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The Molecular Phenotype of Heart Allograft Biopsies
Mario C Deng
Associate Professor of Medicine
Director of Cardiac Transplantation Research
Center for Advanced Cardiac Care
Department of Medicine
Columbia University
USA
modern medicine & immortality
New York Times Magazine Jan 30, 2000
heart transplant milestones
Electrical VAD (Portner)
Human HLtx (Reitz)
Fk506MMFAzathioprin
SirolimusNeoral
ATGAMSteroids
OKT3
1965 1975 1985 1995 2005
Animal Htx (Shumway) First human Htx (Barnard)
Endomyocardial Biopsy (Caves) Copeland re-Htx (Copeland)
Baby Htx (Bailey)
CsA
H. Genome (Lander)
Daclizumab
(1959)
(1967)
(1973)
(1974)
(1981)
(1984)
(2000)
Chemogenomics
Allomap (Deng)(2005)
2009
heart transplant survival by era
0
20
40
60
80
100
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Years
1982-1991 (N=18,854)
1992-2001 (N=35,146)
2002-6/2006 (N=12,369)
All comparisons significant at p < 0.0001
HALF-LIFE 1982-1991: 8.8 years; 1992-2001: 10.5 years; 2002-6/2006: NA
Su
rviv
al (
%)
ISHLT Taylor D et al. J Heart Lung Transplant 2008;27: 937-983
heart transplant cause of death
CAUSE OF DEATH
0-30 Days
(N = 3,006)31 Days –
1 Year
(N = 2,722)
>1 Year –
3 Years
(N = 2,135)
>3 Years –
5 Years
(N = 1,857)
>5 Years –
10 Years
(N = 4,054)
>10 Years
(N = 2,107)
CARDIAC ALLOGRAFT VASCULOPATHY 52 (1.7%) 127 (4.7%) 298 (14.0%) 299 (16.1%) 581 (14.3%) 309 (14.7%)
ACUTE REJECTION 193 (6.4%) 338 (12.4%) 220 (10.3%) 82 (4.4%) 69 (1.7%) 26 (1.2%)
LYMPHOMA 2 (0.1%) 54 (2.0%) 85 (4.0%) 96 (5.2%) 195 (4.8%) 73 (3.5%)
MALIGNANCY, OTHER 1 (0.0%) 57 (2.1%) 218 (10.2%) 340 (18.3%) 749 (18.5%) 392 (18.6%)
CMV 4 (0.1%) 34 (1.2%) 16 (0.7%) 3 (0.2%) 5 (0.1%) 1 (0.0%)
INFECTION, NON-CMV 393 (13.1%) 896 (32.9%) 276 (12.9%) 180 (9.7%) 442 (10.9%) 213 (10.1%)
GRAFT FAILURE 1,257 (41.8%) 500 (18.4%) 499 (23.4%) 379 (20.4%) 765 (18.9%) 353 (16.8%)
TECHNICAL 233 (7.8%) 28 (1.0%) 17 (0.8%) 17 (0.9%) 36 (0.9%) 20 (0.9%)
OTHER 162 (5.4%) 175 (6.4%) 187 (8.8%) 147 (7.9%) 339 (8.4%) 175 (8.3%)
MULTIPLE ORGAN FAILURE 356 (11.8%) 268 (9.8%) 117 (5.5%) 102 (5.5%) 309 (7.6%) 190 (9.0%)
RENAL FAILURE 20 (0.7%) 25 (0.9%) 36 (1.7%) 65 (3.5%) 225 (5.6%) 173 (8.2%)
PULMONARY 133 (4.4%) 108 (4.0%) 96 (4.5%) 85 (4.6%) 172 (4.2%) 99 (4.7%)
CEREBROVASCULAR 200 (6.7%) 112 (4.1%) 70 (3.3%) 62 (3.3%) 167 (4.1%) 83 (3.9%)
ISHLT Taylor D et al. J Heart Lung Transplant 2008;27: 937-983
CD8+
Allo Agallo MHC
allo APC
Allo Agself MHC
self APC
TCR
rejection
tolerance
T-cell activating signals#1 T-cell receptor#2 CD28 #2a CD40L#3 IL2, IL15 etc
Bone marrow
Recipientimmune response
Cardiac allograft
B-cellspleenlymphnode
thymus
CD8+ Cytotox
CD8+ supprCD8+28- suppr
monocyte CD3+ T-cell
CD4+
CD4+Th1
CD4+Th2
CD4+/25+
CD4+/45RO+
allograft rejection
systems biology strategy
RejectionRejection QuiescenceQuiescence
genome
transcriptome
proteome
metabolome
phenome
CD8+
Allo Agallo MHC
allo APC
Allo Agself MHC
self APC
TCR
rejection
tolerance
T-cell activating signals#1 T-cell receptor#2 CD28 #2a CD40L#3 IL2, IL15 etc
Recipientimmune response
Cardiac allograft
B-cell
CD8+ Cytotox
CD8+ supprCD8+28- suppr
monocyte CD3+ T-cell
CD4+
CD4+Th1
CD4+Th
2
CD4+/25+
CD4+/45RO+
allograft rejection
RejectionRejection QuiescenceQuiescence
genome
transcriptome
proteome
metabolome
phenome
organ systemic
phenome echo clinical
proteome histo, Cd4 biomarkers
transcriptome RT-PCR, ISH, array Allomap
genome DNA-sequencing DNA-sequencing
endomyocardial biopsy
mild
severe
