Supplementary Appendix

Transcription

1 Supplementary Appendix This appendix has been provided by the authors to give readers additional information about their work. Supplement to: Lindsley RC, Saber W, Mar BG, et al. Prognostic mutations in myelodysplastic syndrome after stem-cell transplantation. N Engl J Med 2017;376: DOI: /NEJMoa

2 Table of Contents Supplementary methods... 3 Supplementary Figure S1 Distribution of mutations identified at low-frequency (< 20 patients) in the cohort... 5 Supplementary Figure S2 Distribution of number of driver mutations per patient... 6 Supplementary Figure S3 Variant allele fraction distribution by gene... 7 Supplementary Figure S4 Number of TP53 mutations per patient and effect on overall survival... 9 Supplementary Figure S5 TP53 mutation VAF distribution and and effect on overall survival Supplementary Figure S6 - TP53 mutation type and effect on overall survival Supplementary Figure S7 Clinical and genetic characteristics of TP53 mutations based on age Supplementary Figure S8 - TP53 mutations and pre-hct IPSS Supplementary Figure S9 Effect of complex karyotype on overall survival in patients with TP53 mutations Supplementary Figure S9 Distribution of co-mutations in patients with TP53 mutations Supplementary Figure S10 Mutation profile in patients 40 years of age with no TP53 mutation Supplementary Figure S11 Mutation profile in patients < 40 years of age with no TP53 mutation Supplementary Figure S12 Overall survival in 6 MDS subgroups identified by recursive partitioning analysis Supplementary Figure S13 Cumulative incidence of relapse and transplant-related mortality in all patients according to conditioning intensity Supplementary Figure S14 Cumulative incidence of relapse and NRM based on terminal nodestp53 mutations, based on JAK2 mutation status and conditioning intensity Supplementary Figure S15 Cumulative incidence of relapse and NRM in patients < 40 years of age without TP53 mutations, based on presence or absence of high risk features and according to conditioning intensity Supplementary Figure S16 Clinical and genetic characteristics of patients with biallelic SBDS mutations Supplementary Figure S17 Spectrum of driver mutations in therapy-related MDS Supplementary Table S1 List of participating transplant centers Supplementary Table S2 - Complete patient-related characteristics of the cohort Supplementary Table S3 - Complete transplantation-related characteristics of the cohort Supplementary Table S4 Complete disease-related clinical characteristics of the cohort Supplementary Table S5 - List of sequenced genes Supplementary Table S6- Criteria for identification of candidate driver mutations, which were subject to subsequent manual curation Supplementary Table S7 - Patient- and variant-level data in the entire cohort Supplementary Table S8 Frequency and distribution of mutations Supplementary Table S9 - Univariate associations of mutations with overall survival Supplementary Table S10 - Univariate associations of patient characteristics with overall survival Supplementary Table S11 - Univariate associations of transplant characteristics with overall survival Supplementary Table S12 - Univariate associations of disease characteristics with overall survival Supplementary Table S13 - Multivariable Cox model for OS Supplementary Table S14 - Multivariable Fine-Gray model for Relapse Supplementary Table S15 - Multivariable Fine-Gray model for NRM Supplementary Table S16 Association between gene mutations and primary or therapy-related MDS Supplementary Table S17 Association between gene mutations and older age ( 40 years) Supplementary Table S18 Competing risks regression models for relapse and non-relapse mortality according to conditioning intensity for terminal nodes of recursive partitioning model Supplementary Table S19 Competing risks regression models for relapse and non-relapse mortality after reduced-intensity conditioning (RIC) HCT according to gene mutation Supplementary Table S20 Competing risks regression models for relapse and transplant-related mortality after myeloablative conditioning (MAC) HCT according to gene mutation Supplementary Table S21 Clinical characteristics of patients with JAK2 mutated MDS... 95

3 Supplementary Table S22 Association between patient characteristics and overall survival in JAK2 mutated patients Supplementary Table S23 Cause of death according to terminal nodes of recursive partitioning model References

4 Supplementary methods Samples: Patient peripheral blood samples were collected prior to initiation of the preparative conditioning regimen. Samples were collected in Anticoagulant Citrate Dextrose Solution, Solution A (ACD-A) tubes and shipped for overnight delivery at ambient temperature. Blood samples were aliquotted into 1 milliliter cryovials on the day of receipt and maintained in -80 degrees Celsius frozen storage until the time of DNA extraction. Germline samples were uniformly unavailable. Library construction: Native genomic DNA was sheared and library constructed per manufacturer protocol (Agilent). Libraries were then quantified and pooled up to 24 samples per lane in equimolar amounts totaling 500ng of DNA. Each pool was then hybridized to Agilent Custom SureSelect In Solution Hybrid Capture RNA baits, consisting of probes, spanning kbp. Each capture reaction was washed, amplified, and sequenced on two lanes of an Illumina HiSeq bp paired end run. Variant Calling and annotation Fastq files were aligned to hg19 version of the human genome with BWA Single nucleotide and small insertion and deletion calling was performed with samtools mpileup and Varscan Pindel ,2 was used for FLT3-ITD calling at the specific genomic locus located at chromosome 13:28,608,000-28,608,600. Variants were annotated to include information about cdna and amino acid changes, sequence depth, number and percentage of reads supporting the variant allele, population allele frequency in 1000 Genomes release 2.2.2, 3 the Exome Sequencing Project, 4 Exome Aggregation Consortium, 5 and presence in Catalogue of Somatic Mutations in Cancer (COSMIC), version Variants were excluded if they had fewer than 15 total reads at the position, had fewer than 5 alternate reads, had variant allele fraction < 2%, fell outside of the target coordinates, had excessive read strand bias, had excessive number of calls in the local region, caused synonymous changes, or were recurrent small insertions/deletions at low variant allele fraction adjacent to homopolymer repeat regions. No germline tissue was available for evaluation of somatic status of mutations. As such, individual single nucleotide substitutions and small insertions or deletions were evaluated as candidate drivers of MDS or bone marrow failure based on gene-specific characteristics, as outlined in Table S4, then curated manually and classified as MDS driver mutations or pathogenic bone marrow failure mutations based on genetic criteria and literature review. 7,8 Variant level details are available in Table S5. All interpretation of variants was blinded to clinical characteristics and thus agnostic to variables including age, sex, treatment status, and clinical outcomes; the genetic analysis was completed and locked prior to merging with any clinical data. RAS pathway definition We used literature review of functional, genomic, and clinical evidence to define RAS pathway mutations prior to analysis and without reference to our sequencing results: NRAS, KRAS, PTPN11, CBL, NF1, RIT1, FLT3, KIT. Although the precise molecular consequence of individual mutations on intracellular signaling can be distinct, these mutations as a whole share a common role in the biology of myeloid malignancies. Specifically, mutations that cause aberrant activation of RAS/MAPK signaling are late events in disease progression that drive leukemic transformation of MDS We analyzed the impact of each gene individually and the impact of all pathway members together without post-hoc exclusions. Curation of DDX41 variants DDX41 has been reported to be mutated in the germline in the context of familial predisposition to MDS and somatically during disease intitiation or progression. 11,12 Because several of the reported disease-associated alleles are common in population-based exome studies and the effect size may be small or modified by genetic context, we uses variant allele fraction, population allele frequecy, and literature reports to assign DDX41 variants to candidate germline or somatic status for subsequent analyses. No germline material was available for definitive assessment. Statistics 3

