Not for publication or presentation...Not for publication or presentation l. IB09-07s Clinical Significance of Genome-wide Variation in Unrelated Hematopoietic Cell Transplantation

Not for publication or presentation

A G E N D A CIBMTR WORKING COMMITTEE FOR IMMUNOBIOLOGY Honolulu, Hawaii Thursday, February 17, 2011, 12:15 pm– 4:45 pm Co-Chair: Carlheinz Müller, MD, PhD, German National Bone Marrow Donor Registry Telephone: +49-731-1507-10; Fax: +49-731-1507-51; E-mail: [email protected] Co-Chair: David Miklos, MD, PhD, Stanford University Telephone: 650-725-4626; Fax: 650-724-6182; E-mail: [email protected] Co-Chair: Marcelo Fernandez-Vina, PhD, M. D. Anderson Cancer Center Telephone: 713-792-8750; Fax: 713-792-8503; E-mail: [email protected] Statisticians: Michael Haagenson, MS, CIBMTR Statistical Center Telephone: 612-884-8609; Fax: 612-884-8661; E-mail: [email protected] Fiona Kan, MS, MA, CIBMTR Statistical Center Telephone: 612-884-8612; Fax: 612-884-8661; E-mail: [email protected] John Klein, PhD, CIBMTR Statistical Center Telephone: 414-456-8280; Fax: 414-456-6513; E-mail: [email protected] Tao Wang, PhD, CIBMTR Statistical Center Telephone: 414-456-4339; Fax: 414-456-6513; E-mail: [email protected] Co-Scientific Dir: Stephanie Lee, MD, MPH, Fred Hutchinson Cancer Research Center Telephone: 206-667-5160; Fax: 206-667-1034; E-mail: [email protected] Co-Scientific Dir: Stephen Spellman, MBS, CIBMTR Immunobiology Research Telephone: 612-617-8334; Fax: 612-362-3488; E-mail: [email protected] 1. Welcome and introduction (M Fernandez-Vina) 12:15 pm 2. Minutes of Immunobiology Working Committee at Tandem 2010 12:20 pm (M Fernandez-Vina) (Attachment 1) 3. Completed project summary (published or submitted work) 12:25 pm

a. R03-70s McDermott DH, Conway SE, Wang T, Ricklefs SM, Agovi M, Porcella SF, Tran HTB, Milford E, Spellman S and Abdi R. Donor and recipient chemokine receptor CCR5 genotype is associated with survival after bone marrow transplantation. Blood, March 2010; 115:2311-2318.

b. R04-74s Venstrom J, Gooley TA, Spellman SR, Pring J, Malkki M, Dupont B, Petersdorf

E, Hsu KC. Donor activating KIR3DS1 is associated with decreased acute GVHD in unrelated allogeneic hematopoietic stem cell transplantation. Blood, April 2010; 115:3162-3165.

c. R04-98s Spellman S, Bray R, Rosen-Bronson S, Haagenson M, Klein JP, Flesch S, Vierra-

Green C and Anasetti C. The detection of donor-directed, HLA-specific alloantibodies in recipients of unrelated hematopoietic cell transplantation is predictive of graft failure. Blood, April 2010; 115:2704-2708.

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d. IB06-07s Nguyen Y, Al-Lehibi A, Gorbe E, Li E, Haagenson M, Wang T, Spellman S, Lee

SJ and Davidson NO. Insufficient evidence for association of NOD2/CARD15 or other inflammatory bowel disease-associated markers on GVHD incidence or other adverse outcomes in T-replete, unrelated donor transplantation. Blood, April 2010; 115:3625-3631.

e. IB06-03 Valcárcel D, Sierra J, Wang T, Kan F, Gupta V, Hale GA, Marks D, McCarthy

PL, Oudshoorn M, Petersdorf EW, Ringdén O, Setterholm M, Spellman SR, Waller EK, Gajewski JL, Marino SR, Senitzer D, Lee SJ. One antigen mismatched related vs. HLA-matched unrelated donor hematopoietic transplantation in adults with acute leukemia: CIBMTR results in the era of molecular typing. In press. Biol Blood Marrow Transplant., Published online: DOI: 10.1016/j.bbmt.2010.07.022.

f. IB07-01 Woolfrey A, Klein JP, Haagenson M, Spellman SR, Petersdorf E, Oudshoorn M,

Gajewski J, Hale GA, Horan J, Battiwalla M, Marino SR, Setterholm M, Ringden O, Hurley CK, Flomenberg N, Anasetti C, Fernandez-Vina M and Lee SJ. HLA-C Antigen mismatches are associated with worse outcomes in unrelated donor peripheral blood stem cell transplantation. In press. Biol Blood Marrow Transplant, Published online: DOI: 10.1016/j.bbmt.2010.09.012.

g. R02-40s Cooley S, Weisdorf DJ, Guethlein LA, Klein JP, Wang T, Le CT, Marsh SGE,

Geraghty D, Spellman S, Haagenson MD, Ladner M, Trachtenberg E, Parham P and Miller JS. Donor selection for natural killer cell receptor genes leads to superior survival after unrelated transplantation for acute myelogenous leukemia. Blood, October 2010; 116:2411-2419.

h. IB06-06 Shaw P, Kan F, Ahn KW, Spellman SR, Aljurf M, Ayas M, Burke M, Cairo MS,

Chen AR, Davies SM, Frangoul H, Gajewski J, Gale RP, Godder K, Hale GA, Heemskerk MBA, Horan J, Kamani N, Kasow KA, Chan KW, MD18; Lee SJ, Leung WH, Lewis VA, Miklos D, Oudshoorn M, Petersdorf EW, Ringdén O, Sanders J, Schultz KR, Seber A, Setterholm M, Wall DA, Yu L and Pulsipher MA. Outcomes of pediatric bone marrow transplantation for leukemia and myelodysplasia using matched sibling, mismatched related or matched unrelated donors. Blood. November 2010; 116:4007-4015.

i. IB06-04 Dong L, Wu T, Gao ZY, Zhang MJ, Kan F, Spellman SR, Tan XY, Zhao YL,

Wang JB, Lu DP, Miklos D, Petersdorf E, Fernandez-Vina M and Lee SJ. The outcomes of family haploidentical hematopoietic stem cell transplantation in haematological malignancies are not associated with patient age. In press. Biol Blood Marrow Transplant, Published online: DOI: 10.1016/j.bbmt.2010.12.703.

j. IB07-02 Marino SR, Lin S, Maiers M, Haagenson M, Spellman S, Klein JP, Binkowski

TA, Lee SJ, and van Besien K. Identification by random forest method of HLA class I amino acid substitutions associated with lower survival at day 100 in unrelated donor hematopoietic cell transplantation. In press. Bone Marrow Transplantation.

k. IB05-03s Shamim Z, Faucher S, Spellman S, Decker W, Haagenson M, Wang T, Lee SJ,

Ryder LP, and Muller K. Polymorphism in the genes encoding human interleukin-7 receptro-alpha and outcomes after allogeneic hematopoietic cell transplantation with matched unrelated donor. Submitted.

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4. Research Repository update and accrual tables (S Spellman) (Attachment 2) 12:25 pm

5. Proposed studies and discussion for Immunobiology Working Committee 12:35 pm a. Voting guidelines (C Müller) b. PROP 0610-02 Impact of amino acid substitution at peptide binding pockets of HLA

class I molecules on hematopoietic cell transplantation (HCT) outcome (J Pidala/C Anasetti) (Attachment 3) c. PROP 1210-21 Analysis of the NIMA effect on the outcome of unrelated PBSC/BM

transplantation (G Ehninger) (Attachment 4) d. PROP 1210-45 Impact of CLTA4 single nucleotide polymorphisms on outcome after

unrelated donor transplant (M Jagasia) (Attachment 5) e. PROP 1210-53 Evaluation of the impact of allele homozygosity at HLA loci on outcome

(C Hurley/A Woolfrey/M Maiers) (Attachment 6) f. PROP 1210-56 KIR genotyping and immune function in MDS patients prior to unrelated

donor transplantation (E Warlick/J Miller) (Attachment 7) g. PROP 1210-60 Evaluation of the impact of potentially non-immunogenic HLA-C allele

level mismatch (M Fernandez-Vina/M Setterholm) (Attachment 8) h. PROP 1210-62 Effect of Rituximab and ABO mismatch (D Miklos) (Attachment 9) i. Proposal voting

6. International Histocompatibility and Immunogenetics Workshop 2:00 pm Collaboration update (E Petersdorf)

a. R04-75s CGP and post-transplant complication (IHWG)(E Petersdorf)

Manuscript preparation

b. R04-76s Identification of functional SNPs (IHWG) (E Petersdorf)


c. IB05-02s The effect of a single MHC class I mismatch withnumerous sequence differences on the clinical outcome of unrelated HSCT (M Heemskerk)


d. IB06-05 Use of high-resolution HLA data from the National Marrow Donor Program for the IHWG in hematopoietic cell transplantation (E Petersdorf)


e. IB07-04 Employing advanced bioinformatic methods for predicting peptide specificities of HLA molecules in the characterization of permissible mismatches in hematopoietic cell transfer (IHWG) (S Buus)

Analysis

f. IB07-05 Impact of donor-recipient ethnicity on risk of acute GVHD among HLA-A, B, C, DRB1, DQB1, DPB1 matched unrelated transplants (IHWG) (Y Morishima) (Attachment 10)


g. IB07-06 HLA–DP epitope study (IHWG) (B Shaw) (Attachment 11)


h. IB07-07 HLA-DR15 and Transplant Outcome (IHWG) (A Gratwohl)


i. IB09-01s Clinical importance of MHC haplotypes in umbilical cord blood transplantation (E Petersdorf) - no update

Data collection

j. IB09-03s Clinical relevance of cytokine/immune response gene polymorphisms in umbilical cord blood transplantation (E Petersdorf) - no update

Data collection

k. IB09-05s Identification of functional SNPs in umbilical cord blood transplantation (E Petersdorf) - no update

Data collection

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l. IB09-07s Clinical Significance of Genome-wide Variation in Unrelated Hematopoietic Cell Transplantation (E Petersdorf) - no update

Protocol development

BREAK – 15 minutes 2:20 pm 7. Studies in progress (Attachment 12)

HLA GENES – CLASSICAL MATCHING (C Müller)

2:35 pm

a. IB08-02 Evaluation of HLA matching requirements in unrelated hematopoietic stem cell transplantation for nonmalignant disorders (J Horan/A Woolfrey) (Attachment 13) - update

Analysis

b. IB09-02 Non-permissive HLA-DPB1 disparities based on T cell alloreactivity (K Fleischhauer) (Attachment 14) - update


c. IB10-05 Evaluation of a Scoring System for HLA Mismatching: HistoCheck (R Blaszcyk/ C Hurley) (Attachment 15) - update


d. IB10-07 Use of HLA Structure and Function Parameters to Understand the Relationship between HLA Disparity and Transplant Outcomes (LA Baxter-Lowe) (Attachment 16) - update

Data file preparation

e. IB06-02 Mismatching for low expression HLA loci in matched unrelated donor transplants (M Fernandez-Vina) - no update


CYTOKINE/CHEMOKINE

(Chair: M Fernandez-Vina) 3:00 pm

a. IB05-03s Genetic polymorphisms in the genes encoding human interleukin-7 receptor-α: Prognostic significance in allogeneic stem cell transplantation (K Müller) - no update


b. IB08-04s Immune response gene polymorphisms in unrelated donor stem cell transplantation in children (K Müller) – no update


NK/KIR

(Chair: M Fernandez-Vina) 3:00 pm

a. R02-40s/R03-63s KIR Program Project/NK receptor acquisition (J Miller/E Trachtenberg) (Attachment 17) - update

Ongoing

b. R04-74s KIR functional significance (IHWG) (B Dupont/K Hsu/J Venstrom) (Attachment 18) - update


c. IB07-03 Analysis of Killer Immunoglobulin-like Receptor(KIR) ligands in reduced intensity conditioning (RIC)allogeneic hematopoietic stem cell transplantation (HSCT) (R Sobecks) – no update

Data file preparation

d. IB08-06 Analysis of Killer Immunoglobulin-Like Receptor (KIR) ligands in umbilical cord blood transplantation (R Sobecks) - no update


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SENSITIZATION/TOLERANCE (Chair: M Fernandez-Vina)

3:15 pm

a. IB06-11s The effect of NIMA in cord blood transplantation (V Prasad/L Baxter-Lowe/J Kurtzberg) - (Attachment 19) - update


b. IB09-08 A retrospective study on impact of donor and recipient birth order on outcome of HLA-identical sibling stem cell transplantation (SCT) in hematological malignancies reported to the CIBMTR (C Dobbelstein) (Attachment 20) - update


c. R03-65s HY antigen (D Miklos) - no update Manuscript preparation d. GV04-01 Non-identical twin transplant for leukemia

(J Barrett) (Attachment 21) – no update Protocol development

e. IB06-09s Detection of HLA antibody to the mismatched antigen in single antigen HLA-mismatched unrelated donor transplants: Is it a predictor of graft-versus-host disease outcome? (S Arai/D Miklos) - no update


f. IB06-10 Evaluation of the impact of the exposure to NIMA during fetal life and breast feeding and to the IPA during pregnancy on the clinical outcome of HSCT from haploidentical family members (J van Rood) – (Attachment 16) - no update


g. IB10-02 Development of GVHD prevention diagnostic test (R Somogyi/L Greller) - no update

Typing

OTHER GENES

(Chair: C Müller) 3:30 pm

a. IB08-08 Genome-Wide Association in Unrelated Donor Transplant Recipients and Donors: A Pilot Study (R Goyal) - (Attachment 22) - update

Typing

b. IB10-01 Donor and Recipient Telomere Length as Predictors of Outcomes after Hematopoietic Stem Cell Transplant in Patients with Acquired Severe Aplastic Anemia (S Gadalla) (Attachment 23) - update

Typing

c. IB07-08 SNPs in the P53 pathway (P53, MDM2, ATM AND P21/WAF1) and transplant outcome after unrelated hematopoietic stem cell transplantation (B Dupont) – no update


d. IB07-09 To develop and test a prognostic index for survival in CML MUD cohorts (A Dickinson) - no update


e. IB09-04s Association of donor and recipient gene polymorphisms of drug metabolisms [GSTP, GSTT, GSTM and UGT (2B17, 2B7, 2B28)] and innate immune response [CD14, TIRAP, and NALPs (1 and 3)] with outcomes after allele matched unrelated hematopoietic stem cell transplantation (V Rocha) - no update

Typing

f. IB09-06s/RT09-04s Genetic polymorphisms and HCT related mortality Re: Pre-HCT conditioning in matched unrelated donor HCT (T Hahn) – no update


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g. IB10-03 TLR and HMGB1 gene polymorphisms in unrelated

haematopoietic stem cell transplantation (K Müller/B Kornblit) – no update

Typing

h. IB10-04 A validation study of the role of base excision repair pathway as a predictor of outcome after hematopoietic stem cell transplant (B Thyagrajan /M Arora) – no update

Typing

MINOR HISTOCOMPATIBILITY ANTIGENS

(Chair: C Müller) 3:45 pm

a. IB10-06 Identification of Common, Clinically Significant, Minor Histocompatibility Antigens through Stem Cell Transplant Donor/Patient Polymorphism Disparities (P Armistead) – no update

Deferred

8. Deferred studies pending accrual 3:45 pm

a. R04-80s HLA matching in unrelated cord blood transplants (S Rodriguez-Marino) - no update

Data collection

b. IB06-13 HLA disparity in unrelated cord blood transplantation: Delineation of factors contributing to transplant outcomes (L Baxter-Lowe) - no update

Data collection

c. IB08-05s Evaluation of lymphotoxin alpha (LTA) alleles in relation to relapse in AML and CML (P Posch) - no update

Data collection

9. Feedback from Committee (C Müller) 3:50 pm

10. Closing remarks (Chair: C Müller) 4:00 pm

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Not for publication or presentation Attachment 1

MINUTES CIBMTR WORKING COMMITTEE FOR IMMUNOBIOLOGY Orlando, Florida Wednesday, February 24, 2010, 12:15 pm– 4:45 pm Co-Chair: Effie Petersdorf, MD, Fred Hutchinson Cancer Research Center Telephone: 206-667-5244; Fax: 206-667-5255; E-mail: [email protected] Co-Chair: David Miklos, MD, PhD, Stanford University Telephone: 650-725-4626; Fax: 650-724-6182; E-mail: [email protected] Co-Chair: Marcelo Fernandez-Vina, PhD, M. D. Anderson Cancer Center Telephone: 713-792-8750; Fax: 713-792-8503; E-mail: [email protected] Statisticians: Michael Haagenson, MS, CIBMTR Statistical Center Telephone: 612-884-8609; Fax: 612-884-8661; E-mail: [email protected] Fiona Kan, MS, MA, CIBMTR Statistical Center Telephone: 612-884-8612; Fax: 612-884-8661; E-mail: [email protected] John Klein, PhD, CIBMTR Statistical Center Telephone: 414-456-8280; Fax: 414-456-6513; E-mail: [email protected] Tao Wang, PhD, CIBMTR Statistical Center Telephone: 414-456-4339; Fax: 414-456-6513; E-mail: [email protected] Co-Scientific Dir: Stephanie Lee, MD, MPH, Fred Hutchinson Cancer Research Center Telephone: 206-667-5160; Fax: 206-667-1034; E-mail: [email protected] Co-Scientific Dir: Stephen Spellman, MBS, National Marrow Donor Program Telephone: 612-617-8334; Fax: 612-362-3488; E-mail: [email protected] 1. Welcome and Introduction

Dr. Effie Petersdorf called the meeting to order at 12:20 pm. She introduced the Immunobiology Working Committee (IBWC) leadership along with the newly appointed chair Carlheinz Mueller, MD, PhD, from the Zentrales Knochenmarkspender-Register Deutschland (ZKRD). Mr. Spellman thanked Dr. Petersdorf for her contributions as a co-chair to the IBWC over the past five years and presented her with a small gift from the CIBMTR for her service.

2. Minutes of Immunobiology Working Committee at Tandem 2009

The minutes were approved as written.

3. Accrual Tables Mr. Spellman referred the group to the accrual table summaries in the agenda.

4. Proposed Studies and Discussion for Immunobiology Working Committee a. Voting guidelines for proposals were discussed by Dr. David Miklos. He reviewed the

guidelines and discussed the purpose of evaluating the proposals. He reminded committee members to consider what proposals best represent the Immunobiology Working Committee (IBWC) purpose, and to consider whether data are available at the NMDP or CIBMTR to support the proposal. IBWC members were encouraged to write comments and to vote the extremes, where 0=low priority, 1=medium priority and 2=high priority.

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b. Discuss proposals to evaluate donor specific anti-HLA antibodies (DSA) in cord blood

transplants (CBT) Mr. Spellman informed the group that the IBWC received three proposals focused on

evaluating the impact of DSA in CBT. He explained that the analyses require pre-transplant recipient plasma and that the NMDP Repository initiated plasma processing and storage in July 2008. There are currently 208 single-cord blood unit (CBU) transplants and 186 multi-CBU transplants in the inventory. A power calculation based on published DSA rates in CBT from the Japanese Red Cross suggests that ~350 single CBU transplants would be required to detect a significant difference (5% level) in engraftment with 80% power. Annual accrual rates suggest the study could proceed within 1-2 years. The study is further complicated by the need for high resolution HLA typing data on the CBU and recipient and the impact of DSA screening practices at Transplant Centers on the selection of specific cords. NMDP is prioritizing retrospective high resolution testing of CBT pairs to address the first issue. A survey of IBWC members suggests that half of the centers represented screen for potential DSA prior to CBT, and consider this information in cord selection. The final study population will likely need to be restricted to non-screening centers.

c. PROP 0509-01 TLR and HMGB1 gene polymorphisms in unrelated haematopoietic stem cell

transplantation Dr. Brian Kornblit presented this proposal which would validate the Danish group’s previous

findings of associations between TLR and HMGB1 gene polymorphisms in patients and donors and overall survival, progression-free survival and transplant related mortality after allogeneic HCT. Drs. Kornblit and Muller intend to use the cohort of 850 Caucasian cases from Dr. Muller’s previous study IB05-03s. The genotyping methods are already established in the laboratory and the DNA is ready to go. They will evaluate SNPs with minor allele frequencies greater than 1 percent. The Committee raised concerns about the sample size and suggested possibly expanding the number of pairs following the initial analysis.

d. PROP 0609-03 A validation study of the role of base excision repair pathway as a predictor

of outcome after hematopoietic stem cell transplant Dr. Mukta Arora presented this proposal, which intends to validate the results of a study

performed at the University of Minnesota where they found correlation between polymorphisms in the Base Excision Repair pathway and incidence of TRM and relapse. This will be a study on an NMDP cohort of approximately 1000 adult Caucasian patients who underwent myeloablative allogeneic HCT from an unrelated matched (8/8 HLA matched at HLA-A, B, C and DRB1 alleles) donor for treatment of acute leukemias or CML. The Committee noted that the study may need to control for the influence of DP mismatching.

e. PROP 0909-02 Evaluation of a scoring system for HLA mismatching: HistoCheck Dr. Carolyn Hurley presented this proposal as co-principal investigator with Dr. Rainer

Blasczyk. This study will evaluate the scoring system for HLA mismatches called Histocheck. Histocheck is readily accessible on the internet and is being used by some transplant centers to prioritize HLA mismatches for donor selection. A previous analysis by the International Histocompatibility Workshop Group (IHWG) found no correlation between the Histocheck score and transplant outcome, but these results have not been published. This study would use a data set already prepared by the CIBMTR/NMDP for the evaluation of HLA mismatch scoring, e.g. Dr. Rene Duquesnoy’s MatchMaker data set, to minimize the dataset preparation efforts at the Statistical Center. The Committee strongly felt that the results of the analysis should be published regardless of the findings. There was a suggestion

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to include cord blood transplants in this study but others felt that numbers are small, cords are not fully typed, and inclusion of cords would complicate the analysis.

f. PROP 1209-29 Identification of common, clinically significant, minor histocompatibility

antigens through stem cell transplant donor/patient polymorphism disparities Dr. Paul Armistead presented this proposal which has the central hypothesis that GvHD is

mediated by immunodominant minor Histocompatibility Antigens (mHA) presented in the context of specific HLA molecules. Some of these mHA occur at high frequency in patients undergoing allogeneic SCT. These high frequency mHA can be identified by genomic screening methods. It has been shown that mHA mediate acute GvHD. This study would evaluate and type 612 donor/recipient pairs, which would give 80% power to find an odds ratio of 2.0. This proposal would focus on potential mHA that could be presented in the context of HLA-A*02 and would look at 10/10 matched unrelated donor transplants for AML, ALL, CML and MDS. This study will require samples. The Committee suggested Dr. Armistead not include cases from MD Anderson, since they were included in his preliminary analysis and restrict to HLA-A*0201 positive cases.

g. PROP 1209-39 Use of HLA structure and function parameters to understand the relationship

between HLA disparity and transplant outcomes Dr. Marcelo Fernandez-Vina presented this proposal in Dr. Lee Ann Baxter-Lowe’s absence.

This proposal would evaluate an HLA mismatch scoring algorithm based on the location of mismatched residues. Factors incorporated into the scoring system for allorecognition potential include high and low frequency allorecognition factors. Some of the high frequency allorecognition factors include high TCR docking potential, such as identical docking amino acids and conservation of surrounding amino acids, as well as peptide differences, such as the sequence and orientation of the peptides. The low frequency allorecognition factors include TCR docking disruption without differences in peptide binding or orientation. The hypothesis is that TCR docking is critical for allorecognition. The goal is to look at HLA-A and -B mismatches first and then consider HLA-C and -DRB1 separately. This study would use a data set already prepared by the CIBMTR/NMDP for the evaluation of HLA mismatch scoring, e.g. Dr. Rene Duquesnoy’s MatchMaker data set, to minimize the dataset preparation efforts at the Statistical Center. The Committee suggested that the study population be restricted to T cell replete transplants.

h. Proposal voting was completed at this time.

5. Discussion about Advisory Committee recommendations and response to them about ways to improve the Immunobiology Working Committee (IBWC) Dr. Stephanie Lee presented a summary of the Advisory Board recommendations for the IBWC and IBWC leadership response to these suggestions. She solicited feedback from the group. Dr. John Hansen noted the collaboration with the BMTCTN Biomarker committee and that a proposal for comprehensive sample collection was under preparation. The main concerns with collection of samples through BMTCTN were the limited sample size available for accrual through the BMTCTN trials and limitations on phenotype data collection.

Break - A break was taken from 2:00 pm - 2:15 pm.

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6. Studies in Progress OTHER GENES

a. IB08-08 Genome-wide association in unrelated donor transplant recipients and donors: a pilot study Dr. Rakesh Goyal presented an update. This study examines the genetic differences between recipients who experience grade III-IV acute GvHD (G+) vs. those who experience no acute GvHD (G-). The study population consists of 10/10 allele-matched Caucasians receiving their first transplant with standard ablative conditioning and no T-cell manipulation. The study is currently in the typing stage.

b. IB09-06s/RT09-04s Genetic polymorphisms and HCT related mortality re: pre-HCT conditioning in matched unrelated donor HCT Dr. Theresa Hahn presented an update to her study with the co-principal investigator Dr. Lara Sucheston in the audience. Specific aims of this study include 1) To undertake a Genome Wide Association Study (GWAS) to map the independent and joint effects of recipient and donor genetic variation associated with TRM after HLA-matched unrelated donor BMT; 2) To determine the modifying effects of conditioning regimens on associations between recipient and/or donor genetic variants and TRM; and 3) To replicate the top genetic associations in an independent cohort of high resolution 10/10 HLA-matched BMT recipient-unrelated donor pairs. The NHLBI initial review requested to add donors to the cohort. The grant was resubmitted in November 2009 and received a score of 15 and has a percentile rank of 2%. Dr. Hahn expects to hear the final decision in May and will most likely receive the grant in July 2010.

c. IB10-01 Donor and recipient telomere length as predictors of outcomes after hematopoietic stem cell transplant in patients with acquired severe aplastic anemia Dr. Shahinaz Gadalla presented an update to her study. The specific aims of this study are to 1) determine pre-transplant blood telomere length in SAA who received unrelated HSCT compared with age-matched controls and patients with dyskeratosis congenita; 2) assess the relationships between telomere length, and post-transplant outcomes in patients who received unrelated donor HSCT for SAA; and 3) identify factors that modify the association between recipient and/or donor telomere length, and post-transplant outcomes in those individuals. The study population consists of patients less than 40 years of age. There are currently 395 cases with samples. This study currently has funding, and the PIs are preparing for DNA extraction.

d. IB07-08 SNPs in the P53 pathway (P53, MDM2, ATM AND P21/WAF1) and transplant outcome after unrelated hematopoietic stem cell transplantation

No update was given. e. IB07-09 To develop and test a prognostic index for survival in CML MUD cohorts No update was given. f. IB09-04s Association of donor and recipient gene polymorphisms of drug metabolisms

[GSTP, GSTT, GSTM and UGT (2B17, 2B7, 2B28)] and innate immune response [CD14, TIRAP, and NALPs (1 and 3)] with outcomes after allele matched unrelated hematopoietic stem cell transplantation No update was given.

SENSITIZATION/TOLERANCE a. IB06-11s The effect of NIMA in cord blood transplantation

Dr. Vinod Prasad presented this study which looks at the influence of HLA mismatching in non-inherited maternal antigens (NIMA) on the relationship between HLA disparity and outcomes of cord blood transplants. This study tests the hypotheses that NIMA tolerance favorably influences outcomes of cord blood transplants. The COBLT study contained 102 transplants that have NIMA typing available. A preliminary 80% power calculation showed

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that the survival rates of 50% vs. 30% for NIMA vs. non-NIMA groups would require 450-500 patients if 3/6, 4/6, and 5/6 were combined, so additional cases are needed. The study population will consist of transplants with a single-cord unit. The main effect variable will be NIMA matches vs. NIMA mismatches. Standard outcomes will be evaluated during this study. The NMDP/CIBMTR has identified approximately 600 single cord transplant cases treated for malignancy for inclusion in the study. In addition, Eurocord has expressed interest in collaborating on the project. The NMDP will support the collection and HLA testing of maternal samples from the NMDP Cord Blood Bank Network to facilitate the analysis. Due to the anticipated low frequency of NIMA mismatches the study will likely employ a matched case control strategy to maximize power to detect a difference between NIMA matched cases versus mismatched controls.

b. IB09-08 A retrospective study on impact of donor and recipient birth order on outcome of HLA-identical sibling stem cell transplantation (SCT) in hematological malignancies reported to the CIBMTR

Dr. Christiane Dobbelstein presented her study which evaluates outcome after HLA-identical sibling or matched unrelated donor (MUD) transplants when considering birth order of the recipient and donor. There are 11,745 HLA-identical siblings and 2052 MUD transplants in the current selection. Specific aims include the primary objective of evaluating relapse rate after HLA-identical sibling stem cell transplantation in hematological malignancies depending on birth order of donor and recipient. Secondary objectives include evaluating disease-free-survival (DFS), overall survival (OS), non-relapse-mortality, acute and chronic GvHD after HLA-identical sibling stem cell transplantation in hematological malignancies depending on birth order of donor and recipient and to check if there is a similar association between relapse rate, DFS, OS, NRM, and acute and chronic GvHD in HLA-identical MUD SCT depending on relative ages of the donor and recipient as a surrogate for birth order. The study is currently in protocol development and will soon be distributed to the whole working committee to form a writing committee. There will also be planned subset analyses.

c. R03-65s HY antigen No update was given. d. IB06-09s Detection of HLA antibody to the mismatched antigen in single antigen HLA-

mismatched unrelated donor transplants: is it a predictor of graft-versus-host disease outcome?

No update was given. e. IB06-10 Evaluation of the impact of the exposure to NIMA during fetal life and breast

feeding and to the IPA during pregnancy on the clinical outcome of HSCT from haploidentical family members

No update was given. NK/KIR a. R02-40s/R03-63s KIR Program Project/NK receptor acquisition Dr. Jeffrey Miller presented this ongoing project which has recently looked at KIR genotype

assignments and the effect of KIR genotypes on outcome. Donor DNA samples were typed for 15 individual KIR genes by Dr. Elizabeth Trachtenberg using a validated single nucleotide polymorphism (SNP)-based MALDI-TOF assay. KIR genotypes were assigned as haplotypes of A/A or B/x where the B haplotype checks for the presence of 2DS2, 2DL5, 2DS3/S5, 3DS1, 2DS. Further definitions include Centromeric/Telomeric Segments: Cen/Tel A/A, A/B, B/B with a B-content score between 0 and 4. Multivariate analysis shows that KIR Cen-B/B donors are associated with the lowest relapse risk. Multivariate analysis also shows that donors with >2 KIR B-gene motifs decrease relapse and improve survival. Additional analyses showed that donors with > 2 KIR-B gene motifs protect against relapse in HLA matched or HLA mismatched transplants. Donor KIR genotype does not affect outcomes for

11


ALL. This is an ongoing project with more analysis planned in the near future. b. R04-74s KIR functional significance (IHWG)

Dr. Katharine Hsu presented this study which analyzed an NMDP cohort of samples. The study looks at KIR genotyping of donors. The analysis showed that there is increased overall survival, less transplant-related mortality for donor KIR3DS1 homozygous compared to other KIR3DS1 groups but no significant difference in relapse. Upon further subset analysis, the effects appeared mostly in AML. The IHWG found reduced relapse with donors who have KIR2DS5 & KIR3DS1. KIR2DS2 in AML showed no significant difference.

c. IB07-03 Analysis of Killer Immunoglobulin-like Receptor (KIR) ligands in reduced intensity conditioning (RIC) allogeneic hematopoietic stem cell transplantation (HSCT) Dr. Ronald Sobecks presented this study which has gone through some changes since the protocol was sent out a few months ago. First, this study may incorporate genotyping. Second, comments from the writing committee are being incorporated into the protocol. The study population will change to 1999-2007. The number of missing KIR ligands will likely be quantified as 0 vs. 1 vs. 2 vs. 3 instead of present vs. absent. The study design will incorporate recipient and donor KIR ligands. The outcomes will be acute and chronic GvHD, overall survival, relapse and disease-free survival. Samples are available on the whole cohort. Typing will either use the IHWG typing or will go through the NMDP retrospective typing project.

d. IB08-06 Analysis of Killer Immunoglobulin-Like Receptor (KIR) ligands in umbilical cord blood transplantation

No update was given. CYTOKINE/CHEMOKINE a. IB05-03s Genetic polymorphisms in the genes encoding human interleukin-7 receptor-α:

Prognostic significance in allogeneic stem cell transplantation No update was given. b. IB08-04s Immune response gene polymorphisms in unrelated donor stem cell transplantation

in children No update was given. HLA GENES – CLASSICAL MATCHING a. IB06-04 Effect of age on outcome in patients undergoing related HLA-mismatched/

haploidentical stem cell transplantation Dr. Stephanie Lee gave an update on the study in Dr. Lujia Dong’s absence. The study has a

draft manuscript in progress and has a poster presentation at the Tandem BMT Meetings this year. The study was limited by numbers, and no significant age effect was found.

b. IB08-02 Evaluation of HLA matching requirements in unrelated hematopoietic stem cell transplantation for nonmalignant disorders Dr. John Horan presented this study in Dr. Ann Woolfrey’s absence. The hypothesis of this study is that the effect of HLA matching in unrelated hematopoietic stem cell transplantation in patients with non-malignant disorders differs from its effect in patients with malignant diseases. Some reasons for this are that 1) by reducing the risk for relapse, GVHD may be beneficial in patients with malignancies. In patients with non-malignant disorders, on the other hand, GVHD is not beneficial. 2) Patients with non-malignant disorders tend to be younger, and, therefore may be less apt to develop and die from GVHD. 3) Patients with many non-malignant disorders are prone to graft failure. Patients are eligible if they received an unrelated BM or PB graft for a nonmalignant disease, including severe aplastic anemia, immunodeficiency disorders, inherited disorders, and autoimmune disorders between 1988 and 2007. They are also eligible if they received reduced intensity, non-myeloablative, non-traditional ablative or standard myeloablative regimens. Patients are ineligible if either they received an unrelated cord blood graft or they or their donor lack high resolution typing of

12


HLA-A, B, C and DRB1. Outcomes include survival, acute and chronic GVHD, transplant-related mortality and graft failure. Main effect will be the impact of allele and antigen mismatching at the A, B, C, DRB1 and DQB1 loci (available for all pairs). The effect of DPB1 will be estimated if the numbers are sufficient. Analysis will likely only include the 0 mismatches, 1 mismatch and the 2 mismatches groups. There are only 56 patients with three or more mismatches.

c. IB09-02 Non-permissive HLA-DPB1 disparities based on T cell alloreactivity Dr. Marcelo Fernandez-Vina presented this study in Dr. Katharina Fleischhauer’s absence.

This study intends to validate the findings from the Italian group. Specific aims of this study are 1) to validate in vitro an algorithm for definition of non-permissive HLA-DPB1 disparities predictive of mortality after unrelated HSCT, and 2) to analyze the role of HLA-DPA1 in determining the immunogenic T cell epitope (TCE) relevant for non-permissive HLA-DP disparities. The study population consists of 1281 myeloablative transplants that are 10/10 allele matched, DPB1 mismatched and DPA1 typed pairs available in the NMDP database. An analysis will be performed looking at TCE3 and TCE4, which are grouped as matched, permissive, non-permissive and potentially permissive. The primary endpoint is overall survival with secondary endpoints of TRM, acute GvHD and relapse. There will also be a Brier Score comparison at 1, 3 and 5 years of survival for both TCE3 and TCE4. The protocol has been distributed to the whole working committee and the writing committee is being formed.

d. IB06-02 Mismatching for low expression HLA loci in matched unrelated donor transplants No update was given.

7. International Histocompatibility and Immunogenetics Workshop Group (IHWG) Collaboration

Update Dr. Effie Petersdorf gave a summary of the IHWG studies that showed progress over the past year. a. R04-75s CGP and post-transplant complication No update was given. b. R04-76s Identification of functional SNPs SNPs Genotyping includes MHC exon centric Illumina SNP panels genotyped for HLA

10/10 for 3,310 transplants; HLA 9/10 for 1,622 transplants; and HLA 8/10 or less for 457 transplants (which is the ethnicity panel). Currently, the analyses are ongoing. Comparisons of recipient and donor minor allele frequencies (MAF) show little difference between them. Recipient and donor SNP mismatching has a low percentage around HLA-A, -B, -C, -DRB1 and -DQB1

c. IB07-05 Impact of donor-recipient ethnicity on risk of acute GVHD among HLA-A, B, C, DRB1, DQB1, DPB1 matched unrelated transplants Dr. Petersdorf presented this update for Dr. Yasuo Morishima. The purpose of this study is to compare the incidence of acute GVHD between ethnic groups based on the same background. This is a large scale IHWG HCT database of 5555 pairs looking at 10/10 HLA allele matched transplants. The GVHD prophylaxis includes T cell replete stem cell sources and leukemia and MDS patients. Results obtained from this analysis will become basic data for further international analysis of HLA mismatched unrelated HSCT and for donor exchange of unrelated donor. There were 2062 Asian & Pacific/Asian & Pacific pairs, 2419 Caucasian/Caucasian pairs, 39 African American/African American pairs, 21 Hispanic/Hispanic pairs, 2 Native American/Native American pairs, 268 mismatched race pairs (in non-JMDP) and 744 unknown donor ethnicity. Multivariate analysis showed that after adjusting for clinical factors, Caucasian pairs and mismatched race pairs have more acute GvHD (both grades II-IV and grades III-IV) than Asian/Pacific pairs. African American

13


pairs also have more grades III-IV acute GvHD than the Asian/Pacific pairs. Multivariate analysis also shows that after adjusting for clinical factors, Caucasian, Hispanic, African American and mismatched race pairs all have a higher overall mortality rate than Asian/Pacific pairs.

d. IB07-06 HLA–DP epitope study Dr. Petersdorf provided the update for Dr. Bronwen Shaw. The hypothesis is that mismatching for T cell epitopes (TCE) in the HLA-DP molecule may be as important or more important as allele-matching for predicting outcome of unrelated donor HSCT. The study evaluated group-specific matching rather than allele-specific DPB1 matching where the groups were TCE-disparate as “Non-Permissive” and TCE-matched as all others. The study population included 5838 10/10 (HLA-A,-B,-C,-DRB1,-DQB1) allele-level matched pairs . The analysis considered groups of 1) DPB1 allele-mismatched (n=4490) or 12/12 (n=1348) or TCE disparate (TCED) in GvH or HvG, or TCE matched (TCEM). The multivariate results were adjusted for classical variables with the TCEM and DPB1 allele mismatched serving as the reference group. The multivariate results show that the 10/10 TCE disparate group has significantly higher TRM, grades III-IV acute GvHD and overall mortality than the TCE matched DPB1 allele-mismatched group. Multivariate results also show that the 12/12 DPB1 matched group has lower TRM, grades III-IV acute GvHD and higher relapse rates than the TCE matched DPB1 allele-mismatched group. Ongoing work will include defining the impact of the TCE in the 9/10 HLA matched transplants (N=3315) as well as completing an analysis of particular diseases (B cell malignancies) and determining the role of hypervariable region (HvR) disparity in defining non-permissive mismatches. The Committee questioned whether the selection for permissible vs. non-permissive DP mismatches was feasible in the general transplant population.

e. IB05-02s The effect of a single MHC class I mismatch with numerous sequence differences on the clinical outcome of unrelated HSCT Dr. Petersdorf presented the update for this study. The hypothesis is that HCT mismatched for a single MHC class I allele that has many amino acid differences on the α helices and β sheet of the molecule, leads to a more successful outcome compared to HCT with other single class I HLA mismatches. There are 654 recipient-donor pairs with a single class I mismatch (196 HLA-A; 88 HLA-B; 370 HLA-C) in the study population. Some current discussion points for this study include highly divergent HLA mismatches do not lead to superior survival results when compared to other single class I mismatches. The difference with the previous study is that in the Dutch study, the transplants were mainly performed with T-cell depleted grafts and in the IHWG/CIBMTR group; they were performed with T-cell replete grafts. The next steps include repeating the study with patients who received T-cell depleted grafts. An analysis of 2,324 T-repleted and 534 T-depleted transplants is currently ongoing. Future plans are to include functional similarity or dissimilarity of substituted amino acids in the analysis.

f. IB06-05 Use of high-resolution HLA data from the National Marrow Donor Program for the IHWG in hematopoietic cell transplantation Dr. Petersdorf presented the update for this study. The hypothesis is that the risks associated with HLA mismatched transplantation are influenced by 1) the number of mismatched residues within α1 and α2 of class I and DRB and DQB, 2) the location of the amino acid mismatch, and 3) the nature of the mismatched amino acids. The study population is restricted to single mismatches (a set of 2572 cases). The global p-values for the HLA loci are as follows: HLA-A p=0.27, HLA-B p=0.05, HLA-C p=0.01, HLA-DRB1 p=0.97, and HLA-DQB1 p=0.04. The study plans to look at the specific residues at HLA-B, -C, and -DQB1, but perhaps not at HLA-A or -DRB1, based on the global p-values.

