2019 Taiwan and Japan Hematology Forum

Integration of Genetic and Clinical Information

can Improve Risk Stratification of de novo

Myelodysplastic Syndrome (MDS) Patients

Hsin-An Hou, MD, M.Sc., PhD.

Division of Hematology, Department of Internal Medicine,

National Taiwan University Hospital (NTUH), Taipei, Taiwan

Company Affiliation:

Celgene Research support

Abbvie, Astellas, BMS, Celgene, Chugai, IQVIA,

Johnson & Johnson, Kirin, Merck Sharp & Dohme,

Novartis, Pfizer, PharmaEssential, Roche, Takeda,

Honorarium

Celgene, IQVIA, Novartis Advisory Board

Disclosure and Conflicts of Interests

MDS AMLCHIP or ARCH

0.5-1.0%/year30-40%

Low-riskHigh-risk

Tefferi A, et al, N Engl J Med. 2009;361(19):1872-85

Ades L, et al, Lancet. 2014;383(9936):2239-52

Rafael Bejar, et al, Blood. 2014;124:2793-2803

Steensma DP et al, Blood 2015 Jul 2;126(1):9-16

Malcovati L et al, Blood 2017;129:3371-78

Cytopenia

➢ ANC < 1.8K/uL

➢ Hb < 10 g/dL

➢ PLT < 100 K/uL

Ineffective hematopoiesis

➢ Clonal stem cell disorder

➢ Abnormal differentiation, maturation and apoptosis/pyroptosis

➢ Quantitative and qualitative defects

➢ Genetic basis

➢ Higher risk of leukemia transformation

CCUS

Malcovati, Blood, 2017

Progression risk relative to VAF and number and type of mutations

Highest progression risk = SF3B1, SRSF2, U2AF1 or DNMT3A, TET2, or ASXL1 + other

Milestones in MDS: Disease Subtype, Risk Stratification and Treatment

1938

Rhoads:RA

1963

Rhoads:

RA

1978

1980

1982

1989

1997

(2012)

(2016)

(2008)

(2001)

1999

2000

(2006)

2004

2005

2011

Linman, Bagby:

Preleukemia

Streuli:

Dysmyelopoiesis

Erslev:

ESA

WHO

List:

Lenalidomide

Yoshida, Bejar:

Genetics

Bennett:

FAB

Greenberg:

IPSS

IPSS-R

Cheson:

IWG

Fenaux:

Azacitidine

2006

Decitabine

Incorporation of SF3B1 Status in 2016 WHO Classification

Malcovati et al Blood 2011

Papaemmanuil et al NEJM 2011

MDS WHO Classification: from 2008 to 2016

RCUD (RA, RN & RT)

RCMD

RARS

RAEB-1

RAEB-2

WHO 20081

1. Vardiman JW, et al. Blood 2002;100:2292–302; WHO revised classification, 2008; 2. Arber et al, Blood. 2016; 127(20):2391-2405; 3. Germing U, et al, Leukemia.

2012;26(6):1286-1292; 4. Mallo M, et al, Leukemia. 2011;25(1):110-120; 5. Schanz J, T¨et al, J Clin Oncol. 2012;30(8):820-829

MDS-RS

MDS-RS-SLD

MDS-RS-MLD

15% or 5% if SF3B1 mutation is present

MDS with isolated del(5q)del(5q) alone or with 1 additional

abnormality except -7 or del (7q)3-5

MDS-EB-1

MDS-EB-2 BM 10%-19% or PB 5%-19% or Auer rods

BM 5%-9% or PB 2%-4%, no Auer rods

MDS-U

MDS del(5q)

MDS-SLD

MDS-MLDBM <5%, PB <1%, no Auer rods

MDS-U

1% blood blasts

single lineage dysplasia and pancytopenia

based on defining cytogenetic abnormality

Refractory cytopenia of childhood

WHO 20162

recorded on at least 2 separate occasions

Refractory cytopenia of childhood

IPSS1

Parameter Score

Blasts

< 5% 0

5-10% 0.5

11-20% 1.5

21-30% 2

Cytogenetics

Good 0

Intermediate 0.5

Poor 2

Cytopenia

0/1 0

2/3 0.5

Risk

groupScore

OS

(years)

L 0 5.7

Int-1 0.5-1 3.5

Int-2 1.5-2 1.2

H 2.5 0.4

WPSS2

Parameter Score

WHO category

RA, RARS, 5q- 0

RCMD, RCUD-RS 1

RAEB-1 2

RAEB-2 3

Cytogenetics

Good 0

Intermediate 1

Poor 2

RBC transfusion

Yes 1

No 0

Risk

groupScore

OS

(years)