5 Overall survival (OS) was defined as time from transplant until death from any cause. Subjects not confirmed dead were censored at the time last known to be alive. Univariate and multivariable analyses of OS were performed using Cox regression. Hazard ratios (HR) with 95% confidence intervals (CI) and Wald p-values were reported for covariates in multivariable Cox models. OS was estimated using the method of Kaplan and Meier (KM), and reported with 95% CIs based on Greenwood s formula. Differences in survival curves were assessed using log-rank tests. Median follow-up was calculated using the reverse KM method. Transplant-related mortality (NRM) was defined as death without relapse. Deaths after relapse were defined as competing risks (CR). Cumulative incidence between groups was assessed using Gray s test, and estimates were reported with 95% CIs. Cusing the method of Fine and Gray. Each mutation was assessed in the full data set individually with or without adjustment for those clinical and modifiable features identified as significantly associated with the outcome of interest through stepwise forward and backward procedures. OS used a Cox proportional hazards model and was adjusted for patient age in decade, Karnofsky performance status, IPSS-R risk score, therapy-related MDS, year of transplant, donor/recipient sex matching, and donor category. Time to NRM used a Fine and Gray competing risks regression model, and was adjusted for patient age in decade, donor category, IPSS-R risk score and Karnofsky performance status. Time to relapse used a Fine and Gray competing risks regression model, and was adjusted for IPSS-R risk score, conditioning intensity, bone marrow blasts at diagnosis, the use of in vivo T-cell depletion, TBI use, therapy-related MDS, monosomal karyotype and Karnofsky performance status. Recursive partitioning was used to generate a hierarchical model for OS integrating clinical and genetic characteristics. The variables considered included all mutations and non-modifiable clinical variables of age and sex; number of prior lines of therapy and prior diagnosis; therapy-related or de-novo MDS; Karnofsky performance score; pre-disposing conditions, monosomal karyotype, and cytogenetic risk factors; CMV infection; and blast, hemoglobin, platelet, and absolute neutrophil counts. Chi-square and Fisher s exact test was used to test for association between pairs of categorical variables. Odds ratios (OR) and 95% CIs were calculated for binary outcomes from contingency tables or logistic regression. The Wilcoxon rank-sum test was used to assess a location shift in the distribution of continuous variables between two groups. Descriptive statistics (proportions, medians, etc.) were reported with 95% CI or range. All p-values were two-sided, and adjustments for multiple hypothesis testing was performed using the method of Benjamini and Hochberg. Case definitions Cases were defined according to WHO criteria or published transplant literature. Therapy-related MDS was defined based on prior exposure to chemotherapy and/or radiation. 13 Transplant conditioning regimens were classified based on chemotherapy regimen and/or total body irradiation dose as myeloablative, reducedintensity, or non-myeloablative. 14 Availability of data All clinical and genetic data are available for request through CIBMTR: 4

6 Supplementary Figure S1 Distribution of mutations identified at low-frequency (< 20 patients) in the cohort. Shown is the frequency of driver mutations, limited to genes mutated in< 20 patients (<1.3%). Due to their low frequency in the study cohort, these mutations were not evaluated for their association with clinical outcomes. # mutated cases ETNK1 ATM BRCC3 CEBPA RAD21 PIGA BCORL1 GNB1 RIT1 GNAS MPL STAT3 EP300 LUC7L2 FLT3 SDS BRAF CALR CSNK1A1 CREBBP TERT SMC3 SH2B3 NOTCH1 TERC IKZF1 PDS5B KIT CTCF SMC1A SETD2 XRCC2 CBLB STAT5B RPS Frequency 5

7 Supplementary Figure S2 Distribution of number of driver mutations per patient Shown is number of driver mutations per patient in the study cohort (Median = 2, Mean = 2.8). 6

8 Supplementary Figure S3 Variant allele fraction distribution by gene Shown are variant allele fractions according to individual genes. Horizontal lines within boxes indicate median VAF. Box limits indicate the 25th and 75th percentiles, whiskers extend to the 10th and 90th percentiles, and outliers are represented by dots. 7

9 Supplementary Figure S4 Distribution of per-patient maximum somatic VAF Shown is the maximum variant allele fraction of putative somatic mutations in myeloid drivers. To infer the most abundant, clonal mutations in patients with multiple mutations, the mutation with higher VAF was used. Mutations in genes associated only with germline bone marrow failure syndromes are not included. Dotted line shows the median maximum somatic VAF (23%) and the limits of the grey shading indicate the 25th and 75th percentiles 8

10 Supplementary Figure S4 Number of TP53 mutations per patient and effect on overall survival In patients with TP53 mutations, 35% had more than one detectable TP53 mutation. Panel A shows the number of patients with TP53 mutated MDS based on number of distinct TP53 mutations. Panel B shows Kaplan Meier curves for OS in patients with TP53 mutations based on the presence of one or more than one independent TP53 mutation. Surival of patients with more than one TP53 mutation was not significant different than those with one TP53 mutation (HR 1.20, , p=0.18). 9