14


g. IB07-04 Employing advanced bioinformatic methods for predicting peptide specificities of HLA molecules in the characterization of permissible mismatches in hematopoietic cell transfer

No update was given. h. IB07-07 HLA-DR15 and transplant outcome No update was given. i. IB09-01s Clinical importance of MHC haplotypes in umbilical cord blood transplantation No update was given. j. IB09-03s Clinical relevance of cytokine/immune response gene polymorphisms in umbilical

cord blood transplantation No update was given. k. IB09-05s Identification of functional SNPs in umbilical cord blood transplantation No update was given. l. IB09-07s Clinical significance of genome-wide variation in unrelated hematopoietic cell

transplantation No update was given.

8. Deferred studies pending accrual a. GV04-01 Non-identical twin transplant for leukemia No update was given. b. IB06-13 HLA disparity in unrelated cord blood transplantation: Delineation of factors

contributing to transplant outcomes No update was given. c. IB08-05s Evaluation of lymphotoxin alpha (LTA) alleles in relation to relapse in AML and

CML No update was given. d. R04-80s HLA matching in unrelated cord blood transplants No update was given.

9. Feedback from Committee The Immunobiology working committee leadership asked for comments from the whole group as to what could be done differently for the meeting or for the committee in general. No comments were made.

10. Completed project summary (published or submitted work) a. R02-40s/R03-63s Cooley S, Trachtenberg E, Bergemann TL, Saeteurn K, Klein J, Le C, Marsh

SGE, Guethlein LA, Parham P, Miller JS, Weisdorf DJ. Donors with group B KIR haplotypes improve relapse-free survival after unrelated hematopoietic cell transplantation for acute myelogenous leukemia. Blood, January 2009; 726-732.

b. R03-57s Shah R, Selby ST, Yokley B, Slack RS, Hurley CK, Posch PE. TNF, LTA and TGFB1 genotype distributions among acute graft versus host disease (aGVHD) subsets after HLA-matched unrelated hematopoietic stem cell transplantation: a pilot study. Tissue Antigens, July 2009, 74(1):50-56.

c. IB05-01s Spellman S, Warden MB, Haagenson M, Pietz BC, Goulmy E, Warren EH, Wang T, Ellis TM. Effects of mismatching for minor histocompatibility antigens on clinical outcomes in HLA-matched, unrelated hematopoietic stem cell transplants. Biol Blood Marrow Transplant, July 2009, 15(7):856-863.

15


d. R01-60 Baxter-Lowe L, Maiers M, Spellman S, Haagenson M, Wang T, Fernandez-Vina M, Marsh SGE, Horowitz M, Hurley CK. HLA-A disparities illustrate challenges for ranking the impact of HLA mismatches on bone marrow transplant outcomes in the United States. Biol Blood Marrow Transplant, August 2009, 15(8):971-981.

e. IB06-08 Anderson E, Grzywacz B, Wang H, Wang T, Haagenson M, Spellman S, Blazar BR, Miller JS, and Verneris MR. Limited role of MHC class I chain–related gene A (MICA) typing in assessing graft-versus-host disease risk after fully human leukocyte antigen–matched unrelated donor transplantation. Blood, November 2009, 114: 4753-4754.

f. R04-98s Spellman S, Bray R, Rosen-Bronson S, Haagenson M, Klein JP, Flesch S, Vierra-Green C and Anasetti C. The detection of donor-directed, HLA-specific alloantibodies in recipients of unrelated hematopoietic cell transplantation is predictive of graft failure. In press. Blood, prepublished online January 2010.

g. R03-70s McDermott DH, Conway SE, Wang T, Ricklefs SM, Agovi M, Porcella SF, Tran HTB, Milford E, Spellman S and Abdi R. Donor and recipient chemokine receptor CCR5 genotype is associated with survival after bone marrow transplantation. In press. Blood, prepublished online January 2010.

h. IB06-07s Nguyen Y, Al-Lehibi A, Gorbe E, Li E, Haagenson M, Wang T, Spellman S, Lee SJ and Davidson NO. Insufficient evidence for association of NOD2/CARD15 or other inflammatory bowel disease-associated markers on GVHD incidence or other adverse outcomes in T-replete, unrelated donor transplantation. Submitted.

i. IB06-03 Valcárcel D, Sierra J, Wang T, Kan F, Gupta V, Hale GA, Marks D, McCarthy PL, Oudshoorn M, Petersdorf EW, Ringdén O, Setterholm M, Spellman SR, Waller EK, Gajewski JL, Marino SR, Senitzer D, Lee SJ. One antigen mismatched related vs. HLA-matched unrelated donor hematopoietic transplantation in adults with acute leukemia: CIBMTR results in the era of molecular typing. Submitted.

j. IB07-02 Marino SR, Lin S, Maiers M, Haagenson M, Spellman S, Lee SJ, Klein JP, Binkowski TA, and van Besien K. Mismatched unrelated donor stem cell transplantation: identification of HLA class I amino acid substitutions associated with survival at day 100. Submitted.

k. IB07-01 Woolfrey A, Klein JP, Haagenson M, Spellman SR, Petersdorf E, Oudshoorn M, Gajewski J, Hale GA, Horan J, Battiwalla M, Marino SR, Setterholm M, Ringden O, Hurley CK, Flomenberg N, Anasetti C, Fernandez-Vina M and Lee SJ. HLA-C Antigen mismatches are associated with worse outcomes in unrelated donor peripheral blood stem cell transplantation. Submitted.

l. IB06-06 Shaw P, Kan F, Ahn KW, Spellman SR, Aljurf M, Ayas M, Burke M, Cairo MS, Chen AR, Davies SM, Frangoul H, Gajewski J, Gale RP, Godder K, Hale GA, Heemskerk MBA, Horan J, Kamani N, Kasow KA, Chan KW, MD18; Lee SJ, Leung WH, Lewis VA, Miklos D, Oudshoorn M, Petersdorf EW, Ringdén O, Sanders J, Schultz KR, Seber A, Setterholm M, Wall DA, Yu L and Pulsipher MA. Outcomes of pediatric bone marrow transplantation for leukemia and myelodysplasia using matched sibling, mismatched related or matched unrelated donors. Submitted.

11. Closing Remarks

Dr. Petersdorf thanked the committee members for extending their efforts in getting the studies submitted and published in the last year. She also thanked everyone for their participation. The meeting was adjourned at 4:15 pm.

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Based on the voting results, the priority scores for this year’s proposals is as follows:

Proposal # and PI Average Priority Score (0 - 2)*

Assigned Hours for July 2010 - June

2011 PROP 0509-01 (Müller/Kornblit) 1.86 180 PROP 1209-29 (Armistead) 1.64 70 PROP 0609-03 (Arora) 1.47 70 PROP 1209-39 (Baxter-Lowe) 1.25 230 PROP 0909-02 (Blasczyk/Hurley) 1.17 230

* The final score is the weighted average.

17


Accrual Summary for Immunobiology Working Committee

Characteristics of recipients of first transplants reported to the CIBMTR and NMDP

CIBMTR

HLA-identical Sibling

CIBMTRAlternative

Related

CIBMTR Unrelated (non-US)

CIBMTRUnrelated

(US)Variable N (%) N (%) N (%) N (%)Number of patients 41129 6287 7480 24288Number of centers 460 387 195 165Age, median (range), years 30 (<1-82) 21 (<1-82) 27 (<1-75) 33 (<1-80)Age at transplant

< 10 y 5767 (14) 1841 (29) 1626 (22) 4496 (19)10-20 y 7192 (17) 1186 (19) 1230 (16) 3320 (14)20-29 y 7585 (18) 961 (15) 1195 (16) 3124 (13)30-39 y 8153 (20) 878 (14) 1338 (18) 3448 (14)40-49 y 7185 (17) 776 (12) 1128 (15) 4218 (17)≥ 50 y 5240 (13) 641 (10) 962 (13) 5676 (23)

Male sex 23985 (58) 3777 (60) 4444 (59) 14186 (58)Karnofsky prior to transplant > 90% 28851 (73) 3892 (67) 5186 (72) 15994 (71)HLA-A,B,DRB1 groups – low resolution

6/6 41129 (100) 1458 (23) 3771 (50) 17241 (71)5/6 0 1038 (17) 754 (10) 3746 (15)4/6 0 968 (15) 367 ( 5) 990 ( 4)3/6 0 N/A 31 (<1) 9 (<1)Other / < “3/6” / unknown / TBD 0 2823 (45) 2557 (34) 2302 ( 9)

High resolution available ≤ 3/6 N/A N/A 20 (<1) 547 ( 2)4/6 N/A N/A 59 ( 1) 1467 ( 6)5/6 N/A N/A 174 ( 2) 3934 (16)6/6 N/A N/A 359 ( 5) 11338 (47)

HLA high-res. typed/audited (out of 8) N/A N/A 371 ( 5) 12380 (51)Graft type

Bone marrow 30233 (74) 4635 (74) 4726 (63) 13004 (54)Peripheral blood 10483 (26) 1540 (25) 1710 (23) 7741 (32)Cord blood 183 (<1) 31 (<1) 1017 (14) 3338 (14)

Other 193 (<1) 72 ( 1) 23 (<1) 202 ( 1)Conditioning regimen Myeloablative 33732 (82) 5159 (82) 5590 (75) 17568 (72)

Reduced intensity 3545 ( 9) 603 (10) 1096 (15) 3954 (16)Non-myeloablative 1424 ( 3) 226 ( 4) 484 ( 6) 1987 ( 8)Other/To be determined 2428 ( 6) 299 ( 5) 310 ( 4) 779 ( 3)

Donor age, median (range), years 30 (<1-93) 33 (<1-80) 33 (<1-68) 34 (<1-61)

18




CIBMTR


CIBMTRAlternative

Related


CIBMTRUnrelated

(US)Variable N (%) N (%) N (%) N (%)Donor age

< 10 (including UCB tx) 4966 (12) 444 ( 7) 778 (11) 3446 (17)10-19 7095 (18) 772 (13) 53 ( 1) 189 ( 1)20-29 7841 (20) 1267 (21) 1503 (22) 4640 (23)30-39 8017 (20) 1552 (25) 2065 (30) 6037 (30)40-49 6783 (17) 1134 (18) 1457 (21) 4177 (21)≥ 50 5388 (13) 965 (16) 1121 (16) 1725 ( 9)

Disease at transplant AML 10803 (26) 1517 (24) 1762 (24) 7340 (30)ALL 6679 (16) 1220 (19) 1597 (21) 4461 (18)Other leukemia 1022 ( 3) 155 ( 3) 194 ( 3) 1091 ( 4)CML 7945 (19) 969 (15) 1698 (23) 3888 (16)MDS/MPS 2553 ( 6) 323 ( 5) 760 (10) 2809 (12)Non-Hodgkin’s lymphoma 2581 ( 6) 343 ( 5) 206 ( 3) 1469 ( 6)HD-Hodgkin’s lymphoma 269 ( 1) 48 ( 1) 10 (<1) 94 (<1)MYE-plasma cell disorder, MM 975 ( 2) 133 ( 2) 40 ( 1) 128 ( 1)Other malignancies 328 ( 1) 57 ( 1) 30 (<1) 69 (<1)Breast cancer 63 (<1) 22 (<1) 0 4 (<1)Severe aplastic anemia 4274 (10) 473 ( 8) 438 ( 6) 943 ( 4)Inherited ab erythro. diff-funct. 2616 ( 6) 264 ( 4) 213 ( 3) 407 ( 2)SCID & other immune deficienc. 545 ( 1) 551 ( 9) 242 ( 3) 599 ( 2)Inherited abnormal. of platelets 19 (<1) 7 (<1) 11 (<1) 39 (<1)Inherited disorder of metabolism 264 ( 1) 150 ( 2) 179 ( 2) 592 ( 2)Histiocytic disorders 101 (<1) 41 ( 1) 85 ( 1) 297 ( 1)

Autoimmune diseases 17 (<1) 4 (<1) 2 (<1) 8 (<1)Other 26 (<1) 5 (<1) 5 (<1) 33 (<1)

Disease status at transplant Early 15634 (38) 1525 (24) 2496 (33) 6900 (28)Intermediate 5730 (14) 1135 (18) 1649 (22) 5373 (22)Advanced 5369 (13) 1184 (19) 1251 (17) 4277 (18)Non-malignant disease / Other 14396 (35) 2443 (39) 2084 (28) 7738 (32)

GVHD prophylaxis FK506 + (MTX or MMF or steroids) ± other 3059 ( 7) 394 ( 6) 544 ( 7) 9037 (37)

FK506 ± other 418 ( 1) 56 ( 1) 50 ( 1) 870 ( 4)CsA + MTX ± other 20867 (51) 2065 (33) 4415 (59) 7216 (30)CsA ± other 9739 (24) 893 (14) 1732 (23) 4754 (20)MMF ± other 44 (<1) 12 (<1) 20 (<1) 68 (<1)MTX ± other 2952 ( 7) 257 ( 4) 28 (<1) 172 ( 1)T-cell depletion 2858 ( 7) 1397 (22) 470 ( 6) 1194 ( 5)Other / To be determined 1192 ( 3) 1213 (19) 221 ( 3) 977 ( 4)

19




CIBMTR


CIBMTRAlternative

Related


CIBMTRUnrelated

(US)Variable N (%) N (%) N (%) N (%)Donor/Recipient sex match

Male/Male 12400 (32) 2045 (35) 2353 (38) 2237 (36)Male/Female 8383 (22) 959 (17) 1351 (22) 1308 (21)Female/Male 9893 (26) 1426 (25) 1300 (21) 1394 (23)Female/Female 7503 (20) 1335 (23) 1150 (19) 1195 (19)

Donor/Recipient CMV match Negative/Negative 9161 (22) 1392 (22) 1809 (24) 5976 (25)Negative/Positive 5866 (14) 832 (13) 1715 (23) 5762 (24)Positive/Negative 3673 ( 9) 763 (12) 970 (13) 2637 (11)Positive/Positive 15249 (37) 1968 (31) 1846 (25) 3730 (15)Unknown 7180 (17) 1332 (21) 1140 (15) 6183 (25)

Year of transplant 1964-1985 4809 (12) 884 (14) 35 (<1) 11 (<1)1986 1374 ( 3) 257 ( 4) 14 (<1) 18 (<1)1987 1462 ( 4) 248 ( 4) 31 (<1) 34 (<1)1988 1624 ( 4) 244 ( 4) 53 ( 1) 95 (<1)1989 1849 ( 4) 258 ( 4) 100 ( 1) 178 ( 1)1990 1938 ( 5) 316 ( 5) 136 ( 2) 288 ( 1)1991 1883 ( 5) 250 ( 4) 171 ( 2) 410 ( 2)1992 1978 ( 5) 271 ( 4) 226 ( 3) 468 ( 2)1993 1991 ( 5) 273 ( 4) 235 ( 3) 576 ( 2)1994 1835 ( 4) 254 ( 4) 247 ( 3) 709 ( 3)1995 1914 ( 5) 316 ( 5) 324 ( 4) 852 ( 4)1996 1946 ( 5) 312 ( 5) 411 ( 5) 986 ( 4)1997 1650 ( 4) 295 ( 5) 381 ( 5) 1040 ( 4)1998 1487 ( 4) 216 ( 3) 423 ( 6) 1057 ( 4)1999 1341 ( 3) 199 ( 3) 416 ( 6) 1112 ( 5)2000 1432 ( 3) 205 ( 3) 435 ( 6) 1142 ( 5)2001 1411 ( 3) 218 ( 3) 445 ( 6) 1194 ( 5)2002 1332 ( 3) 184 ( 3) 444 ( 6) 1264 ( 5)2003 1135 ( 3) 159 ( 3) 454 ( 6) 1443 ( 6)2004 1340 ( 3) 139 ( 2) 581 ( 8) 1616 ( 7)2005 1403 ( 3) 169 ( 3) 546 ( 7) 1762 ( 7)2006 1152 ( 3) 139 ( 2) 465 ( 6) 1992 ( 8)2007 637 ( 2) 79 ( 1) 275 ( 4) 1904 ( 8)2008 1137 ( 3) 238 ( 4) 309 ( 4) 1826 ( 8)2009 860 ( 2) 145 ( 2) 255 ( 3) 1730 ( 7)2010 209 ( 1) 19 (<1) 68 ( 1) 581 ( 2)

Median follow-up of recipients, mos 90 (<1-465) 85 (1-443) 57 (<1-252) 59 (<1-275)

20


Accrual Summary for First Transplants with Samples Available for Recipient and/or Donor through the NMDP for Adult Donor Transplants

Samples Available for Recipient and Donor

Samples Available for Recipient Only

Samples Available for Donor Only

Variable N Eval N (%) N

Eval N (%) N Eval N (%)

Number of cases 13469 3613 2659Number of centers 188 167 176Age, median (range), years 13469 37 (<1-78) 3613 33 (<1-79) 2657 33 (<1-75)Age at transplant 13469 3613 2657

< 10 y 1608 (12) 605 (17) 451 (17)10-20 y 1708 (13) 516 (14) 379 (14)20-29 y 1912 (14) 499 (14) 380 (14)30-39 y 2140 (16) 543 (15) 406 (15)40-49 y 2629 (20) 662 (18) 480 (18)≥ 50 y 3472 (26) 788 (22) 561 (21)

Male sex 13468 7811 (58) 3613 2105 (58) 2657 1589 (60)Karnofsky prior to transplant > 90% 12654 8917 (70) 3432 2414 (70) 2494 1779 (71)

HLA-A, B, DRB1 groups – high-res 12835 1661 1035

0/6 - 2/6 33 (<1) 20 ( 1) 03/6 156 ( 1) 99 ( 6) 1 (<1)4/6 650 ( 5) 231 (14) 16 ( 2)5/6 2945 (23) 396 (24) 210 (20)6/6 9051 (71) 915 (55) 808 (78)

HLA high-res. typed and audited 11997 11888 (99) 258 252 (98) 94 90 (96)Disease status at transplant 13469 3613 2659

Early 4133 (31) 1130 (31) 753 (28)Intermediate 2949 (22) 883 (24) 617 (23)Advanced 2553 (19) 652 (18) 536 (20)Non-malignant disease / Other 3834 (28) 948 (26) 753 (28)

Graft type 13469 3613 2659 Bone marrow 8151 (61) 2118 (59) 1770 (67)Peripheral blood 5210 (39) 848 (23) 873 (33)Cord blood 0 624 (17) 0Other 108 ( 1) 23 (<1) 16 ( 1)

Conditioning regimen 13469 3613 2659 Myeloablative 9881 (73) 2763 (76) 1983 (75)Reduced intensity 2327 (17) 545 (15) 444 (17)Non-myeloablative 1065 ( 8) 265 ( 7) 183 ( 7)Other/To be determined 196 ( 1) 40 ( 1) 49 ( 2)

Donor age, median (range), years 11658 34 (18-61) 2652 31 (18-61) 2514 34 (18-59)Donor age 12050 3135 2491

< 20 1305 (11) 638 (20) 284 (11)20-29 3029 (25) 787 (25) 613 (25)30-39 4030 (33) 944 (30) 811 (33)40-49 2859 (24) 607 (19) 598 (24)≥ 50 827 ( 7) 159 ( 5) 185 ( 7)

21



Samples Available for

Recipient and Donor Samples Available for Recipient Only




Disease at transplant 13464 3613 2657 AML 4153 (31) 1163 (32) 793 (30)ALL 2405 (18) 680 (19) 536 (20)Other leukemia 569 ( 4) 133 ( 4) 107 ( 4)CML 2459 (18) 666 (18) 475 (18)MDS/MPS 1668 (12) 401 (11) 298 (11)Non-Hodgkin’s lymphoma 920 ( 7) 200 ( 6) 142 ( 5)HD-Hodgkin’s lymphoma 59 (<1) 14 (<1) 9 (<1)MYE-plasma cell disorder, MM 84 ( 1) 23 ( 1) 13 (<1)Other malignancies 34 (<1) 10 (<1) 7 (<1)Breast cancer 4 (<1) 0 0Severe aplastic anemia 465 ( 3) 111 ( 3) 109 ( 4)Inherited ab erythro. diff-funct. 162 ( 1) 52 ( 1) 32 ( 1)SCID & other immune deficienc. 194 ( 1) 59 ( 2) 58 ( 2)

Inherited abnormal. of platelets 12 (<1) 4 (<1) 3 (<1)Inherited disorder of metabolism 169 ( 1) 61 ( 2) 41 ( 2)

Histiocytic disorders 95 ( 1) 33 ( 1) 28 ( 1)Other 12 (<1) 3 (<1) 6 (<1)

GVHD prophylaxis 13469 3613 2659 FK506 ± MMF ± MTX ± steroids ± other 5329 (40) 1206 (33) 868 (33)

FK506 ± other 474 ( 4) 121 ( 3) 75 ( 3)CsA + MTX ± other 4649 (35) 1228 (34) 1035 (39)CsA ± other 1727 (13) 731 (20) 428 (16)MMF ± other 36 (<1) 12 (<1) 5 (<1)MTX ± other 110 ( 1) 20 ( 1) 13 (<1)T-cell depletion 607 ( 5) 170 ( 5) 148 ( 6)Other / To be determined 537 ( 4) 125 ( 3) 87 ( 3)

Donor/Recipient sex match 2926 799 637 Male/Male 1110 (38) 321 (40) 220 (35)Male/Female 649 (22) 168 (21) 137 (22)Female/Male 598 (20) 181 (23) 134 (21)Female/Female 569 (19) 129 (16) 146 (23)

Donor/Recipient CMV match 13469 3613 2659 Negative/Negative 3832 (28) 882 (24) 771 (29)Negative/Positive 3779 (28) 851 (24) 810 (30)Positive/Negative 1732 (13) 422 (12) 345 (13)Positive/Positive 2413 (18) 582 (16) 513 (19)Unknown 1713 (13) 876 (24) 220 ( 8)

22



Samples Available for

Recipient and Donor Samples Available for Recipient Only




Year of transplant 13469 3613 2659 1987 0 0 2 (<1)1988 54 (<1) 9 (<1) 8 (<1)1989 140 ( 1) 6 (<1) 9 (<1)1990 195 ( 1) 20 ( 1) 30 ( 1)1991 278 ( 2) 50 ( 1) 60 ( 2)1992 309 ( 2) 38 ( 1) 90 ( 3)1993 329 ( 2) 68 ( 2) 125 ( 5)1994 418 ( 3) 109 ( 3) 111 ( 4)1995 467 ( 3) 185 ( 5) 115 ( 4)1996 504 ( 4) 227 ( 6) 125 ( 5)1997 588 ( 4) 222 ( 6) 138 ( 5)1998 547 ( 4) 259 ( 7) 137 ( 5)1999 647 ( 5) 217 ( 6) 130 ( 5)2000 794 ( 6) 157 ( 4) 109 ( 4)2001 744 ( 6) 159 ( 4) 106 ( 4)2002 585 ( 4) 127 ( 4) 289 (11)2003 770 ( 6) 188 ( 5) 273 (10)2004 995 ( 7) 288 ( 8) 184 ( 7)2005 1151 ( 9) 282 ( 8) 162 ( 6)2006 1316 (10) 392 (11) 158 ( 6)2007 1249 ( 9) 196 ( 5) 143 ( 5)2008 670 ( 5) 113 ( 3) 73 ( 3)2009 595 ( 4) 231 ( 6) 69 ( 3)2010 124 ( 1) 70 ( 2) 13 (<1)

Median follow-up of recipients, mo 4299 61(<1-257) 959 65 (1-231) 879 76 (3-248)

23


Accrual Summary for First Transplants with Samples Available for Recipient and/or Cord Blood Unit(s) through the NMDP for Cord Blood Transplants

Samples Available -

Recipient and UCB Unit(s)

Samples Available - Recipient

Only

Samples Available – 2 or 3 UCB Units Only

Samples Available - Single UCB Unit Only

Variable N (%) N (%) N (%) N (%)Number of cases 1107 938 77 169Number of centers 111 105 21 69Number of cord blood units in transplant

One 748 (68) 449 (48) 0 169 (100)Two 357 (32) 359 (38) 74 (96) 0Three 2 (<1) 2 (<1) 3 ( 4) 0Unknown 0 128 (14) 0 0

Age, median (range), years 15 (<1-80) 19 (<1-73) 40 (1-73) 6 (<1-68)Age at transplant, by decade

< 10 y 438 (40) 323 (34) 10 (13) 107 (63)10-20 y 204 (18) 155 (17) 4 ( 5) 25 (15)20-29 y 89 ( 8) 117 (12) 7 ( 9) 10 ( 6)30-39 y 79 ( 7) 77 ( 8) 16 (21) 3 ( 2)40-49 y 95 ( 9) 88 ( 9) 14 (18) 8 ( 5)≥ 50 y 202 (18) 178 (19) 26 (34) 16 ( 9)

Male sex 597 (54) 530 (57) 50 (65) 98 (58)Karnofsky prior to transplant > 90% 733 (73) 614 (73) 56 (79) 112 (77)HLA-A, B, DRB1 groups – high-res

0/6 - 2/6 27 ( 3) 28 ( 4) 6 ( 9) 2 ( 2)3/6 131 (14) 131 (20) 11 (18) 17 (14)4/6 394 (43) 281 (43) 34 (55) 42 (35)5/6 253 (28) 174 (26) 11 (18) 41 (34)6/6 112 (12) 47 ( 7) 0 17 (14)

HLA high resolution typed and audited 204 (18) 125 (13) 0 1 ( 1)Disease status at transplant

Early 260 (23) 238 (25) 16 (21) 28 (17)Intermediate 268 (24) 231 (25) 19 (25) 35 (21)Advanced 112 (10) 115 (12) 12 (16) 12 ( 7)Non-malignant disease / Other 467 (42) 354 (38) 30 (39) 94 (56)

Conditioning regimen Myeloablative 693 (63) 582 (62) 31 (40) 108 (64)Reduced intensity 158 (14) 145 (15) 19 (25) 25 (15)Non-myeloablative 179 (16) 155 (17) 23 (30) 22 (13)Other/To be determined 77 ( 7) 56 ( 6) 4 ( 5) 14 ( 8)

Donor/recipient CMV match Negative/Negative 438 (40) 361 (38) 21 (27) 63 (37)Negative/Positive 572 (52) 517 (55) 48 (62) 87 (51)Positive/Negative 0 0 0 0Positive/Positive 0 0 0 0Unknown 97 ( 9) 60 ( 6) 8 (10) 19 (11)

24


Accrual Summary for First Transplants with Samples Available for Recipient and/or Cord Blood Unit(s) through the NMDP for Cord Blood Transplants

Samples Available -

Recipient and UCB Unit(s)

Samples Available - Recipient

Only

Samples Available – 2 or 3 UCB Units Only

Samples Available - Single UCB Unit Only

Variable N (%) N (%) N (%) N (%)Disease at transplant

AML 379 (34) 346 (37) 36 (47) 29 (17)ALL 257 (23) 224 (24) 15 (19) 40 (24)Other leukemia 34 ( 3) 36 ( 4) 0 3 ( 2)CML 22 ( 2) 27 ( 3) 4 ( 5) 3 ( 2)MDS/MPS 97 ( 9) 77 ( 8) 8 (10) 16 ( 9)Non-Hodgkin’s lymphoma 25 ( 2) 13 ( 1) 0 5 ( 3)HD-Hodgkin’s lymphoma 46 ( 4) 38 ( 4) 6 ( 8) 5 ( 3)MYE-plasma cell disorder, MM 4 (<1) 4 (<1) 0 1 ( 1)Other malignancies 3 (<1) 3 (<1) 0 0Breast cancer 31 ( 3) 29 ( 3) 4 ( 5) 3 ( 2)Severe aplastic anemia 24 ( 2) 25 ( 3) 0 9 ( 5)Inherited ab erythro. diff-funct. 65 ( 6) 41 ( 4) 0 23 (14)SCID & other immune deficienc. 2 (<1) 3 (<1) 0 1 ( 1)Inherited abnormal. of platelets 75 ( 7) 42 ( 4) 3 ( 4) 21 (12)Inherited disorder of metabolism 36 ( 3) 23 ( 2) 0 7 ( 4)Histiocytic disorders 2 (<1) 5 ( 1) 0 0Other 4 (<1) 2 (<1) 1 ( 1) 3 ( 2)

GVHD prophylaxis FK506 ± MMF ± MTX ± steroids ± other 349 (32) 335 (36) 25 (32) 39 (23)

FK506 ± other 33 ( 3) 40 ( 4) 8 (10) 12 ( 7)CsA + MTX ± other 33 ( 3) 44 ( 5) 0 13 ( 8)CsA ± other 625 (56) 479 (51) 36 (47) 90 (53)MMF ± other 6 ( 1) 0 0 2 ( 1)MTX ± other 3 (<1) 2 (<1) 0 0Other / To be determined 58 ( 5) 38 ( 4) 8 (10) 13 ( 8)

Year of transplant 2000 1 (<1) 1 (<1) 0 4 ( 2)2001 6 ( 1) 15 ( 2) 0 6 ( 4)2002 2 (<1) 11 ( 1) 0 02003 7 ( 1) 35 ( 4) 0 1 ( 1)2004 12 ( 1) 32 ( 3) 0 7 ( 4)2005 35 ( 3) 73 ( 8) 4 ( 5) 9 ( 5)2006 44 ( 4) 197 (21) 2 ( 3) 10 ( 6)2007 238 (21) 78 ( 8) 12 (16) 34 (20)2008 304 (27) 138 (15) 29 (38) 46 (27)2009 310 (28) 257 (27) 18 (23) 35 (21)2010 148 (13) 101 (11) 12 (16) 17 (10)

Median follow-up of recipients, mo 21 (1-96) 24 (1-96) 12 (1-36) 21 (1-121)

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Study Proposal 0610-02 Study Title: Impact of amino acid substitution at peptide binding pockets of HLA class I molecules on hematopoietic cell transplantation (HCT) outcome Joseph Pidala, MD, MS, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA Claudio Anasetti, MD, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA Introduction: Donor-recipient disparity at HLA loci is associated with greater risk for severe acute graft-versus-host disease as well as inferior survival after unrelated donor allogeneic hematopoietic cell transplantation. The impact of amino acid substitution at peptide binding pockets of the HLA molecule is incompletely understood. A systematic examination of this problem would both provide mechanistic insights into donor-recipient immunoreactivity, and also provide an enhanced method for the informed selection of unrelated volunteer donors. Hypothesis: Amino acid residue substitution at peptide binding pockets of the HLA class I molecule adversely affects transplantation outcome. Specific Aim: Examine the impact of amino acid substitution at HLA class I peptide binding pockets on grade III-IV aGVHD, non-relapse mortality and overall survival following unrelated donor allogeneic hematopoietic cell transplantation. Scientific Justification: Given the limited likelihood of finding a HLA identical sibling donor for potential candidates for allogeneic hematopoietic cell transplantation, there is an important need for the identification of suitably matched unrelated volunteer donors. Molecular typing methods have allowed the definition of allelic disparity between potential donors and recipients not possible with serologic typing methods. Seminal work has characterized the importance of allelic disparity at HLA loci in determining transplantation outcomes including severe acute graft-versus-host disease (aGVHD), non-relapse mortality, and overall survival.1-7 Lee, et al conducted an analysis of NMDP data from 3,857 donor-recipient pairs typed with high-resolution DNA matching at HLA-A, -B, -C, -DRB1, -DQB1, -DQA1, -DPB1, and -DPA1 alleles. Single allele or antigen-level mismatches were associated with greater risk of aGVHD, mortality, and worsened overall survival, and multiple loci mismatches further increased these risks.3 This work has demonstrated the adverse impact of HLA loci allele disparity, as well as the relative importance of disparity at specific loci. These considerations guide the selection of potential unrelated donors. These studies, however, have not defined the importance of specific donor-recipient allele mismatch combinations. In contrast, emerging data has begun to elucidate the impact of specific ‘non-permissive’ allele mismatches on transplantation outcome. Several reports have demonstrated the relationship between specific HLA-DPB1 non-permissive allele mismatches and graft rejection, severe aGVHD, and mortality.8-11 In the largest report to date, Kawase, et al have examined the impact of donor-recipient HLA allele mismatch combinations in an analysis of 5,210 donor-recipient pairs from the Japan Marrow Donor Program.12 In multivariable analysis, 15 non-permissive (defined by their independent prediction of grade III-IV aGVHD) HLA allele mismatch combinations were identified. These included 4 in HLA-A, 1 in HLA-B, 7 in HLA-C, 1 in HLA-DRB1, and 2 in HLA-DPB1. Those with one or greater non-permissive allele mismatches suffered a significantly increased hazard for grade III-IV aGVHD, as well as inferior overall survival. Interestingly, those without non-permissive allele mismatches, but with other allele

26


mismatches had similar severe aGVHD and survival compared to those who were completely matched at all loci. These data have provided insight into the impact of specific allele mismatch combinations on transplantation outcome. In this analysis of 5,210 donor-recipient pairs from the Japan Marrow Donor Program, Kawase, et al also demonstrated that amino acid substitution at position 9 and 116 of the HLA-A molecule, as well as those at positions 9, 77, 80, 99, 116, and 156 in the HLA-C molecule significantly predicted grade III-IV aGVHD.12 With the exception of positions 77 and 80 (associated with KIR2DL ligand in HLA-C), these amino acid positions reside within important peptide binding pockets in the HLA class I molecule. These positions of interest are supported by allied investigation.13-20 Substitution at such amino acid residues of the HLA class I molecule would be expected to alter peptide binding and presentation of antigen to T cells, and thus have importance in determining donor-recipient alloreactivity. The investigation proposed here would examine a focused question of the impact of amino acid substitution at these key positions on HCT outcome utilizing NMDP/CIBMTR data. Patient Eligibility Population:

1. First allogeneic hematopoietic cell transplant recipient and unrelated donor pairs 2. HCT date from 1988 to 2003 (this time frame is selected based on date range in existing NMDP

retrospective high-resolution typing project data set; if possible to extend further, would consider a range of 1988 to 2009, for a one year minimum follow up)

3. Complete high resolution HLA typing performed 4. Diagnoses:

a. Acute myelogenous leukemia b. Acute lymphoblastic leukemia c. Chronic myelogenous leukemia d. Myelodysplastic syndrome

5. Age: no age range limitation; will consider all ages available in data set, and address the impact of age in multivariable modeling.

6. Conditioning regimen: no restrictions; will consider both myeloablative and reduced-intensity regimens in analysis.

7. Graft source: will include both bone marrow and peripheral blood mobilized stem cells Data Requirements: Data collection forms required:

1. Pre-transplant essential data (2400) 2. Confirmation of HLA typing (2500) 3. Post-transplant essential data (2450)

Variables to gather from existing CIBMTR data collection forms: Outcomes of interest:

1. Overall survival (defined as time from date of transplant until death or last follow up) 2. Cumulative incidence of non-relapse mortality 3. Cumulative incidence of grade III-IV aGVHD

Variables of interest: 1. Year of HCT 2. Donor and recipient age 3. Donor and recipient sex 4. Recipient race 5. Donor and recipient CMV serostatus 6. Karnofsky performance status prior to HCT 7. Disease, and remission status

27


8. CIBMTR disease risk category 9. Graft type (peripheral blood, bone marrow) 10. Conditioning regimen (myeloablative, reduced intensity) 11. AGVHD prophylaxis 12. Additional agents received (ATG, campath, rituximab)

Sample Requirements (if study will use biologic samples from the NMDP Research Sample Repository): No NMDP samples are requested in this protocol, only high-resolution HLA typing project data. Study Design (Scientific Plan): We propose examining the independent impact of amino acid substitution at these key (9, 77, 99, 116, and 156) residues on the outcomes of grade III-IV aGVHD, non-relapse mortality and overall survival among unrelated donor-recipient pairs in the existing NMDP database. To estimate the feasibility of this study, we have determined the frequency of amino acid substitution at these residues from existing data (Petersdorf, EW, et al),21 and estimated projected numbers (see table on following page) for the NMDP data (Lee, SJ, et al).3 In the planned analysis, multivariable modeling will examine the association between specific amino acid substitution at these residues and the above outcomes. Analysis will be performed for each residue separately, with comparison for each made to donor-recipient pairs matched at these residues. Another potential approach would be to perform this analysis according to the number of these residues mismatched. In all analyses, patient, disease, and transplant variables will be considered as covariates in multivariable models.

28


Estimated frequency of amino acid substitution at residues of interest in peptide binding region of HLA molecule

Petersdorf (observed) Lee (expected) 0 7 53 Only other substitutions 4 30 116 10 75 77 9 68 99 3 23 156 3 23 9 2 15 9+77+99+116+156 14 105 9+77+156 10 75 9+99+156 7 53 9+156 6 45 9+99+116 5 38 77+156 3 23 116+156 3 23 77+116+156 1 8 77+116 1 8 9+77+99+116 1 8 9+77+99+156 1 8 9+99+116+156 1 8 77+99+156 1 8 77+99+116+156 1 8 Total 93 700 Zero mismatch 11 83 One 27 203 Two 13 98 Three 24 181 Four 3 23 Five 14 105

* “0” defined as no amino acid substitution at any residues within the peptide binding region; “only other substitutions” defined as amino acid substitution at residues other than those of interest (i.e. 9, 77, 99, 116, 156) within the peptide binding region. * Substitution at additional amino acid residues present for each of the categories of residues of interest (i.e. 9, 77, 99, 116, 156) References:

1. Davies SM, Kollman C, Anasetti C, et al. Engraftment and survival after unrelated-donor bone marrow transplantation: a report from the national marrow donor program. Blood. 2000;96:4096-4102.

2. Flomenberg N, Baxter-Lowe LA, Confer D, et al. Impact of HLA class I and class II high-resolution matching on outcomes of unrelated donor bone marrow transplantation: HLA-C mismatching is associated with a strong adverse effect on transplantation outcome. Blood. 2004;104:1923-1930.

3. Lee SJ, Klein J, Haagenson M, et al. High-resolution donor-recipient HLA matching contributes to the success of unrelated donor marrow transplantation. Blood. 2007;110:4576-4583.

4. Morishima Y, Sasazuki T, Inoko H, et al. The clinical significance of human leukocyte antigen (HLA) allele compatibility in patients receiving a marrow transplant from serologically HLA-A, HLA-B, and HLA-DR matched unrelated donors. Blood. 2002;99:4200-4206.

29


5. Petersdorf EW, Longton GM, Anasetti C, et al. Association of HLA-C disparity with graft failure after marrow transplantation from unrelated donors. Blood. 1997;89:1818-1823.