invasive & complication-prone late-stage cellular rejection diagnosis insensitive for humoral rejection significant variability no insight into molecular mechanisms resource-intense
status quo monitoring
rejection grading
0 1
2 3
4
no rejection A, focal infiltrate without necrosis B, diffuse sparse infiltrate w/o necrosis one focus aggressive infiltration/focal myocyte damage A, multifocal aggr infiltr or myoc damage B, diffuse inflamm process with necrosis diffuse, necrosis, edema, hemorrhage
Billingham ME et al. J Heart Lung Transplant 1990;9:587Stewart S et al. J Heart Lung Transplant 2005;24:1710
0 R 1 R
2 R
3 R
AMR
Quilty
current standard of literature
Nielsen H., F.B. Sorensen and B. Nielsen et al., Reproducibility of the acute rejection diagnosis in human cardiac allografts. The Stanford Classification and the International Grading System, J Heart Lung Transplant 12 (1993), pp. 239–243
Fishbein M.C., G. Bell and M.A. Lones et al., Grade 2 cellular heart rejection does it exist?, J Heart Lung Transplant 13 (1994), pp. 1051–1057
Winters G.L., E. Loh and F.J. Schoen, Natural history of focal moderate cardiac allograft rejection. Is treatment warranted?, Circulation 91 (1995), pp. 1975–1980
Milano A., A.L. Caforio and U. Livi et al., Evolution of focal moderate rejection of the cardiac allograft, J Heart Lung Transplant 15 (1996), pp. 456–460
Brunner-La Rocca H.P., G. Sutsch, J. Schneider, F. Follath and W. Kiowski, Natural course of moderate cardiac allograft rejection early and late after transplantation, Circulation 94 (1996), pp. 1334–1338
Mills R.N., D.C. Naftel and J.K. Kirklin et al., Heart transplant rejection with hemodynamic compromise a multiinstitutional study of the role of endomyocardial cellular infiltrate. Cardiac Transplant Research Database, J Heart Lung Transplant 16 (1997), pp. 813–821
Winters G.L., B.M. McManus and Rapamycin Cardiac Rejection Treatment Trial Pathologists, Consistencies and controversies in the application of the ISHLT working formulation for cardiac transplant biopsy specimens, J Heart Lung Transplant 17 (1998), p. 754
Rodriguez E.R. and International Society for Heart and Lung Transplantation, The pathology of heart transplant biopsy specimens revisiting the 1990 ISHLT working formulation, J Heart Lung Transplant 22 (2003), pp. 3–15
Marboe CC, Billingham M, ... Berry G. JHLT 2005;24:S219
CD8+
Allo Agallo MHC
allo APC
Allo Agself MHC
self APC
TCR
rejection
tolerance
T-cell activating signals#1 T-cell receptor#2 CD28 #2a CD40L#3 IL2, IL15 etc
Recipientimmune response
Cardiac allograft
B-cell
CD8+ Cytotox
CD8+ supprCD8+28- suppr
monocyte CD3+ T-cell
CD4+
CD4+Th1
CD4+Th
2
CD4+/25+
CD4+/45RO+
allograft rejection
RejectionRejection QuiescenceQuiescence
genome
transcriptome
proteome
metabolome
phenome
organ systemic
phenome echo clinical
proteome histo, Cd4 biomarkers
transcriptome RT-PCR, ISH, array Allomap
genome DNA-sequencing DNA-sequencing
intragraft cytokine expression
Alvarez CM et al. Clin Transplant 2001;15:228
intragraft cytokine expression
Alvarez CM et al. Clin Transplant 2001;15:228
Author Year No Time Method Result
Zhao 1994 21 early RT-PCR IL6, TGFß+
Van Hoffen 1996 40 first mo‘s ISH/IHC IL6,8,9,10+
Baan 1996 16 < 12 mo RT-PCR IL4,IL6+
Van Besouw 1997 85 > 1 y GIL-ELISA IL6+ TxV
Kimball 1996 62 < 30d ELISA IL6,8+
correlation of IL6 with rejection
Author Year No Time Method Result
Salom 1998 22 early IHC IL6-, IL1, TNF+
Fyfe 1993 40 var ELISA IL4, IL6,TNF-