11 Supplementary Figure S5 TP53 mutation VAF distribution and and effect on overall survival Panel A shows the distribution of TP53 variant allele fractions, where box limits indicate the 25th and 75th percentiles, whiskers extend to the 1st and 99th percentiles, and outliers are represented by dots. The median TP53 VAF was 10% (range 2-85%). Panel B shows Kaplan Meier curves for OS in patients with TP53 mutations according to VAF above or below median. Surival of patients with TP53 VAF 10% was not significant different than those with TP53 VAF < 10% (HR 1.28, , p=0.07). 10

12 Supplementary Figure S6 - TP53 mutation type and effect on overall survival Panel A shows the localization of TP53 mutations based on position within the coding sequence. Missense mutations are shown in green, truncating mutations (frameshift, nonsense, splice site) are shown in red. Panel B shows the proportion of patients with TP53 mutated MDS with missense mutations only (71%, n=205), truncating mutations only (17%, n=48), or both truncating and missense mutations (13%, n=36). Panel C shows Kaplan Meier curves for OS in patients with TP53 mutations according to mutation type, as indicated. Patients with truncating mutations had poor survival compared to those with missense only (HR 1.61, , p = 0.006) or missense plus truncating mutations (HR 1.70, , p = 0.03). Panel C shows the distribution of all TP53 mutations based on amino acid position and mutation type where green are missense and red are truncating (frameshift indels, nonsense, splice site). 11

13 Supplementary Figure S7 Clinical and genetic characteristics of TP53 mutations based on age Panel A shows Kaplan Meier curves for OS in patients 18 according to TP53 mutation status. Panel B shows the distribution of TP53 variant allele fraction in patients according to the indicated age group. No significant differences were identified. Panel C shows the frequency of t-mds in patients according to TP53 mutation status in the indicated age groups. 12

14 Supplementary Figure S8 - TP53 mutations and pre-hct IPSS Panel A shows Kaplan Meier curves for OS in all patients according to IPSS score prior to transplantation. mutation status. Patients are grouped as lower (Low or Intermediate-1) or higher (Intermediate-2 or high) risk groups. Panel B shows the frequency of TP53 mutations in lower or higher risk groups prior to transplantation. Panel C shows Kaplan Meier curves for OS in all patients according to IPSS lower or higher risk groups and TP53 mutation status. 13

15 Supplementary Figure S9 Effect of complex karyotype on overall survival in patients with TP53 mutations. Shown is a Kaplan Meier curves for OS in patients TP53 mutations according to presence or absence of complex karyotype. 14

16 Supplementary Figure S9 Distribution of co-mutations in patients with TP53 mutations. A co-mutation plot shows nonsynonymous mutations in individual genes as labeled on the left. Mutations are depicted by colored bars and each column represents 1 of the 289 patients with TP53 mutations. TP53 PPM1D DNMT3A TET2 ASXL1 RUNX1 U2AF1 SF3B1 SRSF2 ZRSR2 BCOR BCORL1 SBDS EZH2 NRAS KRAS PTPN11 CBL NF1 RIT1 FLT3 WT1 IDH1 IDH2 PRPF8 ETV6 CEBPA GATA2 CUX1 STAG2 RAD21 BRAF CSF3R GNB1 CBLB SETBP1 PHF6 NPM1 JAK2 CALR PIGA STAT3 EP300 LUC7L2 DDX41 ATM NOTCH1 TERT RPS17 15

17 Supplementary Figure S10 Mutation profile in patients 40 years of age with no TP53 mutation A co-mutation plot shows nonsynonymous mutations in individual genes as labeled on the left. Mutations are depicted by colored bars and each column represents 1 of the 1011 patients 40 years of age without TP53 mutation. Colors reflect the three subgroups identified by recursive partitioning: RAS pathway mutation (blue), JAK2 mutation (gold), and all other patients (grey). TP53 NRAS KRAS PTPN11 CBL NF1 RIT1 FLT3 KIT BRAF JAK2 CALR MPL ASXL1 BCOR BCORL1 EZH2 DNMT3A TET2 WT1 IDH1 IDH2 SF3B1 SRSF2 U2AF1 ZRSR2 RUNX1 ETV6 CEBPA GATA2 SETBP1 CUX1 STAG2 PPM1D PHF6 NPM1 Other RAS pathway mutated JAK2 mutated RAS pathway and JAK2 unmutated 16

18 Supplementary Figure S11 Mutation profile in patients < 40 years of age with no TP53 mutation A co-mutation plot shows nonsynonymous mutations in individual genes as labeled on the left. Mutations are depicted by grey bars and each column represents 1 of the 214 patients under 40 years of age without TP53 mutation. Colored bars indicate the two subgroups identified by recursive partitioning: those with at least one high-risk feature (t-mds, platelets < 30 x 10 9 /L at HCT, or bone marrow blasts at diagnosis 15%) or with no high-risk features. Low Risk High Risk ASXL1 RUNX1 GATA2 SETBP1 BCOR STAG2 PIGA WT1 ETV6 NRAS KRAS PTPN11 CBL NF1 BCORL1 EZH2 TET2 DNMT3A U2AF1 SF3B1 SRSF2 ZRSR2 PRPF8 CEBPA IKZF1 CUX1 RAD21 SMC3 IDH1 IDH2 PDS5B FLT3 KIT BRAF CSF3R GNAS PHF6 NPM1 JAK2 CALR SH2B3 STAT3 STAT5B CSNK1A1 ETNK1 CREBBP EP300 LUC7L2 ATM BRCC3 XRCC2 17

19 Supplementary Figure S12 Overall survival in 6 MDS subgroups identified by recursive partitioning analysis. Panel A shows Kaplan Meier curves for OS in all patients in the cohort according to TP53 mutation status and age group. Panel B shows Kaplan Meier curves for OS in patients < 40 years old without TP53 mutations, according to the presence (High Risk) or absence (Low Risk) of at least one of the following clinical features: (t-mds, platelets < 30 x 10 9 /L at HCT, or bone marrow blasts at diagnosis 15%). 18