6. Sasazuki T, Juji T, Morishima Y, et al. Effect of matching of class I HLA alleles on clinical outcome after transplantation of hematopoietic stem cells from an unrelated donor. Japan Marrow Donor Program. N Engl J Med. 1998;339:1177-1185.

7. Speiser DE, Tiercy JM, Rufer N, et al. High resolution HLA matching associated with decreased mortality after unrelated bone marrow transplantation. Blood. 1996;87:4455-4462.

8. Crocchiolo R, Zino E, Vago L, et al. Nonpermissive HLA-DPB1 disparity is a significant independent risk factor for mortality after unrelated hematopoietic stem cell transplantation. Blood. 2009;114:1437-1444.

9. Fleischhauer K, Locatelli F, Zecca M, et al. Graft rejection after unrelated donor hematopoietic stem cell transplantation for thalassemia is associated with nonpermissive HLA-DPB1 disparity in host-versus-graft direction. Blood. 2006;107:2984-2992.

10. Ludajic K, Balavarca Y, Bickeboller H, et al. Impact of HLA-DPB1 allelic and single amino acid mismatches on HSCT. Br J Haematol. 2008;142:436-443.

11. Zino E, Frumento G, Marktel S, et al. A T-cell epitope encoded by a subset of HLA-DPB1 alleles determines nonpermissive mismatches for hematologic stem cell transplantation. Blood. 2004;103:1417-1424.

12. Kawase T, Morishima Y, Matsuo K, et al. High-risk HLA allele mismatch combinations responsible for severe acute graft-versus-host disease and implication for its molecular mechanism. Blood. 2007;110:2235-2241.

13. Ferrara GB, Bacigalupo A, Lamparelli T, et al. Bone marrow transplantation from unrelated donors: the impact of mismatches with substitutions at position 116 of the human leukocyte antigen class I heavy chain. Blood. 2001;98:3150-3155.

14. Hogan KT, Clayberger C, Bernhard EJ, et al. Identification by site-directed mutagenesis of amino acid residues contributing to serologic and CTL-defined epitope differences between HLA-A2.1 and HLA-A2.3. J Immunol. 1988;141:2519-2525.

15. Macdonald WA, Purcell AW, Mifsud NA, et al. A naturally selected dimorphism within the HLA-B44 supertype alters class I structure, peptide repertoire, and T cell recognition. J Exp Med. 2003;198:679-691.

16. Madden DR. The three-dimensional structure of peptide-MHC complexes. Annu Rev Immunol. 1995;13:587-622.

17. Marsh S, Parham, P, Barber, L. The HLA Facts Book: Academic Press; 2000. 18. Mattson DH, Shimojo N, Cowan EP, et al. Differential effects of amino acid substitutions in the

beta-sheet floor and alpha-2 helix of HLA-A2 on recognition by alloreactive viral peptide-specific cytotoxic T lymphocytes. J Immunol. 1989;143:1101-1107.

19. Shimojo N, Cowan EP, Engelhard VH, Maloy WL, Coligan JE, Biddison WE. A single amino acid substitution in HLA-A2 can alter the selection of the cytotoxic T lymphocyte repertoire that responds to influenza virus matrix peptide 55-73. J Immunol. 1989;143:558-564.

20. Tynan FE, Elhassen D, Purcell AW, et al. The immunogenicity of a viral cytotoxic T cell epitope is controlled by its MHC-bound conformation. J Exp Med. 2005;202:1249-1260.

21. Petersdorf EW, Hansen JA, Martin PJ, et al. Major-histocompatibility-complex class I alleles and antigens in hematopoietic-cell transplantation. N Engl J Med. 2001;345:1794-1800.

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Characteristics of recipients receiving first transplants for AML, ALL, CML or MDS that are high-resolution typed for HLA-A, -B, -C and –DRB1 and are 8/8 allele-matched or 7/8 allele-

matched with single mismatch at Class I locusa

Characteristics of patients N Eval N (%)Number of patients 7573Number of centers 176Recipient age, median (range), years 7572 39 (<1-74)Age at transplant 7572

≤ 10 y 607 ( 8)11-20 y 856 (11)21-30 y 1106 (15)31-40 y 1258 (17)41-50 y 1578 (21)Over 50 2167 (29)

Male sex 7571 4267 (56)Karnofsky prior to transplant > 90 6972 4903 (70)Disease at transplant 7573

AML 2989 (39)ALL 1624 (21)CML 1649 (22)MDS 1311 (17)

Disease status at transplant 7573Early 2956 (39)Intermediate 1994 (26)Advanced 1825 (24)Other 798 (11)

Graft type 7573Bone marrow 4370 (58)PBSC 3203 (42)

Conditioning regimen 7573Traditional myeloablative 5054 (67)Reduced intensity 1139 (15)Non-myeloablative 397 ( 5)Non-traditional myeloablative 849 (11)To be determined/Other 134 ( 2)GVHD prophylaxis 7573FK506 + (MTX or MMF or steroids) ± other 3249 (43)FK506 ± other 264 ( 3)CsA + MTX ± other 2690 (36)CsA ± other (no MTX) 780 (10)MMF ± other (no CsA) 18 (<1)MTX ± other (no CsA) 44 ( 1)T-cell depletion 279 ( 4)Other 249 ( 3)

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Continued. Characteristics of patients N Eval N (%)HLA Matching for HLA-A, -B, -C and -DRB1 7573

8/8 allele matched 5435 (72)Single MM at HLA-A 727 (10)Single MM at HLA-B 339 ( 4)Single MM at HLA-C 1072 (14)

Donor/Recipient sex match 6912 Male/Male 2647 (38)Male/Female 1706 (25)Female/Male 1246 (18)Female/Female 1313 (19)

Donor/Recipient CMV match 7573 Negative/Negative 2218 (29)Negative/Positive 2209 (29)Positive/Negative 979 (13)Positive/Positive 1355 (18)Unknown 812 (11)

Donor age, median (range), years 6873 35.6 (18.3-61.2)Donor age 7573

18-19 79 ( 1)20-29 1959 (26)30-39 2559 (34)40-49 1803 (24)50 and older 473 ( 6)Unknown 700 ( 9)

Year of transplant 7573 1988 14 (<1)1989 52 ( 1)1990 77 ( 1)1991 110 ( 1)1992 150 ( 2)1993 160 ( 2)1994 230 ( 3)1995 255 ( 3)1996 259 ( 3)1997 299 ( 4)1998 297 ( 4)1999 352 ( 5)2000 421 ( 6)2001 434 ( 6)2002 369 ( 5)2003 462 ( 6)2004 636 ( 8)2005 752 (10)2006 819 (11)2007 797 (11)2008 425 ( 6)2009 203 ( 3)

Median follow-up of recipients, mo (range) 2675 60 (2-252) a - Data has not been CAP-modeled.

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Study Proposal 1210-21 Study Title: Analysis of the NIMA effect on the outcome of unrelated PBSC/BM transplantation Gerhard Ehninger, University Hospital Carl Gustav Carus, Dresden, Germany Jon J. van Rood, Leiden University Medical Center, Leiden, The Netherlands Alexander H. Schmidt, DKMS German Bone Marrow Donor Center, Tuebingen, Germany Specific Aims: Analyze the NIMA effect for unrelated adult donor PBSC/BM transplantation for hematological malignancies, including myelodysplasia. Primary study end point:

1. Transplant-related mortality (TRM) Secondary study end points:

1. Neutrophil engraftment (ANC ≥ 500/mm3 for 3 consecutive lab values on different days) 2. Platelet engraftment (≥ 20 x 109/l, ≥ 50 x 109/l) 3. Incidence of grade II-IV and III-IV aGVHD 4. Incidence of cGVHD 5. Relapse 6. Overall mortality 7. Treatment failure (relapse or death)

Scientific Justification: The relevance of the NIMA effect, i.e., the lifelong modulating impact of non-inherited maternal antigens on the immune response of the child, for the outcome of allogeneic stem cell transplantation is under scientific debate for many decades. In haploidentical sibling transplantation, a reduction of GVHD risk after transplantation through the NIMA effect could be observed (van Rood et al., 2002). Especially for older patients, unrelated NIMA-matched cord blood transplantation resulted in lower transplant-related mortality, overall mortality, and treatment failure (van Rood et al., 2009). No studies so far have analyzed the NIMA effect for unrelated adult donor stem cell transplantation. The main problem in this setting lies in the general unavailability of maternal HLA phenotype data. In the proposed study, we will ask stem cell donors to provide maternal samples from buccal swabs for HLA typing. A verification of the NIMA effect for unrelated adult donor stem cell transplantation could have considerable impact on unrelated donor search. For example, a maternal sample could routinely be requested on the CT level. Patient eligibility population:

1. Patients with AML, ALL, CML, NHL, or MDS who have undergone a T-cell repleted unrelated bone marrow (BM) or peripheral blood stem cell (PBSC) donation.

2. HLA typing result of patients and donors must be available at high resolution at least for the HLA loci A, B, C, and DRB1.

3. Patient’s donor must be from DKMS German Bone Marrow Donor Center. 4. For donors with at least one mismatch: Sample of the donor’s mother must be available.

(Sample and HLA typing of the donor’s mother will be provided by DKMS.) Data requirements: Primary outcome:

- Transplant-related mortality: Death without evidence of disease recurrence. Event will be summarized by the cumulative incidence estimate with relapse as competing risk.

33


Secondary outcomes: - Neutrophil engraftment: Achievement of ANC ≥ 500/mm3 for 3 consecutive lab values on

different days. Event will be summarized by the cumulative incidence estimate with death as competing risk.

- Platelet engraftment (≥ 20 x 109/l, ≥ 50 x 109/l): Achievements of platelets ≥ 20 x 109/l and platelets ≥ 50 x 109/l. Events will be summarized by the cumulative incidence estimate with death as competing risk.

- Incidence of grade II-IV and III-IV aGVHD: Development of grades II-IV and III-IV acute GVHD (Glucksberg system). Events will be summarized by the cumulative incidence estimate with death as competing risk.

- Incidence of cGVHD: Development of symptoms in any organ system fulfilling the criteria of chronic GVHD. Event will be summarized by the cumulative incidence estimate with death as competing risk.

- Relapse: Recurrence of disease. Event will be summarized by the cumulative incidence estimate with TRM as competing risk.

- Overall mortality: Time to death from any cause. Event will be summarized by a survival curve. Cases will be censored at the time of last follow-up.

- Treatment failure (relapse or death): Time to death or relapse. Event will be summarized by a survival curve. Cases will be censored at the time of last follow-up.

Patient-related: - Age: in decades - Gender: female vs. male - Karnofsky score at transplant: < 90 vs. 90-100

Disease-related: - Disease at transplant: AML, ALL, CML, NHL and MDS - Disease stage at transplant: early vs. intermediate vs. advanced

Donor- and transplant-related: - Stem cell source: BM vs. PBSC - Conditioning regimen: myeloablative vs. non-myeloablative - GVHD prophylaxis: CSA + MTX +/- others vs. CSA + others (no MTX) vs. MTX + others

(no CSA) vs. FK506 +/- others vs. MMF +/- others vs. others - Time from diagnosis to transplant: ≤ 1 year vs. > 1 year - HLA match: no MM vs. 1 MM + NIMA match vs. 1 MM + NIMA MM vs. 2 MM + NIMA

match vs. 2MM + 1NIMA MM vs. 2MM + 2NIMA MM - Gender match: M-M vs. M-F vs. F-M vs. F-F - Donor recipient CMV status: -/- vs. -/+ vs. +/- vs. +/+ - Donor age: in decades - Year of transplant: 1992-2009 - ATG: yes vs. no

Sample Requirements: No repository samples are needed. Study Design:

- To summarize the characteristics of the data set, descriptive tables of patient-, disease-, and donor-/transplant-related variables will be reported. - For discrete variables, the number of cases and their respective percentages will be

calculated. X2 tests will be used to compare discrete variables between the various groups according to 5.5.5 with the “1 MM + NIMA MM” group as reference.

- For continuous variables, the median and ranges will be calculated. The Kruskal-Wallis test will be used to compare continuous variables between the various groups according to 5.5.5 with the “1 MM + NIMA MM” group as reference.

- Probabilities for overall mortality (5.2.6) or treatment failure (5.2.7) will be calculated using the Kaplan-Meier estimator with variance estimated by Greenwood’s formula. Values for other outcomes given in 5.1 and 5.2 will be calculated according to cumulative incidence using a Taylor series linear approximation to estimate the variance.

34


- Multivariate analysis will be performed using the proportional hazards model to compare the various groups according to 5.5.5 with the “1 MM + NIMA MM” group as reference.

Additional Information: - Based on actual donor-recipient pairs and allele frequencies of the donor population, we

estimate about 5% of all mismatches to be NIMA matches. We expect to observe about 50% of the NIMA matches for the C locus.

- A pilot study was carried out in order to estimate the feasibility of collecting maternal DNA samples. Within this pilot, 150 donors were contacted and asked to forward information material and buccal swabs to their mothers. 68 mothers provided samples for HLA high-resolution typing of the mismatched locus of the original transplantation. 6 additional mothers provided their DKMS donor IDs and declared informed consent for scientific evaluation of their HLA data within the project, leading to a total return rate of 49.3% (74/150).Within the pilot, 3 NIMA matches were observed on the allele level (4 on the antigen level).

- Assumptions for power calculation: TRM for the 1 MM + NIMA MM group: 45%; TRM for the 1 MM + NIMA match group: 30%; 5% of MM are NIMA matches; type I error α=0.05; study power 1-β=0.8.

- From these data we calculated a required sample size of 1,804 donor-recipient pairs (including 90 with NIMA matches) using Fisher’s exact test. Based on the return rate of 49.3% that was derived from the pilot study, 3,657 donors have to be contacted to reach this sample size.

References: 1. Van Rood JJ, Loberiza FR, Zhang MJ, et al. Effect of tolerance to noninherited maternal

antigens on the occurrence of graft-versus-host disease after bone marrow transplantation from a parent or an HLA-haploidentical sibling. Blood 2002; 99(5): 1572-7.

2. Van Rood JJ, Stevens CE, Smits J, et al. Reexposure of cord blood to noninherited maternal HLA antigens improves transplant outcome in hematological malignancies. Proc Natl Acad Sci USA. 2009; 106: 19952-7.

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Characteristics of recipients receiving first t-replete transplants for AML, ALL, CML, MDS or NHL and are high-resolution typed for HLA-A, -B, -C and –DRB1 *

Characteristics of patients N Eval N (%) Number of patients 1381 Number of centers 115 Recipient age, median (range), years 1380 46 (<1-74) Age at transplant 1380 ≤ 10 y 51 ( 4) 11-20 y 100 ( 7) 21-30 y 177 (13) 31-40 y 182 (13) 41-50 y 296 (21) Over 50 y 574 (42)

Male sex 1379 789 (57) Karnofsky prior to transplant > 90 1221 783 (64) Disease at transplant 1381

AML 630 (46) ALL 237 (17) CML 135 (10) MDS 229 (17) NHL 150 (11)

Disease status at transplant 1381 Early 474 (34) Intermediate 282 (20) Advanced 279 (20) Other 346 (25)

Graft type 1381 Bone marrow 301 (22) PBSC 1080 (78)

HLA matching for HLA-A, -B, -C and -DRB1 1381 Well matched 1028 (75) Partially matched 294 (21) Mismatched 59 ( 4)

High-resolution typing for HLA-A,-B,-C and -DRB1 1381 8/8 allele-matched 1028 (74) 7/8 single mismatch 294 (21) 6/8 two mismatches 49 ( 4) 5/8 three mismatches 7 ( 1) 4/8 four mismatches 3 (<1)

Conditioning regimen 1381 Myeloablative 895 (65) Reduced intensity 282 (20) Nonmyeloablative 133 (10) Other/To be determined 71 ( 5)

GVHD prophylaxis 1381 FK506 + (MTX or MMF or Steroids) ± other 912 (66) FK506 ± other 61 ( 4) CsA + MTX ± other 233 (17) CsA ± other (no MTX) 106 ( 8) MMF ± other 3 (<1) MTX ± other (no CsA) 6 (<1) Other/To be determined 60 ( 4)

36


Continued.

Characteristics of patients N Eval N (%) Donor/recipient sex match 964

Male/Male 416 (43) Male/Female 255 (26) Female/Male 135 (14) Female/Female 158 (16)

Donor/recipient CMV match 1381 Negative/Negative 356 (26) Negative/Positive 335 (24) Positive/Negative 100 ( 7) Positive/Positive 147 (11) Unknown 443 (32)

Donor age, median (range), years 951 31.9 (18.3-60) Donor age 1381

18-19 32 ( 2) 20-29 367 (27) 30-39 362 (26) 40-49 164 (12) 50 and older 26 ( 2) Unknown 430 (31)

Year of transplant 1381 1995 1 (<1) 1996 0 1997 4 (<1) 1998 1 (<1) 1999 13 ( 1) 2000 30 ( 2) 2001 20 ( 1) 2002 32 ( 2) 2003 69 ( 5) 2004 130 ( 9) 2005 171 (12) 2006 236 (17) 2007 260 (19) 2008 189 (14) 2009 192 (14) 2010 33 ( 2)

Median follow-up of recipients, mo (range) 572 36 (1-149) * Data has not been CAP-modeled.

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Study Proposal 1210-45 Study Title: Impact of CTLA4 single nucleotide polymorphisms on outcome after unrelated donor transplant Madan Jagasia, MBBS, MS, Vanderbilt-Ingram Cancer Center, Department of Medicine, Nashville, TN USA William Clark, MD, Massey Cancer Center, Virginia Commonwealth University, Richmond, VA USA Bipin N Savani, MD, Vanderbilt-Ingram Cancer Center, Department of Medicine, Nashville, TN USA Specific Aims:

1. To study the impact of CTLA4 SNPS on cumulative incidence of relapse after unrelated donor transplant for hematological malignancies

2. To study the impact of CTLA4 SNPS on cumulative incidence of acute and chronic GVHD after unrelated donor transplant for hematological malignancies

3. To study the interaction of CTLA4 SNPs and dose of thymoglobulin/ATG in cumulative incidence of relapse and GVHD (both acute and chronic) after unrelated donor transplant for hematological malignancies

Scientific Justification: Allogeneic hematopoietic cell transplantation (allo-HCT) is a potentially curative treatment modality for a variety of hematological malignancies. Graft-versus-host disease (GVHD) and relapse of the underlying malignancies remain an important problem. Although a variety of clinical factors, both pre and post-transplant have been identified to predict these events, true biologic prediction and individualization of outcomes has eluded us. Cytotoxic T-Lymphocyte Antigen 4 (CTLA4): The fundamental event in the cascade of both GVHD and graft-versus-leukemia (GVL) effect remain the initial interaction between the antigen presenting cell (APC) and donor T cell. In addition to the cognate T-cell receptor (TCR)-Human Leukocyte Antigen (HLA) recognition, engagement of the co-receptors on APC and T cells is required for the occurrence of a productive immune response. These-co-stimulatory signals are mediated via CD80/CD86 molecules on APCs and CD 28 or CTLA4 receptors on T cells, leading to positive or negative regulation of T-cell activation. CTLA4 (or CD152) is a T-cell surface molecule that negatively regulates T-cell activation. It also plays a crucial role in maintenance of T cell homeostasis, balance of T0helper cell subsets, T cell differentiation and is establishment of central and peripheral T cell tolerance. Multiple investigators have studied various SNPs of CTLA4 gene and the association with clinical outcomes. Results are discordant due to inhomogeneous datasets, and differing ethnic populations. Most studies have shown an association with GVHD outcomes.1-4 Pilot Data from Vanderbilt University Medical Center (ASBMT 2011): Adult patients undergoing allo-HCT (cord transplant excluded) at a single center (1999-2008), with a minimum survival of 120 days, and with the availability of pre-transplant recipient and donor germline DNA samples were included (n=164). Ten tagSNPs of the CTLA-4 gene (rs231775, rs231779, rs11571315, rs231777, rs3087243, rs16840252, rs231725, rs4553808, rs10197010, rs11571316) were identified using previously published criteria. All SNPs analyzed passed quality control [test of Hardy-Weinberg equilibrium (HWE) P > 0.001, MAF > 0.10, SNP call rate > 0.95, and pair-wise r2 value less than 0.8]. In univariate analyses, the donor SNP rs4553808 A/A genotype was associated with improved RFS (P=0.019) and OS (P=0.033) compared to the A/G or G/G genotype. Multivariable analysis, adjusted for

38


stem cell source (peripheral blood vs. bone marrow), GVHD subtype (classic/overlap vs. acute subtypes/none), and disease risk (low vs. intermediate vs. high) were performed to test the association of the significant SNPs with RFS and OS. rs4553808 A/A genotype (HR 2.44, P=0.015), classic/overlap GVHD (HR 2.98, P=0.002) and low/intermediate disease risk (HR 2.16, P=0.03) were independent predictors of superior OS. rs4553808 A/A genotype (HR 2.39, P=0.016), and classic/overlap GVHD (HR 3.84, P=0.001) were independent predictors of superior RFS. Our study confirms reports that genetic variation in donor CTLA-4 is associated with outcome after allo-HCT and may allow for identification of patient subsets that may benefit from pre-emptive modulation of immunosuppressive therapy. Our study contrasts with the other published studies, as the number of SNPs anlayzed is more extensive. rs4553808 is in the promoter region of CTLA4 and has been associated with response to ipilumamab (CTLA4-antibody) in melanoma.5 Thymoglobulin/ATG and Unrelated Donor Transplant: The role of thymoglobulin/ATG in unrelated transplant continues to evolve. The dosing of thymoglobulin/ATG remains controversial with data suggesting a range of effective doses.6-11 The dosing of thymoglobulin/ATG is weight based, while the target of thymoglobulin/ATG i.e. donor T cells and host APCs are not a function of weight. Our hypothesis is that patients with CTLA4 SNPs that render the donor T-cells “hyporesponsive” will be at a higher risk of ATG/Thymoglobulin related negative events (i.e. relapse) and will have a significantly decreased chance of acute and chronic GVHD. In order to do such an analysis, a large dataset of patients with a uniform diagnosis and preparative regimen are needed. Although it would be ideal to study only one type of regimen intensity, most studies have shown similar outcome data irrespective of regimen intensity, suggesting that efficacy of GVL in effecting a cure is independent of regimen intensity. The efficacy of GVT is a dependent on tumor type, thus we would propose to limit the analysis to only AML, as relapse after allo-SCT continues to remain a major challenge in this diagnostic category. CIBMTR is well suited and poised to conduct this study. DNA samples will be needed form the NMDP biorepository. The SNP analysis can be conducted at Vanderbilt University Medical Center. If our hypothesis is correct, it will allow us to individualize the use of thymoglobulin in recipients of unrelated allo-HCT. Patient Eligibility Population:

‐ Adult (≥ 18 years) recipients of first unrelated allo-HCT (excluding cord transplants) ‐ AML in CR1 or CR2 ‐ HLA 8/8, 7/8. Haplo-identical donors excluded ‐ Transplanted between 2000-2007 ‐ Any preparative regimen (myeloablative, reduced intensity, non-myeloablative) ‐ Any GVHD prophylaxis (except in vitro T cell depletion, or use of in vivo/in vitro alemtuzumab) ‐ Availability of adequate donor DNA samples for SNP analyses

Outcomes to be Studied:

1. Acute GVHD: Occurrence of grade II-IV GVHD will be analyzed based on Glucksberg’s staging of skin, gastrointestinal and liver disease.

2. Chronic GVHD: Occurrence of symptoms in any organ system fulfilling the criteria of chronic GVHD and classified as limited or extensive.

3. Relapse: time to onset of recurrence either in BM or extramedullary. This event will be summarized by cumulative incidence estimate with TRM as the competing risk.

39


4. Transplant-related mortality: time to death without evidence of disease relapse. This event will be summarized as cumulative incidence estimate with relapse as the competing risk.

5. Leukemia-free survival: will be defined as time to relapse or death from any cause. Patients are censored at last follow-up.

6. Overall survival: Time to death, patients censored at last follow-up.

Variables: Patient-related (at the time of transplant):

‐ Age at transplant: (continuous and <20 vs. 20-40 vs. >40) ‐ Gender: female vs. male ‐ Karnofsky performance status: <90% vs. ≥90%

Disease-related: ‐ CIBMTR risk status at transplant

Transplant-related: ‐ Conditioning regimen (TBI vs. non-TBI) ‐ Myeloablative versus other (reduced intensity plus non-myeloablative) ‐ Donor age 18-19 vs. 20-29 vs. 30-39 vs. > 40y ‐ Donor-recipient gender: M-M vs. M-F vs. F-M vs. F-F ‐ HLA-matching and degree of match (8/8; 7/8; 6/8) ‐ CMV status of donor and recipient: +/+ vs. +/- vs. -/- ‐ Source of stem cells: BM vs. PBSC ‐ CD34 cell dose, x 106/kg: BM, above median vs. below median; PBSC, above median vs. below

median ‐ CD3 cell dose, x 106/kg: BM, above median vs. below median; PBSC above median vs. below

median ‐ Year of transplant ‐ GVHD prophylaxis: CSA+/-other (not MTX) vs. CSA+MTX+/-other vs. FK506+/-other vs. T-

cell depletion ‐ T-cell depletion: Thymoglobulin or ATG, dose of thymoglobulin/ATG, number of days of

administration, time before stem cell infusion. Cohorts will be divided as thymoglobulin <5 mg/kg (ATG equivalent < 40 mg/kg); 5-7.5 mg/kg (40-60 mg/kg); >7.5 mg/kg (>60 mg/kg)

Post-transplant: ‐ Acute GVHD: none vs. II-IV, I-II vs. III-IV ‐ GVHD beyond day 100: late acute, chronic GVHD (limited or extensive) ‐ Relapse: yes/no ‐ Chimerism: (for reduced intensity and non-ablative only) CD3 and CD33 at day 30, day 100

CTLA4 SNPs: ‐ Donor SNPs

Study Design: Clinical: Descriptive tables of patient-, disease-, and transplant-related factors will be prepared. The tables will list median and range for continuous variables and percent of total for categorical variables. Characteristics of patients will be compared using the chi-square test for categorical variables and the Wilcoxon two-sample test for continuous variables. Comparing the outcomes in the different transplant groups will require adjustment for two sources of bias: differences in time to transplant and differences in the baseline patient characteristics. The date of achieving CR will be the starting point for both the groups. To adjust for differences in the baseline characteristics, Cox proportional hazards regression will be used. Probability of LFS and OS will be calculated using Kaplan-Meier estimator. Comparison of survival curves will be done using the log-rank test.

40


Genetic: Ten tagSNPs of the CTLA-4 gene (rs231775, rs231779, rs11571315, rs231777, rs3087243, rs16840252, rs231725, rs4553808, rs10197010, rs11571316) will be analyzed. Univariate logistic regression will performed to test for association between cumulative incidence of relapse (with TRM as competing risk), cumulative incidence of acute GVHD, and chronic GVHD and each SNP using an unadjusted, additive genetic model. P-values will be adjusted using false discovery rate (FDR). Multivariate analyses incorporating significant univariate clinical factors and significant SNPs (identified from univariate analyses) will be built. Interaction of CTLA4 SNPs and thymoglobulin use and dose will be specifically analyzed. Sample Size: The population frequencies of the CTLA4 SNPs are known. In our pilot study, rs 4553808 was an independent predictor of relapse adjusted for other important pre-transplant predictors. Sample size estimate is based on the assumption of finding this SNP as a significant predictor in the proposed study. Based on data from CIBMTR, there are 1172 cases of AML that meet our eligibility criteria. The minor allele of rs 4553808 is G and has a population frequency of 12%-26.3%. Assuming a conservative estimate of 12%, the population frequency of the 3 genotypes GG, AG and AA are 1.44%, 21.1% and 77.4%. Thus the ratio of AA to AG+ GG is 3.43 (77.5%/22.5%). We assumed ratio of AA to AG+ GG as 3:1 for purpose of sample size calculation. Assuming an effect size of effect size of 1.5, accrual period of 7 years, follow up of 3 years, alpha of </= 0.01, power of 0.99, we will need 288 patients. As the regimen intensity is a known variable in impacting relapse, we would like to use a random subset of 288 patients from each regimen intensity (i.e., ablative, reduced intensity, and non-ablative). This strategy will minimize the impact of regimen intensity being a potential confounder.

References:

1. Perez-Garcia A, De la Camara R, Roman-Gomez J et al. CTLA-4 polymorphisms and clinical outcome after allogeneic stem cell transplantation from HLA-identical sibling donors. Blood 2007;110:461-467.

2. Vannucchi AM, Guidi S, Guglielmelli P et al. Significance of CTLA-4 and CD14 genetic polymorphisms in clinical outcome after allogeneic stem cell transplantation. Bone Marrow Transplant. 2007;40:1001-1002.

3. Azarian M, Busson M, Lepage V et al. Donor CTLA-4 +49 A/G*GG genotype is associated with chronic GVHD after HLA-identical haematopoietic stem-cell transplantations. Blood 2007;110:4623-4624.

4. Sellami MH, Bani M, Torjemane L et al. Effect of donor CTLA-4 alleles and haplotypes on graft-versus-host disease occurrence in Tunisian patients receiving a human leukocyte antigen-identical sibling hematopoietic stem cell transplant. Hum.Immunol. 2010

5. Breunis WB, Tarazona-Santos E, Chen R et al. Influence of cytotoxic T lymphocyte-associated antigen 4 (CTLA4) common polymorphisms on outcome in treatment of melanoma patients with CTLA-4 blockade. J.Immunother. 2008;31:586-590.

6. Bacigalupo A, Lamparelli T, Bruzzi P et al. Antithymocyte globulin for graft-versus-host disease prophylaxis in transplants from unrelated donors: 2 randomized studies from Gruppo Italiano Trapianti Midollo Osseo (GITMO). Blood 2001;98:2942-2947.

7. Bacigalupo A, Lamparelli T, Barisione G et al. Thymoglobulin prevents chronic graft-versus-host disease, chronic lung dysfunction, and late transplant-related mortality: long-term follow-up of a randomized trial in patients undergoing unrelated donor transplantation. Biol.Blood Marrow Transplant. 2006;12:560-565.

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8. Finke J, Schmoor C, Lang H, Potthoff K, Bertz H. Matched and mismatched allogeneic stem-cell transplantation from unrelated donors using combined graft-versus-host disease prophylaxis including rabbit anti-T lymphocyte globulin. J.Clin.Oncol. 2003;21:506-513.

9. Finke J, Bethge WA, Schmoor C et al. Standard graft-versus-host disease prophylaxis with or without anti-T-cell globulin in haematopoietic cell transplantation from matched unrelated donors: a randomised, open-label, multicentre phase 3 trial. Lancet Oncol. 2009;10:855-864.

10. Schleuning M, Gunther W, Tischer J, Ledderose G, Kolb HJ. Dose-dependent effects of in vivo antithymocyte globulin during conditioning for allogeneic bone marrow transplantation from unrelated donors in patients with chronic phase CML. Bone Marrow Transplant. 2003;32:243-250.

11. Zander AR, Kroger N, Schleuning M et al. ATG as part of the conditioning regimen reduces transplant-related mortality (TRM) and improves overall survival after unrelated stem cell transplantation in patients with chronic myelogenous leukemia (CML). Bone Marrow Transplant. 2003;32:355-361.

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Characteristics of adult recipients with AML in CR1 or CR2 receiving first T-replete transplants that are high-resolution typed and 7/8 or 8/8 for HLA-A, -B, -C and –DRB1 *

Characteristics of patients N Eval N (%) Number of patients 1172

Number of centers 119 Recipient age, median (range), years 1172 47 (18-74) Age at transplant 1172

18-20 y 35 ( 3) 21-30 y 178 (15) 31-40 y 162 (14) 41-50 y 282 (24) Over 50 y 515 (44)

Male sex 1172 602 (51) Karnofsky prior to transplant > 90 1065 777 (73) Disease status at transplant 1172

1st complete remission (Early) 755 (64) 2nd complete remission (Intermediate) 417 (36)


Conditioning regimen 1172 Myeloablative 757 (65) Reduced intensity 274 (23) Non-myeloablative 117 (10) To Be Determined/Other 24 ( 2)

GVHD prophylaxis 1172 FK506 + (MTX or MMF or steroids) ± other 700 (60) FK506 ± other 45 ( 4) CsA + MTX ± other 234 (20) CsA ± other (no MTX) 150 (13) MMF ± other (no CsA) 4 (<1) MTX ± other (no CsA) 2 (<1) Other/To be determined 37 ( 3)

HLA matching out of 8 (high-resolution) 1172 7/8 293 (25) 8/8 879 (75)

Donor/recipient sex match 1144 Male/Male 406 (35) Male/Female 326 (28) Female/Male 181 (16) Female/Female 231 (20)

Donor/Recipient CMV match 1172 Negative/Negative 330 (28) Negative/Positive 401 (34) Positive/Negative 138 (12) Positive/Positive 246 (21) Unknown 57 ( 5)

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Continued.

Characteristics of patients N Eval N (%) Donor age, median (range), years 1150 34 (18.5-60.4) Donor age 1172

18-19 14 ( 1) 20-29 362 (31) 30-39 417 (36) 40-49 279 (24) 50 and older 78 ( 7) Unknown 22 ( 2)

Year of transplant 1172 2000 72 ( 6) 2001 82 ( 7) 2002 70 ( 6) 2003 88 ( 8) 2004 153 (13) 2005 205 (17) 2006 244 (21) 2007 258 (22)

Median follow-up of recipients, mo (range) 485 48 (3-125) * Data has not been CAP-modeled.

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Study Proposal 1210-53 Study Title: Evaluation of the impact of allele homozygosity at HLA loci on outcome NMDP Histocompatibility Advisory Group Carolyn Hurley, Georgetown University Medical Center, Washington, DC USA Ann Woolfrey, Fred Hutchinson Cancer Research Center, Seattle, WA USA Martin Maiers, National Marrow Donor Program, Minneapolis, MN USA Hypothesis: Mismatches in which either the donor or the recipient is homozygous for the mismatched locus will have outcome indistinguishable from a 7/8 mismatched transplant and exhibit better outcome than a 6/8 mismatched transplant. Primary outcomes for the study are:

— Overall survival (OS) — Disease-free survival (DFS) — Graft failure (Neutrophil engraftment)

Secondary outcomes for the study are: — Acute graft versus host disease grades II-IV and III-IV

Specific Aims: To compare the outcome of unrelated donor transplants in which the recipient and/or the donor is homozygous at a single mismatched HLA locus to: (1) 7/8 matches in which the donor and the recipient are heterozygous for the mismatched locus and (2) 6/8 matches in which the donor and the recipient are each heterozygous and differ for 2 alleles at the same locus or multiple loci. Scientific Justification: There is no consensus among transplant centers as to how homozygosity should be handled in determined the degree of mismatch between a patient and a potential donor. In an article published in 2001 (1), the Seattle transplant group suggested that risk of graft failure was increased if the recipient of a bone marrow graft was HLA homozygous at the mismatched class I locus. 471 donor-recipient pairs were studied; primary graft failure was observed in 26 patients and secondary failure in two. 202 pairs were matched for HLA-A, B, C with 66 of those also matched for DRB1, DQB1, DPB1. 76 pairs were mismatched for one allele/antigen with variable matching at class II loci. Of the 7 recipients homozygous at the mismatched locus, 5 exhibited graft failure compared to 7 of the 98 heterozygous recipients (p-value<0.001). An increased percent of homozygous recipients with multiple class I mismatches also showed more frequent graft failure compared to heterozygous recipients with multiple mismatches. Based on these data, the level of mismatch for homozygous recipients with a mismatch at that locus is counted as 2 mismatches in Seattle’s current matching criteria (A. Woolfrey, personal communication). However, this count of mismatches is not standard among transplant centers. In a recent discussion among registries, it was realized that registries varied in the design of their search algorithm regarding the tally of mismatches when donor and/or recipient were homozygous. In 2009, the World Marrow Donor Association published a generalized discussion of a registry’s matching algorithm (2). In this document, it was recommended that “If an individual is homozygous (or being treated as homozygous), the single Ag/ allele is only considered once in counting the mismatches (that is, A*01:02, – patient and A*03:02, – donor would have one allelic mismatch, not two).” A table in supplemental material gives examples of homozygous recipient (AA) vs. heterozygous donor (AB) being counted as a single mismatch in the HvG vector and heterozygous recipient (AB) vs. homozygous donor (BB) being counted as a single mismatch in the GvH vector. An option to consider AA vs. BB as a total of two

45


mismatches was also described. Since counting of mismatches in homozygotes is not standard, this may result in a different selection of potential matches when an international registry search is performed. Study Population: The study population consists of patients receiving their first myeloablative marrow or peripheral blood stem cell unrelated donor transplantation for the treatment of AML, ALL, CML or MDS. Transplant pairs must be high resolution typed for HLA-A, B, C, DRB1, DQB1 and DPB1 through the NMDP retrospective high resolution typing program. Categories of HLA matching to be included in the study: Donor Recipient Match (A B C DRB1 (3)) Comment A*0201, A*0201 Homozygous

A*0201, A*0201 Homozygous 8/8 Suggested by WG

Comparison group A*0201, A*0301 Heterozygous

A*0201, A*0201 Homozygous

7/8 Vector: HvG Pairs of interest


A*0201, A*0301 Heterozygous

7/8 Vector: GvH Pairs of interest



7/8 (may be called 6/8) Vector: Both Insufficient cases so exclude



7/8 Vector: Both Comparison group



6/8 Vector: Both Comparison group

Outcomes: Primary outcomes:

— Overall survival (OS) – Time to death from any cause. Event will be summarized by a survival curve. Cases will be analyzed at the time of last follow-up. There are no competing risks.

— Disease-free survival (DFS) – Survival without recurrence of primary disease. Events are disease relapse or death. Cases will be analyzed at the time of last follow-up. There are no competing risks.

— Neutrophil engraftment: Achievement of ANC ≥ 500 for 3 consecutive lab values on different days. Event will be summarized by the cumulative incidence estimate with death as a competing risk.

Secondary outcomes: — Acute GVHD: Development of grades II-IV and III-IV acute GVHD using the Glucksberg

system. Events will be summarized by the cumulative incidence

Variables to be Analyzed: Main Effect to be tested:

— Impact for donors and/or recipients who are homozygous at a single mismatched locus to pairs heterozygous for the mismatched locus with 7/8 or 6/8 match grade scores

Patient-related (at time of transplant): — Age: in decades (0-9, 10-19, 20-29, 30-39, 40-49, 50 and older). — Gender: female vs. male — Karnofsky score at transplant: < 90 vs. 90-100

Disease-related: — Disease at transplant

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— Subanalysis by each disease: ALL, AML, CML and MDS — Disease status prior to transplant: early vs. intermediate vs. advanced vs. others — Subanalysis by disease stage: early, intermediate and advanced

Transplant-related: — Source of stem cells: marrow (BM) vs. peripheral blood stem cells (PB) — Donor age: in decades (18-29, 30-39, 40-49, 50 and older) — Year of transplant: (1988-2003) — Gender match: M-M vs. M-F vs. F-M vs. F-F — Donor/recipient CMV status: -/- vs. -/+ vs. +/- vs. +/+ vs. Unknown — Conditioning regimen: Traditional Myeloablative vs. non-traditional myeloablative — GvHD prophylaxis: Tacrolimus +/-others vs. CSA +/-others vs. TCD vs. others — ATG use: Yes vs. no

Study Design: Donor-recipient pairs who are typed for HLA alleles at HLA-A,-B, -C, -DRB1 where either the donor and/or the recipient are homozygous at the single mismatched locus will be compared to 7/8 and 6/8 matched pairs in which the mismatched locus is not homozygous in both the recipient and the donor. A comparison will also be made to 8/8 matched pairs in which the locus in question is homozygous. Mismatches will be treated in both the GvH (graft vs. host disease), HvG (graft failure) or both directions (survival). To summarize the characteristics of the dataset, descriptive tables of patient-, disease and transplant-related factors will be reported. For discrete factors, the number of cases and their respective percentages will be calculated. Chi-Square tests will be used to compare discrete factors between the HLA matched vs. mismatched groups. For continuous factors, the median and ranges will be calculated. The Kruskal-Wallis test will be used to compare the continuous factors between the HLA matched vs. mismatched groups. Probabilities for overall survival and disease-free survival will be calculated using the Kaplan-Meier estimator with variance estimated by Greenwood's formula. Comparison of survival curves will be done using the log-rank test. Values for other outcomes listed in section 5 will be calculated according to cumulative incidence using a Taylor series linear approximation to estimate the variance. Multivariate analyses will be performed using the proportional hazards model to compare the homozygous locus mismatched vs. heterozygous mismatched groups. Models will be fit to determine which risk factors may be related to a given outcome. All variables will be tested for the affirmation of the proportional hazards assumption. Factors violating the proportional hazards assumption will be adjusted for first before the stepwise model building approach will be used in developing models for the primary and secondary outcomes. References:

1. Petersdorf EW, Hansen JA, Martin PJ, Woolfrey A, Malkki M, Gooley T, Storer B, Mickelson E, Smith A, Anasetti C. Major-histocompatibility-complex class I alleles and antigens in hematopoietic-cell transplantation. N Engl J Med 2001: 345:1794-1800.