George 1997 484 < 8 wk ELISA IL6,IL8,TNF-
Ruan 1992 113 ? IHC IL6-,IL2+,IFN+
Van Besouw 1995 49 < 90d culture IL6-,IL4-,IL2,IFN+
Lagoo 1996 328 < 8 wk RT-PCR IL6-
Grant 1996 187 <2y RT-PC/ELISA IL6-,IL2+
Grant 1996 259 <2y ELISA IL6-
no correlation of IL6 with rejection
Deng 1998 115 <3mo ELISA IL6-
systems biology strategy
00
Phenotype 1Phenotype 1
proteome
transcriptome
metabolome
phenome/physiome
genome
Phenotype 2Phenotype 2
bot
tom
-to-
top
top-to-b
ottom
...We used mouse transplants to annotate pathogenesis-based transcript sets (PBTs) that reflect major biologic events in allograft rejection—cytotoxic T-cell infiltration, interferon-γ effects and parenchymal deterioration. We examined the relationship between PBT expression, histopathologic lesions and clinical diagnoses in 143 consecutive human kidney transplant biopsies for cause. PBTs correlated strongly with one another, indicating that transcriptome disturbances in renal transplants have a stereotyped internal structure. This disturbance was continuous, not dichotomous, across rejection and nonrejection. PBTs correlated with histopathologic lesions and were the highest in biopsies with clinically apparent rejection episodes. Surprisingly, antibody-mediated rejection had changes similar to T-cell mediated rejection. Biopsies lacking PBT disturbances did not have rejection. PBTs suggested that some current Banff histopathology criteria are unreliable, particularly at the cut-off between borderline and rejection...many transcriptome changes previously described in rejection are features of a large-scale disturbance characteristic of rejection but occurring at lower levels in many forms of injury. PBTs represent a quantitative measure of the inflammatory disturbances in organ transplants, and a new window on the mechanisms of these changes.
Mueller TF et al. Am J Transplant 2007;7:1
pathogenesis-based transcript sets
endomycardial array & rejection
Karason K et al. BMC Cardiov Dis 2006;6:29
endomycardial array & rejection
Karason K et al. BMC Cardiov Dis 2006;6:29
endomycardial array & rejection
Karason K et al. BMC Cardiov Dis 2006;6:29
endomycardial array & rejection
Karason K et al. BMC Cardiov Dis 2006;6:29
endomycardial array & rejection
Karason K et al. BMC Cardiov Dis 2006;6:29
…Methods: Endomyocardial tissue samples and serum were obtained in connection with clinical biopsies ... Endomyocardial RNA,..were analysed with DNA microarray. Genes showing up-regulation during rejection followed by normalization after the rejection episode were evaluated further with real-time RT-PCR…ELISA was performed to investigate whether change in gene-regulation during graft rejection was reflected in altered concentrations of the encoded protein in serum…Results…CCL9 was significantly upregulated during rejection (p < 0.05)…There were no changes in CXCL9 and CXCL10 serum concentrations during cardiac rejection…Conclusion: We conclude, that despite a distinct up-regulation of CXCL9 mRNA in human hearts during cardiac allograft rejection, this was not reflected in the serum levels of the encoded protein. Thus, in contrast to previous suggestions, serum CXCL9 does not appear to be a promising serum biomarker for cardiac allograft rejection. The lack of success in the identification of cardiac rejection biomarkers in the current study indicates that expression profiling of immunological active cells of the heart recipient may be a better way to identify cardiac rejection biomarkers.