20 Supplementary Figure S13 Cumulative incidence of relapse and transplant-related mortality in all patients according to conditioning intensity. Panel A shows cumulative incidence of relapse and transplant-related mortality (NRM) in the entire cohort (n = 1514). Panels B and C show cumulative incidence of relapse and NRM in patients receiving myeloablative and reduced-intensity conditioning. 19

21 Supplementary Figure S14 Cumulative incidence of relapse and NRM based on terminal nodestp53 mutations, based on JAK2 mutation status and conditioning intensity. Shown are cumulative incidence of relapse and transplant-related mortality (NRM) patients in patients receiving myeloablative (MAC, solid lines) or reduced-intensity (RIC, dashed lines) conditioning. Panel A shows the total cohort according to presence (red) or absence (black) of TP53 mutations. Panel B shows patients 40 years of age without TP53 mutations according to presence (red) or absence (black) of RAS pathway mutations. Panel C shows patients 40 years of age without TP53 or RAS pathway mutations according to presence (red) or absence (black) of JAK2 mutations. P values are found in Table S18. 20

22 Supplementary Figure S15 Cumulative incidence of relapse and NRM in patients < 40 years of age without TP53 mutations, based on presence or absence of high risk features and according to conditioning intensity. Shown are cumulative incidence of relapse and transplant-related mortality (NRM) in patients < 40 years of age without TP53 mutations, based on presence or absence of high risk features in all patients (Panel A). Panels B and C show cumulative incidence of relapse and non-relapse mortality based on presence or absence of high risk features in patients according to treatment with myeloablative (solid lines) or reduced-intensity (dashed) conditioning. 21

23 Supplementary Figure S16 Clinical and genetic characteristics of patients with biallelic SBDS mutations. Panel A shows the age at MDS diagnosis of patients with two SBDS mutations (n = 7), one SBDS mutation (n = 11), or zero SBDS mutations (n = 1496). Box limits indicate the 25th and 75th percentiles, whiskers extend to the 1st and 99th percentiles, and outliers are represented by dots. Panel B shows sex-specific percentile height of patients with two or one SBDS mutations. Red circles depict the 2 patients known clinically to have Shwachman-Diamond Syndrome. Panel C shows the frequency of somatic TP53 mutations in patients with two SBDS mutations (100%, 7/7), one SBDS mutation (27%, 3/11), or zero SBDS mutations (19%, 279/1496). 22

24 Supplementary Figure S17 Spectrum of driver mutations in therapy-related MDS. The co-mutation plot shows the spectrum of driver mutations in t-mds, where mutations are depicted as colored bars and each column represents one of the 311 t-mds patients in the cohort. 23

25 Supplementary Table S1 List of participating transplant centers. Center name Helen DeVos Children's Hospital UCSF Benioff Children's Hospital - Oakland Stanford Health Care The Children's Hospital of Denver Louisiana State University Children's Hospital Steven and Alexandra Cohen Children's Medical Center of New York Montefiore Medical Center Rady Children's Hospital San Diego Children's Hospital of Wisconsin Washington University/St Louis Children's Hospital University of Tennessee Christiana Care Cook Children's Medical Center Cancer Transplant Institute at Virginia G. Piper Cancer Center Children's National Medical Center Roger Williams Medical Center Stony Brook University Hospital Pediatric BMT Program, Doernbecher Children's Hospital (OHSU) Scripps Blood & Marrow Transplant Program Johns Hopkins All Children's Hospital Children's Medical Center - Dallas Sarah Cannon BMT Center at Centennial Medical Center Cancer Institute of New Jersey Avera McKennan Transplant Institute Nationwide Children's Hospital SSM Health Saint Louis University Hospital The Children's Mercy Hospitals and Clinics Ann & Robert H. Lurie Children's Hospital of Chicago Banner Blood and Marrow Transplant Program 4564 University of Louisville Hospital/James Brown Cancer Center Dartmouth-Hitchcock Medical Center Children's Healthcare of Atlanta at Egleston Memorial Sloan Kettering Cancer Center - Peds Memorial Sloan-Kettering Cancer Center Sutter Cancer Center Georgia Cancer Center at Augusta University Health Beth Israel Deaconess Medical Center UT Southwestern Medical Center - BMT Program Duke University Medical Center; Pediatric BMT The Coleman Foundation Blood and Marrow Transplant Center, Rush Univer University of California-Davis Cancer Center St Jude Children's Research Hospital Blood & Marrow Transplant Center, Florida Hospital Medical Group Fox Chase Temple University Hospital Bone Marrow Transplant Program UMass Memorial Medical Center 24

26 University of Mississippi Medical Center - Jackson Philadelphia Children's Hospital Banner University Medical Center - Tucson Tufts New England Medical Center Cincinnati Children's Hospital Medical Center Mayo Clinic Florida - Jacksonville Indiana Blood & Marrow Transplantation Seidman Cancer Center - University Hospitals Case Medical Center Henry Ford Hospital Bone Marrow Transplant Program University of Kentucky Medical Center North Shore University Hospital University of Colorado Hospital Cedars-Sinai Medical Center University of Miami - Adults University of Pittsburgh Medical Center Osborn Hematopoietic Malignancy & Transplantation Program Oklahoma University Medical Center Dana Farber Cancer Institute & Boston Children's Hospital Yale New Haven Hospital Jewish Hospital Blood and Marrow Transplant Center University of Maryland School of Medicine Utah Blood and Marrow Transplant Program- Adults Latter Day Saints Hospital Thomas Jefferson University Medical City Dallas Hospital University of California, San Diego Medical Center Wake Forest Baptist Health Baylor College of Medicine Medical University of South Carolina West Penn Hospital Shands HealthCare & University of Florida University of North Carolina Hospitals - Chapel Hill Northwestern Memorial Hospital Mount Sinai Medical Center - New York UCLA Hematologic Malignancy/Stem Cell Transplantation Program University of Iowa Hospital & Clinics University of Nebraska Medical Center University of Rochester Medical Center University of Alabama Birmingham University of California - San Francisco - Adults Penn State Hershey Medical Center University of Wisconsin Hospital and Clinics Froedtert & Medical College of Wisconsin Virginia Commonwealth University Massey Cancer Center BMT Program Roswell Park Cancer Institute University of Chicago Medical Center Loyola University Medical Center Indiana University Hospital/Riley Hospital for Children 25