2. Bochtler W, Maiers M, Bakker JN, Oudshoorn M, Marsh SG, Baier D, Hurley CK, Muller CR. World Marrow Donor Association framework for the implementation of HLA matching programs in hematopoietic stem cell donor registries and cord blood banks. Bone Marrow Transplant. 2010:

3. Lee SJ, Klein J, Haagenson M, Baxter-Lowe LA, Confer DL, Eapen M, Fernandez-Vina M, Flomenberg N, Horowitz M, Hurley CK, Noreen H, Oudshoorn M, Petersdorf E, Setterholm M, Spellman S, Weisdorf D, Williams TM, Anasetti C. High-resolution donor-recipient HLA matching contributes to the success of unrelated donor marrow transplantation. Blood 2007: 110:4576-4583.

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Characteristics of recipients receiving first transplants for AML, ALL, CML or MDS with a myeloablative conditioning regimen and high-resolution typed for HLA-A, -B, -C,

-DRB1, -DQB1 and –DPB1 by homozygosity groups *

Characteristics of patients 7/8 HVG

MM N (%)

7/8 GVH MM

N (%)

7/8 Both Directions

N (%)

6/8 Both Directions

N (%) Number of patients 69 98 1084 633Number of centers 45 52 131 108 Recipient age, median (range), years 34 (<1-60) 27 (<1-61) 32 (<1-65) 28 (<1-60) Age at transplant

≤ 10 y 8 (12) 16 (16) 131 (12) 92 (15) 11-20 y 8 (12) 13 (13) 170 (16) 132 (21) 21-30 y 8 (12) 24 (24) 185 (17) 114 (18) 31-40 y 23 (33) 16 (16) 229 (21) 138 (22) 41-50 y 13 (19) 19 (19) 256 (24) 118 (19) Over 50 y 9 (13) 10 (10) 113 (10) 39 ( 6)

Male sex 38 (55) 48 (49) 590 (54) 358 (57) Karnofsky prior to transplant > 90 49 (73) 65 (69) 763 (72) 459 (74) Disease at transplant

AML 19 (28) 29 (30) 332 (31) 167 (26) ALL 17 (25) 34 (35) 271 (25) 187 (30) CML 25 (36) 24 (24) 354 (33) 219 (35) MDS 8 (12) 11 (11) 127 (12) 60 ( 9)

Disease status at transplant Early 28 (41) 34 (35) 383 (35) 240 (38) Intermediate 16 (23) 35 (36) 335 (31) 205 (32) Advanced 17 (25) 19 (19) 278 (26) 156 (25) Other 8 (12) 10 (10) 88 ( 8) 32 ( 5)

Graft type Bone marrow 63 (91) 86 (88) 964 (89) 595 (94) PBSC 6 ( 9) 12 (12) 120 (11) 38 ( 6)

Number of patients 69 98 1084 633GVHD prophylaxis FK506 + (MTX or MMF or steroids) ± other 18 (26) 24 (24) 252 (23) 105 (17)

FK506 ± other 2 ( 3) 0 15 ( 1) 6 ( 1) CsA + MTX ± other 34 (49) 55 (56) 559 (52) 346 (55) CsA ± other (no MTX) 9 (13) 11 (11) 102 ( 9) 70 (11) MTX ± other (no CsA) 0 0 7 ( 1) 7 ( 1) T-cell depletion 4 ( 6) 5 ( 5) 108 (10) 61 (10) Other/To be determined 2 ( 3) 3 ( 3) 41 ( 4) 38 ( 6)

Donor/Recipient sex match Male/Male 18 (28) 33 (35) 346 (33) 197 (31) Male/Female 17 (26) 26 (27) 247 (24) 142 (23) Female/Male 16 (25) 15 (16) 223 (21) 160 (25) Female/Female 14 (22) 21 (22) 230 (22) 131 (21)

Donor/Recipient CMV match Negative/Negative 24 (35) 33 (34) 326 (30) 205 (32) Negative/Positive 22 (32) 28 (29) 300 (28) 182 (29) Positive/Negative 10 (14) 16 (16) 171 (16) 101 (16) Positive/Positive 8 (12) 16 (16) 227 (21) 126 (20) Unknown 5 ( 7) 5 ( 5) 60 ( 6) 19 ( 3)

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Continued.

Characteristics of patients 7/8 HVG

MM N (%)

7/8 GVH MM

N (%)

7/8 Both Directions

N (%)

6/8 Both Directions

N (%) Donor age, median (range), years 34 (20-54) 37 (19-57) 36 (19-59) 37 (18-59) Donor age

18-19 0 2 ( 2) 6 ( 1) 6 ( 1) 20-29 19 (28) 18 (18) 247 (23) 159 (25) 30-39 24 (35) 38 (39) 397 (37) 226 (36) 40-49 19 (28) 30 (31) 297 (27) 194 (31) 50 and older 3 ( 4) 7 ( 7) 95 ( 9) 43 ( 7) Unknown 4 ( 6) 3 ( 3) 42 ( 4) 5 ( 1)

Number of patients 69 98 1084 633Year of transplant

1988 1 ( 1) 0 3 (<1) 1 (<1) 1989 0 1 ( 1) 3 (<1) 3 (<1) 1990 3 ( 4) 3 ( 3) 19 ( 2) 16 ( 3) 1991 1 ( 1) 0 35 ( 3) 35 ( 6) 1992 4 ( 6) 7 ( 7) 41 ( 4) 39 ( 6) 1993 4 ( 6) 5 ( 5) 50 ( 5) 44 ( 7) 1994 1 ( 1) 6 ( 6) 71 ( 7) 49 ( 8) 1995 6 ( 9) 4 ( 4) 93 ( 9) 56 ( 9) 1996 3 ( 4) 6 ( 6) 86 ( 8) 61 (10) 1997 6 ( 9) 10 (10) 88 ( 8) 55 ( 9) 1998 7 (10) 8 ( 8) 101 ( 9) 57 ( 9) 1999 6 ( 9) 10 (10) 112 (10) 46 ( 7) 2000 7 (10) 10 (10) 107 (10) 65 (10) 2001 5 ( 7) 14 (14) 133 (12) 59 ( 9) 2002 9 (13) 7 ( 7) 68 ( 6) 34 ( 5) 2003 2 ( 3) 1 ( 1) 18 ( 2) 8 ( 1) 2004 0 0 0 0 2005 0 0 3 (<1) 0 2006 0 2 ( 2) 11 ( 1) 0 2007 0 1 ( 1) 5 (<1) 2 (<1) 2008 3 ( 4) 2 ( 2) 17 ( 2) 2 (<1) 2009 1 ( 1) 1 ( 1) 20 ( 2) 1 (<1)

Median follow-up of recipients, mo (range) 107 (22-234) 120 (12-158) 115 (3-240) 132 (22-225) * Data has not been CAP-modeled.

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Study Proposal 1210-56 Study Title: KIR genotyping and Immune function in MDS Patients Prior to Unrelated Donor Transplantation Erica Warlick, MD, University of Minnesota, Minneapolis, MN USA Daniel Weisdorf, MD, University of Minnesota, Minneapolis, MN USA Julie Ross, PhD, University of Minnesota, Minneapolis, MN USA David Lagarspada, PhD, University of Minnesota, Minneapolis, MN USA Jeffrey Miller, MD, University of Minnesota, Minneapolis, MN USA Specific Aims: The overarching hypothesis it that NK cell function correlates with disease progression in MDS and could lead to therapeutic immune strategies to control MDS. In the cohort selected here, we presume that all patients have advanced disease based on their need for transplantation. These results will be compared to a separate analysis on a prospective study on newly diagnosed MDS patients and those undergoing therapy. The following specific aims for this proposal support our hypothesis:

1. Determine whether advanced MDS patients requiring transplant have a KIR genotype that deviates from a cohort of normal controls

2. Determine function of NK cells and suppressor cells (T regulatory cells and myeloid derived suppressor cells) in MDS patients requiring transplant

3. Analyze pre-transplant frequencies and function for ability to predict post transplant outcomes Scientific Justification: Immune dysregulation has long been implicated for a role in the pathogenesis of myelodysplastic syndromes as evident by the clinical responses to immune modulating drugs (cyclosporine and ATG) as well as the potential curative potential with allogeneic bone marrow transplantation. Much of the attention on immune dysregulation in the past has been focused on B and T cell changes noted in MDS patients citing correlations with autoimmune disorders especially in lower risk MDS1, 2 and notable findings of V-beta and J region skewing in oligoclonal T cell populations3-5 as potential contributors to disease. We summarize here the current literature regarding immune dysregulation in MDS with respect to NK cells, T cell/T regulatory cells, myeloid suppressor cells, alterations of this immune profile in response to therapy and propose clinical interventions based on these data. Natural Killer Cell Function in MDS: Alterations in NK cell cytotoxicity has been noted in MDS patients. In brief review of NK cell biology, a number of both activating and inhibitory receptors are present on NK cells and mediate their cytotoxicity. Natural cytotoxicity receptors (NCRs) NKp46, NKp30 as well as activating receptor NKG2D are constitutively active activating receptors involved in NK-mediated cytotoxicty6, 7. Few published reports evaluating NK cell characteristics in MDS patients, and the three most detailed studies reveal contradictory results. Kaledjian and colleagues showed that despite normal numbers of NK cells and activating receptors NKp46, NKp30, and NKG2D, cytolytic function and response to IL-2 stimulation was decreased.8 Based on these data they questioned the possibility of soluble/humoral factors impacting NK cell cytotoxicity. Kiladjian’s data showed that a representative population of the NK cells (25-44%) in these MDS patients were clonal harboring the same clone as the MDS cells the existence of a dual population of NK cells suggesting the possible contribution of the cytogenetic abnormalities to the NK cell dysfunction8. Conversely, Epling-Burnette and colleagues found that NK cells fell into two categories of cytotoxicity: low function and normal function. The low function NK cell group was most notably present in the high risk MDS patients: 1) INT2 or high risk by IPSS, 2) those with abnormal cytogenetics, and 3) those in

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RAEB-1, RAEB-2, and AML WHO categories. Additionally, they found decreased activating receptors NKp30 and NKG2D in MDS patients with only NKG2D presence correlated with level of cytotoxicity. The importance of the various receptors in terms of cytotoxicty was tested with antibody assay blocking of the various receptors, and NKG2D was identified as the primary receptor responsible for the killing. Epling’s group addressed the question of a soluble factor contributing to the decreased NK cytotoxicity by evaluating for soluble MICA and MICB (the receptor ligands for NKG2D) and found no evidence for this. Additionally, in contrast to Kiladjian’s reports, Epling found that IL-2 treatment was able to increase the presence of the activating receptors as well as increase NK cell mediated cytotoxicity9. In contrast to the above two groups that evaluated peripheral blood NK cell function in MDS patients, Carlsten’s group recently published their findings regarding bone marrow NK cell function. Their studies focused on the CD56dim cytotoxic NK cells assessing presence of activating and inhibitory receptors, assessing actual cytotoxicity, and performing blast analysis. They noted down-regulation of both DNAM-1 (DNAX-accessory molecule 1) and NKG2D on bone marrow NK cells with an inverse correlation with increased blasts and noted a more central role of DNAM-1 compared with NGK2D. They found no significant changes in NCRs. The loss of DNAM-1 was more pronounced in those with blast percentage > 5% possibly consistent with clonal expansion in the absence of functional cytotoxic NK cells. The peripheral blood NK cell evaluation interestingly revealed no down-regulation of DNAM-1, NKG2D, or the NCRs NKp30 or NKp46. NK cell degranulation was assessed and both bone marrow and peripheral blood NK cells showed decreased degranulation after coincubation with K562, despite the presence of normal receptor expression in the peripheral blood NK cells. Analysis of CD34+ blasts interestingly showed ongoing expression of the DNAM-1 Ligands CD155 and CD112 but rare expression of the NKG2D ligands MIC and ULBP. Assessment of direct killing of autologous blasts by the BM NK cells showed that these autologous NK cells were not able to kill the blasts10. In summary, the data for NK cell alterations in MDS reveal discrepant results citing either soluble/humoral factors decreasing peripheral blood NK cell mediated cytotoxicity 8 versus decreased activating receptors (NKp30, NKp46, NKG2D) with NKG2D the primary effector 9 versus decreased expression of bone marrow NK cell activating receptor DNAM-1 contributing to the NK cell dysfunction10. The above studies include relatively small sample sizes raising the need for NK cell profile analysis on a larger scale. T Cell Alterations in MDS: Given the association with autoimmune disorders, the aberrant inflammatory milieu with altered levels of TNF-alpha,etc, and the impact of immunosuppressive therapy in MDS, there is extensive literature on T cell alterations found in those patients with MDS. Characterization of the T cell repertoire in MDS patients has demonstrated T cell expansion with recurrent patterns of V-Beta and J region skewing suggestive of an ongoing antigenic immune response3,5,11. Specific in vitro work by Smith et al. investigated the possibility of auto-reactive T cells and noted the presence of inhibitory T cells in patients with MDS11 with subsequent clinical studies showing in vivo success using anti T-cell therapies (anti-thymocyte globulin (ATG)4,12 and cyclosporine-A (CSA))13. Subsequent investigations revealed an abnormal T cell repertoire and T cell-mediated inhibition of bone marrow CFU-GM4 as a potential mechanism to the response to ATG. More recent data has suggested that this clonal T cell expansion of effector cells (CD8+/CD57+/CD28-) is not limited to MDS with features historically associated with response to immunosuppressive therapy such as marrow cellularity, age, IPSS category, etc and is likely common to most MDS subgroups14. The influence of T regulatory cells (Tregs), defined as CD4+, CD25high, Foxp3+, on immune surveillance and development of autoimmunity or suppression of host immune response has been widely researched. Numerous studies in solid tumors and now hematologic malignancies are citing increased numbers of Tregs in those patients with active malignancies and progressive disease. The variable course of disease in

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MDS with early/low grade MDS often mimicking an autoimmune type disorder with increase inflammatory milieu, increased apoptosis and response to immunosuppressive therapies and later/higher grade MDS showing more of a rapid progression to leukemia and a more proliferative nature raises the question of Treg impact on MDS disease progression and pathophysiology. A number of studies to date have evaluated the impact of Tregs in MDS. Kordasti et al showed a correlation of increased peripheral blood T regulatory cells in more advanced MDS with higher percentage of cells in those with more aggressive WHO classification (5q/RA < RCMD< RAEB < MDS/MPD), higher blast percentage, higher IPSS score, and more complex cytogenetics. The Tregs were found to be polyclonal by spectratyping suggesting that they may arise by peripheral expansion15. Hamdi et all found a similar increase Tregs in MDS patients but slightly different pattern with higher levels in both RA/RARS and RAEB compared with RCMD16. Kotsiandis et al went on to assess the kinetics, function and bone marrow trafficking of Tregs in MDS and analyzed both peripheral blood and bone marrow. They found Treg expansion in both PB and BM in later stage MDS compared with normal controls/early MDS. Treg frequency and number showed a direct correlation with disease progression or response to therapy with increases at times of disease progression, stability with disease stability, and decrease with response to therapy17. Functionally, the T regulatory cells retained their function in late stage MDS, theoretically consistent with their impact on suppressing immune attack on the underlying disease, and in early stage MDS were less efficient at suppression of polyclonal T cell proliferative responses again theoretically consistent with the higher degree of autoimmunity in early stage disease. Alfinito et al investigated the immune profile in MDS patients as well and confirmed the increased Treg frequency in INT-2/High risk patients and lower numbers in Low/INT-1 patients. They also noted increased CD8 cells and B cells in the BM of low/INT-1 patients. Combined these data can support the role of Tregs in leukemic progression INT-2/High risk MDS and a more autoimmune milieu in the low/INT-1 patients18. In summary, these data would suggest that Tregs play a key role in immune modulation in MDS and have a significant role in progression of disease but further larger scale analysis is warranted. Patient Eligibility Population:

— Myelodysplastic Syndromes (MDS) including the following subtypes (FAB): - RA - RARS - RAEB1/2 - RAEB t

— 1st Allogeneic HSCT1988-2005 — De novo and Treatment related MDS will both be included — Age : All — Donor: Related or Unrelated — Stem Cell source: BM, PB, or UCB — Those patients with viable call and DNA samples available

Data Requirements: (Data to Be Collected) Patient-related:

— Age — Gender — KPS and HCT-CI prior to transplant

Disease-related: — FAB subtype of MDS at diagnosis (RA/RARS/RAEB 1 or 2/RAEBt) — IPSS Score at Diagnosis — Percentage blasts at diagnosis — Blood counts at diagnosis: Hgb, WBC, platelet, ANC — Cytogenetics at diagnosis — Transfusion dependence:

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- PRBC: yes or no - Platelets: yes or no

— Therapy Prior to transplant - Conventional Induction Type chemotherapy: yes or no - Hypomethylating Agents (azacitidine or decitabine): yes or no - Immunomodulatory Medications (thalidomide/lenalidomide/ATG+CSA): yes or no - Prior Erythropoietin Use: Yes or No - No Therapy: yes or no

— Time from Diagnosis to Transplant — Disease Status at the time of transplant

- % Blasts: 0-4%;< 5% - Advanced Disease (RAEB1/2) vs. Less Advanced Disease (RA/RARS) - CR vs. untreated vs. treated with <5% blasts vs. treated with > 5% blasts

Transplant-related: — Donor Type (related vs. unrelated) and HLA matching — Graft Source ( BM, PB, cord blood) — GVHD Prophylaxis — Conditioning Intensity (Myeloablative vs. RIC) — Year of transplant (1995-1998, 1999-2003, 2003-2006)

Outcomes: — KIR genotype comparison between MDS subsets and normal controls (we already have

transplant donor control (n~2500) data from KIR genotyped cohorts performed as a part of J. Miller’s NCI P01)

— Comparison of NK cell, T regulatory cell and myeloid derived suppressor cell immune phenotype between various subsets of MDS patients

— Assessment for correlation between Immune phenotype and post transplant outcomes of DFS, relapse, OS

Sample Requirements:

— DNA sample for KIR genotyping (n=971, excluding samples already analyzed in the initial P01 donor/recipient cohort of 209 already received and analyzed):

— One aliquot of viable cells collected pre-transplant should be sufficient for these studies - NK Cell Functional Analysis - T regulatory and myeloid derived suppressor cell number

We would like to pilot this functional data with an initial request of 120 samples, assuming that 50% will yield interpretable results (samples need to be both viable and have enough NK cells for analysis). This will allow a better assessment of assay variability and projected endpoints. We hypothesize that this initial assessment will tell us if the cohort will stratify into a high and low function group compared to normal subjects. If the cohort exhibits uniform low function, then we could conclude that patients eligible for unrelated transplant are advanced and beyond the early phases of hypothesized intact immune integrity, important in disease progression. However, if a dichotomy of immune function is found (i.e. patients stratify into a high and low group), additional samples will be requested to allow correlation between immune function and MDS characteristics to better understand this biology.

Study Design:

— DNA samples from all MDS patients will be used for KIR genotyping by the laboratory of Jeffrey Miller. KIR genotype profile will be compared with normal controls (available through Dr. Miller’s lab) to assess for any meaningful deviation between the two groups

— NK cell and T regulatory (Treg) cell and myeloid derived suppressor numbers and functional analysis will be performed by Dr. Miller’s laboratory. Initial studies will be descriptive to

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assess for meaningful patterns of immune dysregulation observed in the various MDS subsets. Methods for high-resolution dual function 9-color flow cytometry will evaluate CD107a degranulation (as a surrogate marker of NK cell killing) and interferon gamma production in addition to surface markers CD56, CD3, CD45, NKG2A, CD158a, CD158b, CD158e. Thawed samples will be tested after resting overnight and then stimulation with IL-12/IL-18 and class I negative K562 targets. The Miller lab has extensive experience with this assay and it has been validated with frozen samples. Treg assays have been developed and validated and the assay for myeloid derived suppressor cells is in progress.

— As immune profile patterns are identified across the MDS subsets, correlation with transplant outcomes will be studied

Statistical Support: The work proposed here falls within the scope of Miller’s NK P01 and will be supported by the biostatistical core of that grant either through the U of MN site (Dr, Chap Le and colleagues) or the MCOW (Dr. John Klein and colleagues). Clinical outcome data will be needed from the NMDP/CIBMTR including survival, DFS, relapse, TRM, acute GVHD, and chronic GVHD for the MDS patients included here. References:

1. Marisavljevic D, Kraguljac N, Rolovic Z. Immunologic abnormalities in myelodysplastic syndromes: clinical features and characteristics of the lymphoid population. Med Oncol. 2006;23:385-392.

2. Billstrom R, Johansson H, Johansson B, Mitelman F. Immune-mediated complications in patients with myelodysplastic syndromes--clinical and cytogenetic features. Eur J Haematol. 1995;55:42-48.

3. Epperson DE, Nakamura R, Saunthararajah Y, Melenhorst J, Barrett AJ. Oligoclonal T cell expansion in myelodysplastic syndrome: evidence for an autoimmune process. Leuk Res. 2001;25:1075-1083.

4. Molldrem JJ, Jiang YZ, Stetler-Stevenson M, Mavroudis D, Hensel N, Barrett AJ. Haematological response of patients with myelodysplastic syndrome to antithymocyte globulin is associated with a loss of lymphocyte-mediated inhibition of CFU-GM and alterations in T-cell receptor Vbeta profiles. Br J Haematol. 1998;102:1314-1322.

5. Melenhorst JJ, Eniafe R, Follmann D, Nakamura R, Kirby M, Barrett AJ. Molecular and flow cytometric characterization of the CD4 and CD8 T-cell repertoire in patients with myelodysplastic syndrome. Br J Haematol. 2002;119:97-105.

6. Miller JS. Biology of natural killer cells in cancer and infection. Cancer Invest. 2002;20:405-419. 7. Wu J and Lanier LL. Natural killer cells and cancer. Adv Cancer Res. 2003;90:127-156. 8. Kiladjian JJ, Bourgeois E, Lobe I, et al. Cytolytic function and survival of natural killer cells are

severely altered in myelodysplastic syndromes. Leukemia. 2006;20:463-470. 9. Epling-Burnette PK, Bai F, Painter JS, et al. Reduced natural killer (NK) function associated with

high-risk myelodysplastic syndrome (MDS) and reduced expression of activating NK receptors. Blood. 2007;109:4816-4824.

10. Carlsten M, Baumann BC, Simonsson M, et al. Reduced DNAM-1 expression on bone marrow NK cells associated with impaired killing of CD34(+) blasts in myelodysplastic syndrome. Leukemia. 2010;24:1607-1616.

11. Smith MA and Smith JG. The occurrence subtype and significance of haemopoietic inhibitory T cells (HIT cells) in myelodysplasia: an in vitro study. Leuk Res. 1991;15:597-601.

12. Molldrem JJ, Caples M, Mavroudis D, Plante M, Young NS, Barrett AJ. Antithymocyte globulin for patients with myelodysplastic syndrome. Br J Haematol. 1997;99:699-705.

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13. Jonasova A, Neuwirtova R, Cermak J, et al. Cyclosporin A therapy in hypoplastic MDS patients and certain refractory anaemias without hypoplastic bone marrow. Br J Haematol. 1998;100:304-309.

14. Epling-Burnette PK, Painter JS, Rollison DE, et al. Prevalence and clinical association of clonal T-cell expansions in Myelodysplastic Syndrome. Leukemia. 2007;21:659-667.

15. Kordasti SY, Ingram W, Hayden J, et al. CD4+CD25high Foxp3+ regulatory T cells in myelodysplastic syndrome (MDS). Blood. 2007;110:847-850.

16. Hamdi W, Ogawara H, Handa H, Tsukamoto N, Nojima Y, Murakami H. Clinical significance of regulatory T cells in patients with myelodysplastic syndrome. Eur J Haematol. 2009;82:201-207.

17. Kotsianidis I, Bouchliou I, Nakou E, et al. Kinetics, function and bone marrow trafficking of CD4+CD25+FOXP3+ regulatory T cells in myelodysplastic syndromes (MDS). Leukemia. 2009;23:510-518.

18. Alfinito F, Sica M, Luciano L, et al. Immune dysregulation and dyserythropoiesis in the myelodysplastic syndromes. Br J Haematol. 2010;148:90-98.

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Characteristics of recipients receiving first transplants for MDS where DNA and viable cells are available for the proposed study *

Characteristics of patients N Eval N (%) Number of patients 971

Number of centers 116 Recipient age, median (range), years 971 44 (<1-73) Age at transplant 971

≤ 10 y 81 ( 8) 11-20 y 94 (10) 21-30 y 85 ( 9) 31-40 y 152 (16) 41-50 y 232 (24) Over 50 327 (34)

Male sex 971 555 (57) Karnofsky prior to transplant > 90 903 580 (64) Disease status at transplant 971

Early 233 (24) Advanced 454 (47) Other 284 (29)


Conditioning regimen 971 Traditional myeloablative 688 (71) Reduced intensity 132 (14) Non-myeloablative 71 ( 7) Non-traditional myeloablative 74 ( 8) To Be Determined/Other 6 ( 1)

Donor/recipient sex match 971 Male/Male 363 (37) Male/Female 222 (23) Female/Male 192 (20) Female/Female 194 (20)

GVHD prophylaxis 971 FK506 + (MTX or MMF or steroids) ± other 256 (26) FK506 ± other 20 ( 2) CsA + MTX ± other 448 (46) CsA ± other (no MTX) 99 (10) MMF ± other (no CsA) 3 (<1) MTX ± other (no CsA) 6 ( 1) T-cell depletion 131 (13) Other 8 ( 1)

HLA Matching for HLA-A, -B, -C and -DRB1 971 Well-matched 501 (52) Partially matched 302 (31) Mismatched 168 (17)

56


Continued.

Characteristics of patients N Eval N (%) Donor/Recipient CMV match 971

Negative/Negative 320 (33) Negative/Positive 290 (30) Positive/Negative 146 (15) Positive/Positive 199 (20) Unknown 16 ( 2)

Donor age, median (range), years 971 36 (18-60) Donor age 971

18-19 6 ( 1) 20-29 246 (25) 30-39 395 (41) 40-49 254 (26) 50 and older 70 ( 7)

Year of transplant 971 1988 2 (<1) 1989 6 ( 1) 1990 16 ( 2) 1991 16 ( 2) 1992 17 ( 2) 1993 26 ( 3) 1994 39 ( 4) 1995 52 ( 5) 1996 45 ( 5) 1997 69 ( 7) 1998 64 ( 7) 1999 61 ( 6) 2000 73 ( 8) 2001 86 ( 9) 2002 68 ( 7) 2003 119 (12) 2004 143 (15) 2005 69 ( 7)

Median follow-up of recipients, mo (range) 289 73 (5-222) * - Data has not been CAP-modeled.

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Study Proposal 1210-60 Study Title: Evaluation of the impact of potentially non-immunogenic HLA-C allele level mismatch Marcelo Fernandez-Viña, PhD, MD Anderson Cancer Center, Houston, TX USA Michelle Setterholm, National Marrow Donor Program, Minneapolis, MN USA Specific Aims:

1. To determine the effect of a putative non-immunogenic HLA mismatches on the outcome of unrelated donor transplantation.

2. To compare the impact of the single mismatch in the alleles C*03:03/C*03:04 with other single antigen and/or allele level mismatches in HLA-C on the outcome of unrelated donor transplantation.

3. To compare the impact of the single mismatch in the alleles C*03:03/C*03:04 with other single antigen or allele level mismatches in HLA-C or HLA-A, B or DRB1 on the outcome of unrelated donor transplantation.

4. To compare the impact of the single mismatch in the allele C*03:03/C*03:04 on the outcome of unrelated donor transplantation with the outcome of transplants in which the patient and the unrelated donors are fully matched in HLA-A, B, C or DRB1 loci.

5. To compare the outcome of transplants with one allele or antigen mismatch in HLA-A, B, C and DRB1 with an additional mismatch in the alleles C*03:03/C*03:04 with the outcome of transplants in which the patient and the unrelated donors present two mismatches in alleles or antigens of HLA-A, B, C or DRB1 loci excluding mismatches in C*03:03/C*03:04.

6. To compare the outcome of transplants with one allele or antigen mismatch in HLA-A, B ,C and DRB1 with an additional mismatch in the alleles C*03:03/C*03:04 with the outcome of transplants in which the patient and the donor present a single antigen or allele level mismatches HLA-A, B, C or DRB1 other than C*03:03/C*03:04.

Scientific Justification: A study evaluating the cytotoxic T-lymphocyte precursor (CTLp) frequencies directed against incompatibilities at the HLA-A, -B, and -C locus in donor-recipient pairs (1) showed a significant correlation between HLA class I incompatibilities (p < 0.001) and high CTLp frequencies. The analysis of HLA amino acid sequences in the HLA-C allele mismatched group showed that mismatches involving alleles with amino acid differences at five polymorphic positions 97, 99, 113, 114, and 116 situated at the peptide binding groove always resulted in the high CTLp frequency group, while the low/absent CTLp frequency group included mainly pairs with the mismatch in the alleles C*03:03/C*03:04 that differ only by one amino acid replacement at residue 91. This residue is not a contact site with the T-cell receptor is not part of any of the pockets that accommodate the peptide side chains in the antigen recognition site. Two CIBMTR outcome studies (2-3) including marrow and PBSC as source of allogeneic HSC from unrelated donors have shown that the isolated allele level mismatch in HLA-C do no not associate with detrimental effects in any outcome. These two alleles (C*03:03 and C*03:04) associate frequently with B*15:01 in European populations and with B*40:02 in Asian populations; in unrelated HSC transplantation many of the donor/recipient pairs that match in HLA-B and carry B*15:01 turn presenting an allele level mismatch in these subtypes of HLA-C*03. It appears that in most populations no other allele of HLA-C associates often with two alleles of the same HLA-C group. We propose that the most predominant allele level mismatch in HLA-C in these transplant cohorts included the pair of alleles C*03:03/C*03:04. Therefore, we propose the apparent lack of impact in outcome of the HLA-C allele level mismatch results from the lack of immunogenicity of

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the mismatch C*03:03/C*03:04 that was demonstrated in the in-vitro experiments (1). It is conceivable that other allele level mismatches in HLA-C have a negative impact in outcome. The proposed comparisons may prove that the mismatch in the alleles HLA-C*03:03/C*03:04 is tolerable; if this was to be the case then the availability of well matched unrelated donors may be expanded for significant number of patients, in particular those carrying HLA-B*15:01 (present in > 12.5 percent of European subjects) and B*40:02 (present in > 6.0 percent of Asian subjects). Patient Eligibility Population: The study population consists of patients receiving their first marrow or peripheral blood stem cell unrelated donor transplantation for the treatment of AML, ALL, CML or MDS. Transplant pairs must be high resolution typed for HLA-A, B, C, DRB1, DQB1 and DPB1 through the NMDP retrospective high resolution typing program. Outcomes to be Studied:

1. Overall survival 2. Acute GVHD 3. Chronic GVHD 4. Relapse 5. DFS 6. TRM

Variables to be Analyzed: Main effect to be tested:

— C*03:03 vs. C*03:04 single mismatches (7/8) vs. any other HLA-C locus mismatches vs. any other single HLA mismatches (7/8) vs. 8/8

Patient-related (at time of transplant): — Age: in decades (0-9, 10-19, 20-29, 30-39, 40-49, 50 and older). — Gender: female vs. male — Karnofsky score at transplant: < 90 vs. 90-100

Disease-related: — Disease at transplant

- Subanalysis by each disease: ALL, AML, CML and MDS — Disease status prior to transplant: early vs. intermediate vs. advanced vs. others

- Subanalysis by disease stage: early, intermediate and advanced Transplant-related:

— Source of stem cells: marrow (BM) vs. peripheral blood stem cells (PB) — Donor age: in decades (18-29, 30-39, 40-49, 50 and older) — Year of transplant: (1988-2007) — Gender match: M-M vs. M-F vs. F-M vs. F-F — Donor/recipient CMV status: -/- vs. -/+ vs. +/- vs. +/+ vs. Unknown — Conditioning regimen: Traditional Myeloablative vs. reduced intensity — GvHD prophylaxis: Tacrolimus +/-others vs. CSA +/-others vs. others

Study Design: To compare the impact of the single mismatch in the alleles C*03:03/C*03:04 with:

— Other single antigen and/or allele level mismatches in HLA-C — Other single antigen or allele level mismatches in HLA-C or HLA-A, B or DRB1 — Transplants fully matched in HLA-A, B, C or DRB1 loci — Transplants in which the patient and the unrelated donors present two mismatches in alleles or

antigens of HLA-A, B, C or DRB1 loci excluding mismatches in C*03:03/C*03:04

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— Transplants in which the patient and the donor present a single antigen or allele level mismatches HLA-A, B, C or DRB1 other than C*03:03/C*03:04.

To summarize the characteristics of the dataset, descriptive tables of patient-, disease and transplant-related factors will be reported. For discrete factors, the number of cases and their respective percentages will be calculated. Chi-Square tests will be used to compare discrete factors between the HLA matched vs. mismatched groups. For continuous factors, the median and ranges will be calculated. The Kruskal-Wallis test will be used to compare the continuous factors between the HLA matched vs. mismatched groups. Probabilities for overall survival and disease-free survival will be calculated using the Kaplan-Meier estimator with variance estimated by Greenwood's formula. Comparison of survival curves will be done using the log-rank test. Values for other outcomes listed in section 5 will be calculated according to cumulative incidence using a Taylor series linear approximation to estimate the variance. Multivariate analyses will be performed using the proportional hazards model to compare the homozygous locus mismatched vs. heterozygous mismatched groups. Models will be fit to determine which risk factors may be related to a given outcome. All variables will be tested for the affirmation of the proportional hazards assumption. Factors violating the proportional hazards assumption will be adjusted for first before the stepwise model building approach will be used in developing models for the primary and secondary outcomes. References:

1. Oudshoorn M, Doxiadis II, van den Berg-Loonen PM, Voorter CE, Verduyn W, Claas FH. Functional versus structural matching: can the CTLp test be replaced by HLA allele typing? Hum Immunol. 2002 Mar;63(3):176-84.

2. Lee SJ, Klein J, Haagenson M, Baxter-Lowe LA, Confer DL, Eapen M, Fernandez-Vina M, Flomenberg N, Horowitz M, Hurley CK, Noreen H, Oudshoorn M, Petersdorf E, Setterholm M, Spellman S, Weisdorf D, Williams TM, Anasetti C. High-resolution donor-recipient HLA matching contributes to the success of unrelated donor marrow transplantation. Blood. 2007 Dec 15;110(13):4576-83.

3. Woolfrey A, Klein JP, Haagenson M, Spellman S, Petersdorf E, Oudshoorn M, Gajewski J, Hale GA, Horan J, Battiwalla M, Marino SR, Setterholm M, Ringden O, Hurley C, Flomenberg N, Anasetti C, Fernandez-Vina M, Lee SJ. HLA-C Antigen Mismatch Is Associated with Worse Outcome in Unrelated Donor Peripheral Blood. Stem Cell Transplantation. Biol Blood Marrow Transplant. 2010 Sep 24.

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Characteristics of recipients receiving first transplants for AML, ALL, CML or MDS with high-resolution HLA typing for HLA-A, -B, -C, -DRB1, and –DQB1 by HLA matching groups *

Characteristics of patients C*03:03/ C*03:04

N (%)

7/8 HLA-C MM

N (%)

7/8 HLA- Other MM

N (%)

8/8 Matched

N (%)Number of patients 146 927 1345 5451Number of centers 65 144 148 170Recipient age, median (range), years 34 (<1-68) 37 (<1-72) 34 (<1-74) 41 (<1-74)Age at transplant

≤ 10 y 19 (13) 74 ( 8) 144 (11) 401 ( 7)11-20 y 20 (14) 127 (14) 186 (14) 554 (10)21-30 y 25 (17) 136 (15) 224 (17) 775 (14)31-40 y 23 (16) 168 (18) 234 (17) 899 (16)41-50 y 35 (24) 205 (22) 259 (19) 1138 (21)Over 50 y 24 (16) 217 (23) 298 (22) 1683 (31)

Male sex 76 (52) 532 (57) 730 (54) 3094 (57)Karnofsky prior to transplant > 90 92 (68) 627 (73) 864 (68) 3509 (70)Disease at transplant

AML 54 (37) 354 (38) 513 (38) 2173 (40)ALL 35 (24) 217 (23) 342 (25) 1104 (20)CML 37 (25) 211 (23) 299 (22) 1181 (22)MDS 20 (14) 145 (16) 191 (14) 993 (18)

Disease status at transplant

Early 56 (38) 317 (34) 498 (37) 2203 (40)Intermediate 41 (28) 280 (30) 392 (29) 1370 (25)Advanced 37 (25) 253 (27) 310 (23) 1287 (24)Other 12 ( 8) 77 ( 8) 145 (11) 591 (11)

Conditioning regimen Myeloablative 125 (86) 747 (81) 1098 (82) 4158 (76)Reduced intensity 16 (11) 124 (13) 170 (13) 874 (16)Non-myeloablative 5 ( 3) 39 ( 4) 51 ( 4) 319 ( 6)Other/TBD 0 17 ( 2) 26 ( 2) 100 ( 2)

Graft type

Bone marrow 95 (65) 591 (64) 820 (61) 3045 (56)PBSC 51 (35) 336 (36) 525 (39) 2406 (44)

HLA-DQB1 matching

Allele-matched 124 (85) 812 (88) 1097 (82) 4991 (92)Single allele mismatch 22 (15) 111 (12) 238 (18) 449 ( 8)Double allele mismatch 0 4 (<1) 10 ( 1) 11 (<1)

HLA-DPB1 matching

Allele-matched 9 ( 6) 68 ( 7) 93 ( 7) 415 ( 8)Single allele mismatch 55 (38) 316 (34) 405 (30) 1501 (28)Double allele mismatch 23 (16) 196 (21) 250 (19) 815 (15)HLA-DPB1 data missing 59 (40) 347 (37) 597 (44) 2720 (50)

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Continued.