VL 0 11.8

L 1 5.5

Int 2 4

H 3-4 2.2

VH 5-6 0.8

MDAPSS3-4

Parameter Score

Blasts

5-10% 1

11-29% 2

Cytogenetics

7abn/Cplx 3

PLTs

<30 3

30-49 2

50-199 1

WBC >20 2

Hb < 12 2

Age

60-64 1

65 2

ECOG 2 2

RBC transfusion-No 0

Risk

groupScore

OS

(years)

L 0-4 4.5

Int-1 5-6 2.1

Int-2 7-8 1.2

H 9 0.5

IPSSR5

Parameter Score

Blasts

2% 0

>2-<5% 0.5

5-10% 1.5

>10% 2

Cytogenetics

Very Good 0

Good 0.5

Intermediate 2

Poor 3

Very poor 4

Hb 8-<10 1

<8 1.5

ANC <0.8 0.5

PLTs 50-<100 0.5

<50 1

Risk

groupScore

OS

(years)

VL 1.5 8.8

L >1.5-3 5.3

Int >3-4.5 3.0

H >4.5-6 1.6

VH >6 0.8

1. Greenberg P, et al, Blood.1997;89:2079–2088; 2. Malcovati L, et al, Haematologica. 2011;96:1433–1440; 3. Kantarjian H, et al, Cancer. 2008;113:1351–1361;

4. Garcia-Manero G, et al, Leukemia. 2008;22:538–543. 5. Greenberg PL, et al, Blood. 2012;120:2454–2465.

RAS Pathway

NRAS/KRAS, CBL, NF1, PTPN11

Receptor/Kinase

JAK2, MPL, FLT3, GNAS, FBXW7, KIT, GPRC5A

Transcription

CEBPA, RUNX1, ETV6, GATA 2, SETBP1, PHF6, NCOR2, IRF1

DNA methylation

DNMT3A, TET2, IDH1/2

Chromatin modification

ASXL1, EZH2, KDM6A, ATRX,

MLL/PTD

RNA Splicing

SF3B1, SRSF2, ZRSR2, U2AF1,

U2AF2, SF1, LUC7L2

Cohesin/CTCF

STAG2, CTCF, SMC3, SMC1A,

RAD21

DNA repair

ATM, BRCC3, DCLRE1C,

FANCL

Molecular Genetic Alterations in MDS Pathogenesis

Other

TP53, NPM1, WT1, UAMB4,

GNB1, PIGA, etc

Proliferation/Survival Epigenetic modification

Block differentiation

NEJM, 2011, 364, 2496-2506; Nature, 2011, 478, 64-69; Education program from EHA and ASH, 2013-2017, 13th MDS Meeting

Ades L, et al, Lancet. 2014;383(9936):2239-52

Rafael Bejar, et al, Blood. 2014;124:2793-2803

➢ Transcription factors

➢ Kinase signaling

➢ Cohesins➢ DNA repairs

Frequency of Genetic Alterations in MDS

Prognostic Relevance of Point Mutations in IPSS Model

➢ 18 genes by NGS in 439 FAB-defined MDS patients

➢ 51% of all patients had at least one point mutation

Bejar et al, N Engl J Med 2011; 364:2496-2506

944 MDS Pts ( 2008 WHO)

➢ median age: 72.8 y/o

➢ 77.1% supportive care

➢ NGS in 104 genes

Haferlach et al, Leukemia 28:241-247,2014

Nazha et al, Leukemia 30, 2214–2220, 2016

A combination of conventional factors (age, gender, and IPSS-R) and

mutations in 14 genes (CBL, NARS, KRAS, ETV6, NPM1, LAMB4,

NF1, PRPF8, RUNX1, TET2, ASXL1, EZH2, STAG2, and TP53) as a

novel prognostic model.

Nazha et al.

➢ 508 primary and secondary MDS

➢ 20% supportive care

➢ NGS in 62 genes

➢ EZH2, SF3B1, and TP53 into IPSS-R.

MDS in Asia ➢ Chen, B. et al. Clinical and cytogenetic features of 508

Chinese patients with myelodysplastic syndrome and

comparison with those in Western countries. Leukemia

2005; 19, 767–775

➢ Yang, Y. T. et al. IPSS-R in 555 Taiwanese patients with

primary MDS: integration of monosomal karyotype. Am. J.