27 Texas Transplant Institute The Blood and Marrow Transplant Program at Northside Hospital New York Presbyterian Hospital University of Kansas Massachusetts General Hospital Colorado Blood Cancer Institute Abramson Cancer Center University - Pennsylvania Medical Center The Ohio State University Medical Center Emory University Mayo Clinic Arizona and Phoenix Children's Hospital Vanderbilt University Oregon Health and Science University Duke University - Adults Cleveland Clinic The University of Michigan Mayo Clinic Rochester Karmanos Cancer Institute Baylor University Medical Center The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins Masonic Cancer Center University of Minnesota Memorial Sloan Kettering Cancer Center - Adults Hackensack University Medical Center Washington University School of Medicine H Lee Moffitt Cancer Center City of Hope National Medical Center Fred Hutchinson Cancer Center Dana Farber Cancer Institute at Brigham and Women's Hospital - Adults MD Anderson Cancer Center Hospital De Clinicas Curitiba University Hospital, Rigshospitalet University Hospital Charite - Virchow University Hospital Charite Kinderklinik - Virchow University Leipzig, BMT Center University Hospital Mainz Deutsche Klinik fur Diagnostik - Wiesbaden UKE Hamburg, Klinik und Poliklinik fur Stammzelltransplantation Leiden University Medical Center 26

28 Supplementary Table S2 - Complete patient-related characteristics of the cohort. Karnofsky performance score No. of patients (%) (54) < (28) Missing 278 (18) Recipient age at transplant (2) (4) (4) (5) (11) (26) 60 and older 719 (47) Recipient sex Male 912 (60) Female 602 (40) Recipient race Caucasian 1397 (92) African American 57 (4) Asian 37 (2) Pacific Islander 3 (0) Native American 2 (0) Other 18 (1) Recipient ethnicity Caucasian, non-hispanic 1324 (87) African American, non-hispanic 54 (4) Asian race, non-hispanic 37 (2) Pacific Islander race, non-hispanic 3 (0) Native American race, non-hispanic 2 (0) Hispanic, Caucasian race 73 (5) Hispanic, African American race 3 (0) Missing 18 (1) 27

29 Supplementary Table S3 - Complete transplantation-related characteristics of the cohort. No. of patients (%) Conditioning regimen (1) Myeloablative 789 (52) Reduced intensity 582 (38) Non-myeloablative 130 (9) Missing 13 (1) Conditioning regimen (2) Myeloablative- TBI-based 116 (8) Myeloablative Chemo-based 673 (44) Reduced Intensity TBI-based 54 (4) Reduced Intensity Chemo-based 528 (35) Non-myeloablative TBI-based 85 (6) Non-myeloablative Chemo-based 45 (3) Missing 13 (1) Conditioning regimen (3) MAC-Cy/TBI/others 7 (0) MAC-CY/TBI 101 (7) MAC-TBI/others 8 (1) MAC-Bu/Flu 173 (11) MAC-Thiotepa based 8 (1) MAC-Bu/Cy/others 3 (0) MAC-Bu/Cy 216 (14) MAC-Bu/Flu + others 42 (3) MAC-Bu + others 188 (12) MAC-Flu/Melphalan 11 (1) MAC-Treosulfan-based 16 (1) MAC-others 16 (1) RIC-TBI+others 54 (4) RIC-Bu/Flu/others 3 (0) RIC-Bu/Flu 270 (18) RIC-Flu/Melphalan 210 (14) RIC-Flu/Melphalan/others 24 (2) RIC-Bu/others 2 (0) RIC-others 19 (1) NST-TBI/Cy/Flu 59 (4) NST-TBI/Flu 26 (2) NST-Flu/Cy 15 (1) NST-others 30 (2) Missing 13 (1) TBI use > 500 single dose or > 800 fractionated dose 116 (8) < 500 single dose or < 800 fractionated dose but other agents delivered at MAC dose 9 (1) single dose or fractionated dose 55 (4) TBI 200 cgy 134 (9) TBI, dosage missing 83 (5) Non-TBI regimen 1117 (74) 28

30 GVHD prophylaxis Ex vivo T-cell depletion 21 (1) CD34 selection 32 (2) Cyclophosphamide-based 19 (1) Tacrolimus-based 1134 (75) CSA-based 242 (16) None reported 24 (2) Missing 42 (3) in vivo T-cell depletion No 843 (56) Yes 605 (40) Missing 66 (4) Donor sex Male 972 (64) Female 454 (30) Missing 88 (6) Sex match (donor/recipient) M/M 610 (40) M/F 362 (24) F/M 250 (17) F/F 204 (13) Missing 88 (6) Graft type Bone marrow 221 (15) PBSC 1114 (74) Cord Blood 168 (11) Missing 11 (1) Donor type Unrelated 1165 (77) Related 181 (12) Cord blood 168 (11) Donor group/graft source 8/8 URD, PBSC 709 (47) HLA-identical siblings, BM 12 (1) HLA-identical siblings, PBSC 169 (11) 8/8 URD, BM 154 (10) 7/8 URD, BM 34 (2) 7/8 URD, PBSC 126 (8) 6/8 URD, BM 21 (1) 6/8 URD, PBSC 115 (8) UCBT 174 (11) Recipient CMV Negative 619 (41) Positive 877 (58) Missing 18 (1) CMV match (donor/recipient) Neg/Neg 439 (29) Neg/Pos 459 (30) 29

