SUPPLEMENTAL APPENDIX METZELER ET AL.: SPECTRUM AND PROGNOSTIC RELEVANCE OF DRIVER GENE MUTATIONS IN ACUTE MYELOID LEUKEMIA

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1 SUPPLEMENTAL APPENDIX TO METZELER ET AL.: SPECTRUM AND PROGNOSTIC RELEVANCE OF DRIVER GENE MUTATIONS IN ACUTE MYELOID LEUKEMIA

2 SUPPLEMENTAL METHODS Treatment protocols In the AMLCG-1999 trial 1-3 (clinicaltrials.gov identifier NCT ; recruitment period, ; patients included in this study were recruited between 1999 and 2004), patients <60 years were randomized to receive double induction with either one cycle of TAD-9 followed by one cycle of HAM on day 21, or two cycles of HAM 21 days apart (TAD-9: thioguanine 100 mg/m2 twice daily on days 3-9, cytarabine 100 mg/m²/d continuous infusion on days 1+2 and 100 mg/m² twice daily on days 3-8, and daunorubicin 60 mg/m² on days 3-5; HAM: cytarabine 3 g/m² twice daily on days 1-3 and mitoxantrone 10 mg/m² on days 3-5). Patients <60 years underwent upfront randomization to undergo consolidation either with one cycle of TAD-9 followed by three years of monthly cytarabine-based maintenance chemotherapy, or a single TAD-9 consolidation course followed by autologous stem cell transplantation (and no maintenance). Younger patients with matched sibling donors were allowed to undergo allogeneic SCT in first complete CR. Patients 60 years were randomized to receive induction therapy with one cycle of either TAD-9 or HAM, followed by a second HAM cycle on day 21 only if 5% residual blasts were present in the BM on day 16. All patients 60 years were to receive one cycle of TAD-9 consolidation followed by maintenance therapy. In the AMCG-2008 trial 4 (NCT ; recruitment period, ), patients <60 years and fit patients up to the age of 70 were randomized to receive either double induction chemotherapy with TAD-9 and HAM (21 days apart), or dose-dense induction therapy according to the sham regimen (cytarabine 3 g/m² [1 g/m² in patients 60 years] twice daily on days 1,2,8 and 9; and mitoxantrone 10 mg/m² on days 3,4,10 and 11). Allogeneic transplantation in first CR from a matched related or unrelated donor was recommended for all patients except those with favorable genetic features (favorable cytogenetics or cytogenetically normal patients with mutated NPM1 and no FLT3-ITD) and good response to induction chemotherapy. For patients without a 2

3 donor, patients unable or unwilling to undergo allotransplantation, and those with a favorable risk profile, postremission therapy consisted of one cycle of TAD-9 for consolidation, followed by 3 years of cytarabine-based maintenance therapy. Less fit patients aged 60 years, and all patients aged 70 years, were randomized to receive induction therapy according to the HAM regimen (cytarabine, 1g/m² per dose) followed by a second HAM induction cycle on day 21 only if a bone marrow aspirate on day 16 showed 5% blasts, or to dose-dense induction with sham (cytarabine, 1g/m² per dose). Postremission therapy in this group consisted of one cycle of TAD-9 for consolidation, followed by 3 years of maintenance therapy. Sequencing and data analysis Sequencing, variant calling and variant classification Sequence enrichment was performed using a targeted, multiplexed amplicon-based approach (Haloplex, Agilent, Boeblingen, Germany) starting from ng of genomic DNA, and the resulting libraries were sequenced on an Illumina MiSeq instrument (Illumina, San Diego, USA) using 2x250 bp paired-end reads. Data analysis was performed using a custom, in-house data analysis pipeline based on Linux shell scripts. Reads were quality trimmed and sequencing adapters were removed using Trimmomatic. 5 Alignment to the human reference genome (version hg19) was performed using the BWA-MEM algorithm. Single nucleotide variants (SNVs) and short insertions or deletions (InDels) were called using VarScan 2 and Pindel, respectively. 6,7 A minimum coverage of 30x, variant allele frequency (VAF) of >2%, and a VARSCAN-assigned variant p value of <.001 were used as thresholds for SNV calling. Sequence variants were functionally annotated using ANNOVAR. 8 To differentiate known and putative leukemia driver mutations from known germline polymorphisms and variants of unclear significance, we used a multi-step algorithm as outlined in Supplemental Figure 1. Only nonsynonymous variants in coding regions and mutations affecting 3

