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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