31 Pos/Neg 162 (11) Pos/Pos 379 (25) Missing 75 (5) Donor ethnicity Caucasian, non-hispanic 1086 (72) African American, non-hispanic 40 (3) Asian race, non-hispanic 27 (2) Native American race, non-hispanic 8 (1) Hispanic, Caucasian race 51 (3) Hispanic, African American race 1 (0) Hispanic, Native American race 1 (0) Hispanic, Unknown race 16 (1) Missing 284 (19) Donor race Caucasian 1130 (75) African American 41 (3) Asian 27 (2) Pacific Islander 7 (0) Native American 9 (1) Other 54 (4) Missing 246 (16) Donor age (11) (3) (35) (20) (15) (8) Missing 122 (8) Year of transplant (grouped) (4) (16) (21) (22) (33) (4) 30

32 Supplementary Table S4 Complete disease-related clinical characteristics of the cohort. No. of patients (%) Therapy-related MDS Primary MDS 1203 (79) t-mds 311 (21) Prior diagnosis for t-mds Predisposing condition 15 (1) HL 13 (1) NHL 74 (5) Breast cancer 56 (4) ALL 28 (2) CLL 37 (2) Other hem 39 (3) Missing 1252 (83) Predisposing conditions None reported 1394 (92) Yes 120 (8) Type of predisposing condition Inherited bone marrow failure syndrome 7 (0) Aplastic anemia 45 (3) Idiopathic thrombocytopenic purpura 9 (1) Paroxysmal nocturnal hemoglobinuria 3 (0) Other hematologic condition 46 (3) Other non-hematologic condition 10 (1) None reported 1394 (92) Bone marrow blasts at HCT (%) (25) (18) (16) (19) Missing 341 (23) Absolute neutrophil count at HCT (x 109/L) (59) < (32) Missing 137 (9) Platelets at HCT (x 10 9 /L ) (36) (23) < (36) Missing 85 (6) Hemoglobin (g/dl) (39) (35) <8 307 (20) Missing 81 (5) 31

33 Cytogenetic risk group Very good 10 (1) Good 569 (38) Intermediate 269 (18) Poor 287 (19) Very poor 125 (8) Missing 254 (17) Monosomal karyotype No 1090 (72) Yes 210 (14) Missing 214 (14) Prior MDS therapy None 509 (34) HMA only 761 (50) Chemo only 60 (4) HMA and Chemo 64 (4) Missing 120 (8) IPSS-R prior to HCT Very low 119 (8) Low 287 (19) Intermediate 340 (22) High 223 (15) Very high 171 (11) Missing 374 (25) IPSS-R prior to HCT Low 149 (10) Intermediate (29) Intermediate (24) High 61 (4) Missing 346 (24) MDS Classification at diagnosis MDS-NOS 226 (15) Refractory anemia 163 (11) Refractory anemia with excess blasts 136 (9) Refractory anemia with ring sideroblasts 81 (5) RAEB (17) RAEB (19) RCMD 201 (13) RCMD/RS 29 (2) 5q-syndrome 16 (1) Missing 114 (8) 32

34 Supplementary Table S5 - List of sequenced genes ANKRD26 CSF1R FANCC IDH2 NOTCH2 RPL11 SETBP1 TINF2 ASXL1 CSF3R FANCD2 IKZF1 NPM1 RPL23 SETD2 TP53 ATM CSNK1A1 FANCE JAK2 NRAS RPL26 SF1 U2AF1 ATRX CTC1 FANCF JAK3 PALB2 RPL35A SF3A1 U2AF2 B2M CTCF FANCG KIT PDS5B RPL36 SF3B1 VPS45 BCOR CUX1 FANCI KRAS PHF6 RPL5 SH2B3 WAS BCORL1 DDX41 FANCL LUC7L2 PIGA RPS10 SLX4 WRAP53 BRAF DKC1 FANCM MIR-142 PIGT RPS15 SMC1A WT1 BRCA1 DNMT3A FLT3 MPL PPM1D RPS17 SMC3 XRCC2 BRCA2 ELA2 G6PC3 MRE11A PRPF40B RPS19 SRSF2 ZRSR2 BRCC3 EP300 GATA1 MYD88 PRPF8 RPS24 STAG1 BRIP1 ERCC4 GATA2 MYH9 PTEN RPS26 STAG2 CALR ETNK1 GFI1 NBN PTPN11 RPS27A STAT3 CBL ETV6 GNAS NF1 RAD21 RPS29 STAT5B CBLB EZH2 GNB1 NHP2 RAD51C RPS7 TERC CEBPA FANCA HAX1 NOP10 RIT1 RUNX1 TERT CREBBP FANCB IDH1 NOTCH1 RMRP SBDS TET2 33

35 Supplementary Table S6- Criteria for identification of candidate driver mutations, which were subject to subsequent manual curation. Gene Criterion Gene Criterion FANCL type:frameshift KIT aa_range:810,830 and ExAC_Freq<0.01 aa_range:540,600 and ExAC_Freq<0.01 type:splice_site type:nonframeshift and ExAC_Freq<0.01 type:frameshift FANCM type:frameshift KRAS type:missense and ExAC_Freq<0.01 type:nonframeshift and ExAC_Freq<0.01 type:splice_site LUC7L2 type:frameshift FLT3 aa_match:d835 aa_match:n841 type:splice_site aa_match:y842 MPL aa_match:515 type:nonframeshift and aa_range:800,850 and aa_match:505 aa_match:n676 type:nonframeshift and aa_range:500,520 aa_match:n663 MRE11A type:frameshift aa_match:f691 aa_match:v592 type:splice_site aa_match:v579 MYD88 aa_match:l265 type:missense and aa_range:569,700 and MYH9 type:frameshift type:nonframeshift and aa_range:569,700 and type:frameshift and aa_range:569,648 type:splice_site G6PC3 type:frameshift type:missense and ExAC_Freq<0.01 NBN type:frameshift type:splice_site type:missense and ExAC_Freq<0.01 type:splice_site GATA1 type:frameshift type:missense and ExAC_Freq<0.01 NF1 type:frameshift type:splice_site type:missense and ExAC_Freq<0.01 type:splice_site GATA2 type:frameshift NHP2 type:frameshift type:splice_site type:splice_site type:missense and aa_range:200,398 type:missense and ExAC_Freq<0.01 position: , NOP10 type:frameshift GFI1 type:missense and ExAC_Freq<0.01 GNAS aa_match:r201 type:splice_site GNB1 aa_match:k57 type:missense and ExAC_Freq<0.01 aa_match:i80 NOTCH1 type:missense and aa_range:617,738 andexac_freq<0.01 aa_match:k89 type:missense and aa_range:500,616 andexac_freq<0.01 #aa_match:d76 type:frameshift and aa_range:2061,2555 #aa_match:k78 and aa_range:2061,2555 #aa_match:n88 NOTCH2 type:frameshift and aa_range:2010,2471 HAX1 type:frameshift and aa_range:2010,2471 NPM1 type:frameshift type:splice_site NRAS type:missense and ExAC_Freq<0.01 type:missense and ExAC_Freq<0.01 type:nonframeshift and ExAC_Freq<0.01 IDH1 aa_match:r132 #aa_match:g12 type:nonframeshift and aa_range:126,138 #aa_match:g13 IDH2 aa_match:r172 #aa_match:v14 aa_match:r140 #aa_match:i24 type:nonframeshift and aa_range:134,146 #aa_match:g60 type:nonframeshift and aa_range:164,180 #aa_match:q61 IKZF1 type:frameshift #aa_match:t74p #aa_match:a146 type:splice_site PALB2 type:frameshift JAK2 aa_match:v617f type:missense and aa_range:505,547 type:splice_site aa_match:v683 PDS5B type:frameshift aa_match:r867 aa_match:d873 type:splice_site aa_match:p933 JAK3 type:missense and ExAC_Freq<0.01 type:nonframeshift and ExAC_Freq<