N (%)

7/8 HLA-C MM

N (%)

7/8 HLA- Other MM

N (%)

8/8 Matched

N (%)GVHD prophylaxis

FK506 + (MTX or MMF or steroids) ± other 53 (36) 346 (37) 522 (39) 2442 (45)

FK506 ± other 2 ( 1) 28 ( 3) 49 ( 4) 196 ( 4)CsA + MTX ± other 65 (45) 363 (39) 477 (35) 1866 (34)CsA ± other (no MTX) 14 (10) 97 (10) 150 (11) 557 (10)MMF ± other 0 3 (<1) 2 (<1) 14 (<1)MTX ± other (no CsA) 2 ( 1) 4 (<1) 9 ( 1) 32 ( 1)T-cell depletion 8 ( 5) 47 ( 5) 76 ( 6) 171 ( 3)Other/To be determined 2 ( 1) 39 ( 4) 60 ( 4) 173 ( 3)

Donor/Recipient sex match

Male/Male 51 (36) 304 (35) 405 (33) 1966 (40)Male/Female 40 (29) 196 (23) 291 (24) 1234 (25)Female/Male 21 (15) 198 (23) 256 (21) 828 (17)Female/Female 28 (20) 171 (20) 267 (22) 901 (18)

Donor/Recipient CMV match

Negative/Negative 54 (37) 271 (29) 352 (26) 1615 (30)Negative/Positive 41 (28) 267 (29) 361 (27) 1619 (30)Positive/Negative 24 (16) 126 (14) 197 (15) 669 (12)Positive/Positive 21 (14) 189 (20) 273 (20) 912 (17)Unknown 6 ( 4) 74 ( 8) 162 (12) 636 (12)

Donor age, median (range), years 37 (19-56) 36 (18-61) 37 (19-60) 35 (18-61)Donor age

18-19 1 ( 1) 5 ( 1) 6 (<1) 68 ( 1)20-29 33 (23) 205 (22) 285 (21) 1486 (27)30-39 43 (29) 350 (38) 446 (33) 1811 (33)40-49 47 (32) 242 (26) 360 (27) 1235 (23)50 and older 16 (11) 64 ( 7) 117 ( 9) 298 ( 5)Unknown 6 ( 4) 61 ( 7) 131 (10) 553 (10)

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Continued.


N (%)

7/8 HLA-C MM

N (%)

7/8 HLA- Other MM

N (%)

8/8 Matched

N (%)Year of transplant

1988 0 0 7 ( 1) 12 (<1)1989 1 ( 1) 6 ( 1) 12 ( 1) 39 ( 1)1990 4 ( 3) 7 ( 1) 20 ( 1) 54 ( 1)1991 4 ( 3) 12 ( 1) 28 ( 2) 81 ( 1)1992 3 ( 2) 20 ( 2) 34 ( 3) 100 ( 2)1993 5 ( 3) 23 ( 2) 38 ( 3) 106 ( 2)1994 5 ( 3) 33 ( 4) 42 ( 3) 161 ( 3)1995 12 ( 8) 44 ( 5) 50 ( 4) 160 ( 3)1996 8 ( 5) 47 ( 5) 42 ( 3) 175 ( 3)1997 6 ( 4) 49 ( 5) 49 ( 4) 204 ( 4)1998 8 ( 5) 46 ( 5) 64 ( 5) 197 ( 4)1999 9 ( 6) 61 ( 7) 64 ( 5) 222 ( 4)2000 7 ( 5) 66 ( 7) 76 ( 6) 283 ( 5)2001 5 ( 3) 72 ( 8) 92 ( 7) 276 ( 5)2002 8 ( 5) 53 ( 6) 40 ( 3) 275 ( 5)2003 7 ( 5) 52 ( 6) 95 ( 7) 317 ( 6)2004 16 (11) 85 ( 9) 91 ( 7) 457 ( 8)2005 13 ( 9) 71 ( 8) 130 (10) 564 (10)2006 12 ( 8) 69 ( 7) 126 ( 9) 641 (12)2007 9 ( 6) 55 ( 6) 127 ( 9) 627 (12)2008 2 ( 1) 40 ( 4) 85 ( 6) 324 ( 6)2009 2 ( 1) 16 ( 2) 33 ( 2) 176 ( 3)

Median follow-up of recipients, mo (range) 84 (8-234) 82 (3-229) 64 (2-240) 60 (3-252)* Data has not been CAP-modeled.

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Study proposal 1210-62 Study Title: Effect of Rituximab and ABO Mismatch David Miklos, MD, PhD, Stanford University, Stanford, CA USA Specific Aims: Reanalyze the same data set for impact of donor and recipient ABO effect, especially ABO minor mismatch impact on OS, III-IV acute GVHD, and NRM using the usual CIBMTR clinical outcomes analysis. If the CIBMTR review of the 435 lymphoma patients shows ABO effect in relation to rituximab then EBMT abstract (Miklos planning to attend already). Ongoing correlative studies at Stanford:

1. Measuring ABO specific B cells using Jerne Plaque Technique 14, 28, and 56 days after allo-HCT in ABO mismatched and matched recipients

2. Developing B cell specific A and B tetramer FACS reagent. Significance:

1. Avoid ABO minor mismatch 2. Consider CTN clinical trial randomizing rituximab treatment for ABO mismatch patients.

Scientific Justification: Introduction: The clinical impact of ABO blood group mismatching between hematopoietic cell transplant (HCT) patients and their donors remains controversial. HCT recipients with antibodies against ABO antigens expressed by donor are ABO major mismatches (ex. Rec: O DNR: A) while minor ABO minor mismatch describes the converse of donor plasma containing antibodies against recipient expressed ABO antigen. ABO impact varies by conditioning regimen, graft source, donor engraftment chimerism, and institution transfusion practices. We recently analyzed clinical outcomes for all allogeneic transplants performed at Stanford University since 1986 as a function of donor and recipient ABO types.

Seebach et al. BBMT 2005; CIBMTR ABO Mismatch Analysis Material and Methods: From January 1986 thru January 2010, 1955 patients underwent allogeneic HCT with ages ranging 3-74 (median 46 years). Myeloablative conditioning including total body irradiation high-dose chemotherapy alone was used for 1443 transplants. Nonmyeloablative conditioning (n=512) included single 200TBI (n=193), and total lymphoid irradiation – antithymoglobulin (TLI-ATG; n=319). Overall, donor and recipient ABO typing identified 63% ABO-matched, 15% ABO-major mm, 15% ABO-minor mm, and 7% ABO-bidirectional mm. ABO mismatch distribution did not differ by donor or recipient age, conditioning regimen, or disease. HLA-matched related donors accounted for 57.8% of grafts, and their sibling recipients were HLA matched ##% (p=sig compared to URD).

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Results: Considering all 1955 patients, ABO minor mm had decreased overall survival compared to ABO matched transplants (p<0.05) this mortality deficit developed by 100 days after HCT (p<0.01) and was predominately due to nonrelapse mortality. In contrast, ABO-major mm and bidirectional mm were similar to ABO matched transplants through 2 years. Univariate analysis decreased OS and 100 day survival in ABO minor mm associated with 1) bone marrow graft source, and 2) non rituximab treated diseases (AML, ALL, MDS, CML).

Overall Survival is Worst63%

15% 15% 7%

OS Following Myeloablative Conditioning

n= 1955

n=1443

OS Following Nonmyeloablative Conditioning

p=0.012 vs ABO Match

p<0.05 vs ABO Match

n=512

p=0.012 vs ABO Match

ABO matched

ABO Major

Mismatch

ABO Minor

Mismatch

ABO Bidirect ional

Mismatch

ABO Minor

Mismatch

ABO Minor

Mismatch

ABO Minor

Mismatch

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ABO matched

ABO minor mismatch

Bone Marrow Grafts Onlyn= 833

P<0.01 vs ABO Match

ABO Minor Mismatch Effect is Absent in NHL and CLL Patients

AML, ALL, MDS, and CML NHL and CLL

FollowingMyeloablative Conditioning

ABO matched

ABO minor mismatch

ABO matched

ABO minor mismatch

P<0.05

We have subsequently gone on to score for patients who received rituximab within 6 months of allo-HCT and show that ABO minor mismatch and ABO matched patients have similar OS. Ludajic et al. studied 154 patients with unrelated donors mixing T cell depletion, graft source (BM v PBSC), and diseases, but report ABO minor mismatch provides a RR=4 [CI: 1.70-10.56; p=0.002] The authors suggest ABO minor

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mismatches may play a role in aGVHD pathophysiology, and recommend ABO matching (Ludajic et al. BBMT 2010). We hypothesize: ABO is an important immune response target after alloHCT for donor B cells that can be overcome by in vivo B cell depletion. Ratanatharathorn et al for CIBMTR GVHD Committee Brit Journal of HAem. 145: 816 - 824.

The 435 lymphoma patient CIBMTR analysis shows significantly lower incidence of transplant related mortality (TRM) [RR=0.68, 0.47-1.0; p=0.05] with lower acute GVHD III-IV RR=0.55, 0.34-0.91]. But importantly, ABO mismatch was not evaluated (Confirmed with S. Spellman). We are preparing the Stanford ABO minor mismatch experience for ASBMT.

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Characteristics of patients receiving HLA-identical sibling or unrelated donor peripheral blood transplants for Non-Hodgkin’s Lymphoma between 1999 and 2004, reported to CIBMTR.

Characteristics of patients No Rituximab N (%)

Rituximab N (%) P-valued

Number of patients 256 179 Age, median (range), years 50 (22-70) 50 (22-67) 0.42Age at transplant 0.65

21-40 43 (17) 34 (19) 41-50 80 (31) 60 (34) 51-70 133 (52) 85 (47)

Male sex 169 (66) 121 (68) 0.71Performance score 0.32

< 90 89 (35) 54 (30) 90-100 163 (64) 119 (66) Unknown 4 ( 2) 6 ( 3)

Histology of lymphoma 0.07Small cell 18 ( 7) 4 ( 2) Follicular 133 (52) 92 (51) Diffuse large cell 45 (18) 46 (26) Mantle cell 53 (21) 32 (18) Other a 7 ( 3) 5 ( 3)

Disease status prior to transplant 0.07PR partial remission 90 (35) 63 (35) CR complete remission 55 (21) 47 (26) Rel sensitive 58 (23) 50 (28) Rel resistant/untreated/unknown/progressive disease 51 (20) 18 (10) Missing 2 ( 1) 1 ( 1)

Donor 0.002HLA-identical siblings 208 (81) 122 (68) Unrelated donor 48 (19) 57 (32)

Number of chemotherapy regimen 0.69< 3 lines 95 (37) 72 (40) 3-6 lines 153 (60) 100 (56) > 6 lines 8 ( 3) 7 ( 4)

Radiation therapy, yes 54 (21) 40 (22) 0.75Conditioning regimen 0.84

Myeloblative 117 (46) 80 (45) Non-myeloblative 139 (54) 99 (55)

GVHD prophylaxis <0.001CSA + MTX ± other 103 (40) 46 (26) CSA ± other 84 (33) 45 (25) FK506 + MTX ± other 40 (16) 63 (35) FK506 ± other 25 (10) 22 (12) Other b 4 ( 2) 3 ( 2)

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Continued.

Characteristics of patients No Rituximab N (%)

Rituximab N (%) P-valued

Donor/Recipient sex match 0.76Male / Male 92 (36) 75 (42) Male / Female 46 (18) 32 (18) Female / Male 77 (30) 46 (26) Female / Female 39 (15) 25 (14) Unknown 2 ( 1) 1 ( 1)

Donor pregnancy 0.37Male donor 138 (54) 107 (60) Female, no pregnancy 17 ( 7) 14 ( 8) 1 or more pregnancies 60 (23) 30 (17) Unknown 41 (16) 28 (16)

Donor/Recipient CMV match 0.05D(-)/R(-) 66 (26) 57 (32) D(-)/R(+) 51 (20) 43 (24) D(+)/R(-) 32 (13) 30 (17) D(+)/R(+) 92 (36) 42 (23) Unknown 15 ( 6) 7 ( 4)

Year of transplant < 0.0011999-2000 87 (34) 24 (13) 2001-2004 169 (66) 155 (87)

Number of doses of rituxan received N/A1 dose -- 83 (46) 2 doses -- 64 (36) 3 doses -- 20 (11) 4 doses -- 9 ( 5) 5 doses -- 3 ( 2)

Time from last dose of rituxan to transplant N/A< 3 months -- 120 (67) 3-6 months -- 59 (33)

ABO Matching for recipient/donor 0.87ABO matched 152 (59) 104 (58) Minor ABO mismatch 47 (18) 30 (17) Major ABO mismatch 44 (17) 33 (18) Bidirectional mismatch 12 ( 5) 10 ( 6) Unknown blood type for recipient and/or donor 1 (<1) 2 ( 1)

HLA-matching 0.01HLA-identical sibling 208 (81) 122 (68) Well-matched 38 (15) 42 (23) Partially matched 8 ( 3) 10 ( 6) Mismatched 2 ( 1) 5 ( 3)

Median (range) time from diagnosis to transplant, months 25 (2-194) 22 (4-196) 0.04Median follow-up of survivors, months 39 (3-88) 30 (3-82)

Abbreviations: CSA = Cyclosporine, MTX = Methotrexate, FK506 = Tacrolimus.

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2011 BMT TANDEM MEETINGS ABSTRACT Universal role for HLA-C and KIR2DL ligand mismatch in severe acute graft-versus-host disease after unrelated donor hematopoietic stem cell transplantation (U-HSCT) in Japanese and Caucasian transplant recipients: an analysis on behalf of International Histocompatibility Working Group in Hematopoietic Cell Transplantation Takakazu Kawase1, Yasuo Morishima2, Mari Malkki3, Ted A Gooley3, Katharine C Hsu4, Bo Dupont4, Peter Bardy5, Alejandro Madigral6, Jean-Denis Bignon7, Stephen Spellman8, Andrea Velardi9 and Effie W Petersdorf3 1Japan Marrow Donor Program, Tokyo, Japan; 2Department of Hematology and Cell Therapy, Aichi Cancer Center, Nagoya, Aichi, Japan; 3Division of Clinical Research, Fred Hutchinson Cancer Research Center, Seattle, WA, United States; 4Memorial Sloan-Kettering Cancer Center, New York, NY, United States; 5Department of Haematology and Oncology, The Queen Elizabeth Hospital, Woodville, South Australia, Australia; 6The Anthony Nolan Research Institute Royal Free Hospital, London, United Kingdom; 7Establissement Francais Sang, Nantes, France; 8National Marrow Donor Program, Minneapolis, United States and 9Division of Hematology and Clinical Immunology, University of Perugia, Perugia, Italy. Donor HLA mismatching is a known risk factor for morbidity and mortality after unrelated hematopoietic cell transplantation (HCT). The frequency of donor and recipient HLA phenotypes differs between ethnically diverse populations, as does the incidence of acute graft-versus-host disease (GVHD). Certain high-risk HLA mismatches are responsible for acute GVHD risk in the Japanese experience (Kawase et al Blood 2007). We tested the hypothesis that clinical outcome after HLA-C mismatched unrelated HCT depends on the specific mismatch combinations, and that risks are different depending on the HLA phenotypes of the transplant population. The International Histocompatibility Working Group dataset enables us to compare clinical outcome between Caucasian and Japanese populations to test these hypotheses. High resolution HLA typing was available for the Japanese (n=5986) and Caucasian pairs (n=9379). Multivariable Cox regression models adjusted for HLA matching status other than HLA-C and non-HLA factors known to influence GVHD risk. In both Japanese and Caucasian recipients, the presence of an HLA-C mismatch/KIR ligand match was associated with increased risk of grades III-IV acute GVHD compared to an HLA-C match (HR 1.69 [p<0.001] and 1.23 [p<0.001], respectively). KIR2DL ligand mismatching had an even stronger effect among the Japanese than the Caucasian recipients (HRs for HLA-C allele mismatch and KIR mismatch in GVH direction were 2.51 (p<0.001) and 1.21 (p=0.017), respectively). Since it is known that certain mismatches that occur in the Japanese population occur very infrequently (if at all) in the Caucasian population, and vice verse, we conducted the same analyses but limited to subjects who have HLA-C mismatch combinations occurring in more than 10 subjects in both populations. Compared to HLA-C matches, HLA-C mismatch/KIR ligand match and HLA-C mismatch/KIR ligand mismatch were associated with a statistically significantly increased risk of GVHD in Japanese (HR 1.39 [p=0.003] and HR 3.13 [p<0.001], respectively) but not in Caucasian recipients (HR 1.18 [p=0.128] and HR 0.91 [p=0.588]). These results suggest that the magnitude of risks associated with HLA-C disparity after unrelated HCT may be different in recipients of Japanese compared to Caucasian background, particularly in the presence of a KIR ligand mismatch. Risks may also depend on the specific HLA-C allele mismatch combinations.

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AMERICAN SOCIETY OF HEMATOLOGY (ASH) ABSTRACT 2010 Permissive HLA-DPB1 Mismatching Compared to a Non-Permissive Mismatching Significantly Improves Overall Survival Following Allogeneic Transplantation In Patients with Both 10/10 and 9/10 Matched Unrelated Donors Bronwen E. Shaw, PhD, MRCP, FRCPath1, Katharina Fleischhauer, PhD2*, Mari Malkki3*, Theodore Gooley, PhD4*, Elisabetta Zino, PhD2*, Stephen Spellman, PhD5*, Yasuo Morishima, MD6, Andrea Velardi, MD, PhD7*, Peter Bardy, MD8*, Jean-denis Bignon9*, J. Alejandro Madrigal, MD, PhD10 and Effie W. Petersdorf, MD3*

1Haemato.-Oncology, Anthony Nolan Trust/ Royal Marsden Hospital, Sutton, Surrey, United Kingdom 2San Raffaele Scientific Institute, Milan, Italy 3Fred Hutchinson Cancer Research Center, Seattle, WA 4Fred Hutchinson Cancer Research Center, Seattle, WA, USA 5Center for International Blood and Marrow Transplant Research, Minneapolis, MN 6Hematology and Cell Therapy, Aichi Cancer Center, Nagoya, Japan 7Univeristy of Perugia, Perugia, Italy 8Royal Adelaide Hospital, Adelaide, SA, Australia 9EFS Pays de Loire, Nantes, France 10The Royal Free Hospital, The Anthony Nolan Rsch. Inst., London, United Kingdom It is well established that the use of a donor matched for 9-10/10 alleles at HLA-A,-B,-C,-DRB1,-DQB1 significantly improves overall survival (OS) after unrelated donor (UD) haematopoietic stem cell transplantation (HSCT). Whilst the matching status for HLA-DPB1 alleles has been shown to influence transplant complications (relapse and graft-versus-host disease (GVHD), its impact on survival has not been well defined. The current unmet need in clinical practice is an approach to stratify selection criteria when a clinician is confronted with the choice between several 10/10 or 9/10 matched unrelated donors. There is now considerable interest in exploring different types of matching criteria to define permissive HLA-DPB1 mismatches which may be associated with an improved outcome. We have previously shown that HLA-DPB1 permissiveness can be functionally defined by the characterization of shared T cell epitopes (TCE) recognized by alloreactive T cells. In this model, allelic HLA mismatches are classified as permissive if they do not involve TCE disparities, and as non-permissive if they do. Using this concept, we developed two overlapping algorithms of permissivity for allelic HLA-DPB1 mismatches, on the basis of 3 (TCE3) or 4 (TCE4) groups of DPB1 alleles encoding immunogenic TCE. Data from relatively small prospective studies has shown a worse outcome to be associated with non-permissive DPB1 TCE disparities. Here, we present outcomes in 9123 UD-HSCT pairs, collected through the International Histocompatibility Working Group (IHWG). The cohort was comprised of 5809 10/10 matched transplant pairs and 3314 9/10 matched pairs. Within the 10/10 and 9/10 matched pairs three groups of patients were identified: 1. Zero DPB1 mismatches (i.e. allele matched), 2. Permissive DPB1 mismatch, 3. Non-permissive DPB1 mismatch. The model was adjusted for disease severity, source of stem cells, conditioning regimen, use of T-cell depletion, patient/donor gender and patient age.

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In line with DPB1 allele frequencies in worldwide populations, the number of transplants scored as permissive was higher for TCE3 (4398/7270 [60.4%]) than for TCE4 (2577/7270 [35.4%]). Using the DPB1 permissive mismatch transplants as the reference group (either 10/10 or 9/10 matched), we showed that DPB1 allelic matches resulted in similar survivals to DPB1 permissive mismatches, both in the 10/10 (HR 0.96, p=0.498 for TCE3 and HR 0.99, p=0.85 for TCE4) and the 9/10 setting (HR 0.97, p=0.70 for TCE3 and HR 0.99, p=0.96 for TCE4). In contrast, survival was significantly worse in the presence of a non-permissive TCE3 or TCE4 mismatch, both in the 10/10 (HR 1.15, p=0.0005 for TCE3 and HR 1.13, p=0.0035 for TCE4) and in the 9/10 matched setting (HR 1.13, p=0.0140 for TCE3 and HR 1.11, p=0.0448 for TCE4). The survival detriment appeared to be due to a significantly increased non-relapse mortality (TCE3: 10/10 HR 1.27, p<0.001 and 9/10 HR 1.21, p=0.0001; TCE4: 10/10 HR 1.24, p<0.001 and 9/10 HR 1.13, p=0.0514), as well as an increase in grades II-IV acute GVHD (TCE3: 10/10 HR 1.17, p<0.001 and 9/10 HR 1.29, p<0.001; TCE4: 10/10 HR 1.12, p=0.0035 and 9/10 HR 1.19, p<0.0001). There was no significant difference in disease relapse between permissive and non-permissive mismatched pairs. Finally, using the 10/10 DPB1 permissive mismatched group as a reference, we found survival to be similar for 10/10 DPB1 non-permissive (HR 1.15) and 9/10 DPB1 permissive (HR 1.20) or DPB1 allele matched (HR 1.17) transplants. In conclusion, our results suggest that extending donor selection to include HLA-DPB1 both allelic and functional TCE matching may result in better prediction of survival for patients. These findings provide an attractive new algorithm to stratify donor choice when several well-matched UD are identified.

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TO: Immunobiology Working Committee Members FROM: Steve Spellman, MBS; Co-Scientific Director for the Immunobiology WC Stephanie Lee, MD, MPH; Co-Scientific Director for the Immunobiology WC RE: Studies in Progress Summary HLA GENES IB05-02s: Effect of single Class I mismatching on unrelated donor hematopoietic stem cell transplantation (HCT) (MBA Heemskerk): This analysis will assess whether HCT with HLA class I mismatched unrelated donors with few or many amino acid sequence differences in the α-helices and β-sheet of the molecule vary in outcome to gain insight into potentially permissible mismatches. This is a collaborative study with the International Histocompatibility Working Group (IHWG). Analysis is underway. IB06-02: Impact of mismatches in low expression HLA loci on the outcome of unrelated donor transplantation (HCT) (M Fernandez-Vina): This study proposes to investigate the role of incompatibilities in the HLA DRB3/4/5, DQ and DP (low expression) loci (LEL) on the outcome of unrelated HCT and hypothesizes that the effects of these loci are weak, cumulative and only demonstrable in combination with mismatches in other loci. A draft manuscript is underway and it is expected to be submitted in 2011. IB06-05: Use of high-resolution HLA data from the National Marrow Donor Program for the International Histocompatibility Working Group (IHWG) in hematopoietic cell transplantation (HCT) (E Petersdorf): The goal of the study is to to define the clinical importance of mismatching at specific HLA loci and at class I and II amino acid residues. This is a collaborative study with the International Histocompatibility Working Group (IHWG). Analysis is underway. IB07-04: Employing advanced bioinformatic methods for predicting peptide specificities of HLA molecules in the characterization of permissible mismatches in hematopoietic cell transplantation (HCT) (S Buus): This study proposes to identify a way to use a bioinformatic tool, MHCNetPan (M. Nielsen, et al., PLoS ONE2, e796, 2007) to define the distance for each pairwise donor-recipient HLA class I and II allele mismatch that are most strongly associated with post-transplant risks of acute GVHD and mortality. This is a collaborative study with the IHWG. Analysis is underway. IB07-05: Impact of donor-recipient ethnicity on risk of acute GVHD (GVHD) among HLA -A, B, C, DRB1, DQB1, DPB1 matched unrelated donor transplants (Y Morishima): The objective of the study is to evaluate the role of recipeint and donor race on clinical outcomes after unrelated donor HCT. The analysis found that Japanese transplants experienced lower risks of acute GVHD, relapse and mortality compared to other ethnic groups and was presented at ASH 2009. This is a collaborative study with the IHWG. A draft manuscript is underway.

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IB07-06: Human leukocyte antigen: DP epitope study (B Shaw): This study proposes to determine the clinical importance of permissible and non-permissible T-cell epitope defined HLA-DPB1 mismatches. This is a collaborative study with the IHWG. An oral abstract was presented at ASH 2010 and a draft manuscript is underway. IB07-07: HLA -DR15 and hematopoietic stem cell transplantation (HCT) outcome (A Gratwohl): This study tests the hypothesis that the presence of HLA-DR15 influences clinical outcome after unrelated donor HCT. Results presented at EBMT 2009 suggest that DR15 does not confer a special susceptibility to alloimmune effects. This is a collaborative study with the IHWG. A draft manuscript is underway. IB08-02: HLA matching for unrelated hematopoietic cell transplantation (HCT) for non-malignant disorders (J Horan / A Woolfrey): This study will evaluate the impact of high resolution HLA-A, B, C, DRB1, DQB1 and DPB1 matching on the risks for mortality, GVHD and relapse after unrelated donor HCT for non-malignant disease. An abstract was accepted for oral presentation at Tandem 2011.The PIs are currently coding chimerism data so that the final analyses can be completed. IB09-02s: Non-permissive HLA-DPB1 disparities based on T-cell alloreactivity (K Fleischhauer): This study will validate the previous finding that HLA-DPB1 disparities in unrelated donor HCT can be classified as permissive and non-permissive according to T cell alloreactivity patterns and determine whether HLA-DPA1*0201 contributes to alloreactivity. Non-permissive HLA-DPB1 disparities were associated with increased risk of NRM and with protection from disease relapse in non-permissive GVH. The inclusion of HLA-DPA1 in the scoring scheme did not improve the assignments. An abstract was submitted to EBMT 2011. IB10-05: Evaluation of a scoring system for HLA mismatching: HistoCheck (R Blasczyk / CK Hurley): This study will evaluate the correlation between HistoCheck HLA allele dissimilarity scoring between mismatched HLA alleles/antigens and unrelated donor HCT outcome. The results of the analysis found no correlation between HistoCheck score and outcomes. An abstract was accepted for oral presentation at Tandem 2011 and a draft manuscript is underway. CYTOKINE/CHEMOKINE R04-75s: Functional significance of cytokine gene polymorphisms in modulating risk of post-transplant complications (E Petersdorf): This study is designed to identify immune response gene variants that are associated with risks of acute GVHD, relapse and mortality after unrelated donor HCT. This is a collaborative study with the IHWG. A draft manuscript is underway. IB05-03s: Genetic polymorphisms in the genes encoding human IL-7 Receptor-a: Prognostic significance in allogeneic stem cell transplantation (HCT) (K Muller): This study is designed to validate previous results from a single center analysis that suggested that donors carrying the +1237G variant in the alpha chain of IL-7R are associated with increased treatment related mortality and acute GVHD. The manuscript was submitted and rejected by Blood. Revisions are underway and resubmission to the European Journal of Immunology planned for early 2011. NK/KIR R02-40s/R03-63s: Choosing donors with favorable KIR B genotypes for unrelated hematopoietic cell transplantation (HCT) results in superior relapse protection and better relapse-free survival for patients with acute myeloid leukemia (AML) (J Miller): This is an ongoing study in support of Dr. Miller’s NK Biology program project grant.

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R04-74s: Functional significance of Killer-IG-ligand genes in HLA-matched and mismatched unrelated hematopoietic cell transplantation (HCT) (K Hsu / B DuPont): The purpose of this study is to determine the influence of donor KIR genotypes and haplotypes on HCT for leukemia. This is a collaborative study with the IHWG and is an ongoing effort. An abstract was accepted for oral presentation at Tandem 2011. IB07-03: Analysis of KIR ligands in reduced intensity conditioning allogeneic hematopoietic stem cell transplantation (HCT) (R Sobecks): The objectives of this study are to evaluate the clinical effects of KIR ligand absence in recipients of HLA matched and mismatched unrelated donor RIC HCT for myeloid malignancies. Retrospective donor KIR genotyping to determine the relevance of recipeint KIR ligand absence was recently completed. Datafile preparation is underway. OTHER GENES R04-76s: Identification of functional single nucleotide polymorphisms (SNPs): in unrelated hematopoietic cell transplantation (HCT) (E Petersdorf): This study proposes to identify novel major histocompatibility complex resident SNPs of clinical importance. HLA matched pairs were genotyped for 1120 MHC SNPs and correlation with outcome is underway. This is a collaborative study with the IHWG. Analysis is underway. IB07-08: Single nucleotide polymorphisms in the P53 Pathway (P53, MDM2, ATM AND P21/WAF1) and transplant outcome after unrelated haematopoietic stem cell transplantation (HCT) (B DuPont): The goal of the study is to determine if tumor suppressor p53 Arg72Pro polymorphism affects post-transplantation survival. The study found that recipient polymorphism in p53 may be a risk factor for long-term survival. The study was expanded to include RIC regimens and HLA mismatched cases. The committee provided a letter of support and contributed text describing the sample resources available to the study for an R01 grant proposal. This is a collaborative study with the IHWG. Testing is underway. IB07-09: To develop and test a prognostic index for survival in chronic myelogenous leukemia matched unrelated donor cohorts (A Dickinson): The purpose of the study is to validate previous work in HLA-matched sibling transplantation showing that cytokine SNPs were associated with decreased survival and improved prognostic accuracy when combined with the European Group for Blood and Marrow Transplantation (EBMT) risk score in CML patients. The results of the analysis were negative. A draft manuscript is circulating and will be submitted in 2011. IB08-08: Genome-wide association in unrelated donor hematopoietic cell transplant (HCT) recipients and donors (R Goyal): This study hypothesizes that an unbiased recipient-donor genome-wide association (GWA) study will identify genes associated with risk of acute graft versus host disease (aGvHD) after HLA-matched unrelated donor HCT. Testing is underway. IB09-04s: Donor/recipient gene polymorphisms of drug metabolism and in innate immune response post allele-matched unrelated donor hematopoietic stem cell transplantation (HCT) (V Rocha): This study is designed to validate associations between polymorphisms in drug metabolism and innate immune response genes and outcomes previously identified in matched sibling donor HCT in the HLA-matched unrelated donor HCT setting. Testing is underway. IB09-06s/RT09-04*: Genetic susceptibility to transplant-related mortality after matched unrelated stem cell transplant (T Hahn): This is a joint study with the Regimen Related Toxicity working committee and is supported by an R01 grant to Drs. Hahn and Sucheston. This study will test for a genetic association with transplant-related and overall mortality in recipients of myeloablative and reduced intensity conditioning matched unrelated donor HCT. The committee provided a letter of support and contributed text describing the sample resources available to the study for the R01 grant proposal. Testing will begin in early 2011.

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IB09-07s: Clinical significance of genome-wide variation in unrelated donor hematopoietic stem cell transplantation (HCT) (E Petersdorf): This study is designed to assess the impact of genome-wide variation between donors and recepients in HLA matched unrelated donor HCT. This is a collaborative study with the IHWG. The committee provided a letter of support and contributed text describing the sample resources available to the study for a U01 grant proposal. Testing is underway. IB10-01s: Donor and recipient telomere length as predictors of outcomes after hematopoietic stem cell transplant in patients with acquired severe aplastic anemia (S Gadalla): More than one-third of patients with acquired severe aplastic anemia (SAA) have short telomeres. Telomere shortening in peripheral blood is associated with increased risk of malignancies, pulmonary and liver fibrosis, and other complications. This study will explore the role that telomere length plays in outcomes after HCT for SAA. Testing is underway. IB10-03: TLR and HMGB1 gene polymorphisms in unrelated haematopoietic stem cell transplantation (K Müller/ B Kornblit): The objective of this study is to validate single center findings of a correlation between High Mobility Group Box 1 (HBGB1) polymorphism in the recipient and relapse rates following HLA matched unrelated donor HCT. Testing is underway. IB10-04: A validation study of the role of base excision repair pathway as a predictor of outcome after hematopoietic stem cell transplant (M Arora): This study is designed to validate findings from a single center that noted a correlation between SNPs in the DNA repair genes and relapse and TRM after HLA matched unrelated donor HCT. The committee provided a letter of support and contributed text describing the sample resources available to the study for an R01 grant proposal. Testing is underway. SENSITIZATION and TOLERANCE R03-65s: Detection of H-Y antibodies in healthy female donors: Does H-Y presensitization predict male hematopoietic stem cell transplantation (HCT) outcome (D Miklos): Draft manuscript is underway and expected to be submitted in 2011. IB06-09s: Detection of HLA antibody to the mismatched antigen in single antigen HLA-mismatched unrelated donor transplantation (HCT): Is it associated with GVHD outcome? (S Arai): Draft manuscript is underway and expected to be submitted in 2011. IB06-10: Evaluation of the impact of exposure to non-inherited maternal antigens (NIMA) during fetal life and breast feeding and to the inherited paternal antigens during pregnancy on the clinical outcome of hematopoietic cell transplantation (HCT) from haploidentical family members (J Van Rood): This study will evaluate the impact of NIMA on haploidentical related HCT for hematological malignancies. The study requires HLA typing on the patient, donor and patient parents to assign the presence or absence of NIMA matches. A comprehensive review of the available HLA data was completed August 2010. Planning is underway for a combined study with EBMT. IB06-11s: Effect of non-inherited maternal antigens (NIMA) in cord blood transplantation (VK Prasad / LA Baxter-Lowe): This study will test the hypothesis that matching for NIMA in the selection of cord blood units will favorably influence outcomes. The U.S. study population was combined with a similar cohort from Eurocord to augment the power for analysis. A matched case-control analysis was completed and found that NIMA matching had a positive effect on overall survival, disease free survival and relapse. An abstract was submitted to EBMT 2011.

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IB09-08: Donor/recipient birth order in matched sibling hematopoietic stem cell transplantation (HCT) (C Dobbelstein): The study proposes to validate findings from a single center study that found matched sibling HCT utilizing donors born after the recipient yielded superior survival with lower incidences of acute GVHD and relapse. The results of the analysis did not support the hypothesis. A draft manuscript is underway. An abstract is planned for EHA 2011. MINOR HISTOCOMPATIBILITY ANTIGENS GV04-01: Outcomes of non-identical twin transplants for leukemia (AJ Barrett): This study hypothesizes that the shared circulation in utero may create donor-recipient tolerance following HCT between HLA-identical dizygotic twins. A draft protocol is complete and will be circulated to the working committee in 2011. A draft datafile is prepared but requires center confirmation of the dizygotic twins. Approved studies not yet initiated: HLA GENES IB10-07: Use of HLA structure and function parameters to understand the relationship between HLA disparity and transplant outcomes (LA Baxter-Lowe): This study is designed to develop an HLA dispartity scoring system based on the molecular structure and alloreactive function of the mismatched amino acids. The system will then be evaluated for correlation with survival, disease-free survival, relapse, GVHD and engraftment following HLA mismatched unrelated donor HCT. A protocol will be available October 2010. R04-80s: Impact of HLA matching on outcome in pediatric patients undergoing unrelated umbilical cord blood transplantation (SR Marino): Study deferred pending data collection. IB06-13: HLA disparity among unrelated umbilical cord blood transplants (LA Baxter-Lowe): Study deferred pending data collection. IB09-01s: Clinical importance of major histocompatibility complex haplotypes in umbilical cord blood transplantation (E Petersdorf): Study deferred pending data and sample collection. CYTOKINE/CHEMOKINE IB08-05/LK08-04*: Lymphotoxin alpha alleles in acute myelogenous leukemia (AML) relapse (P Posch): This is a joint study with the Acute Leukemia Working Committee and it proposes to determine whether LTA alleles correlate with relapse in AML and CML and to determine if the correlation is associated with high or low LTA production. Study deferred pending in vitro assessment of LTA polymorphism. IB08-04: Immune response gene polymorphisms in unrelated donor hematopoietic cell transplantation (HCT) in children (K Muller): Study awaiting completion of IB05-03s by the same study team. IB09-03s: Clinical relevance of cytokine/immune response genes in umbilical cord blood transplant (E Petersdorf): Study deferred pending data and sample collection. NK/KIR IB08-06: KIR ligands in umbilical cord blood hematopoietic cell transplantation (HCT) (R Sobecks): Study deferred pending data and sample collection. OTHER GENES IB09-05s: Identification of functional single nucleotide polymorphisms (SNPs) in umbilical cord blood transplant (E Petersdorf): Study deferred pending data and sample collection.

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MINOR HISTOCOMPATIBILITY ANTIGENS IB10-06: Identification of common, clinically significant, minor histocompatibility antigens through stem cell transplant donor/patient polymorphism disparities (P Armistead): The central hypothesis of this research project is that many donor/patient mHAg mismatches result from non-synonymous (i.e. encoding a different amino acid) single nucleotide polymorphisms in the coding regions of genes (cSNPs) and that cytotoxic T lymphocytes (CTLs) recognizing these mHAgs can convey GvHD and/or GvL. These two conditions may be separable based upon the tissue specificity of the individual mHAgs. The study will proceed following receipt of funding.

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2011 BMT TANDEM MEETINGS ABSTRACT Evaluation of HLA Matching Requirements in Unrelated Hematopoietic Stem Cell Transplantation for Nonmalignant Disorders Ann Woolfrey, John Horan, Tao Wang, Michael Haagenson, Mouhab Ayas, Lee Ann Baxter-Lowe, Bella Bielorai, Stella Davies, Jason Dehn, Haydar Frangoul, James Gajewski, Vikas Gupta, Gregory A. Hale, Carolyn Hurley, Susana Marino, Philip McCarthy, Paul Orchard, Machteld Oudshoorn, Marilyn S. Pollack, Vijay Reddy, Peter Shaw, Stephen Spellman and Stephanie Lee Previous studies of HLA matching in unrelated donor (URD) transplantation have focused largely on patients with leukemia. The effect of donor-recipient mismatching in patients with nonmalignant disorders (NMD) may differ and remains poorly defined. We analyzed data from the CIBMTR database on 667 URD transplants performed for a NMD between 1995 and 2007. The initial analysis was restricted to donor-recipient pairs that were allele matched at the A, B, C and DRB1 loci (375), matched at 7/8 alleles (191) or matched at 6/8 alleles (101). The median patient age was 9 years and did not differ between the three groups. The distribution of the types of NMD was similar in the three groups. Across all groups, the most common were severe aplastic anemia (54%), immunodeficiencies (18%), inborn errors of metabolism (14%) and histiocytic disorders (10%). Transplants involving 8/8 matched pairs were more likely to have been performed after 2001 (72% vs. 60% vs. 36%, p<0.0001), to involve a Caucasian recipient (78% vs. 68% vs. 62%, p=0.009) and to involve donor and recipients who were both CMV seronegative (39% vs. 24% vs. 27%, P=0.004), respectively. The unadjusted overall survival at five years was 65%, 57% and 47% after transplants involving 8/8, 7/8 and 6/8 matches (p=0.004). The cumulative incidence of grade 2-4 acute GVHD was 43, 40 and 44% (p=0.76) at 100 days and of chronic GVHD was 32, 29, and 30% (p=0.82) at 2 years. The multivariate analyses demonstrated an association between degree of mismatch and mortality. The hazard ratio for 7/8 matched transplants was 1.31 (95% CI, 0.98-1.75, p=0.066) and for 6/8 matched transplants was 1.75 (1.25-2.45, p=0.0012) compared to 8/8 matched pairs. No association between degree of mismatching and acute GVHD II-IV, acute GVHD III-IV or chronic GVHD was noted. Our results underscore the importance of HLA matching in transplants for NMD. They also suggest that the effects of mismatching are mediated by a complication other than GVHD. Graft failure may be an important cause of treatment failure; we are currently collecting the chimerism data necessary to analyze this outcome.