intragraft & PBL expression
Flechner SM et al. Am J Transplant 2004;4:1475
…Our results demonstrate that PBL gene expression profiles in acute rejection are distinctly different from those of normal controls and from patients with well-functioning transplants. Therefore, acute rejection does influence the gene expression profile of the circulating lymphocyte pool. Moreover, despite the fact that surprisingly we found very little common gene expression between PBLs and kidney biopsies, we did identify a large number of lymphocyte-specific genes in the kidney tissue. One interpretation is that there are compartment-specific differences between the PBLs in the circulation and the subset of lymphocytes that are activated and recruited to the transplant kidney during acute rejection. ..these results…may explain the failure of more than a decade of work testing PBLs for an array of activation antigens based on findings in rejecting allografts and other immune models…It is possible that the gene expression profile of the PBLs represents the adequacy of immunosuppression such that the rejecting patients reflect the profile of inadequate immunosuppression as compared with the PBLs sampled from patients with well-functioning transplants... there is a distinct gene expression profile in the PBL pool that correlates with acute rejection and immunosuppression. If these results can be confirmed in a large, prospective trial it would support the use of such profiles as a minimally invasive monitoring strategy for the immunological status of the graft and support the potential of using them to monitor the adequacy of immunosuppression…
current standard of care
…the variability in the grading of heart transplant biopsies suggests the biopsy itself may not be a true gold standard against which all subsequent tests should be compared; this has clear implications for the evaluation of any new molecular diagnostic test if the only end-point is comparison with biopsy grade. Multifactorial end-points combining clinical, hemodynamic and biopsy data would provide a better standard. Indeed, the correlation of these multiple factors and peripheral blood gene expression with biopsy histology may provide a basis for further refining of the biopsy grading system by providing insight into the histologic features that best correlate with immunologic status and clinical outcomes.
Marboe CC, Billingham M, ... Berry G. JHLT 2005;24:S219
networked alloimmunity
Orosz CG. J Heart Lung Transplant 1996;15:1063Baan et al. Transplant Int 1998;11:160Bumgardner et al. Sem Liver Dis 1999;19:189
detection of cytokine transcripts does not imply protein detection of cytokine protein does not imply function function may vary in different contexts composite effects of multiple cytokines are rarely tested unknown cytokines may be involved in rejection cytokine polymorphisms may explain variations in-vitro effects may not reflect in-vivo effects animal data may not translate into in clinical data > nonreductionist research approach necessary
CD8+
Allo Agallo MHC
allo APC
Allo Agself MHC
self APC
TCR
rejection
tolerance
T-cell activating signals#1 T-cell receptor#2 CD28 #2a CD40L#3 IL2, IL15 etc
Recipientimmune response
Cardiac allograft
B-cell
CD8+ Cytotox
CD8+ supprCD8+28- suppr
monocyte CD3+ T-cell
CD4+
CD4+Th1
CD4+Th
2
CD4+/25+
CD4+/45RO+
allograft rejection
RejectionRejection QuiescenceQuiescence
genome
transcriptome
proteome
metabolome
phenome
organ systemic
phenome echo clinical
proteome histo, Cd4 biomarkers
transcriptome RT-PCR, ISH, array Allomap
genome DNA-sequencing DNA-sequencing
Grant AJ et al. Lancet 2002;359:150
differential lymphocyte homing
endomyocardial biopsy