36 Gene Criterion Gene Criterion ANKRD26 type:5utr and ExAC_Freq<0.01 CUX1 type:frameshift ASXL1 type:frameshift and aa_range:400,1540 and aa_range:400,1540 type:splice_site type:splice_site and ExAC_Freq<0.01 DDX41 type:missense and ExAC_Freq<0.01 ATM type:frameshift type:frameshift type:splice_site type:splice_site ATRX type:frameshift type:missense and aa_range:360,430 DKC1 type:frameshift type:splice_site B2M type:frameshift type:missense and ExAC_Freq<0.01 DNMT3A aa_match:r882 type:splice_site type:frameshift BCOR type:frameshift type:splice_site type:splice_site type:missense and aa_range:626,910 and BCORL1 type:frameshift type:missense and aa_range:290,374 and ELANE type:missense and ExAC_Freq<0.01 type:splice_site EP300 type:frameshift BRAF type:missense and aa_range:590,615 BRCA1 type:frameshift type:splice_site ERCC4 type:frameshift type:splice_site type:missense and ExAC_Freq<0.01 type:splice_site BRCA2 type:frameshift ETNK1 aa_match:h243 aa_match:n244 type:splice_site ETV6 type:frameshift type:missense and ExAC_Freq<0.01 BRCC3 type:frameshift type:splice_site type:missense and ExAC_Freq<0.01 type:splice_site type:missense and aa_range:338,424 and BRIP1 type:frameshift #ETS domain type:missense and aa_range:56,123 and type:splice_site #PNT domain CALR type:frameshift and aa_range:352,418 EZH2 type:frameshift CBL type:missense and aa_range:360,430 type:frameshift and aa_range:360,430 type:splice_site type:nonframeshift and aa_range:360,430 type:missense and aa_range:617,738 and type:splice_site type:missense and aa_range:500,616 and CBLB type:missense and aa_range:360,430 FANCA type:frameshift CEBPA type:frameshift type:splice_site type:missense and ExAC_Freq<0.01 FANCB type:frameshift CREBBP type:frameshift type:splice_site type:splice_site FANCC type:frameshift CSF1R aa_match:y969 type:missense and aa_range:543,972 and type:splice_site type:frameshift FANCD2 type:frameshift CSF3R aa_match:t615 type:splice_site aa_match:t618 FANCE type:frameshift type:frameshift and aa_range:618,840 and aa_range:618,840 type:splice_site CSNK1A1 aa_match:e98 FANCF type:frameshift aa_match:d140 type:missense and ExAC_Freq<0.01 type:splice_site CTC1 type:frameshift FANCG type:frameshift type:splice_site type:splice_site CTCF type:frameshift FANCI type:frameshift type:splice_site type:splice_site 35

37 Gene Criterion Gene Criterion PHF6 type:frameshift RPS10 type:frameshift type:splice_site type:missense and ExAC_Freq<0.01 type:missense and aa_range:239,330 and RPS15 type:frameshift type:missense and aa_range:42,132 and PIGA type:missense and ExAC_Freq<0.01 type:missense and ExAC_Freq<0.01 type:frameshift RPS17 type:frameshift PIGT type:frameshift type:missense and ExAC_Freq<0.01 RPS19 type:frameshift PPM1D type:frameshift and aa_range:421,605 and aa_range:421,605 type:missense and ExAC_Freq<0.01 PRPF40B type:frameshift RPS24 type:frameshift type:splice_site type:missense and ExAC_Freq<0.01 PRPF8 type:frameshift RPS26 type:frameshift type:splice_site type:missense and ExAC_Freq<0.01 PTEN type:missense and ExAC_Freq<0.01 RPS27A type:frameshift type:frameshift type:missense and ExAC_Freq<0.01 PTPN11 type:missense and ExAC_Freq<0.01 RPS29 type:frameshift RAD21 type:frameshift type:missense and ExAC_Freq<0.01 type:splice_site RPS7 type:frameshift RAD51C type:frameshift type:missense and ExAC_Freq<0.01 type:splice_site RUNX1 type:frameshift RIT1 aa_match:s35 aa_match:a57 type:splice_site aa_match:f82 type:missense and ExAC_Freq<0.01 aa_match:g95 SBDS type:missense and ExAC_Freq<0.01 aa_match:a77 type:nonframeshift and ExAC_Freq<0.01 aa_match:e81 type:splice_site aa_match:t83 type:frameshift aa_match:y89 aa_match:m90 SETBP1 type:missense and aa_range:855,880 RMRP type:missense and ExAC_Freq<0.01 SETD2 type:frameshift type:nonframeshift and ExAC_Freq<0.01 type:frameshift type:splice_site SF1 type:frameshift RPL11 type:frameshift type:splice_site type:missense and ExAC_Freq<0.01 SF3A1 type:frameshift RPL23 type:frameshift type:splice_site type:missense and ExAC_Freq<0.01 SF3B1 type:missense and aa_range:550,800 and RPL26 type:frameshift SH2B3 type:frameshift type:missense and ExAC_Freq<0.01 type:missense and aa_range:364,441 and RPL35A type:frameshift type:missense and aa_range:195,307 and SLX4 type:frameshift type:missense and ExAC_Freq<0.01 RPL36 type:frameshift type:splice_site SMC1A type:frameshift type:missense and ExAC_Freq<0.01 RPL5 type:frameshift type:splice_site type:missense and ExAC_Freq<