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THE EUROPEAN GROUP FOR BLOOD AND MARROW TRANSPLANTATION (EBMT) ABSTRACT 2011

Non-permissive HLA-DPB1 T cell epitope disparities are associated with non-relapse mortality after unrelated stem cell transplantation and are not dependent on HLA-DPA1 Katharina Fleischhauer, Stephen R Spellman, Tao Wang, Michael Haagenson, Minoo Battiwalla, Lee Ann Baxter-Lowe, Jason W. Dehn, James L. Gajewski, Gregory A. Hale, Martin B. Heemskerk, Stephanie J. Lee, Philip L. McCarthy, David B. Miklos, Machteld Oudshoorn, Marilyn S. Pollack, Susana Marino, Vijay Reddy, David Senitzer, Bronwen Shaw, Edmund K. Waller, Marcelo Fernandez-Vina. Background and Objectives: In unrelated donor (UD) hematopoietic stem cell transplantation (HCT), non-permissive UD-recipient disparity for a T cell epitope (TCE) shared by some human leukocyte antigen (HLA)-DPB1 alleles, mostly in linkage disequilibrium with HLA-DPA1*02:01, has been suggested to be predictive of clinical outcome. Here we validated the HLA-DPB1-TCE algorithm in an independent cohort of UD-HCT, and tested the hypothesis that non-permissivity of the HLA-DPB1-TCE might be dependent on heterodimer formation with HLA-DPA1*02:01, or on the presence of allelic DPA1 mismatches. Design and Methods: 1281 HCT from 10/10 HLA-matched UD, facilitated through the National Marrow Donor Program, for acute lymphoid or myeloid leukemia, chronic myeloid leukemia or myelodysplastic syndrome were included. 239 pairs were fully matched for HLA-DPB1, while 585, 226 and 231 pairs had a permissive or a non-permissive graft versus host (GvH) or host versus graft (HvG) DPB1-TCE mismatch, respectively. When the presence of HLA-DPA1*02:01 or DPA1 mismatching was also considered, about half of the non-permissive mismatches were re-scored as permissive. The predictive value of these algorithms was tested in Cox regression models adjusted for all major clinical variables. Results: Non-permissive HLA-DPB1 TCE disparities were associated with increased non-relapse mortality (NRM), both for GvH (HR1.3, 95%CI 1-1.61, P=0.03) and HvG (HR1.3, 95%CI 1-1.6, P=0.04), as compared to permissive disparities. In contrast, non-permissive GvH but not HvG were more protective from relapse than permissive mismatches (HR0.6, 95%CI 0.4-0.8, P=0.002 GvH and HR1.1, 95%CI 0.80-1.5, P=0.60 HvG). There was a trend for association of non-permissive GvH or HvG with increased GvH disease (P=0.09) and overall survival (OS) (P=0.11), however this did not reach significance, probably as a result of competing impacts. A post-hoc power analysis suggests a 5x larger cohort is required to evaluate OS. The inclusion of DPA1 in the scoring scheme did not have an impact on the results. Conclusions: In our transplant cohort, non-permissive HLA-DPB1 TCE disparities were associated with increased risk of NRM after 10/10 matched UD-HCT and with protection from disease relapse in non-permissive GvH, making it attractive for patients with multiple potential UD. The inclusion of HLA-DPA1 in the scoring scheme does not provide relevant additional information for determining clinically non-permissive HLA-DP disparities.

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2011 BMT TANDEM MEETINGS ABSTRACT Scoring HLA Mismatches by HistoCheck Does Not Predict Clinical Outcome in HCT Carolyn Hurley, PhD, John Klein, PhD, Stephen Spellman , MBS, Michael Haagenson MS, Stephanie J. Lee MD, MPH, Medhat Askar, MD, PhD, Lee Ann Baxter-Lowe, PhD, Jun He, MD, Susan Hsu, PhD, Rainer Blasczyk, MD. High resolution HLA matching at HLA-A, B, C and DRB1 has proven critical for optimal outcomes following unrelated donor (URD) hematopoietic stem cell transplantation (HCT). Approximately 30% of URD HCT facilitated through by the National Marrow Donor Program (NMDP) are mismatched (MM) at one or more loci. Currently, no rating system exists to reliably predict which HLA MM URD should be selected for patients who do not have an HLA allele matched donor. In 2002, Elsner and Blasczyk suggested that a rating system, HistoCheck, might be used to identify acceptable MM. This algorithm is based on the functional similarity of amino acids weighted based on the position of the disparity in the HLA molecule to yield a Dissimilarity Score (DSS). We evaluated the ability of DSS to predict the risk associated with HLA disparity in a population of 744 single allele or antigen HLA-A, B, or C MM myeloablative URD HCT for AML, ALL, CML or MDS facilitated through the NMDP from 1988-2003. All pairs in the study were retrospectively high resolution typed for HLA-A, B, C, DRB1, DQB1 and DPB1. Multivariate models were used to adjust for other significant clinical risk factors. A threshold of p <0.01 was used to define significance due to multiple comparisons. HLA MM were scored using the HistoCheck web-based tool and the patients divided into 4 quartiles: DSS 1.04-2.84 (allele MM), 2.84-13.75 (allele and antigen MM), 13.75-19.385 (antigen MM) and 10.385-36.62 (antigen MM). Using the lowest scoring quartile as the reference, the DSS groups were evaluated for an association with relapse, transplant related mortality (TRM), acute and chronic GVHD, leukemia free survival (LFS), and overall survival in the entire cohort and in subset analyses by disease and disease stage. No significant associations were found between DSS and any outcomes in the overall cohort using the quartile categories or treating DSS as a continuous variable. Higher DSS scores were associated with decreased engraftment in early stage disease (p=0.0003) but not in other disease stages. In disease subset analyses, DSS score was associated with TRM, LFS and OS in MDS. However, the MDS subgroups were very small (n=10-21) diminishing confidence in the association. In summary, DSS does not correlate with transplant outcome, and the HistoCheck scoring system does not provide an effective strategy to rank HLA MM. The dataset used in this study is available to evaluate new algorithms developed for donor selection.

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CIBMTR 1209-39

PROTOCOL FOR USE OF HLA STRUCTURE AND FUNCTION PARAMETERS TO UNDERSTAND THE RELATIONSHIP BETWEEN HLA DISPARITY AND TRANSPLANT

OUTCOMES

DRAFT PROTOCOL Study Chair: Lee Ann Baxter-Lowe, Ph.D.

University of California, San Francisco, Immunogenetics and Transplantation Laboratory, Box 0508 San Francisco, California 94143-0508 USA Telephone: 415-476-3883 Fax: 415-476-0379 E-mail: [email protected]

Study Statistician: Tanya Pedersen, MS CIBMTR

3001 Broadway Street, N.E., Suite 110 Minneapolis, MN 55413 USA Telephone: 612-884-8607 Fax: 612-884-8661

E-mail: [email protected] Scientific Directors: Stephanie Lee, MD, MPH Fred Hutchinson Cancer Research Center 1100 Fairview Ave. North, D5-290 PO Box 19024 Seattle, WA 98109 Telephone: 206-667-6190 Fax: 206-667-1034 E-mail: [email protected] Stephen Spellman, MS CIBMTR National Marrow Donor Program 3001 Broadway Street NE, Suite 500 Minneapolis, MN 55413 USA Telephone: 612-617-8334 Fax: 612-362-3488 E-mail: [email protected]

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Working Committee Chairs: David Miklos, MD/PhD Stanford University Department of Medicine; BMT Division CCSR, Room #2205 269 West Campus Drive Stanford, CA 94305-5170 USA Telephone: 650-725-4626 Fax: 650-724-6182 E-mail: [email protected]

Marcelo Fernandez-Vina, PhD

Laboratory Medicine M. D. Anderson Cancer Center 1515 Holcombe Blvd., Box 423 Houston, TX 77030-4009 USA Telephone: 713-792-8750 Fax: 713-792-8503

E-mail: [email protected] Carlheinz Mueller, MD, PhD Director German National Bone Marrow Donor Registry ZKRD Helmholtzstrabe 10, 89081 Ulm, Germany Telephone: +49 731 1507-10 Fax: +49 731 1507-51 E-mail: [email protected]

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1.0 HYPOTHESIS:

HLA mismatching has been established as an important variable affecting the outcomes of hematopoietic stem cell transplants. It is hypothesized that a scoring system for HLA disparities which is based upon fundamental knowledge of allorecognition is superior to conventional approaches for defining and ranking HLA mismatches. Key elements of the scoring system are the similarity of amino acids in HLA molecules of the donor and recipient that are important for docking of T cell receptors along with differences in amino acids in HLA molecules of the donor and recipient that affect peptide recognition. Since the scoring system is based upon the structure and function of mismatched amino acids, the scores will be referred to as the SF Score. Using conventional definitions of HLA disparity, it has been shown that a single HLA-A, -B, -C, or DRB1 mismatch is associated with a 9-10% decrement in patient survival and that multiple HLA disparities at these loci further diminish survival [1]. If there are sufficient subjects, it is preferable to begin testing this hypothesis using patients receiving a graft from a donor with a single HLA-A, -B, -C, or DRB1 disparity because this will diminish the confounding effects of multiple HLA disparities. The analysis can be expanded to include recipients with grafts that have multiple HLA disparities as required to achieve sufficient numbers or to expand hypothesis testing to pairs with multiple HLA disparities. The primary outcome for this study is overall survival (OS). Secondary outcomes for this study are (1) incidence of grade II-IV and III-IV acute graft vs. host disease (aGVHD) (2) transplant-related mortality (TRM), (3) neutrophil engraftment, (4) platelet engraftment, (5) incidence and severity of chronic graft vs. host disease (cGvHD), (6) disease-free survival (DFS), and (7) disease relapse.

2.0 OBJECTIVES:

2.1 To determine the relationship between HLA disparities ranked by their SF Score and survival, disease-free survival, and transplant related mortality. The reference group for these comparisons will be HLA matched pairs (minimally HLA-A, -B, -C, and DRB1 matched pairs, but could include HLA-DQ and/or -DP matching as required to achieve an optimal control group). The SF scores associated with survival will be determined. If there are sufficient subjects, it is preferable to examine the associations for each locus (HLA-A, -B, -C, or DRB1), HLA Class I (HLA-A, -B, or -C), HLA-Class II (HLA-DRB1), and any single HLA-A, -B, -C, or DRB1 mismatch.

2.2 To determine the relationship between HLA disparities ranked by their SF Score and

GvHD. For this analysis, HLA mismatching in the graft-versus-host direction will be considered. The reference group for these comparisons will be HLA matched pairs (minimally HLA-A, -B, -C, and DRB1 matched pairs, but could include HLA-DQ and/or -DP matching as required to achieve an optimal control group). The SF scores associated with GvHD will be determined. If there are sufficient subjects, it is preferable to examine the associations for each locus (HLA-A, -B, -C, or DRB1), HLA Class I (HLA-A, -B, or -C), HLA-Class II (HLA-DRB1), and any single HLA-A, -B, -C, or DRB1 mismatch.


engraftment of neutrophils and platelets. For this analysis, HLA mismatching in the host-versus-graft direction will be examined. The reference group for these comparisons will be HLA matched pairs (minimally HLA-A, -B, -C, and DRB1 matched pairs, but could include HLA-DQ and/or -DP matching as required to achieve an optimal control group).

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The SF scores associated with engraftment will be determined. If there are sufficient subjects, it is preferable to examine the associations for each locus (HLA-A, -B, -C, or DRB1), HLA Class I (HLA-A, -B, or -C), HLA-Class II (HLA-DRB1), and any single HLA-A, -B, -C, or DRB1 mismatch.


disease relapse. For this analysis, HLA mismatching in the graft-versus-host direction as well as any HLA mismatch will be considered. The reference group for these comparisons will be HLA matched pairs (minimally HLA-A, -B, -C, and DRB1 matched pairs, but could include HLA-DQ and/or -DP matching as required to achieve an optimal control group). The SF scores associated with relapse will be determined. If there are sufficient subjects, it is preferable to examine the associations for each locus (HLA-A, -B, -C, or DRB1), HLA Class I (HLA-A, -B, or -C), HLA-Class II (HLA-DRB1), and any single HLA-A, -B, -C, or DRB1 mismatch.

2.5 To compare SF Scores with conventional methods for evaluating HLA mismatches. For

the endpoints in Objectives 1-4, the relationships observed using SF will be compared with those observed using conventional approaches for defining HLA mismatches (any HLA disparity, low resolution disparities, and high resolution disparities as previously described [1]).

2.6 To determine if SF Scores are useful for HLA-DQ and -DP. If the above objectives show

that SF scoring is equivalent or superior to conventional approaches for defining HLA mismatches for any locus, SF scores for HLA-DQ and -DP will be tested to determine if SF Scores reveal relationships that have not been detectable using conventional methods for defining HLA disparities.

2.7 To use SF Scores to determine the effects of multiple HLA disparities. If the above

objectives show that SF scoring for single HLA disparities is equivalent or superior to conventional approaches for defining HLA mismatches, the combined effects of multiple HLA disparities will be determined using SF Scores.

3.0 SCIENTIFIC JUSTIFICATION:

Many investigations have detected relationships between HLA mismatching and outcomes of bone marrow transplants. The largest studies have consistently observed relationships between HLA-A, -B, -C, and -DRB1 disparities and transplant outcomes [1-4]. In addition, several studies have reported associations between transplant outcomes and HLA-DQ and -DP mismatching, but these relationships are more controversial [1, 2, 5-7]. For patients lacking an HLA matched donor, there are no well accepted guidelines for selecting the optimal donor amongst several donors who have different HLA mismatches. Analysis of cross-reactive antigens (CREG) and amino acid triplets have not been helpful [8, 9]. An algorithm called HistoCheck was created to score HLA disparities using the number of HLA differences and the similarity of amino acid side chains along with some adjustments for position, but this approach has not correlated with transplant outcomes [10-13]. The long-term goal of this project is to develop rational guidelines for selecting optimal HLA mismatched donors by taking into consideration the structure and function of HLA molecules. The first phase of this investigation examined the most frequent HLA-A mismatches to determine if there are particular common disparities that have detectable effects on transplant outcomes [14]. This study concluded that alternative approaches are required because it is not feasible to

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accrue sufficient donor-recipient pairs to determine the risks associated with the most frequent HLA-A disparities in the United States. The current proposal overcomes this limitation by scoring HLA disparities using functional properties of the amino acid that differ between the HLA molecules of the donor and host. Rudolph et al have identified amino acids that are frequently important for binding of T cell receptors to HLA molecules [15]. When these amino acids are changed, the T cell often loses the ability to recognize HLA-peptide complexes. Potential relevance to hematopoietic stem cell transplantation is suggested by observations that HLA mismatches with many amino acid differences do not elicit cytotoxic T lymphocyte responses and can be acceptable for transplantation [16, 17]. Based upon these observations, we hypothesize that the most deleterious HLA disparities are those that are matched for amino acids that are important for T cell docking in combination with amino acid differences that alter peptides that are bound to the HLA molecule. SF Scores which are based upon these parameters will be assigned to every HLA disparity that is observed in the study population.

4.0 STUDY POPULATION:

This investigation will include all patients who meet the following criteria: — High resolution HLA-A, -B, -C, -DR, -DQ, and -DP typing of donor and recipient — Transplant performed in the United States — One of the following underlying diseases: AML, CML, NHL, MDS, ALL — Transplant performed after 1989 — Recipient age <65 — First hematopoietic stem cell graft — Graft from bone marrow or peripheral blood stem cells The study population will be adjusted by a CAP correction.

5.0 OUTCOMES:

5.1 Overall survival: Time to death. Patients are censored at the time of their last follow-up. 5.2 Acute graft-versus-host disease: Incidence of grade II, III and IV GVHD. These will be

based on Glucksberg staging of skin, gastrointestinal or liver disease. 5.3 Chronic graft-versus-host disease: Occurrence of symptoms in any organ system

fulfilling the IBMTR criteria of chronic GVHD. 5.4 Disease-free survival: Time to treatment failure (death or relapse). Patients are censored

at the time of last follow-up. 5.5 Transplant-related mortality: Time to death without evidence of disease recurrence.

Patients are censored at time of relapse or at last follow-up. This event is summarized by the cumulative incidence estimate with relapse as the competing risk.

5.6 Relapse: Time to onset of disease recurrence. Patients never in remission after transplant

will be considered as the event occurring at 1 day post-transplant. Patients will be censored at death in continuous CR, second transplant or, for patients surviving in continuous complete remission, at last contact. This event is summarized by the cumulative incidence estimate.

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5.7 Neutrophil engraftment: Time to neutrophil count (ANC) > 0.5 x 109/L (first of three

consecutive days) will define engraftment. 5.8 Platelet engraftment: Time to platelet count ≥ 50,000 x 109/L will define engraftment.

6.0 VARIABLES TO BE ANALYZED: Patient-related variables (at time of transplant): — Age: < 20y vs. 20-40y vs. ≥ 40y — Gender: female vs. male — Karnofsky performance score: ≥ 90% vs. < 90% — Disease-related — Underlying disease: AML, CML, NHL, MDS, ALL — Time from diagnosis to transplant for CML: ≤1 year vs. > 1 year — IBMTR risk group: standard vs. high risk Donor- and graft-related variables: — Donor age: in decades (18-29, 30-39, 40-49, ≥ 50) — Donor-recipient gender: M-M vs. M-F vs. F-M vs. F-F — Donor-recipient CMV status: +/+ vs. +/- vs. -/+ vs. -/- — Cell dose: dose x106/kg: — Source of stem cells: marrow (BM) vs. peripheral blood stem cells (PB) — T cell depletion: T cell depletion vs. no T cell depletion Transplant-related variables: — Conditioning regimen: Myeloablative vs. non-myeloablative — GVHD prophylaxis (To be determined) — Year of transplant — Transplant center Post-transplant variables: — Engraftment: yes vs. no — Acute GVHD: 0-1 vs. 2-4 and 0-2 vs. 3-4 — Chronic GVHD: none vs. limited vs. extensive — Donor cell infusion: yes vs. no — Survival — Last contact HLA Matching variables: — HLA locus — HLA mismatching

- SF Score (thresholds to be determine from data analysis) - Any HLA mismatch as previously described [1] - Low resolution HLA mismatches as previously described [1] - High resolution HLA mismatches as previously described [1]

7.0 STUDY DESIGN:

To summarize the characteristics of the dataset, descriptive tables of patient-, disease- and transplant-related factors will be reported.

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— For discrete factors, the number of cases and their respective percentages will be calculated. Chi-Square tests will be used to compare discrete factors between the HLA matched vs. mismatched groups.

— For continuous factors, the median and ranges will be calculated. The Kruskal-Wallis test will be used to compare the continuous factors between the HLA matched vs. mismatched groups.

A descriptive table and/or figures will be prepared to describe the HLA matching of the study population using SF Scores and conventional methods. Probabilities for overall survival and disease-free survival will be calculated using the Kaplan-Meier estimator with variance estimated by Greenwood's formula. Comparison of survival curves will be done using the log-rank test. Values for other outcomes will be calculated according to cumulative incidence using a Taylor series linear approximation to estimate the variance. Multivariate analyses will be performed using the proportional hazards model to compare the HLA matched with either SF Score or conventional HLA mismatching. Models will be fit to determine which risk factors may be related to a given outcome. All variables will be tested for the affirmation of the proportional hazards assumption. Factors violating the proportional hazards assumption will be adjusted for first before the stepwise model building approach will be used in developing models for the primary and secondary outcomes.

8.0 REFERENCES:

1. Lee, S.J., et al., High-resolution donor-recipient HLA matching contributes to the success of unrelated donor marrow transplantation. Blood, 2007.

2. Flomenberg, N., et al., Impact of HLA class I and class II high-resolution matching on outcomes of unrelated donor bone marrow transplantation: HLA-C mismatching is associated with a strong adverse effect on transplantation outcome. Blood, 2004. 104(7): p. 1923-30.

3. Morishima, Y., et al., Effects of HLA allele and killer immunoglobulin-like receptor ligand matching on clinical outcome in leukemia patients undergoing transplantation with T-cell-replete marrow from an unrelated donor. Biol Blood Marrow Transplant, 2007. 13(3): p. 315-28.

4. Petersdorf, E.W., et al., Major-histocompatibility-complex class I alleles and antigens in hematopoietic-cell transplantation. N Engl J Med, 2001. 345(25): p. 1794-800.

5. Gallardo, D., et al., Hla-DPB1 mismatch in HLA-A-B-DRB1 identical sibling donor stem cell transplantation and acute graft-versus-host disease. Transplantation, 2004. 77(7): p. 1107-10.

6. Shaw, B.E., et al., Diverging effects of HLA-DPB1 matching status on outcome following unrelated donor transplantation depending on disease stage and the degree of matching for other HLA alleles. Leukemia. 24(1): p. 58-65.

7. Petersdorf, E.W., et al., The biological significance of HLA-DP gene variation in haematopoietic cell transplantation. Br J Haematol, 2001. 112(4): p. 988-94.

8. Duquesnoy, R., et al., HLAMatchmaker-defined triplet matching is not associated with better survival rates of patients with class I HLA allele mismatched hematopoietic cell transplants from unrelated donors. Biol Blood Marrow Transplant, 2008. 14(9): p. 1064-71.

9. Wade, J.A., et al., HLA mismatching within or outside of cross-reactive groups (CREGs) is associated with similar outcomes after unrelated hematopoietic stem cell transplantation. Blood, 2007. 109(9): p. 4064-70.

10. DeLuca, D.S. and R. Blasczyk, HistoCheck. Evaluating structural and functional MHC similarities. Methods Mol Biol, 2007. 409: p. 395-405.

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11. Elsner, H.A., et al., HistoCheck: rating of HLA class I and II mismatches by an internet-based software tool. Bone Marrow Transplant, 2004. 33(2): p. 165-9.

12. Heemskerk, M.B., et al., The HistoCheck algorithm does not predict T-cell alloreactivity in vitro. Bone Marrow Transplant, 2005. 36(10): p. 927-8.

13. Shaw, B.E., et al., Scoring for HLA matching? A clinical test of HistoCheck. Bone Marrow Transplant, 2004. 34(4): p. 367-8; author reply 369.

14. Baxter-Lowe, L.A., et al., HLA-A disparities illustrate challenges for ranking the impact of HLA mismatches on bone marrow transplant outcomes in the United States. Biol Blood Marrow Transplant, 2009. 15(8): p. 971-81.

15. Rudolph, M.G., R.L. Stanfield, and I.A. Wilson, How TCRs bind MHCs, peptides, and coreceptors. Annu Rev Immunol, 2006. 24: p. 419-66.

16. Heemskerk, M.B., et al., Allogeneic MHC class I molecules with numerous sequence differences do not elicit a CTL response. Hum Immunol, 2005. 66(9): p. 969-76.

17. Heemskerk, M.B., et al., Highly diverged MHC class I mismatches are acceptable for haematopoietic stem cell transplantation. Bone Marrow Transplant, 2007. 40(3): p. 193-200.

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Table 1. Characteristics of patients with AML, ALL, CML or MDS and are typed for HLA-A, -B, -C, -DRB1, -DQB1 and –DPB1 for the HLA Matchmaker project using high resolution typing data from the National Marrow Donor Program by matched vs. single mismatch for HLA-A, -B or -C

8/8 Matched

HLA-A MM

HLA-B MM

HLA-C MM

Variable N (%) N (%) N (%) N (%)Number of patients 1507 179 88 333Number of centers 103 55 44 80Age, median (range), years 34 (<1-63) 25 (<1-60) 29 (2-56) 33 (<1-65)Age at transplant

≤ 10 y 141 ( 9) 31 (17) 11 (13) 32 (10)10-19 y 168 (11) 41 (23) 15 (17) 53 (16)20-29 y 283 (19) 32 (18) 18 (20) 57 (17)30-39 y 377 (25) 33 (18) 20 (23) 67 (20)40-49 y 390 (26) 28 (16) 16 (18) 95 (29)50 y and older 148 (10) 14 ( 8) 8 ( 9) 29 ( 9)

Male sex 848 (56) 96 (54) 46 (52) 184 (55)Karnofsky prior to transplant > 90 1162 (77) 141 (79) 65 (74) 248 (74)Disease at transplant

AML 370 (25) 50 (28) 24 (27) 98 (29)ALL 352 (23) 55 (31) 20 (23) 80 (24)CML 717 (48) 68 (38) 43 (49) 145 (44)MDS 68 ( 5) 6 ( 3) 1 ( 1) 10 ( 3)

Graft type Marrow 1402 (93) 168 (94) 82 (93) 309 (93)Peripheral blood 105 ( 7) 11 ( 6) 6 ( 7) 24 ( 7)

Disease status at transplant Early 834 (55) 81 (45) 46 (52) 155 (47)Intermediate 673 (45) 98 (55) 42 (48) 178 (53)

Conditioning Regimen

Traditional myeloablative 1472 (98) 176 (98) 85 (97) 325 (98)Nontraditional ablative 35 ( 2) 3 ( 2) 3 ( 3) 8 ( 2)

Donor/recipient sex match Male/Male 572 (38) 52 (29) 35 (40) 120 (36)Male/Female 371 (25) 41 (23) 20 (23) 84 (25)Female/Male 276 (18) 44 (25) 11 (13) 64 (19)Female/Female 288 (19) 42 (23) 22 (25) 65 (20)

GVHD prophylaxis

Tacrolimus + (MTX or MMF or steroids) ± other 296 (20) 19 (11) 23 (26) 74 (22)

Tacrolimus ± other 2 (<1) 0 1 ( 1) 4 ( 1)CsA + MTX ± other 890 (59) 110 (61) 49 (56) 165 (50)CsA ± other (no MTX) 57 ( 4) 5 ( 3) 1 ( 1) 7 ( 2)MTX ± other (no CsA) 7 (<1) 2 ( 1) 0 3 ( 1)T-cell depletion 254 (17) 43 (24) 14 (16) 80 (24)Other 1 (<1) 0 0 0

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Table 1. Continued.

8/8 Matched

HLA-A MM

HLA-B MM

HLA-C MM

Variable N (%) N (%) N (%) N (%)Donor/Recipient CMV match

Negative/Negative 558 (37) 60 (34) 27 (31) 122 (37)Negative/Positive 410 (27) 38 (21) 26 (30) 100 (30)Positive/Negative 242 (16) 37 (21) 12 (14) 46 (14)Positive/Positive 245 (16) 41 (23) 22 (25) 59 (18)Unknown 52 ( 3) 3 ( 2) 1 ( 1) 6 ( 2)

Donor age, median (range), years 36 (18-59) 36 (20-59) 37 (21-57) 36 (19-59)Donor age

18-19 13 ( 1) 0 0 3 ( 1)20-29 376 (25) 48 (27) 21 (24) 74 (22)30-39 601 (40) 66 (37) 34 (39) 130 (39)40-49 421 (28) 48 (27) 25 (28) 100 (30)50 and older 96 ( 6) 17 ( 9) 8 ( 9) 26 ( 8)

Year of transplant 1988 10 ( 1) 1 ( 1) 0 01989 28 ( 2) 3 ( 2) 0 2 ( 1)1990 38 ( 3) 4 ( 2) 1 ( 1) 7 ( 2)1991 58 ( 4) 5 ( 3) 1 ( 1) 13 ( 4)1992 78 ( 5) 10 ( 6) 3 ( 3) 15 ( 5)1993 70 ( 5) 8 ( 4) 6 ( 7) 16 ( 5)1994 118 ( 8) 14 ( 8) 4 ( 5) 24 ( 7)1995 104 ( 7) 16 ( 9) 8 ( 9) 30 ( 9)1996 118 ( 8) 12 ( 7) 5 ( 6) 31 ( 9)1997 144 (10) 12 ( 7) 8 ( 9) 28 ( 8)1998 141 ( 9) 13 ( 7) 7 ( 8) 31 ( 9)1999 140 ( 9) 22 (12) 9 (10) 39 (12)2000 172 (11) 22 (12) 11 (13) 39 (12)2001 134 ( 9) 24 (13) 17 (19) 32 (10)2002 128 ( 8) 8 ( 4) 6 ( 7) 20 ( 6)2003 26 ( 2) 5 ( 3) 2 ( 2) 6 ( 2)

HLA-DQB1 matching Allele-matched 1389 (92) 157 (88) 75 (85) 281 (84)Single mismatch 114 ( 8) 21 (12) 13 (15) 50 (15)Double mismatch 4 (<1) 1 ( 1) 0 2 ( 1)

HLA-DPB1 matching Allele-matched 225 (15) 23 (13) 19 (22) 48 (14)Single mismatch 833 (55) 88 (49) 46 (52) 189 (57)Double mismatch 449 (30) 68 (38) 23 (26) 96 (29)

Median follow-up of recipients, mo 109 (3-245) 113 (11-229) 96 (35-203) 108 (8-223)

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AMERICAN SOCIETY OF HEMATOLOGY (ASH) ABSTRACT 2009 Selection of Donors with Favorable KIR B Genotypes for Unrelated Hematopoietic Cell Transplantation Results in Superior Relapse Protection and Better Relapse-Free Survival for Patients with AML Sarah Cooley1, Lisbeth Guethlein, PhD2*, Elizabeth Trachtenberg, PhD3*, Martha Ladner3*, Xianghua Luo4*, Chap T Le5*, Tao Wang, PhD6*, John P. Klein, PhD6*, Steven G.E. Marsh, PhD7*, Stephen Spellman8*, Michael D Haagenson, MS9*, Daniel J. Weisdorf, MD1*, Peter Parham2* and Jeffrey S. Miller10

1Hematology, Oncology and Transplantation, University of Minnesota, Minneapolis, MN 2Structural Biology, Standford University, Stanford, CA 3Children's Hospital & Research Center Oakland, Oakland, CA 4Masonic Cancer Center, University of Minnesota, Minneapolis, MN 5Biostatistics, University of Minnesota, Minneapolis, MN 6Center for International Blood and Marrow Transplant Research, Medical College of Wisconsin, Milwaukee, WI 7Anthony Nolan Research Institute, London 8National Marrow Donor Program, Minneapolis, MN 9Center for International Blood and Marrow Transplant Research, Minneapolis, MN 10University of Minnesota, Minneapolis, MN Unrelated donor (URD) transplants from donors with KIR group B/x genotypes (vs. A/A) confer a significant relapse-free survival (RFS) benefit for patients with acute myeloid leukemia (AML) (RR: 0.70 [95% CI: 0.55-0.88]; P = .002; Blood 2009; 113[3]). This new analysis was designed to investigate the beneficial effect of KIR B donors and to develop a donor selection strategy to improve clinical outcomes after hematopoietic cell transplantation (HCT) for leukemia. Based on an analysis of 27 unique KIR haplotype sequences we identified common centromeric and telomeric gene content motifs. KIR A haplotypes contain a Cen-A motif (defined by the presence of the inhibitory KIR gene 2DL3) and a Tel-A motif (defined by the presence of the activating gene 2DS4). The B haplotypes were defined as containing Cen-B (presence of 2DS2 and/or 2DL2) and/or Tel-B (presence of 2DS1), with further subdivisions possible at the allelic level. Thus, based on gene content alone, donor KIR genotypes can be classified as homogyzous A/A or defined by the type (Cen-B or Tel-B) or number (0, 1, 2 or ≥3; B domain content score) of B domains. Multivariate models were used to evaluate the effect of donor KIR genotypes on clinical outcomes after URD transplants facilitated by the National Marrow Donor Program for AML (n=1086) and acute lymphoblastic leukemia (ALL: n=334) between 1988 and 2006. The improved RFS associated with donor KIR B genotypes (Cen-A/B, Cen-B/B, Tel-A/B or Tel-B/B) vs. KIR A genotypes (Cen-A/A or Tel-A/A) in AML was most evident in the 115 donors (10.6%) who were homogyzous for the Cen-B motif (RR 0.72 [95% CI 0.55-0.94]; p=0.014). Likewise, Cen-B/B donors conferred significant protection against relapse (RR 0.34 [95% CI 0.2-0.57]; p < 0.0001); with absolute relapse rates of only 10% (Cen-B/B) vs. ~31% (A/A). Similarly, multivariate models demonstrated that compared to KIR A/A donors, donors with higher KIR B domain content scores resulted in improved RFS (2B motifs: (RR 0.78 [95% CI 0.63-0.95]; p=0.013; n=244) or ≥3 B motifs: (RR 0.76 [95% CI 0.57-1.02]; p=0.07; n=84) and

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less relapse (2B motifs: (RR 0.54 [95% CI 0.39-0.74]; p=0.0001) or ≥3 B motifs: (RR 0.45 [95% CI 0.27-0.74]; p=0.0017). Donor KIR genotype had no effect on rates of graft vs. host disease or treatment related mortality. Importantly, the use of KIR B donors of any type was not associated with any improvement in clinical outcomes for patients with ALL. These data suggest that AML blasts may be particularly sensitive to killing by NK cells and raises the possibility that activating genes present in the donor KIR B haplotypes may uniquely recognize ligands on AML blasts. The KIR B genotype effects were not affected by the degree of HLA matching. Therefore, these data support the consideration of KIR genotyping with HLA typing into the unrelated donor search criteria for patients with AML. To capture the benefit of Cen-B and/or higher B domain content scores we propose that the ~30% of donors who have ≥2 B domains (includes all Cen-B/B donors) be given preference over donors with 0 or 1 KIR B domains. In this large retrospective dataset, assignment by that rule resulted in significant improvements in RFS (RR 0.80 [95% CI 0.68-0.94]; p=0.0063) and relapse (RR 0.53 [95% CI 0.41-0.69]; p<0.0001). A prospective trial to test this strategy is planned.

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2011 BMT TANDEM MEETINGS ABSTRACT

Donor KIR2DS1 and KIR3DS1 are associated with improved outcomes following unrelated allogeneic stem cell transplantation for acute myeloid leukemia.

Jeffrey M. Venstrom1, Gianfranco Pittari1, Joseph Chewning1, Ted A. Gooley2, Stephen Spellman3, Michael Haagenson3, Meighan M. Gallagher1, Mari Malkki2, Effie Petersdorf2, Bo Dupont1, Katharine C. Hsu1 1 Memorial Sloan-Kettering Cancer Center; New York, NY 2 Fred Hutchinson Cancer Research Center; Seattle, WA 3 Center for International Blood and Marrow Transplant Research; Minneapolis, MN Background: Current concepts of NK alloreactivity are based on donor/recipient KIR ligand mismatching and on donor KIR genotype variants, while little is known about the role of specific activating KIR genes. Specifically, the importance of the telomeric KIR2DS1 and KIR3DS1genes, and the centromeric KIR2DS2 gene has not been studied in detail. We tested the hypothesis that individual donor activating KIR genes would influence the outcome of AML patients undergoing unrelated allogeneic HCT. Methods: Donor KIR genotyping was performed for 1229 AML patients receiving transplants between 1989 and 2008 from 9/10 or 10/10 HLA-matched unrelated donors with outcome data provided by the CIBMTR. HLA genotyping was verified through the NMDP retrospective typing program. Cox regression was used to examine the association between donor KIR genotype and HCT outcome. Models were adjusted for age, disease severity, donor/patient gender, T-cell depletion, conditioning, and HLA mismatch. Results: 422 donors were KIR2DS1-positive, 429 donors were KIR3DS1-positive (n=355 KIR2DS1pos, KIR3DS1pos), and 630 donors were KIR2DS2-positive. The presence of donor KIR2DS1 was associated with lower relapse [HR 0.76 (0.61-0.96), p=0.02], even after adjusting for presence of KIR2DS2 [adjusted HR: 0.77 (0.61-0.97), p=0.02] and presence of donor KIR3DS1, with which it shares strong positive LD [adjusted HR 0.71 (0.51-0.99), p=0.04]. Donor KIR3DS1 was associated with reduced non-relapse mortality [HR 0.68 (0.50-0.92), p=0.01] and decreased overall mortality [HR 0.79 (0.63-0.98), p= 0.03] even after adjusting for donor KIR2DS1. Interestingly, after adjusting for KIR3DS1, KIR2DS1 positivity had little association with overall mortality [HR 1.00 (0.80-1.25, p=.98)]. There was no statistically significant association of donor KIR2DS2 on survival, relapse, or non-relapse mortality. Furthermore, the protective effect of donor centromeric KIR B-haplotype homozygosity on relapse was not statistically significant [CenBB vs. CenAA, HR 0.8 (0.55-1.15), p=0.23]. Conclusion: Individual activating KIR may mediate independent effects in unrelated allogeneic HCT for AML, with KIR2DS1 protecting from relapse and KIR3DS1 protecting from non-relapse mortality.

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THE EUROPEAN GROUP FOR BLOOD AND MARROW TRANSPLANTATION (EBMT) ABSTRACT 2011

Impact of matching at non-inherited maternal antigens (NIMA+) on outcomes after 5/6 or 4/6 HLA mismatched unrelated cord blood transplantation (UCBT) for malignant hematological diseases. A matched pair analysis on behalf of Eurocord-EBMT, Netcord, NMDP and CIBMTR. Vanderson Rocha, Duncan Purtill, Mei-Jie Zhang, Stephen Spellman, Annalisa Ruggeri, Vinod Prasad, Christina Navarette, Gesine Koegler, Etienne Beaudoux, Lee Ann Baxter-Lowe, Mary Horowitz, Jon J van Rood, Joanne Kurtzberg, Eliane Gluckman and Mary Eapen NIMA+ UCBT for patients with hematological malignancies has been associated with decreased relapse incidence (RI) and non-relapse mortality (NRM) (van Rood JJ et al, PNAS 2009). We examined for the effect of NIMA+ on UCBT outcomes in a separate cohort; 508 patients with hematologic malignancies who received 5/6 or 4/6 UCBT (284 UCBTs were facilitated by NMDP banks and 224, by Netcord banks). Most patients had acute leukemia and were aged ≤ 16 years at UCBT. 52 patients were NIMA+, accounting for a frequency of 10%. Due to the relatively low frequency of NIMA+ UCBT, a matched-pair analysis was performed. Cases and controls were matched for previously identified prognostic factors for NRM; HLA-match 5/6 vs. 4/6, patient age (≤ 16 vs. > 16 years), disease status (CR1 vs. CR2/CR3 vs. not in remission) and conditioning regimen (myeloablative TBI, myeloablative non-TBI, reduced intensity regimens). In addition, cases and controls were matched on disease and cell dose (infused TNC ≤ 3 vs. > 3 x 107/kg). 48 NIMA+ UCBT recipients were matched to 116 NIMA- UCBT recipients. 4 NIMA+ cases could not be matched and were excluded from the analysis. There were no differences in neutrophil recovery, acute and chronic GVHD after NIMA+ and NIMA- UCBT. NRM after NIMA+ UCBT was lower compared to NIMA- UCBT but this did not reach statistical significance (HR 0.45, p=0.12). Relapse was lower after NIMA+ UCBT (HR 0.43, p=0.05) compared with NIMA- UCBT. Consequently, overall mortality (HR 0.46, p=0.02) and treatment failure (relapse or death; inverse of disease-free survival) (HR 0.42 p=0.03) were lower after NIMA+ UCBT compared to NIMA- UCBT. These data suggest higher overall and disease-free survival as well as enhanced leukemia control in recipients matched to donor non-inherited maternal antigen.