Hematol. 2014; 89, E142–E149.

➢ Korea- Lee, J. H. et al. Application of different prognostic

scoring systems and comparison of the FAB and WHO

classifications in Korean patients with myelodysplastic

syndrome. Leukemia 2003; 17, 305–313.

➢ Japan- Yoshizatoet al, Genetic abnormalities in

myelodysplasia and secondary acute myeloid leukemia:

impact on outcome of stem cell transplantation. Blood.

2017;129(17):2347-2358

Prognostic Relevance of Molecular Profiling in IPSS-R Model

Mutation-based Prognostic Risk Models in MDS:

⚫ Comprehensive analyses of cytogenetics and 25 molecular genes in 426 de novo MDS patients.

⚫ Evaluation of molecular pattern in different subgroups of MDS patients

⚫ Prognostic relevance of integrated prognostic system

It remains unclear whether integrated analysis of genetic alterations with IPSS-R can further improve the prognostic relevance in Asia.

Blood Cancer J. 2018 Apr 4;8(4):39. doi: 10.1038/s41408-018-0074-7

Molecular Gene Mutations in 426 MDS Patients:77.4% had either genetic alterations (66.9%) or cytogenetic changes (37%)

0.0

5.0

10.0

15.0

20.0

25.0

4.5 3.1

1.4 0.0

1.2 0.9

12.2

2.6

13.8

9.9

4.2

0.9

22.5

6.3

1.2

13.6

11.0 9.6

7.3

6.3

0.5 0.5 0.0 0.0

9.6

3.3

%

Cohesin complex

(7.3%)Splicing

factors

(39.0%)

Transcription

Factor

(14.3%)

RAS signaling

(8.7%)DNA

methylation

(24.4%)

Chromatin

modification

(24.2%)

Others

(12.9%)Receptor

/Kinase

(2.1%)

0.0

5.0

10.0

15.0

20.0

25.0

Very high

High

Intermediate

Very low/Low

Frequency of Mutations in 25 Genes with Different IPSS-R

0%

20%

40%

60%

80%

100%

Number of Gene Mutations based on 2016 WHO ClassificationIncreased number of somatic mutations predicted poorer outcome

Relative Frequency and Pairwise co-occurrence of Genetic Alterations

The length of the arc corresponds to the frequency of the first gene mutation, and the width of the ribbon corresponds to the proportion of the second gene mutation.

Pairwise Associations among Gene Mutations in MDS Patients

Variables

Overall Survival Leukemia-free Survival

95% CI 95% CI

RR Lower Upper P value RR Lower Upper P value

Age 1.024 1.014 1.034 <0.001* 0.997 0.983 1.011 0.713

Gender (Male vs. female) 1.248 0.922 1.687 0.151 1.251 0.753 2.079 0.387

IPSS-R scores# 0.306 0.208 0.450 <0.001* 0.211 0.102 0.437 <0.001*

CBL mutation 2.292 1.164 4.513 0.016* 2.561 0.943 6.955 0.065

RUNX1 mutation 1.021 0.679 1.536 0.920 1.381 0.729 2.617 0.322

IDH2 mutation 1.957 1.045 3.665 0.036* 1.798 0.577 5.602 0.311

DNMT3A mutation 1.571 1.028 2.401 0.037* 1.789 0.875 3.658 0.111

TET2 mutation 1.233 0.829 1.835 0.301 1.270 0.642 2.513 0.493

ASXL1 mutation 1.557 1.040 2.329 0.031* 2.009 1.066 3.788 0.031*

EZH2 mutation 1.292 0.724 2.304 0.385 1.413 0.587 3.402 0.440

SRSF2 mutation 1.084 0.684 1.719 0.731 1.168 0.530 2.576 0.700

ZRSR2 mutation 1.212 0.782 1.879 0.389 1.035 0.477 2.249 0.930

Cohesin mutation†† 1.232 0.764 1.986 0.392 2.620 1.281 5.359 0.008*

TP53 mutation 9.524 6.067 14.950 <0.001* 14.669 6.664 32.288 <0.001*

Multivariate Analysis (Cox regression) for the SurvivalMedian follow-up: 43.2 months

Abbreviation: RR, relative risk; CI, confidence interval; IPSS-R, revised international prognostic scoring system; * P value <0.05 was considered significant# IPSS-R scores: Lower IPSS-R scores (very low- and low-risk) vs. others ††Cohesin genes, including RAD21, STAG1, STAG2, SMC1A and SMC3A