mild
severe
invasive & complication-prone late-stage cellular rejection diagnosis insensitive for humoral rejection significant variability no insight into molecular mechanisms resource-intense
highly sensitive for rejection strong negative predictive value positive test >need for further workup non-invasive easily repeatable on outpatient basis low complication rate decreased costs
status quo monitoring future monitoring
systemic IL6 & HTx
1 2 3 4 5 6 7 8Bx time
050
100150200
pg/ml
MOF
stable
15 pts, < 3 mo posttx, at EMB time IL6 ELISA, RHC, echo IL2 & rej 2+ IL6 & prognosis
0 5 10 15 20 25 30RAP
050
100150200250300
IL6
Deng et al. Transplantation 1995;60:1118
malaise
graft dysfunction
under
cellul rej humor rej infectionSIRS
overimmunosuppression
bolus steroids cyclophosph reduced IS antibiotics
HTx management
Deng et al. Transplantation 1998;65:1255
CARGO clinical study summary Overview
Cardiac Allograft Rejection Gene expression Observational study = “CARGO” 8 center, 4-year observational study initiated in 2001 (22% of US HTx). 629 patients, 4917 post-transplant encounters
Hypothesis Gene expression profiling of peripheral blood mononuclear cells can discriminate ISHLT grade 0 rejection (quiescence) from moderate/severe (ISHLT grade ≥ 3A) rejection
Design & ResultProspective, blinded validation study of 20 gene algorithm demonstrated ability to distinguish Grade 3A rejection from quiescence
Deng/Eisen/Mehra et al. Am J Transplant 2006;6:150
Algorithm development Real-time PCR
20-gene algorithm to distinguish rejection from quiescence (AlloMap molecular testing)
Candidate gene selection 285 Leukocyte microarray
Database / literature mining
252 candidate genes
Validation Prospective, blinded, statistically-powered (n = 270)
Additional samples tested to further define performance (n > 1000)
DevelopmentDevelopment~1 year~1 year(PCR)(PCR)
ClinicalClinical ValidationValidation
~1 year~1 year(Molecular Test)(Molecular Test)
DiscoveryDiscovery~2 years~2 years
(microarray)(microarray)
II
IIII
IIIIII
Study Design Prospective Multi-center Non-blinded Randomized Non-inferiority
Patients 2-5 years post-Tx ≥ 18 years old Stable outpatients
Invasive Monitoring Attenuation through Gene Expression IMAGEQuestionHow do theGEP-based study restrictions affect clinical implementation?
Pham/Deng/Kfoury et al. J Heart Lung Transplant 2007;26:808ClinicalTrials.gov identifier NCT00351559
• HypothesisTo determine whether the monitoring of acute
rejectionusing GEP is not inferior compared to the use of the EMB with respect to the event-free survival
Decrease in LV function, defined as LVEF change ≥ 25% compared with the baseline, or enrollment value, as measured by echocardiography
Development of clinically overt rejection (heart failure, hemodynamic compromise)
Death from any cause
Invasive Monitoring Attenuation through Gene Expression IMAGE
EnrollmentVisit &
Randomization
Gene Profiling Arm
Year 2 - 3 Year 4 - 5
Clinic x x x x x
Echo x x x x x
GEP/EMB x x x x x
Study End
Biopsy Arm
~2 year follow-up
CARGOII Study
2000 2005 2010
CARGO study start
CARGO study completion/CLIA approval
Allomap Medicare reimbursement
FDA approval IVDMIA
IMAGE Study
(2001)
(2005)
(2006)
(2008)
(2009)
(2010)
Allomap implementation milestones
regulatory transitions – CLIA>FDA
CLIA approval 2005 FDA approval 2008 – safety & efficacy Center for Devices & Radiological Health In-vitro diagnostic multivariate index assay IVDMIA FDA-director Daniel Schultz comment:
“…Allomap is an example of how advancements in science and technology are leading to new medical care diagnostics…“
Phase
Figure 2: Strategies in gene expression based biomarker test development.