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CIBMTR IB09-08

THE IMPACT OF DONOR AND RECIPIENT BIRTH ORDER ON OUTCOME OF HLA-IDENTICAL SIBLING AND UNRELATED DONOR STEM CELL TRANSPLANTATION IN

HEMATOLOGICAL MALIGNANCIES

REVISED PROTOCOL Study Chair: Christiane Dobbelstein, MD Hannover Medical School Carl Neuberg Str. 1 30625 Hannover, Germany Telephone: +49-511-532-0 Fax: +49-511-532-3611 E-mail: [email protected] Study Co-Chairs: Matthias Eder, MD Hannover Medical School Carl Neuberg Str. 1 30625 Hannover, Germany Telephone: +49-511-532-9207 Fax: +49-511-532-5133 E-mail: [email protected] Arnold Ganser, MD Hannover Medical School Carl Neuberg Str. 1 30625 Hannover, Germany Telephone: +49-511-532-0 Fax: +49-511-532-5550 E-mail: [email protected] Study Statistician: Michael Haagenson, MS CIBMTR 3001 Broadway Street, N.E., Suite 110 Minneapolis, MN 55413 USA Telephone: 612-884-8609 Fax: 612- 884-8661 E-mail: [email protected]

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Scientific Directors: Stephanie Lee, MD, MPH Fred Hutchinson Cancer Research Center P.O. Box 19024, D5-290 1100 Fairview Avenue North Seattle, WA 98109 USA Telephone: 206-667-5160 Fax: 206-667-1034 E-mail: [email protected] Stephen Spellman, MS CIBMTR National Marrow Donor Program 3001 Broadway Street NE, Suite 500 Minneapolis, MN 55413 USA Telephone: 612-617-8334 Fax: 612-362-3488 E-mail: [email protected] Working Committee Chairs: David Miklos, MD, PhD Stanford University Department of Medicine; BMT Division CCSR, Room #2205 269 West Campus Drive Stanford, CA 94305-5170 USA Telephone: 650-725-4626 Fax: 650-724-6182 E-mail: [email protected] Carlheinz Mueller, MD, PhD Director German National Bone Marrow Donor Registry ZKRD Helmholtzstrabe 10, 89081 Ulm, Germany Telephone: +49 731 1507-10 Fax: +49 731 1507-51 E-mail: [email protected] Marcelo Fernandez-Vina, PhD Laboratory Medicine M. D. Anderson Cancer Center 1515 Holcombe Blvd., Box 423 Houston, TX 77030-4009 USA Telephone: +1 713-792-8750 Fax: +1 713-792-8503 E-mail: [email protected]

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1.0 SPECIFIC OBJECTIVES:

The objectives of this study are: 1.1 Primary Objective: To evaluate relapse rate after HLA-identical sibling stem cell

transplantation in hematological malignancies depending on birth order of donor and recipient.

1.2 Secondary Objectives: To evaluate disease-free survival (DFS), overall survival (OS), non-

relapse mortality, acute and chronic GvHD after HLA-identical sibling stem cell transplantation in hematological malignancies depending on birth order of donor and recipient.


Allogeneic stem cell transplantation (SCT) is the most effective treatment for many hematological malignancies. However, relapse and graft-versus-host-disease (GvHD) remain the most important complications of allogeneic SCT. Since HLA disparity between donor and recipient is the most critical factor that governs the severity of GvHD, HLA-identical siblings are optimal donors for allogeneic SCT. Besides well established risk factors for the outcome of SCT such as patient age, HLA-match between donor and recipient, and stage of disease, the impact of primacy of birth in HLA-identical sibling transplantation has been described in a retrospective analysis (Bucher, Blood 2007). In this study, three groups were defined: firstborn donor (FD), firstborn recipient (FR), and others (FO). In a cohort of 311 patients, first-born patients had the best survival, the lowest incidence of acute graft-versus host disease (aGvHD), and a reduced relapse mortality rate. The underlying mechanism may include pre-existing microchimerism by fetomaternal and transmaternal sibling cell trafficking. Along this line we retrospectively analyzed HLA-identical sibling transplantation for patients with de novo AML at our institution based on patients’ and donors’ ages. Patients were assigned to one of two groups: recipients (R) with an older sibling donor (D) (D>R group) and patients who are older than their donors (R>D group). In de novo AML, a trend to superior overall survival, lower incidence of aGvHD ≥II°, and a lower relapse incidence were observed in recipients transplanted from younger sibling donors. If only patients transplanted with non-T-cell depleted grafts after standard myeloablative conditioning in first CR are considered, a significantly better survival with significantly lower relapse incidence and mortality has been observed. Recently, we have extended our study cohort to all consecutive patients with a SCT indication for hematological malignancies and an HLA-identical sibling donor at our center from 1995 to 2007. In this retrospective analysis patients transplanted from a younger sibling donor have a superior outcome in terms of relapse incidence and relapse mortality. In multivariate analysis, stage of disease and birth order have been found to be two independent risk factors for both relapse incidence as well as relapse mortality. In univariate analysis these effects are more pronounced in myeloid as compared to lymphoid diseases.

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Gratwohl studied 1704 HLA-identical sibling donor transplants for aplastic anemia, performed 1980-2007, dividing pairs into donor older and patient older. They did not see any difference in graft rejection, transplant-related mortality, or overall survival which was confirmed in multivariate analysis. However, this study was smaller and limited to patients diagnosed with aplastic anemia. To further evaluate the impact of patients’ and donors’ birth order on outcome of HLA-identical sibling SCT in hematological malignancies, we propose to perform a multi-center retrospective analysis using the data of the CIBMTR registry. We intend to evaluate all patients with an HLA-identical sibling donor, but would like to focus on patients with AML and CML especially. As a control we propose to analyse outcome of HLA-identical (8/8) MUD SCT for RI, OS, LFS, TRM, acute and chronic GvHD, respectively. If birth order (or relative age) only matters if recipient and donor share the same mother, these effects are not expected to be seen in HLA-identical MUD SCT.

3.0 STUDY POPULATION: The retrospective analysis will be conducted on data from subjects with the following criteria: 3.1 Diagnosis of hematological malignancies (AML, ALL, MDS, CML) 3.2 Adults or pediatric patients (any age) 3.3 Allogeneic stem cell transplantation from HLA-identical siblings donors since 1990 up to

December 2007 3.4 Recipient and donor of different ages at the time of transplant, but no more than 15 years

apart All data required for the study is available through the CIBMTR database. No supplemental data collection will be required.

4.0 OUTCOMES:

4.1 Disease relapse – Development of clinical relapse of the primary disease as defined by the CIBMTR. The event will be summarized by the cumulative incidence estimate and patients analyzed at last follow-up. Death is a competing risk.

4.2 Overall survival – Time to death from any cause. Event will be summarized by Kaplan-

Meier estimate. Cases will be analyzed at the time of last follow-up. There are no competing risks.

4.3 Disease-free survival - Survival without recurrence of primary disease. Events are disease

relapse or death and are summarized by Kaplan-Meier estimate. Cases will be analyzed at the time of last follow-up. There are no competing risks.

4.4 Treatment-related mortality - Death in continuous remission of primary disease. Event will

be summarized by the cumulative incidence estimate with relapse as a competing risk.

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4.5 Acute GVHD: Development of Grades II-IV and III-IV acute GVHD. Event will be summarized by the cumulative incidence estimate. Cases will be analyzed at time of last follow-up. Death is a competing risk. Second transplant is a censoring event.

4.6 Chronic GVHD: Development of symptoms in any organ system fulfilling the criteria of

extensive chronic GVHD. The event will be summarized by the cumulative incidence estimate. Patients will be analyzed at last follow-up. Death is a competing risk and second transplant is a censoring event.

5.0 VARIABLES TO BE ANALYZED:

* indicates baseline

Main effect: — Donor -recipient birth order: recipient older* vs. donor older.

Patient and donor related:

— Patient and donor sex match: MM* vs. MF vs. FF vs. FM — Patient and donor CMV: -/-* vs. +/- vs. +/+ vs. -/+ — Race: White* vs. Hispanic vs. African American vs. All others — Pretransplant KPS: 90-100* vs. <90

Disease related:

— Disease: AML* vs. ALL vs. MDS vs. CML — Disease stage: early*, intermediate, advanced

Transplant related:

— Conditioning regimen: myeloablative conditioning* vs. reduced intensity/nonmyelablative conditioning

— Stem cell source: BM* or PBSC — GVHD prophylaxis: calcineurin inhibitor + methotrexate* vs. calcineurin inhibitor

without methotrexate vs. T-cell depletion — Use of ATG for conditioning or GVHD prophylaxis: no* vs. yes — Year of transplant: TBD (1990-2007)

6.0 STUDY DESIGN:

Statistical considerations: We will first examine the characteristics of the recipients of HLA-matched sibling transplants with particular attention to the spectrum of factors known to be associated with transplant outcomes. Cohorts will be compared with Chi-square tests, t-tests or non-parametric testing as appropriate. Cumulative incidence curves will be used in a competing risks setting, death being treated as a competing event to calculate probability of acute and chronic GvHD, NRM and relapse rate. Probabilities of survival and DFS will be calculated using the Kaplan-Meier estimate; the log-rank test will be used for univariate comparisons. Risk factors for outcomes will be evaluated in multivariate analyses, using Cox proportional hazards for DFS and survival. All models will include the main effect of interest (R>D vs. D>R) as well as other covariates with statistical significance level of less than 0.05. Interactions will be tested for all variables in the final model

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at the significance level of 0.01. Statistical analyses will be performed with SAS software package. Planned subset analyses are:

— myeloid leukemias: AML and CML — early stage diseases — T-cell replete grafts — myeloablative conditioning — peripheral blood stem cells as graft source — cohort consisting of all these subsets (myeloid disease, early stage disease, no TCD,

myeloablative conditioning, PBSC) 7.0 REFERENCES:

1. Bucher et al. Blood 2007; 110: 468-9 2. Dobbelstein et al. Haematologica 2008: 93 (s1):136 3. Dobbelstein et al. Blood 2008; 112: 343 4. Gratwohl et al. Blood 2009; 114; 5569

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Table 1. Characteristics of patients with HLA matched sibling donors receiving a first transplant for AML, ALL, CML or MDS where donor/recipient pairs are split by donor age compared to recipient age

Matched Siblings

recipient older than donor

Matched Siblings

donor older than recipient

P-value

Variable N (%) N (%) Number of patients 6089 5788 Number of centers 331 347 Age at transplant, median (range), years 35 (2-75) 31 (<1-72) < 0.0001Age at transplant in decades < 0.0001

0 – 9 y 327 ( 5) 668 (12) 10 – 19 y 829 (14) 925 (16) 20 – 29 y 1066 (18) 1111 (19) 30 – 39 y 1450 (24) 1300 (22) 40 – 49 y 1421 (23) 1147 (20) 50 and older 996 (16) 637 (11)

Difference in age between recipient and donor < 0.0001Donor 10-15 years older 0 583 (10) Donor 5-9 years older 0 1807 (31) Donor 2-4 years older 0 2464 (43) Donor 1- < 2 years older 0 934 (16) Recipient 1- < 2 years older 863 (14) 0 Recipient 2-4 years older 2568 (42) 0 Recipient 5-9 years older 2001 (33) 0 Recipient 10-15 years older 657 (11) 0

Race 0.06Caucasian 4482 (74) 4349 (75) African American 207 ( 3) 163 ( 3) Hispanic 273 ( 4) 278 ( 5) Other 1127 (19) 998 (17)

Male sex 3417 (56) 3415 (59) 0.002Karnofsky prior to transplant > 90 4679 (77) 4457 (77) 0.84Disease at transplant < 0.0001

AML 2348 (39) 2223 (38) ALL 1265 (21) 1470 (25) CML 1938 (32) 1652 (29) MDS 538 ( 9) 443 ( 8)

Stem cell source 0.04Bone marrow 4305 (71) 4191 (72) Peripheral blood stem cells (PBSC) 1784 (29) 1597 (28)

Disease status at transplant 0.08Early 4834 (79) 4686 (81) Intermediate 213 ( 3) 199 ( 3) Advanced 1042 (17) 903 (16)

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Table 1. Continued.

Matched Siblings


Matched Siblings


P-value

Variable N (%) N (%) Conditioning regimen

Myeloablative 5550 (91) 5372 (93) 0.0009Reduced intensity/nonmyeloablative 539 ( 9) 416 ( 7)

Donor/recipient sex match 0.02Male/Male 1872 (31) 1878 (32) Male/Female 1390 (23) 1249 (22) Female/Male 1545 (25) 1537 (27) Female/Female 1282 (21) 1124 (19)

ATG given 0.09Yes 219 ( 4) 176 ( 3) No 5870 (96) 5612 (97)

GVHD prophylaxis 0.66Calcineurin inhibitor + MTX ± other 4551 (75) 4360 (75) Calcineurin inhibitor ± other (No MTX) 1199 (20) 1125 (19) T-cell depletion 339 ( 6) 303 ( 5)

Interval from dx to TX, months 7.2 (0.3-253) 6.9 (0.2-321) 0.008Donor/recipient CMV match < 0.0001

Negative/Negative 1576 (26) 1490 (26) Negative/Positive 1219 (20) 715 (12) Positive/Negative 463 ( 8) 784 (14) Positive/Positive 2831 (46) 2799 (48)

Donor age, median (range), years 30 (<1-69) 36 (1-79) < 0.0001Donor age in decades < 0.0001

0-9 617 (10) 304 ( 5) 10-19 1036 (17) 732 (13) 20-29 1293 (21) 1057 (18) 30-39 1511 (25) 1189 (21) 40-49 1187 (19) 1292 (22) 50 and older 445 ( 7) 1214 (21)

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Table 1. Continued.

Matched Siblings


Matched Siblings


P-value

Variable N (%) N (%) Year of transplant 0.41

1990 544 ( 9) 544 ( 9) 1991 505 ( 8) 519 ( 9) 1992 507 ( 8) 506 ( 9) 1993 533 ( 9) 472 ( 8) 1994 433 ( 7) 442 ( 8) 1995 409 ( 7) 438 ( 8) 1996 464 ( 8) 415 ( 7) 1997 383 ( 6) 333 ( 6) 1998 309 ( 5) 302 ( 5) 1999 235 ( 4) 237 ( 4) 2000 264 ( 4) 226 ( 4) 2001 225 ( 4) 198 ( 3) 2002 229 ( 4) 212 ( 4) 2003 207 ( 3) 161 ( 3) 2004 274 ( 4) 242 ( 4) 2005 277 ( 5) 262 ( 5) 2006 210 ( 3) 206 ( 4) 2007 81 ( 1) 73 ( 1)

Median follow-up of survivors, mo 78 (1-233) 82 (0.6-228) 0.46

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Table 2. Univariate probabilities for HLA matched sibling cases where recipient is older than donor vs. donor is older than recipient

Variable

Matched Siblingsrecipient older

than donor Prob (range)

Matched Siblings donor older

than recipient Prob (range) P-value

Overall survival – 1 year 64 (63-65)% 65 (64-67)% 0.10 Overall survival – 2 years 57 (56-58)% 58 (57-60)% 0.12 Overall survival – 3 years 54 (53-55)% 56 (54-57)% 0.14 Disease free survival – 1 year 60 (59-61)% 61 (60-62)% 0.23 Disease free survival – 2 years 54 (52-55)% 55 (54-57)% 0.07 Disease free survival – 3 years 51 (50-53)% 53 (52-55)% 0.05 Relapse – 1 year 15 (14-16)% 16 (15-17)% 0.15 Relapse – 2 years 18 (17-19)% 19 (18-20)% 0.32 Relapse – 3 years 19 (18-20)% 20 (19-21)% 0.36 Treatment related mortality – 1 year 25 (24-26)% 23 (22-24)% 0.009 Treatment related mortality – 2 years 28 (27-29)% 26 (25-27)% 0.003 Treatment related mortality – 3 years 30 (29-31)% 27 (26-28)% 0.003 Grades II-IV acute GvHD – 100 days 40 (39-41)% 41 (39-42)% 0.37 Grades III-IV acute GvHD – 100 days 17 (16-18)% 17 (16-18)% 0.56 Chronic GvHD – 1 year 33 (31-34)% 32 (31-34)% 0.73 Chronic GvHD – 2 years 35 (33-36)% 35 (33-36)% 0.97 Chronic GvHD – 3 years 35 (34-36)% 35 (34-36)% 0.71

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Table 3. Causes of death for HLA matched sibling cases where recipient is older than donor vs. donor is older than recipient

Matched Siblings


Matched Siblings donor older

than recipient P-value Cause of Death group N (%) N (%) 0.05 Graft failure/Graft rejection 17 ( 1) 25 ( 1) Infection 482 (17) 419 (16) Interstitial pneumonitis 203 ( 7) 150 ( 6) ARDS 82 ( 3) 79 ( 3) Acute GvHD 196 ( 7) 195 ( 7) Chronic GvHD 162 ( 6) 143 ( 5) Recurrent primary disease 905 (31) 948 (36) Organ failure 315 (11) 280 (11) Secondary malignancy 34 ( 1) 27 ( 1) Hemorrhage 140 ( 5) 105 ( 4) Accidental death 5 (<1) 8 (<1) Vascular death 7 (<1) 9 (<1) Prior malignancy 5 (<1) 2 (<1) GvHD and Interstitial pneumonitis 79 ( 3) 59 ( 2) Other 116 ( 4) 113 ( 4) Unknown 135 ( 5) 103 ( 4)

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CIBMTR GV04-01

OUTCOME OF NON-SYNGENEIC TWIN TRANSPLANTS FOR ACUTE LEUKEMIA, CML AND MDS

DRAFT PROTOCOL

Study Chair: John Barrett, MD NHLBI Research Division of Intramural Research 10 Center Drive, Building 10-CRC Bethesda, MD 20892-1202 USA Telephone: 301-402-4170 Fax: 301-480-2664 E-mail: [email protected] Study Statistician: Michael Haagenson, MS CIBMTR 3001 Broadway Street, N.E., Suite 110 Minneapolis, MN 55413 USA Telephone: 612-884-8609 Fax: 612- 884-8661 E-mail: [email protected] Scientific Directors: Stephanie Lee, MD, MPH Fred Hutchinson Cancer Research Center P.O. Box 19024, D5-290 1100 Fairview Avenue North Seattle, WA 98109 USA Telephone: 206-667-5160 Fax: 206-667-1034 E-mail: [email protected] Stephen Spellman, MS CIBMTR National Marrow Donor Program 3001 Broadway Street NE, Suite 100 Minneapolis, MN 55413 USA Telephone: 612-617-8334 Fax: 612-362-3488 E-mail: [email protected]

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Working Committee Chairs: David Miklos, MD, PhD Stanford University Department of Medicine; BMT Division CCSR, Room #2205 269 West Campus Drive Stanford, CA 94305-5170 USA Telephone: 650-725-4626 Fax: 650-724-6182 E-mail: [email protected] Marcelo Fernandez-Vina, PhD Laboratory Medicine M. D. Anderson Cancer Center 1515 Holcombe Blvd., Box 423 Houston, TX 77030-4009 USA Telephone: 713-792-8750 Fax: 713-792-8503 E-mail: [email protected] Carlheinz Mueller, MD, PhD Director German National Bone Marrow Donor Registry ZKRD Helmholtzstrabe 10, 89081 Ulm, Germany Telephone: +49 731 1507-10 Fax: +49 731 1507-51 E-mail: [email protected]

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1.0 HYPOTHESIS:

Shared circulation in utero may create donor-recipient tolerance following stem cell transplants between HLA-identical dizygotic twins.

2.0 OBJECTIVES:

2.1 To compare acute and chronic GVHD incidence and severity, relapse incidence and survival in HLA identical dizygotic twin transplants and a matched group of HLA identical sibling transplants for acute leukemia, CML and MDS.

2.2 To compare transplant-related mortality and disease-free survival in HLA identical dizygotic twin transplants and a matched group of HLA identical sibling transplants for acute leukemia, CML and MDS.


Since the first description of freemartin cattle, who tolerate skin grafts from their dizygotic twin sibling, it has been known for many years that allogeneic tolerance can be established by shared circulation between dizygotic twins in utero (reviewed in 1). In man red cell blood group analysis has shown that shared circulation only rarely results in macrochimerism. However, recently introduced techniques of red cell typing detected 0.01% red cell microchimerism in around 8% of dizygotic twins indicating that they shared circulations at some stage during gestation (2). Because of limited numbers of analyses and lack of sensitivity of techniques to detect very low degrees of chimerism, this figure may be an under-estimate of the total number of mixed chimeric dizygotic twins. Although there are only three reports of mutual MLC non-reactivity in non HLA-compatible microchimeric dizygotic twins (3-5), data from organ transplantation in experimental models and man suggests that microchimerism of blood cells derived from the transplanted organ can establish tolerance (6). Furthermore it may suffice for a twin’s immune system to be exposed to the sibling’s antigenic repertoire for only a brief period during thymic development to establish tolerance without persisting chimerism. Stem cell transplants between mutually immune tolerant twin siblings might therefore assume the outcome characteristics of identical twin transplants with zero or low severity of GVHD, rapid immune recovery and consequently lower early non-relapse mortality but a higher relapse rates because the GVL effect is abrogated.

The CIBMTR database contains the largest database documenting immunological events between HLA identical dizygotic twins. This permits a comparison of transplant outcomes between HLA matched non-twin sibling transplants and non-identical twin transplants to determine whether outcomes differ possibly due to microchimerism-induced tolerance between donor and recipient.


Non-identical twins: — HLA-matched fraternal twin donors (not syngeneic) — AML, ALL, CML and MDS, all disease stages — First transplant, 1990-2008 — Both adults and children will be included — Myeloablative and reduced intensity/non-myeloablative

Comparable population of HLA identical non-twin sibling transplants:

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Patients with similar major pretransplant features (diagnosis), conditioning regimen intensity (myeloablative, reduced intensity, nonmyeloablative), and year of transplant with the twin(s) but receiving an HLA matched sibling transplant at the sites providing fraternal twin data will be selected from the CIBMTR database, and matched 3-4:1 based on at least 3 characteristics. This will limit the number of non-twin patients and help control for center characteristics.

5.0 OUTCOMES:

Primary outcomes: 5.1 Acute GVHD: Development of Grades II-IV and III–IV acute GVHD using standard

CIBMTR criteria. Events will be summarized by the cumulative incidence estimate with death as a competing risk. Second transplant is a censoring event.

5.2 Chronic GVHD: Development of symptoms in any organ system fulfilling the criteria of limited or extensive chronic GVHD. The event will be summarized by the cumulative incidence estimate. Patients will be analyzed at last follow-up. Death is a competing risk, and second transplant or relapse is a censoring event. Cases will be analyzed at time of last follow-up.

5.3 Disease relapse: Events are disease recurrence. Events will be summarized by the cumulative incidence estimate with death in continuous complete remission as a competing risk. For patients surviving in continuous complete remission, patients will be censored at date of last contact.

5.4 Overall survival: Time to death from any cause. Events will be summarized by a survival curve. Cases will be censored at last follow-up.

Secondary outcomes: 5.5 Treatment-related mortality: time to death in continuous complete remission; patients are

censored at time of last follow-up. Relapse is a competing event. 5.6 Disease-free survival: time to treatment failure (death or relapse); patients are censored at

last contact. 6.0 VARIABLES TO BE ANALYZED:

* baseline

Main effect: — HLA-identical sibling* vs. HLA-identical dizygotic (fraternal) twins

Patient related: — Age (TBD based on data) — Race: White* vs. Non-white — Karnofsky score (90-100* vs. <80)

Disease related: — Diagnosis (AML*, ALL, MDS, CML) — Stage of disease (early*, intermediate, advanced)

Transplant related: — Conditioning regimen (myeloablative conditioning vs. reduced intensity/nonmyeloablative or

specific regimens) — GvHD prophylaxis (cyclosporine ± methotrexate ± other* vs. tacrolimus ± methotrexate ±

other; vs. T-cell depletion) – may delete TCD later

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— Gender match (F-M vs. others*) — CMV status of donor and patient (-/-* vs. others) — Donor age (TBD based on data) — Year of transplantation (TBD based on data) — Graft source (bone marrow* vs. PBSC) — Use of ATG for conditioning or GVHD prophylaxis: no* vs. yes

Other variables not expected to be related to whether fraternal twins or singletons so they can be ignored — TBI — Cytogenetic risk groups for AML, MDS and ALL — Graft cell counts: NC/kg for bone marrow, CD34/kg for peripheral blood — Time from diagnosis to transplant — Parity of female donors: nulliparous vs. parous — Birth order of singletons (expect equally distributed donor older and younger than pt)

7.0 STATISTICAL ANALYSIS:

We will first determine the fraternal twin pairs by examining the age differences between recipient and HLA-identical sibling donor (excluding syngeneic transplants). Because of differences in donor age coding (exact birth date vs. age in years), we will first consider all pairs with birthdate listed for both donor and recipient. Pairs with the same birthday are considered fraternal twins (n=143). Pairs where donor birthdate is not available but the listed age is the same as the recipient and less than 300 days (10 months) different were also considered fraternal twins (n=68) since people tend to round down to whole numbers when reporting their age. Two groups were excluded: (1) Donor listed age greater than recipient calculated age but by less than 300 days, since rounding could not account for the age difference (n=110); and (2) where recipient and donor birthdates are available but < 300 days apart, since these look like errors in recording the birthdates (n=69). [1/4/11: based on stats comments, we will confirm fraternal twin status with centers for all pairs with age +/- 300 days (n=390). Donor date of birth: MM/DD/YYYY Relationship of donor and recipient: 1=syngeneic, identical, monozygotic twin 2=fraternal, non-identical, dizygotic twin 3=HLA-identical sibling form on different days] To assemble a comparison cohort, we will then select all HLA-identical (non syngeneic) pairs with the same diagnosis, conditioning regimen intensity (myeloablative/not myeloablative), and year (+/- 2 years) at that site. Non-fraternal siblings were defined as an age difference > 300 days. 13 fraternal twins were excluded from the analysis because an appropriate non-fraternal sibling pair could not be identified. [1/4/11: stats comments, ok to match on 3 of 4 factors] Matching will be 3-4:1. Cohorts will be compared with Chi-square tests, t-tests or non-parametric testing as appropriate for the variables listed in Section 6.0. Cumulative incidence curves will be used in a competing risks setting, death being treated as a competing event to calculate probability of acute and chronic GVHD, TRM and relapse rate.

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Patients will be censored at second transplant for GVHD. For patients transplanted not in complete remission, patients who die within 28 days posttransplant are attributed to the transplant (coded as treatment-related-death, not as relapse), and patients alive beyond 28 days posttransplant and never achieving remission posttransplant are considered as having acute leukemia or MDS recurrence at one day after transplant (coded as relapse and time from transplant to relapse is one day.) [1/4/11: stats comment: rewrite to reflect matching] Probabilities of survival and DFS will be calculated using the Kaplan-Meier estimate; the log-rank test will be used for univariate comparisons. Risk factors for outcomes will be evaluated in multivariate analyses, using Cox proportional hazards for acute and chronic GVHD, relapse and survival. All models will include the main effect of interest (fraternal twin vs. non twin) as well as other covariates with statistical significance level of less than 0.05. Interactions will be tested for all variables in the final model at the significance level of 0.01. Statistical analyses will be performed with SAS software package. Center effect will not be tested because of the very small number of twins from each center, and we expect the selection algorithm for non twin patients to control for center characteristics. Planned subset analyses are: — Disease type (CML in particular) — Disease stage (early stage in particular)

8.0 REFERENCES:

1. Billingham RE, Head JR. Current trends in reproductive immunology: an overview. J Reprod Immunol. 1981;3 :253-65.

2. van Dijk, B A; Boomsma, D I; de Man, A J. Blood group chimerism in human multiple births is not rare. American Journal of Medical Genetics. 1966, 61:264-268

3. Thomsen M, Hansen HE Dickmeiss E, MLC and CML studies in the family of a pair of HLA haploidentical chimeric twins. Scand J Immunol. 1977, 6:523.

4. Angela E, Robinson E, North D, A case of twin chimerism. J Med Genet. 1976, 13:528. 5. Vietor HE, Hamel BC, van Bree SP, van der Meer EM, Smeets DF, Otten BJ, Holl RA, Claas

FH. Immunological tolerance in an HLA non-identical chimeric twin. Hum Immunol. 2000 Mar;61(3):190-2.

6. Triulzi DJ, Nalesnik MA. Microchimerism, GVHD, and tolerance in solid organ transplantation. Transfusion. 2001;41: 419-26.

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Table 1. Characteristics of patients who received a HLA identical sibling first transplant for AML, ALL, CML or MDS from 1990 to 2007 from a non-identical twin and reported to the CIBMTR

HLA-Identical

Fraternal Twins* HLA-Identical Non-twins # P-value

Variable N Eval N (%) N Eval N (%) Number of patients 198 2261 Number of centers 107 107 Age, median (range), years 198 31 (1 – 72) 2261 33 (0.4-74) 0.27 Recipient age, in decades 0.01

0 – 9 years 26 (13) 150 ( 7) 10 – 19 years 31 (16) 321 (14) 20 – 29 years 34 (17) 469 (21) 30 – 39 years 42 (21) 598 (26) 40 – 49 years 38 (19) 440 (19) 50 and older 27 (14) 283 (13)

Recipient race 198 2261 0.001 Caucasian/White 152 (77) 1512 (67) African American/Black 5 ( 2) 74 ( 3) Asian/Pacific Islander 14 ( 7) 257 (11) Hispanic 15 ( 8) 89 ( 4) Native American 0 1 (<1) Other/Multiple/Unknown 12 ( 6) 328 (15)

Male sex 198 123 (62) 2261 1309 (58) 0.25 Karnofsky Performance Score, 90 – 100 195 154 (79) 2229 1790 (80) 0.65

Disease 198 2261 0.0001 AML 68 (34) 938 (41) ALL 51 (26) 456 (20) CML 59 (30) 775 (34) MDS 20 (10) 92 ( 4)

Disease status 198 2261 0.23 Early 121 (61) 1414 (63) Intermediate 34 (17) 427 (19) Advanced 33 (17) 361 (16) Other 10 ( 5) 59 ( 3)

Stem cell source 198 2261 0.55 Bone marrow 135 (68) 1588 (70) PBSC 63 (32) 673 (30)

Conditioning regimen 198 2261 0.002 Myeloablative 175 (88) 2111 (93) Reduced intensity 4 ( 2) 9 (<1) Nonmyeloablative 19 (10) 141 ( 6) GvHD Prophylaxis 198 2261 < 0.001 (Tacrolimus or Cyclosporine) + MTX ± other 125 (63) 1657 (73)

(Tacrolimus or Cyclosporine) ± other (no MTX) 45 (23) 420 (19)

Ex vivo T-cell depletion 9 ( 5) 107 ( 5) Other 19 (10) 77 ( 3)

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Table 1. Continued.

HLA-Identical Fraternal Twins*

HLA-Identical Non-twins # P-value

Variable N Eval N (%) N Eval N (%) Donor/Recipient sex match 198 2261 0.006

M/M 87 (44) 741 (33) M/F 34 (17) 506 (22) F/M 36 (18) 568 (25) F/F 41 (21) 446 (20)

Donor/Recipient CMV match 198 2261 0.20 Negative/Negative 62 (31) 578 (26) Negative/Positive 28 (14) 302 (13) Positive/Negative 14 ( 7) 217 (10) Positive/Positive 84 (42) 1084 (48) Unknown 10 ( 5) 80 ( 4)

Donor Age, median (range), years 198 31 (1-72) 2261 32 (0.24-74) 0.39 Donor age by decade 198 2261 0.12

0-9 years 25 (13) 148 ( 7) 10-19 years 32 (16) 333 (15) 20-29 years 33 (17) 488 (22) 30-39 years 42 (21) 540 (24) 40-49 years 39 (20) 425 (19) 50 and older 27 (14) 305 (14)

ATG given 198 2261 0.17 Yes 5 ( 3) 50 ( 2) No 193 (97) 2211 (98)

Difference in age by birth dates, Median (range)* (in days) 133 0 (0-0) 2261 393.5 (-8313-17273) N/A

Difference in age by specified age, Median (range)* (in months) 198 0.35 (-5.9–9.8) 2261 -13.6 (-567–273) N/A

Year of transplant 198 2261 0.01 1990 20 (10) 154 ( 7) 1991 13 ( 7) 176 ( 8) 1992 12 ( 6) 196 ( 9) 1993 18 ( 9) 240 (11) 1994 12 ( 6) 216 (10) 1995 14 ( 7) 187 ( 8) 1996 13 ( 7) 131 ( 6) 1997 12 ( 6) 114 ( 5) 1998 8 ( 4) 92 ( 4) 1999 8 ( 4) 73 ( 3) 2000 4 ( 2) 55 ( 2) 2001 11 ( 6) 31 ( 1) 2002 4 ( 2) 57 ( 3) 2003 7 ( 4) 62 ( 3) 2004 18 ( 9) 141 ( 6) 2005 13 ( 7) 157 ( 7) 2006 7 ( 4) 139 ( 6) 2007 4 ( 2) 40 ( 2)

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Table 1. Continued.

HLA-Identical Fraternal Twins*

HLA-Identical Non-twins # P-value

Variable N Eval N (%) N Eval N (%) Median follow-up of recipients in months 107 72 (2.6–225) 1225 66 (0.6-228) 0.57b

* - Difference in age is either (a) 0 days or (b) only donor age (not donor birth date) available and R>D by less than 300 days. # - Matched by disease, conditioning regimen intensity, and year (± 2 years) to fraternal twins from each site. b – Log-rank p-value.

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CIBMTR IB08-08

GENOME-WIDE ASSOCIATION IN UNRELATED DONOR TRANSPLANT RECIPIENTS AND DONORS: A PILOT STUDY

REVISED PROTOCOL

Study Chair: Rakesh K. Goyal, MD Pediatric Hematology-Oncology and BMT Room 328, 4B DeSoto Children’s Hospital of Pittsburgh 3705 Fifth Avenue Pittsburgh, PA 15213 Telephone: 412-692-7610 Fax: 412-692-7693 E-mail: [email protected] Study Co-chairs: Robert E. Ferrell, PhD Graduate School of Public Health A304, Crabtree Hall, 130 DeSoto Street Pittsburgh, PA 15261 Telephone: 412-624-3018 Fax: 412-624-3020 E-mail: [email protected] Bernard J. Devlin, PhD, Robert E. Ferrell, PhD Computational Genetics Program Western Psychiatric Institute & Clinic 3811 O'Hara Street Pittsburgh, PA 15213 Telephone: (412) 246-6642 E-mail: [email protected] Study Statistician: Michael Haagenson, MS CIBMTR 3001 Broadway Street, N.E., Suite 110 Minneapolis, MN 55413 USA Telephone: 612-884-8609 Fax: 612-884-8661 E-mail: [email protected]

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Scientific Directors: Stephanie Lee, MD, MPH Fred Hutchinson Cancer Research Center P.O. Box 19024, D5-290 1100 Fairview Avenue North Seattle, WA 98109 USA Telephone: 206-667-5160 Fax: 206-667-1034 E-mail: [email protected] Stephen R. Spellman, MBS CIBMTR National Marrow Donor Program 3001 Broadway St. N.E. Minneapolis, MN 55413 USA Telephone: 612-617-8334 Fax: 612-362-3488 E-mail: [email protected] Working Committee Chairs: Carlheinz Mueller, MD, PhD Director German National Bone Marrow Donor Registry ZKRD Helmholtzstrabe 10, 89081 Ulm, Germany Telephone: +49 731 1507-10 Fax: +49 731 1507-51 E-mail: [email protected] David Miklos, MD, PhD Stanford University Department of Medicine; BMT Division CCSR, Room #2205 269 West Campus Drive Stanford, CA 94305-5170 USA Telephone: 650-725-4626 Fax: 650-724-6182 E-mail: [email protected] Marcelo Fernandez-Vina, PhD Laboratory Medicine M. D. Anderson Cancer Center 1515 Holcombe Blvd., Box 423 Houston, TX 77030-4009 USA Telephone: +1 713-792-8750 Fax: +1 713-792-8503 E-mail: [email protected]

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1.0 HYPOTHESIS:

We hypothesize that an unbiased recipient-donor genome-wide association (GWA) study will identify genes associated with risk of acute graft versus host disease (aGvHD) after HLA-matched unrelated donor BMT. The study will examine:

1.1 The genetic differences between recipients who experience grade III-IV aGvHD (G+)

versus those who get no aGvHD (G-) 1.2 The genetic differences between donors in the G+ and G- cohorts, and 1.3 Recipient-donor genetic mismatches between the two groups

2.0 OBJECTIVES:

2.1 Contrast frequencies of single nucleotide polymorphisms (SNPs) in G+ versus G- recipient genomes

2.2 Contrast frequencies of SNPs in G+ versus G- donor genomes, and 2.3 Evaluate recipient-donor SNP mismatches


Donor-recipient matching on HLA has its limit in improving patient survival and identification of additional loci that impact aGvHD is important to further progress. Efforts to date have focused on a small number of genes almost all related to inflammation, in BMT recipients. While these are rational candidate genes, they assume that we have an understanding of the mechanisms of aGvHD. Our proposal will globally assess recipient and donor genomes to identify genes associated with risk of aGvHD. Improved understanding of multiple genomic variants and their interactions should help identify recipients at the highest risk of GvHD, develop individualized immunosuppressive strategies, and ultimately improve outcome for allo-BMT recipients.


Unrelated donor transplant recipients who develop grade III-IV aGvHD (G+) will serve as cases and those with no aGvHD (G-) by day 100 posttransplant will serve as controls. Both cohorts will be drawn from the same patient population. HLA disparity is the major genetic determinant of aGvHD. Other risk factors include recipient age, type and status of underlying disease, graft source, conditioning intensity, use of radiation, aGvHD prophylaxis and use of anti-thymocyte globulin (ATG). The study population will be selected to ensure a similar distribution of risk factors in the case and control arms. For this discovery phase of GWA study, to maximize the probability of identifying highly significantly associated variants, we would focus on a homogeneous group of patients. 4.1. Recipient-donor pairs are allele matched for HLA-A, -B, -C, -DRB1, and -DQB1 loci 4.2. Recipient-donor pairs are of self-identified European-American ancestry 4.3. First transplant

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4.4. Standard ablative pretransplant conditioning. Excludes reduced-intensity (mini) transplants 4.5. No T-cell manipulation of transplant graft

5.0 OUTCOMES:

Primary Outcome used to assign cases (G+) and controls (G-): — Acute GVHD: Development of Grades III-IV acute GVHD using Glucksberg system which

grades GVHD based on the pattern and severity of abnormalities in skin, gastrointestinal and liver.


— The frequency of single nucleotide polymorphisms (SNPs) in G+ versus G- recipient genomes.

7.0 STUDY DESIGN:

7.1 Sample Requirements: One-time aliquots of recipient and donor sample pairs of nonviable cells will be requested for DNA extraction and sample preparation. Ideally we would like to get few micrograms yield of high quality DNA from individual subject’s frozen peripheral blood aliquot or EBV transformed B-cell line.

7.2 GWAS Experiments: A pooling strategy will be used for the initial discovery phase of

GWAS. To illustrate the study design, a sample size of 500 recipient-donor pairs with grade III-IV acute GvHD and 500 recipient-donor pairs with no acute GvHD has been used.

Grade III-IV aGvHD (G+) No aGvHD (G-) Recipients R1 R2 Donors D1 D2

All sample processing, DNA isolation and quantitation, pooling of DNA specimens and hybridization to the Human 610 - Quad V1 arrays, will be carried out in the Human Genomics and Proteomics Core Laboratory of the University of Pittsburgh (www.genetics.pitt.edu). GWA scan approach is outlined for R1 cohort in the following schematic. A similar approach will be used for R2, D1 and D2 cohorts.

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7.3 Statistical Analyses

7.3.1 Aim (1): Hypothesis 1: A handful of variants in recipients’ genomes exert detectable influence on G+ versus G- status. Estimate and contrast allele frequencies between R1 and R2. Rank the SNPs according to the P values from the contrast. Cutoff P value for follow-up genotyping of SNPs, using an individual samples from the pooled experiment as well as new, independent samples, will be determined by statistical theory and the data.

7.3.2 Aim (2): Hypothesis 2: A handful of variants in donors’ genomes exert

detectable influence on G+ versus G- status. The same approach outlined in Aim (1) will be used to compare donor groups D1 and D2.