38 Gene Criterion Gene Criterion SMC3 type:frameshift TINF2 type:frameshift type:splice_site type:splice_site SRSF2 aa_match:p95 type:missense and aa_range:201,352 and type:nonframeshift and aa_range:85,100 TP53 type:frameshift type:frameshift and aa_range:85,100 STAG1 type:frameshift type:splice_site type:missense and ExAC_Freq<0.01 type:splice_site U2AF1 aa_match:s34 STAG2 type:frameshift aa_match:q157 type:nonframeshift and aa_range:30-38 type:splice_site type:nonframeshift and aa_range: STAT3 type:missense and aa_range:584,674 U2AF2 type:nonframeshift and ExAC_Freq<0.01 type:nonframeshift and aa_range:584,674 type:frameshift STAT5B type:missense and aa_range:593,670 type:nonframeshift and aa_range:593,670 VPS45 type:missense and ExAC_Freq<0.01 TERC ExAC_Freq<0.01 type:nonframeshift and ExAC_Freq<0.01 TERT type:missense and ExAC_Freq<0.01 type:frameshift type:nonframeshift and ExAC_Freq<0.01 type:frameshift WAS type:missense and ExAC_Freq<0.01 type:nonframeshift and ExAC_Freq<0.01 TET2 type:frameshift type:frameshift type:splice_site WRAP53 type:frameshift type:missense and aa_range:1104,1481 and type:missense and aa_range:1843,2002 and type:splice_site type:nonframeshift and aa_range:1104,1481 and type:missense and ExAC_Freq<0.01 type:nonframeshift and aa_range:1843,2002 and WT1 type:frameshift type:splice_site XRCC2 type:frameshift type:splice_site type:missense and ExAC_Freq<0.01 ZRSR2 type:frameshift type:splice_site MIR142 ExAC_Freq<

39 Supplementary Table S7 - Patient- and variant-level data in the entire cohort. UPN Chr Position_Start Ref Alt Gene cdna AA Variant_Type VAF Depth C T ASXL1 c.c1249t p.r417x Nonsense C T ASXL1 c.c1249t p.r417x Nonsense A ASXL1 c insa p.y425fs fs-ins C T ASXL1 c.c1282t p.q428x Nonsense G ASXL1 c insg p.v435fs fs-ins C T ASXL1 c.c1549t p.q517x Nonsense G T ASXL1 c.g1696t p.e566x Nonsense A G ASXL1 c a>g Splice_Site G T ASXL1 c g>t Splice_Site G A ASXL1 c g>a Splice_Site C G ASXL1 c.c1730g p.s577x Nonsense C T ASXL1 c.c1762t p.q588x Nonsense C T ASXL1 c.c1762t p.q588x Nonsense C T ASXL1 c.c1762t p.q588x Nonsense A ASXL1 c insa p.y591-q592delinsx Nonsense A ASXL1 c insa p.y591-q592delinsx Nonsense A ASXL1 c insa p.y591-q592delinsx Nonsense A ASXL1 c insa p.y591-q592delinsx Nonsense C G ASXL1 c.c1773g p.y591x Nonsense C A ASXL1 c.c1773a p.y591x Nonsense C A ASXL1 c.c1773a p.y591x Nonsense C G ASXL1 c.c1773g p.y591x Nonsense C A ASXL1 c.c1773a p.y591x Nonsense C A ASXL1 c.c1773a p.y591x Nonsense C T ASXL1 c.c1774t p.q592x Nonsense C T ASXL1 c.c1774t p.q592x Nonsense C T ASXL1 c.c1774t p.q592x Nonsense G T ASXL1 c.g1804t p.e602x Nonsense TA ASXL1 c insta p.d616fs fs-ins TAAT ASXL1 c instaat p.d616-i617delinsdx Nonsense T ASXL1 c inst p.i617fs fs-ins A T ASXL1 c.a1852t p.k618x Nonsense C ASXL1 c insc p.a619fs fs-ins ASXL1 c del p del fs-del C T ASXL1 c.c1867t p.q623x Nonsense ASXL1 c del p del fs-del G ASXL1 c insg p.r625fs fs-ins ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del ASXL1 c del p del fs-del A ASXL1 c insa p.h633fs fs-ins A T ASXL1 c.a1900t p.r634x Nonsense G T ASXL1 c.g1903t p.e635x Nonsense ASXL1 c del p del fs-del ASXL1 c del p del fs-del A - ASXL1 c.1926dela p.g642fs fs-del G ASXL1 c insg p.g642fs fs-ins G ASXL1 c insg p.g642fs fs-ins G ASXL1 c insg p.g642fs fs-ins G ASXL1 c insg p.g642fs fs-ins G ASXL1 c insg p.g642fs fs-ins G ASXL1 c insg p.g642fs fs-ins G ASXL1 c insg p.g642fs fs-ins G ASXL1 c insg p.g642fs fs-ins G ASXL1 c insg p.g642fs fs-ins G ASXL1 c insg p.g642fs fs-ins G ASXL1 c insg p.g642fs fs-ins G ASXL1 c insg p.g642fs fs-ins G ASXL1 c insg p.g642fs fs-ins G ASXL1 c insg p.g642fs fs-ins