Tasks
Phase 1
Phase 2
Phase 3
Phase 4
Phase 5
Phase 6
Phase 7
• Imperfect Clinical/Phenotype Standards
• Dichotomous vs. Continuous Phenotype Choices
Challenges
• Multicenter Study
• General Gene Discovery Strategy
• Focused vs. Whole Genome Microarray
• Real time-PCR Validations
• Discriminatory vs. Classifier Genes
• Mathematical Modeling
• Biological Plausibility of the Diagnostic Test Gene List
• Clinical Test Replicability
• Independence of Primary Clinical Validation Cohort
• Prevalence Estimation of Clinical Phenotype of Interest
• Regulatory Approval
• Payer Reimbursement
• Clinical Acceptance
CLINICAL PHENOTYPE CONSENSUSDEFINITION
GENE DISCOVERY
INTERNAL DIFFERENTIAL GENE LIST VALIDATION
DIAGNOSTIC CLASSIFIER DEVELOPMENT
EXTERNAL CLINICAL VALIDATION
CLINICAL IMPLEMENTATION
POST-CLINICAL IMPLEMENTATION STUDIES
Phase
Figure 2: Strategies in gene expression based biomarker test development.
Tasks
Phase 1
Phase 2
Phase 3
Phase 4
Phase 5
Phase 6
Phase 7
• Imperfect Clinical/Phenotype Standards
• Dichotomous vs. Continuous Phenotype Choices
Challenges
• Multicenter Study
• General Gene Discovery Strategy
• Focused vs. Whole Genome Microarray
• Real time-PCR Validations
• Discriminatory vs. Classifier Genes
• Mathematical Modeling
• Biological Plausibility of the Diagnostic Test Gene List
• Clinical Test Replicability
• Independence of Primary Clinical Validation Cohort
• Prevalence Estimation of Clinical Phenotype of Interest
• Regulatory Approval
• Payer Reimbursement
• Clinical Acceptance
CLINICAL PHENOTYPE CONSENSUSDEFINITION
GENE DISCOVERY
INTERNAL DIFFERENTIAL GENE LIST VALIDATION
DIAGNOSTIC CLASSIFIER DEVELOPMENT
EXTERNAL CLINICAL VALIDATION
CLINICAL IMPLEMENTATION
POST-CLINICAL IMPLEMENTATION STUDIES
strategies in GEP test development
Shahzad ,Sinha , Latif, Deng. Standardized operational procedures in clinical gene expression biomarker panel development. In: Scherer A (Ed).John Wiley & Sons 2009
Clarke R et al. Nat Rev Cancer 2008;8:37
dimensionality problem overviewThe application of several high-throughput genomic and proteomic technologies generate high-dimensional data sets•The multimodality of high-dimensional expression data can confound both simple mechanistic interpretations of biology and the generation of complete or accurate gene signal transduction pathways or networks.•The mathematical and statistical properties of high-dimensional data spaces are often poorly understood or inadequately considered, particularly if the number of data points obtained for each specimen greatly exceed the number of specimens.•Data are rarely randomly distributed in high-dimensions and are highly correlated, often with spurious correlations.•The distances between a data point and its nearest and farthest neighbours can become equidistant in high dimensions, potentially compromising the accuracy of some distance-based analysis tools.•Owing to the ‘curse of dimensionality’ phenomenon and its negative impact on generalization performance, the large estimation error from complex statistical models can easily compromise the prediction advantage provided by their greater representation power. •Some machine learning methods address the ‘curse of dimensionality’ in high-dimensional data analysis through feature selection and dimensionality reduction, leading to better data visualization and improved classification.•It is important to ensure that the generalization capability of classifiers derived by supervised learning methods from high-dimensional data is independently validated
invasive vs noninvasive algorithm
Allomap-score?
team patientassessment
clinical status?
graft function?
no biopsy
clinical biopsy
treatment & re-biopsy
3-6mo <207-12 mo < 30> 12 mo < 34
Allomap-score?
team patientassessment
clinical status?
graft function?
no biopsy
clinical biopsy
treatment & re-biopsy
3-6mo <207-12 mo < 30> 12 mo < 34
team patientassessment
clinical status?
graft function?
clinical biopsy
treatment & re-biopsy
protocol biopsy grade?
team patientassessment
clinical status?
graft function?
clinical biopsy
treatment & re-biopsy
protocol biopsy grade?