7.3.3 Aim (3): Hypothesis 3: In addition to known variation, G+ versus G- status is

determined by degree of matching of donor and recipient genotypes. Two sub-hypotheses are possible. 7.3.3.1 H3a: Donor and recipient matching for a handful of detectable novel

variants is critical for good outcome of the transplant 7.3.3.2 H3b: Donor and recipient matching for myriad novel loci, each of

small effect, is critical for good outcome of the transplant. We can test H3b with good power while generating pilot data useful for H3a and their validation will require independent funding.

We will also randomly choose 40 G+ and G- pairs from R1, R2, D1 and D2 groups for individual genotyping on Illumina HapMap610 Arrays. Results from this experiment will be used to discover covariates to refine estimated, pooled allele frequencies.Cutoff P value for follow-up genotyping of SNPs will be determined by statistical theory and the data. Single-marker allelic tests will be performed and SNPs with greatest significance and of those that remain significant at the 0.05 level after corrections for multiple comparisons will be chosen for replication studies in using individual samples from this pooled experiment as well as different independent cohorts of unrelated donor transplants.

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8.0 REFERENCES:

1. Mullighan CG, Petersdorf EW. Genomic polymorphism and allogeneic hematopoietic transplantation outcome. Biol Blood Marrow Transplant. 2006;12:19-27.

2. Dickinson AM, Middleton PG, Rocha V, Gluckman E, Holler E. Genetic polymorphisms predicting the outcome of bone marrow transplants. Br J Haematol. 2004;127:479-490.

3. Goyal R, Lin Y, Livote E, et al. TNF-alpha and TNF Receptor Superfamily Member 1B Polymorphisms Predict Risk of Acute GVHD Following Matched Unrelated Donor BMT in Children. BMT Tandem Meetings. San Diego, CA; 2008:Abstract #1813.

4. Pearson TA, Manolio TA. How to interpret a genome-wide association study. JAMA. 2008;299:1335-1344.

5. Pearson JV, Huentelman MJ, Halperin RF, et al. Identification of the genetic basis for complex disorders by use of pooling-based genome wide single-nucleotide-polymorphism association studies. Am J Hum Genet. 2007;80:126-139.

6. Macgregor S, Zhao ZZ, Henders A, Nicholas MG, Montgomery GW, Visscher PM. Highly cost-efficient genome-wide association studies using DNA pools and dense SNP arrays. Nucleic Acids Res. 2008;36:e35.

7. Craig DW, Huentelman MJ, Hu-Lince D, et al. Identification of disease causing loci using an array-based genotyping approach on pooled DNA. BMC Genomics. 2005;6:138.

8. Benjamini Y, Drai D, Elmer G, Kafkafi N, Golani I. Controlling the false discovery rate in behavior genetics research. Behav Brain Res. 2001;125:279-284.

9. Chowdari KV, Northup A, Pless L, et al. DNA pooling: a comprehensive, multi-stage association analysis of ACSL6 and SIRT5 polymorphisms in schizophrenia. Genes Brain Behav. 2007;6:229-239.

10. Tzeng JY, Devlin B, Wasserman L, Roeder K. On the identification of disease mutations by the analysis of haplotype similarity and goodness of fit. Am J Hum Genet. 2003;72:891-902.

11. Patterson N, Price AL, Reich D. Population structure and eigenanalysis. PLoS Genet. 2006;2:e190.

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Table 1. Characteristics of patients receiving a T-replete, first transplant for any disease where recipient received a myeloablative conditioning regimen and the donor/recipient pairs are Caucasian and are high resolution matched for HLA-A, -B, -C and -DRB1 through the NMDP by Grades acute GvHD 3-4 vs. no acute GvHD by Day 100 post-transplant a

No aGvHD

by Day 100Grades 3-4 aGvHD

by Day 100Variable N eval N (%) N eval N (%)Number of patients 453 411Number of centers 91 83Age at transplant, median (range), years 453 36 (<1-66) 411 39 (<1-67)Age at transplant in decades 453 411

< 10 y 37 ( 8) 25 ( 6)11 – 20 y 39 ( 9) 28 ( 7)21 – 30 y 95 (21) 87 (21)31 – 40 y 87 (19) 71 (17)41 – 50 y 108 (24) 107 (26)Over 50 y 87 (19) 93 (23)

Male sex 453 258 (57) 411 246 (60)Karnofsky prior to transplant > 90 401 310 (77) 394 299 (76)Disease at transplant 453 411

AML 145 (32) 103 (25)ALL 92 (20) 69 (17)Other leukemia 13 ( 3) 18 ( 4)CML 89 (20) 137 (33)MDS 82 (18) 66 (16)NHL 32 ( 7) 18 ( 4)

Disease status at transplant 453 411 Early 168 (37) 168 (41)Intermediate 103 (23) 86 (21)Advanced 94 (21) 96 (23)Other 88 (19) 61 (15)

Stem cell source 453 411 Bone marrow 266 (59) 241 (59)Peripheral blood stem cells (PBSC) 187 (41) 170 (41)

GVHD prophylaxis 453 411 FK506 ± MTX ± MMF ± Steroids ± other 224 (49) 184 (45)FK506 ± other 20 ( 4) 13 ( 3)CsA + MTX ± other 184 (41) 195 (47)CsA ± other (no MTX) 12 ( 3) 17 ( 4)MTX ± other (no CSA) 1 (<1) 1 (<1)Other/To be determined 12 ( 3) 1 (<1)

HLA matching at HLA-DQB1 453 411 Allele-matched 425 (94) 377 (92)One allele mismatch 28 ( 6) 34 ( 8)

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Table 1. Continued.

No aGvHD by Day 100

Grades 3-4 aGvHD by Day 100

Variable N eval N (%) N eval N (%)Donor/Recipient sex match 453 411

Male/Male 199 (44) 155 (38)Male/Female 122 (27) 99 (24)Female/Male 59 (13) 91 (22)Female/Female 73 (16) 66 (16)

Donor/Recipient CMV match 453 411 Negative/Negative 169 (37) 150 (36)Negative/Positive 137 (30) 131 (32)Positive/Negative 60 (13) 51 (12)Positive/Positive 78 (17) 66 (16)Unknown 9 ( 2) 13 ( 3)

Donor age, median (range), years 453 32 (18-60) 411 36 (19-58)Donor age in decades 453 411

18-19 2 (<1) 2 (<1)20-29 171 (38) 120 (29)30-39 155 (34) 166 (40)40-49 99 (22) 99 (24)50 and older 26 ( 6) 24 ( 6)

Time from dx to tx, overall, median (range) months 449 10 (0.9-251) 409 10 (0.6-211)Year of transplant 453 411

1988 0 1 (<1)1989 0 2 (<1)1990 0 8 ( 2)1991 2 (<1) 14 ( 3)1992 6 ( 1) 15 ( 4)1993 8 ( 2) 12 ( 3)1994 14 ( 3) 12 ( 3)1995 12 ( 3) 13 ( 3)1996 12 ( 3) 13 ( 3)1997 15 ( 3) 11 ( 3)1998 10 ( 2) 17 ( 4)1999 17 ( 4) 15 ( 4)2000 23 ( 5) 25 ( 6)2001 31 ( 7) 9 ( 2)2002 24 ( 5) 20 ( 5)2003 45 (10) 32 ( 8)2004 75 (17) 45 (11)2005 72 (16) 62 (15)2006 62 (14) 63 (15)2007 25 ( 6) 22 ( 5)

Median follow-up of survivors, months 300 36 (3-168) 122 36 (2-205)a – Data is not adjusted for the NMDP corrective action plan.

Notes: The cumulative incidence rate for Grades 3-4 acute GvHD is 30 (28-32)%.

Abbreviations: HLA - Human leukocyte antigens, SCID - Severe combined immunodeficiency, FK506 - Tacrolimus, MTX - Methotrexate, MMF - Mycophenolate mofetil, CsA - Cyclosporine, CMV - Cytomegalovirus, dx - diagnosis, tx - transplant.

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CIBMTR IB10-01 DONOR AND RECIPIENT TELOMERE LENGTH AS PREDICTORS OF OUTCOMES AFTER HEMATOPOIETIC STEM CELL TRANSPLANT IN PATIENTS WITH ACQUIRED SEVERE

APLASTIC ANEMIA

REVISED PROTOCOL Study Chairs: Shahinaz M. Gadalla, MD, PhD Department of Cancer Epidemiology and Genetics (DCEG) National Cancer Institute 6120 Executive Boulevard, CGB Room 7019 Rockville, MD Telephone: 301-435-4721 E-mail: [email protected] Sharon A. Savage, M.D. Department of Cancer Epidemiology and Genetics (DCEG) National Cancer Institute Executive Plaza South, EPS Room 7018 Rockville, MD Telephone: 301-496-5785 Fax: 301-496-1854 E-mail: [email protected] Study Statistician: Michael Haagenson, MS CIBMTR 3001 Broadway Street, N.E., Suite 110 Minneapolis, MN 55413 USA Telephone: 612-884-8609 Fax: 612- 884-8661 E-mail: [email protected] Scientific Director: Stephanie Lee, MD, MPH Fred Hutchinson Cancer Research Center 1100 Fairview Ave. North, D5-290 PO Box 19024 Seattle, WA 98109 Telephone: 206-667-6190 Fax: 206-667-1034 E-mail: [email protected]

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Scientific Director: Stephen Spellman, MS CIBMTR National Marrow Donor Program 3001 Broadway Street NE, Suite 500 Minneapolis, MN 55413 USA Telephone: 612-617-8334 Fax: 612-362-3488 E-mail: [email protected] Working Committee Chairs: David Miklos, MD/PhD Stanford University Department of Medicine; BMT Division CCSR, Room #2205 269 West Campus Drive Stanford, CA 94305-5170 USA Telephone: 650-725-4626 Fax: 650-724-6182 E-mail: [email protected] Marcelo Fernandez-Vina, PhD Laboratory Medicine M.D. Anderson Cancer Center 1515 Holcombe Blvd., Box 423 Houston, TX 77030-4009 USA Telephone: 713-792-8750 Fax: 713-792-8503 E-mail: [email protected] Carlheinz Mueller, MD, PhD Director German National Bone Marrow Donor Registry ZKRD Helmholtzstrabe 10, 89081 Ulm, Germany Telephone: +49 731 1507-10 Fax: +49 731 1507-51 E-mail: [email protected]

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1.0 OBJECTIVES: More than one-third of patients with acquired severe aplastic anemia (SAA) have short telomeres. Telomere shortening in peripheral blood is associated with increased risk of malignancies, pulmonary and liver fibrosis, and other complications. The role that telomere length plays in outcomes after hematopoietic stem cell transplantation (HSCT) has not been previously studied.

2.0 SPECIFIC AIMS:

2.1 Determine pre-transplant blood telomere length in patients who received HSCT for SAA

and compare with age-matched controls and patients with dyskeratosis congenita. 2.2 Assess the relationships between recipient and donor telomere length, and post-transplant

outcomes (including death, malignancy and organ fibrosis) in patients who received unrelated donor HSCT for SAA.

2.3 Identify factors that modify the association between recipient and/or donor telomere length,

and post-transplant outcomes in patients who received unrelated donor HSCT, assuming that any such associations are found in (2) above.

3.0 BACKGROUND:

Telomere Biology and Human Disease: Telomeres consist of long TTAGGG nucleotide repeats at chromosome ends that are essential for maintaining chromosomal stability. Telomeric repeats are lost, and telomeres become shorter, with each cell division. Therefore, telomere length correlates with age, tissue type and the replicative history of cells1. Germline mutations in telomere biology genes are present in patients with dyskeratosis congenita (DC), an inherited bone marrow failure and cancer predisposition syndrome. In this disease, all patients have very short telomeres2, but only approximately 50% of them have a germline mutation in one of the known telomere biology genes (DKC1, TERC, TERT, TINF2, NOLA2 or NOLA3), indicating that more genes remain to be discovered. Patients with DC develop severe aplastic anemia (SAA) that does not respond to immunosuppressive medications3; this is the leading cause of death in those patients. Patients with DC also have a very high risk of malignancy, with an observed-to-expected ratio of 11 (all cancers combined) compared with the general population4. Pulmonary fibrosis is also a significant cause of morbidity and mortality in DC patients5;6. Germline mutations in TERT and TERC have also been detected in about 15% of patients with apparently acquired aplastic anemia or myelodysplastic syndrome7. Approximately 8% of patients with familial idiopathic pulmonary fibrosis have mutations in these genes as well8. Whether these occurrences represent unrecognized, incompletely penetrant DC is presently the focus of debate. At the very least, it appears that mutations in telomere biology genes may be associated with variable clinical phenotypes, including adult-onset aplastic anemia and idiopathic pulmonary fibrosis. Several studies have investigated the role of telomere length in cancer risk. Short telomeres were associated with increased risk of bladder, kidney, lung, head and neck9, and breast cancer10, but not in prostate11. Other conditions associated with short telomeres include chronic inflammation, cardiovascular disease12, and sporadic idiopathic pulmonary fibrosis13.

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Acquired Aplastic Anemia (AA): Acquired AA is a rare, complex disorder, characterized by immune-mediated destruction of the bone marrow mostly of unknown etiology. The annual incidence of moderate-to-severe acquired AA is approximately 2 cases/million population, with bimodal age peaks at 15-25 and over 60 years14. Patients with acquired AA appear to have shorter telomeres than controls15, but not as short as those observed in DC16. In DC, telomeres are typically below the first percentile of expected for their age, and significantly shorter than patients with other inherited bone marrow failure syndromes2. AA patients with the shortest telomeres have a poor response to immunosuppressive medications, and more long-term complications12.Telomere shortening in acquired AA is suggested to be due to accelerated proliferation of the hematopoietic progenitor cells in response to bone marrow hypoplasia15. Alternatively, it might be due to mutations in telomere biology genes (see above) since patients with mutations in those genes are expected to have very short telomeres, similar to those observed in DC patients. HSCT is the first line of therapy for SAA patients who have an HLA-matched sibling, with cure rates approaching 90%. Patients without a satisfactory related donor receive immunosuppressive therapy (IST) as first line of management, followed by unrelated donor HSCT in case of IST failure or relapse after initial response17. Outcomes of HSCT in Patients with SAA: Survival after HSCT in SAA has improved significantly during recent decades, primarily due to declining graft failure rates and improving prevention and treatment of graft versus host disease (GVHD). Better survival is associated with the use of matched sibling grafts, young age at transplantation, short duration between diagnosis and transplant, the use of non-radiation based conditioning regimens18;19; and grafts from young unrelated donors to young recipients20. Patients who survive HSCT are at high risk of serious complications, including GVHD, life-threatening pulmonary complications, organ failure, and malignancy. Cancer risk (all sites combined) in patients transplanted for SAA is higher than that reported for the total HSCT population (SIR=9.1 vs. 2.1, respectively)21;22. Patients who received radiation-containing conditioning regimens or those who had chronic GVHD were at higher risk of developing cancers than those who did not21;22. Telomere biology and HSCT: After HSCT, the transplanted hematopoietic cells replicate rapidly to achieve engraftment. This may result in acute telomere attrition, which could contribute to late complications. Studies based on small numbers (4-25 participants)23-28, of mostly long-term HSCT survivors, suggest that telomeres in the recipients’ transplanted cells are shorter than those of their donors. Significant telomere attrition occurs in the first year post-transplant24. HSCT recipients transplanted from younger donors23, or from umbilical cord blood29 had longer telomeres, presumably because they started with longer ones (i.e., their donors were younger). However, none of these studies evaluated the effect of recipients’ and/or donors’ pre-transplant telomere length or their post-transplant attrition on HSCT outcomes.


Telomeres play an important role in cellular aging and genomic stability. Patients with DC are at high risk of HSCT-related complications, possibly because of their germline defects in telomere biology. The associations between telomere length and HSCT outcomes in SAA are not known. It

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is possible that patients with SAA and short telomeres (even if not as short as in patients with DC) are at risk of HSCT-related complications because of limited cellular replicative capacity due to either short telomeres at the beginning, and/or accelerated telomere shortening after HSCT. We propose to determine telomere length in patients who underwent HSCT for SAA and in their donors, to better understand the role of telomere biology in HSCT outcomes. We hypothesize that certain HSCT complications, such as post-transplant malignancies, and tissue/organ fibrosis are associated with reduced telomere length. Studying patients who received HSCT for severe acquired aplastic anemia (SAA) provides an opportunity to answer this question. We expect telomere lengths in SAA patients to range from normal to very short, and we will be able to compare these differences in relation to patient outcomes.

5.0 STUDY DESIGN:

Study participants: The Center for International Blood and Marrow Transplant Research (CIBMTR) is a research affiliate of the International Bone Marrow Transplant Registry (IBMTR) and the National Marrow Donor Program (NMDP). It is comprised of a voluntary working group of more than 500 transplant centers worldwide that contribute detailed patient, disease, transplant, and outcome information, on allogeneic HSCT recipients. In addition, NMDP collects biological samples from all patients/donors pairs involved in unrelated donor HSCT performed under its auspices. This study will include 457 individuals with SAA who received unrelated bone marrow (n=397) or peripheral blood donor transplant (n=60) prior to age 40 between 1988 and 2007, at a CIBMTR center, who also have an available pre-transplant blood sample from both the recipients and their donors stored at the NMDP. Most of the study participants n= 438 (95.42%) have received their transplants in a US center, 11 (2.40%) in Brazil, 1 (0.22%) in Denmark, 4 (0.87%) in Germany, 4 (0.87%) in the Netherlands and 1 (0.22%) in Norway. The demographic characteristics and selected clinical data related to the recipients, as well as the age distribution of the donors are provided in Table 1. A subset of 62 HSCT recipients also have a post-transplant blood sample at day +100. We will use these samples to assess the degree of telomere attrition (as defined in the study predictor section). In addition, two groups are planned: a) 300 healthy individuals (150 young healthy family members of individuals enrolled in different studies at the National Institute of Health and 150 healthy children and adolescent enrolled at the National Institute of Child Health and Human Development) to serve as age-matched healthy controls in analyzing telomere length; b) All DC patients enrolled in the Clinical Genetics Branch IBMFS project (n=40), to serve as positive controls for the SAA patients with very short telomeres. We believe this latter group will be valuable to study, since some SAA patients may have clinically occult DC. DNA extraction and Telomere length measurements: DNA will be extracted by standard methods from peripheral blood mononuclear cells. Care will be taken to ensure that the same method of DNA extraction is used for all of the samples. DNA has already been extracted from the IBMFS cohort DC cases, healthy family members, and the NICHD controls. Telomere length will be determined on genomic DNA by quantitative PCR (Q-PCR). The Q-PCR reaction measures telomere length as a ratio of TTAGGG repeat copy number to a single copy gene number (T/S). This method is simple, rapid and proven to be of value in epidemiological

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studies; in addition it is highly-correlated with the gold standard telomere length measurement technique, Southern blotting (r2=0.68, p= 1.5 × 10–24)30. Fortunately, we have recently demonstrated a high positive correlation between telomere length measurements performed on DNA isolated from buffy coats performed by Q-PCR, and those obtained by flow-FISH in peripheral blood of individuals with IBMFS (r=0.89, p<0.0001) (SA Savage and S Gadalla, unpublished observations). Very short telomeres detected by Flow-FISH are the method of choice for diagnosing DC, but is not practical for epidemiologic studies because it requires fresh or cryopreserved whole blood. Q-PCR will be performed on each sample in triplicate, requiring approximately 100ng of genomic DNA per subject. To ensure validity of the results, we will test for differences in telomere length measurements by sample freezing time. If differences observed, we will frequency matched controls on cases by time of sample collection and length of time in the freezer.

6.0 OUTCOMES:

Study Outcomes: — Death — Post-transplant malignancy — Tissue or organ fibrosis (pulmonary fibrosis, bronchiolitis obliterans, liver cirrhosis, and

scleroderma) Study Predictors: — Recipient pre-transplant telomere length — Donor pre-transplant telomere length — Telomere attrition (defined as the difference between donor telomere length shortly before

HSCT and telomere length in recipient peripheral blood obtained 100 days post-transplant) Recipient and donor telomere length will be categorized in relation to a reference curve of telomere length in healthy controls (300 individuals with ages ranging between 6 months and 65 years of age) stratified by gender. This method has the potential to be clinically applicable, and will provide age- and gender-standardized telomere length values for each study participant. Follow up: Transplant recipients will be followed up from date of transplant to study outcome or censoring (lost to follow up, end of the study, or death in outcomes other than death). CIBMTR collects outcome data at day 100 and 6 month post-transplant for the first year followed by annual data collection. We do not expect more than 10% lost to follow up, based on CIBMTR procedure to ensure data completion that will reach up to ceasing the privilege of the transplant center for searching NMDP database for suitable donors (details are provided in the following webpage: http://www.cibmtr.org/DATA/CIBMTR_Manual/PDF/a00382.pdf


7.1 Determine pre-transplant blood telomere length in patients who received HSCT for SAA and compare with age-matched controls and patients with dyskeratosis congenital. We will use the Wilcoxon rank sum test to compare the distribution of pre-transplant telomere length in SAA cases with age-matched healthy controls, and with DC cases.

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7.2 Assess the relationships between pre-transplant recipient and donor telomere length, and post-transplant outcomes (including death, malignancy and organ fibrosis) in patients who received unrelated donor HSCT for SAA. We will use Kaplan-Meier survival curves to estimate the cumulative incidence of death, post-transplant malignancies, and tissue/organ fibrosis outcomes, stratifying on donor and recipient age- and sex- standardized telomere length (below 25th percentile, 25th-50th percentile, more than 50th percentile for their age and gender, based on TL in healthy controls). Also, we will use Cox regression models to estimate the relative risk and 95% confidence interval of developing each outcome of interest, comparing long to short telomere in age- and gender-standardized categories, adjusting for potential confounders (listed in the next section). Recipient and donor pre-transplant telomere length will be entered in the same model to adjust for the effect of one another, and we will test for interaction between them. To analyze the association of each outcome with the degree of telomere attrition at day100 post-transplant, we will use Kaplan-Meier curves and Cox models. Since post-transplant telomere lengths are available only in 62 out of 457 recipients (demographic and clinical characteristics in Table 1), we will use the R software Nested-Cohort technique31;32 to conduct the survival analyses, by weighting up the 62 sub-sampled recipients to represent the full cohort of 457 recipients. In this analysis, we will allow the weights to be stratified by important demographic and clinical characteristics to satisfy the missing at random assumption. To calculate the absolute cumulative incidence of specified outcomes, accounting for competing risks, we will use standard calculations33.

7.3 Potential confounders and covariates:

7.3.1 Patient-related variables: — Age at transplant — Gender — Race — Co-morbid conditions prior to conditioning (e.g., pulmonary or liver

abnormalities, and CMV infection)

7.3.2 Disease-related variables: — Pre-transplant therapy, including androgen and corticosteroids — Time from diagnosis to transplant — Number of pre-transplant red blood cell and/or platelet transfusions

7.3.3 Transplant-related variables:

— Donor age and gender — Graft type (bone marrow versus mobilized peripheral blood stem cells) — Transplant preparative regimen (i.e., radiation vs. no radiation) — Conditioning regimen (myeloablative versus non-myeloablative/reduced

intensity) — Calendar year of transplant (1988-1995,1996-1999, 2000-2007) — Degree of HLA-matching between recipient and donor

7.4 Identify factors that modify the association between recipient and/or donor telomere length,

and post-transplant outcomes in patients who received unrelated donor HSCT

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The main factors of interest are: GVHD and radiation-containing conditioning regimens. We will test the interaction between 1) GVHD (moderate/severe vs. mild/none), 2) conditioning regimen (radiation vs. none), and telomere length (<25th percentile vs. ≥ 25th percentile of age and gender matched controls) in the final model. In case of significant interaction, we will report stratum-specific RR. Power calculation34: Aim #1 Minimal detectable relative risks, comparing transplant recipients with telomere length <25th percentile to those ≥25th percentile, are shown for a range of expected probability of the outcomes among transplant recipients with longer telomeres. The calculation assumption includes 80% power, and type I error probability=0.05 Probability of outcome in the recipients with telomere length ≥25th percentile Minimal detectable RR

5% 2.5 10% 1.9 15% 1.7 20% 1.6 25% 1.5

Aim#3: Power calculation based on different assumptions: cohort study, α=0.05, two sided test, 2 level exposures that are independent with an independent marginal probabilities of 30% for each exposure arm, multiplicative model, sample size=457, probability of death in patients with longer telomeres (>25 percentile) and no exposure (GVHD/radiation) =0.20, and OR of death for patients with short telomeres=2 OR for 2nd exposure (GVHD/RADIATION)

Interaction ratio of ORs

Power

1.5 2 0.30 1.5 3 0.62 1.5 4 0.80 2.0 2 0.30 2.0 3 0.60 2.0 4 0.78 3.0 2 0.28 3.0 3 0.56 3.0 4 0.73

8.0 HUMAN SUBJECT PROTECTION:

The study will include two sources of data; 1) Demographic, clinical information and blood samples of HSCT recipients (SAA) and donor; 2) Information and DNA samples of the controls. The first will be provided by the CIBMTR and NMDP as coded data, in which study investigators cannot identify individual participants. CIBMTR and NMDP protocols of data and biological sample collection were reviewed and approved by the IRB at each participating center and the Medical college of Wisconsin. For more details please see (http://www.cibmtr.org/DATA/Protocols_IRBs_Conse/Database_Repository_Protocols/index.html).

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The second set of samples will be derived from previously-collected data and samples in the Clinical Genetics Branch family studies and the National Institute of Child Health and Human Development (NICHD). Those studies have been approved by the IRBs of the NCI or NICHD. Based on the provided information, we will apply for IRB exemption. + 200 cGy TBI; Fludarabine + Cy; Fludarabine + ARAC.

9.0 REFERENCES:

1. Aubert G, Lansdorp PM. Telomeres and aging. Physiol Rev. 2008;88:557-579. 2. Alter BP, Baerlocher GM, Savage SA et al. Very short telomere length by flow fluorescence

in situ hybridization identifies patients with dyskeratosis congenita. Blood 2007;110:1439-1447.

3. Al-Rahawan MM, Giri N, Alter BP. Intensive immunosuppression therapy for aplastic anemia associated with dyskeratosis congenita. Int.J.Hematol. 2006;83:275-276.

4. Alter BP, Giri N, Savage SA, Rosenberg PS. Cancer in dyskeratosis congenita. Blood 2009: 25;113(26):6549-57

5. Savage SA, Alter BP. Dyskeratosis congenita. Hematol.Oncol.Clin.North Am. 2009;23:215-231.

6. Walne AJ, Dokal I. Advances in the understanding of dyskeratosis congenita. Br.J.Haematol. 2009: 145(2):164-72

7. Ly H, Calado RT, Allard P, et al. Functional characterization of telomerase RNA variants found in patients with hematologic disorders. Blood 2005;105:2332-2339.

8. Armanios MY, Chen JJ, Cogan JD, et al. Telomerase mutations in families with idiopathic pulmonary fibrosis. N.Engl.J.Med. 2007;356:1317-1326.

9. Wu X, Amos CI, Zhu Y, et al. Telomere dysfunction: a potential cancer predisposition factor. J.Natl.Cancer Inst. 2003;95:1211-1218.

10. Shen J, Terry MB, Gurvich I, et al. Short telomere length and breast cancer risk: a study in sister sets. Cancer Res. 2007;67:5538-5544.

11. Mirabello L, Huang WY, Wong JY, et al. The association between leukocyte telomere length and cigarette smoking, dietary and physical variables, and risk of prostate cancer. Aging Cell 2009

12. Savage SA, Alter BP. The role of telomere biology in bone marrow failure and other disorders. Mech.Ageing Dev. 2008;129:35-47.

13. Alder JK, Chen JJ, Lancaster L, et al. Short telomeres are a risk factor for idiopathic pulmonary fibrosis. Proc.Natl.Acad.Sci.U.S.A 2008;105:13051-13056.

14. Young NS, Kaufman DW. The epidemiology of acquired aplastic anemia. Haematologica 2008;93:489-492.

15. Ball SE, Gibson FM, Rizzo S, et al. Progressive telomere shortening in aplastic anemia. Blood 1998;91:3582-3592.

16. Du HY, Pumbo E, Ivanovich J, et al. TERC and TERT gene mutations in patients with bone marrow failure and the significance of telomere length measurements. Blood 2009;113:309-316.

17. Kurre P, Johnson FL, Deeg HJ. Diagnosis and treatment of children with aplastic anemia. Pediatr.Blood Cancer 2005;45:770-780.

18. Passweg JR, Socie G, Hinterberger W, et al. Bone marrow transplantation for severe aplastic anemia: has outcome improved? Blood 1997;90:858-864.

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19. Bacigalupo A, Oneto R, Bruno B, et al. Current results of bone marrow transplantation in patients with acquired severe aplastic anemia. Report of the European Group for Blood and Marrow transplantation. On behalf of the Working Party on Severe Aplastic Anemia of the European Group for Blood and Marrow Transplantation. Acta Haematol. 2000;103:19-25.

20. Perez-Albuerne ED, Eapen M, Klein J, et al. Outcome of unrelated donor stem cell transplantation for children with severe aplastic anemia. Br.J.Haematol. 2008;141:216-223.

21. Deeg HJ, Socie G, Schoch G, et al. Malignancies after marrow transplantation for aplastic anemia and fanconi anemia: a joint Seattle and Paris analysis of results in 700 patients. Blood 1996;87:386-392.

22. Rizzo JD, Curtis RE, Socie G, et al. Solid cancers after allogeneic hematopoietic cell transplantation. Blood 2009;113:1175-1183.

23. Akiyama M, Hoshi Y, Sakurai S, et al. Changes of telomere length in children after hematopoietic stem cell transplantation. Bone Marrow Transplant. 1998;21:167-171.

24. Akiyama M, Asai O, Kuraishi Y, et al. Shortening of telomeres in recipients of both autologous and allogeneic hematopoietic stem cell transplantation. Bone Marrow Transplant. 2000;25:441-447.

25. Lee J, Kook H, Chung I, et al. Telomere length changes in patients undergoing hematopoietic stem cell transplantation. Bone Marrow Transplant. 1999;24:411-415.

26. Thornley I, Freedman MH. Telomeres, X-inactivation ratios, and hematopoietic stem cell transplantation in humans: a review. Stem Cells 2002;20:198-204.

27. Thornley I, Sutherland R, Wynn R, et al. Early hematopoietic reconstitution after clinical stem cell transplantation: evidence for stochastic stem cell behavior and limited acceleration in telomere loss. Blood 2002;99:2387-2396.

28. de Pauw ES, Otto SA, Wijnen JT, et al. Long-term follow-up of recipients of allogeneic bone marrow grafts reveals no progressive telomere shortening and provides no evidence for haematopoietic stem cell exhaustion. Br.J.Haematol. 2002;116:491-496.

29. Pipes BL, Tsang T, Peng SX, et al. Telomere length changes after umbilical cord blood transplant. Transfusion 2006;46:1038-1043.

30. Cawthon RM. Telomere measurement by quantitative PCR. Nucleic Acids Res. 2002;30:e47.

31. Katki HA, Mark SD. Survival analysis for cohorts with missing covariate information. R News 2008;8:14-19.

32. Mark SD, Katki HA. Specifying and implementing nonparametric and semiparametric survival estimators in two-stage (sampled) cohort studies with missing case data. Journal of the American Statistical Association 2006;101:460-471.

33. Kalbfleisch JD, Prentice RL. The statistical analysis of failure time data. John Wiley & Sons; 2002.

34. William D.Dupont and and Walton D.Plummer, Jr. PS: Power and sample size calculation. 2009. Ref Type: Computer Program.

133


Table 1. Characteristics of patients less than 40 years old receiving a first transplant for severe aplastic

anemia where donor/ recipient pairs are high resolution HLA typed with donor and recipient samples available from the NMDP ab

Pre-transplant

recipient sample available only

Pre- and post- transplant

recipient samples available

Variable N (%) N (%)Number of patients 397 62Number of centers 82 28Age at transplant, median (range), years 14 (<1-39) 18 (<1-39)Age at transplant in decades

0 - 9 y 116 (29) 16 (26)10 – 19 y 158 (40) 20 (32)20 – 29 y 71 (18) 19 (31)30 – 39 y 52 (13) 7 (11)

Race Caucasian 280 (71) 52 (84)African American 37 ( 9) 3 ( 5)Asian/Pacific Islander 20 ( 5) 3 ( 5)Hispanic 57 (14) 4 ( 6)Native American 1 (<1) 0Other 2 ( 1) 0

Male sex 216 (54) 34 (55)Karnofsky prior to transplant > 90 295 (78) 48 (77)HLA matching out of 10 alleles

10/10 167 (42) 28 (45)9/10 109 (28) 11 (18)8/10 69 (17) 16 (26)7/10 28 ( 7) 5 ( 8)≤ 6/10 24 ( 6) 2 ( 3)

Stem cell source Bone marrow 335 (84) 62 (100)Peripheral blood stem cells (PBSC) 60 (15) 0Cord blood 2 ( 1) 0

Sub-disease status for SAA Idiopathic 194 (49) 34 (55)Other 203 (51) 28 (45)

Conditioning regimen Myeloablative 122 (31) 41 (66)Reduced intensity 133 (34) 17 (27)Non-myeloablative 109 (27) 3 ( 5)To be determined 33 ( 8) 1 ( 2)

134


Table 1. Continued.

Pre-transplant




Variable N (%) N (%)GVHD prophylaxis

FK506 ± MTX ± MMF ± steroids ± other 84 (21) 3 ( 5)FK506 ± other 5 ( 1) 0CsA + MTX ± other 151 (38) 32 (52)CsA ± other (No MTX) 48 (12) 10 (16)MMF ± other 1 (<1) 0MTX ± other (No CSA) 3 ( 1) 0T-cell depletion 103 (26) 17 (27)Other 2 ( 1) 0

Donor/Recipient sex match Male/Male 139 (35) 25 (40)Male/Female 106 (27) 12 (19)Female/Male 77 (19) 9 (15)Female/Female 75 (19) 16 (26)

Donor/Recipient CMV match Negative/Negative 123 (31) 16 (26)Negative/Positive 119 (30) 24 (39)Positive/Negative 52 (13) 10 (16)Positive/Positive 95 (24) 12 (19)Unknown 8 ( 2) 0

Donor age, cord blood unit, median (range), years 2.3 (1.4-3.1) N/ADonor age, adult donor, median (range), years 35 (19-61) 37 (20-50)Donor age in decades

0-9 (cord blood) 2 ( 1) 010-19 2 ( 1) 020-29 114 (29) 19 (31)30-39 135 (34) 20 (32)40-49 118 (30) 21 (34)50 and older 26 ( 7) 2 ( 3)

135


Table 1. Continued.

Pre-transplant




Variable N (%) N (%)Year of transplant

1988 1 (<1) 01989 5 ( 1) 2 ( 3)1990 6 ( 2) 4 ( 6)1991 11 ( 3) 1 ( 2)1992 11 ( 3) 3 ( 5)1993 16 ( 4) 7 (11)1994 9 ( 2) 12 (19)1995 9 ( 2) 5 ( 8)1996 9 ( 2) 4 ( 6)1997 26 ( 7) 9 (15)1998 19 ( 5) 8 (13)1999 14 ( 4) 7 (11)2000 27 ( 7) 02001 28 ( 7) 02002 23 ( 6) 02003 21 ( 5) 02004 42 (11) 02005 38 (10) 02006 57 (14) 02007 25 ( 6) 0

Median follow-up of survivors, months 26 (2-194) 122 (37-205)a – NMDP data is not adjusted for the NMDP corrective action plan. b – Sample availability has been determined.

Abbreviations: HLA - Human leukocyte antigens, FK506 - Tacrolimus, MTX - Methotrexate, MMF - Mycophenolate mofetil, CsA - Cyclosporine, CMV - Cytomegalovirus.

Conditioning regimen definitions: • Myeloablative - (Cy/TBI with TBI>500 cGy single or TBI>800 cGy fractionated) ± VP16; Busulfan + Cy; TBI>500 cGy

single or TBI>800 cGy fractionated; Melphalan > 150 mg/m2; Busulfan > 9 mg/Kg; Busulfan + Melphalan. • Reduced Intensity - 500≥TBI>200 cGy single or 5800≥TBI>200 cGy fractionated; Melphalan ≤ 150 mg/m2; Busulfan ≤ 9

mg/Kg; BEAM; CBV; VP16+Cy. • Non-myeloablative - 200 cGy TBI; Fludarabine + 200 cGy TBI; Fludarabine + Cy; Fludarabine + ARAC.

136


STUDY TIMELINE: The study was reviewed and approved by the CIBMTR Immunobiology working committee on July 2009 (see appendix for attached Letter of Approval)

0 6 12 18 months

DNA-extraction for SAA cases

IRB and MTA approval for controls & DC cases

Q-PCR length measurement

Obtaining clinical data files

Data Analysis & Publication

137


STUDY BUDGET: Item Sample description Cost/sample Total cost

Telomere length measurement 976 samples from the NMDP (457×2 pre-transplant+62 post-transplant) 40 DC 300 controls

$45 $59,220

Sample handling and shipment fee (NMDP) 976 samples from NMDP $14 $13,664

DNA extraction 976 sample from NMDP $42 $40,992 DNA aliquot and shipment to Harvard and NMDP $1,744

Sample retrieval and shipment costs 340 sample from NCI $7 $2,380 Westat contract for handling and tracking the specimens $25,000

Total project cost $143,000 * The project was awarded $93,000 from end-of-FY09 year funds awarded to CGB. An intramural research award (IRA) has

been approved to cover the additional cost of the study.

138


APPENDIX:

Table 1. Demographic and transplant related characteristics of all SAA BMT recipients and the subset for whom a post-transplant sample is available

All Recipients n=457

Subset n=62

Variable N (%) N (%)Age at transplant in years

<10 10 – 19 20 – 29 30 – 39

Race Caucasian African American Asian/Pacific Islander Hispanic Native American Other

Gender Male Female

Donor age in years 18-19 20-29 30-39 40-49 50 and older

Type of SAA Idiopathic Other

Source of transplanted cells Bone marrow Peripheral blood stem cells

131 (29) 177 (39)

90(19) 59 (13)

331(72)

39 ( 8) 23 (5)

61 (13) 1 (<1)

2 (1)

250 (55) 207 (45)

2 (<1)

133 (29) 155 (34) 139 (30)

28 ( 6)

228 (50) 229 (50)

397 (87)

60 (13)

16 (26) 20 (32) 19 (31)

7 (11)

52 (84) 3 ( 5) 3 ( 5) 4 ( 6)

0 0

34 (55) 28 (45)

0

19 (31) 20 (32) 21 (34)

2 ( 3)

34 (55) 28 (45)

62 (100)

0Data source: Center for International Blood and Marrow Transplant Research (CIBMTR)

139


STUDY PROCEDURES:

457 Pre + 62 post-transplant blood samples from patients with SAA

457 Pre-transplant blood samples from matched

donors

DNA extraction

Measure Telomere Length

DNA sample from 300 healthy controls + 40 CGB patients with DC

Clinical Data from CIBMTR

Merge data

140


Timeline of HSCT in relation to outcomes and available clinical data collected by CIBMTR

-10

100

0

Days in reference to transplant

Conditioning Regimen Chemotherapy +/- Radiation

Blood Collection

TRANSPLANTATION

At Risk of Acute Complications e.g. graft failure, drug toxicity, acute GVHD, infections

At Risk of chronic and long

term complications

Pre-transplant data: diagnosis, detailed clinical data, donor type, conditioning regimen co-morbid

Transplant related data: Product type, T-cell depletion, total volume, adverse reaction Donor demographics

Follow up data at day 100

Follow up data at 6-month

Follow up data annually

Data on Vital status

141

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