Five Gene Mutations Predict Clinical Outcome

Poor-risk Mutations:

CBL, IDH2, DNMT3A, ASXL1 and TP53

P<0.001

Poor-risk Mutation Absent

5-year rate: 22.1%

Poor-risk Mutation Present

5-year rate: 61.7%Poor-risk Mutation Absent

median 69.9 months

Poor-risk Mutation Present

median 15 monthsP<0.001

41.8% patients harbored at least one poor-risk mutations

Allogeneic HSCT could Improve the Prognosis in High-Risk Patients

Poor-risk Mutations:

CBL, IDH2, DNMT3A, ASXL1 and TP53

P=0.002

Patients with Poor-risk Mutations

Allogeneic HSCT (+)

median 91.4 months

Allogeneic HSCT (-)

median 13.7 months

Overall Survival and Leukemia Transformation Rate according to the IPSS-R Risk Categories and Mutational Status

Patients with these poor-risk mutations had an OS shorter than others in

the same risk group, but similar to those with the next higher risk category.

A new risk model was developed incorporating the weighted coefficients of these factors:

Formula: Age x 0.025 – IPSS-R lower-risk group x 1.184 + CBL x 0.829 + IDH2 x 0.829 + DNMT3A x 0.452 + ASXL1 x

0.442 + TP53 x 2.254. low (score <-0.5; n=84), intermediate (score -0.5~0.5; n=158),

high (score 0.51~1.5; n=129) and very high (score >1.5; n=55).

WHO-defined MDS

Mayo Alliance Prognostic Model for MDS

Genetic risk factors: monosomal karyotype (MK; 4 points); non-MK abnormalities other than single/double 5q (1

point); RUNX1 mutation (1 point); ASXL1 mutation (1 point); absence of SF3B1 mutation (1 point).

Clinical risk factors: age >70 years (2 points); Hb level of <8 g/dL for women and <9 g/dL for men (2 points);

platelet count <75k/uL (1 point); BM blasts >10% (1 point).

Mayo Clin Proc. 2018 Jun 1. pii: S0025-6196(18)30312-4

Mayo ClinicN=357

NTUHN=328

IPSS-RAUC 0.76AIC 227 (logistic fit model)AIC 1943 (Cox regression)

MAPSAUC 0.87AIC 175 (logistic fit model)AIC 1865 (Cox regression)

Mayo Alliance Prognostic Model for MDS

Mayo Clin Proc. 2018 Jun 1. pii: S0025-6196(18)30312-4

International Working Group for the Prognosis of MDS (IWG-PM)

➢ Median F/U: 3.5 years

➢ Transformation in 20% Pts

➢ 95%: driver alterations

➢ Median of 4 oncogenic events per Pt

➢ Genes level

➢ Mutation hotspots

➢ Genetic interaction

✓ Incorporate gene mutations into formal classification i.e. WHO

classification for MDS?

✓ Improve prognostic accuracy and treatment decisions by

consideration of gene mutations in formal prognostic i.e.

IPSS/IPSS-R molecular model? 2018 ASH Meeting

Milestones in MDS: Disease, Risk Stratification and Treatment

1938

Rhoads:RA

1963

Rhoads:

RA

1978

1980

1982

1989

1997

(2012)

(2016)

(2008)

1999

2000(2006)

2004

2005

2011

Linman, Bagby:

Preleukemia

Streuli:

Dysmyelopoiesis

Erslev:

EPO

WHO

List:

Lenalidomide

Yoshida, Bejar:

Genetics

Bennett:

FAB

Greenberg:

IPSS

IPSS-R

Cheson:

IWG

Fenaux:

Azacitidine

2006

Decitabine

2019+

M-IPSS-R

Conclusion

Integration of Genetic and Clinical Information in MDS Prognostication

➢ Early assessment of IPSS-R and mutational profiling of five relevant genes, including

CBL, IDH2, ASXL1, DNMT3A, and TP53, may improve the prognostic stratification of

MDS patients.

➢ Presence of these poor-risk mutations can also risk-stratify the patients independently

of 2016 WHO classification.

➢ The new Mayo Alliance Prognostic Model provides a simple and contemporary

prognostication tool for MDS.

➢ The clinically relevant integrated prognostic system further refines the current prediction

models and may guide the therapeutic decision.

Funding and Grants➢ Ministry of Health and Welfare➢ Ministry of Science and Technology ➢ National Taiwan University (NTU)➢ NTU Hospital