4 splice sites were considered in our analyses. All variants with a population frequency of 0.1% in the National Heart, Lung and Blood Institute (NHLBI) 6500 exomes study or the 1000 genomes project were discarded. 9 Nonsynonymous variants were then classified based on their deduced consequences on the amino acid level. Nonsense and frameshift mutations were considered pathogenic. Missense variants, in-frame insertions/deletions and splice site variants were included if they were previously reported in the Catalogue Of Somatic Mutations In Cancer (COSMIC, version 70) but not listed in the National Center for Biotechnology Information Short Genetic Variations database (dbsnp, version 138). 10,11 Missense variants, in-frame insertions/deletions and splice site variants not fulfilling these criteria were individually assessed based on the available data from COSMIC, dbsnp, the NHLBI 6500 exomes and 1000 genomes cohorts, their location relative to known mutations in the gene, and their predicted functional consequences using the Mutation Taster and Mutation Analyzer algorithms. 8,12 Missense variants of uncertain significance were not counted as driver mutations in downstream analyses. The final list of known and putative driver mutations is presented in Supplemental Table 4A. Copy number analyses The QUANDICO algorithm was used to identify copy number variations (CNVs) based on amplicon sequencing data. 13 A Q score of 50 and an estimated copy number of 1.5 or 2.5 for autosomal genes were used as criteria for CNV detection. Supplemental Table 4B lists regions where copy number alterations were called. Comparison of variant allele frequencies (VAFs) of mutations in different genes We studied the VAFs of mutations in different genes, to differentiate genes that are commonly mutated in a majority of leukemic cells (i.e., mutations present in an early founder clone ) from changes that often affect only a fraction of the sequenced cells, and thus might have been gained relatively late during the evolution of the disease. 4

5 When we compared the VAFs of SNVs and InDels of increasing length, we observed that InDels of 15 base pairs showed a bias towards lower VAFs (Supplemental Figure 8A). Therefore, insertions and deletions of 15 base pairs (n=298; 30% of the total number of 998 indels) were excluded from the downstream between-gene comparisons of VAFs. FLT3-internal tandem duplications (ITDs) accounted for the large majority of the excluded variants (n=242). Once longer InDels were excluded, the VAFs of SNVs and InDels were similar in genes affected by both types of mutations (Supplemental Figure 8B). Since our aim was to use VAFs as a measure for the fraction of cells carrying each mutation, we next employed copy number information from QUANDICO to adjust the VAFs of mutations located in regions affected by CNVs. Analysis of mutually exclusive genetic alterations The mutually exclusive gene set analysis (MEGSA) algorithm was used to identify mutually exclusive sets of genetic alterations. 14 The analysis included all genes mutated in 25 patients, as well as those cytogenetic alterations that are recognized as distinct entities by the WHO classification (i.e., the core-binding factor [CBF] rearrangements [t(8;21)(q22;q22), inv(16)(p13.1q22) and t(16;16)(p13.1;q22)]; the translocations t(6;9)(p23;q34) and inv(3)(q21q26.2) / t(3;3)(q21;q26.2); and balanced rearrangements involving KMT2A). MEGSA was restricted to signatures comprising a maximum of 8 genetic alterations due to computational limitations. Patients with CBF rearrangements were combined into a single group to reduce complexity. Corrected P values are based on 1000 permutations of the data. Definition of clinical end points Clinical endpoints were defined, in accordance with generally accepted criteria, 15 as follows: Complete remission (CR) required a bone marrow (BM) aspirate with cellularity greater than 20% and maturation of all cell lines, less than 5% blasts and no Auer rods; and in the peripheral blood, an absolute neutrophil count of 1,500/µL, platelet count of 100,000/µL, and no leukemic blasts; 5

6 and no evidence of extramedullary leukemia, all of which had to persist for at least 1 month. Relapse was defined by the presence of 5% BM blasts, or circulating leukemic blasts, or the development of extramedullary leukemia. Relapse-free survival (RFS) was measured from the date of CR until the date of relapse or death; patients alive and in CR were censored at last followup. Overall survival (OS) was measured from the date of study entry until the date of death, and patients alive at last follow-up were censored. Univariate and multivariate survival analyses Logistic regression models including trial arm as a covariable were used to study associations between individual variables and CR rate. Cox models stratified for trial arm were used for timeto-event variables, and P values were calculated by the Wald test. Multivariate logistic regression models were constructed for factors associated with achievement of CR, and multivariate Cox proportional hazards models were used to study factors associated with survival endpoints. Gene mutations were included in the multivariate models if they were detected in at least 20 patients and had a univariate P value, not adjusted for multiple comparisons, of.10 (Supplemental Tables 6-8). The following variables representing known risk factors and potential confounders were additionally included in all multivariate models: age, gender, de novo vs secondary AML vs treatment-related AML, Eastern Cooperative Oncology Group (ECOG) performance status, white blood cell (WBC) count, cytogenetic risk categories as defined by the Medical Research Council (MRC) classification, 16 NPM1 mutations and FLT3 internal tandem duplications (FLT3-ITD) or the combined NPM1-mutated, FLT3-ITD-negative genotype, and CEBPA mutations. All multivariate models were also adjusted for trial arm. No variable selection or elimination technique was applied, and all variables were retained in the final models. Since the frequencies of some mutations differed between younger (<60 years) and older ( 60 years) patients, and patients over the age of 60 also received lower-intensity treatment, we tested for potential interactions between each variable and age group. Only significant interactions are reported. 6

7 SUPPLEMENTAL RESULTS Coverage of recurrent driver gene mutations in comparison to published wholegenome / whole-exome sequencing data Our assay covered the entire coding sequence of 37 recurrently mutated genes, while for another 31 genes with known hotspots, only the commonly mutated regions were targeted. To assess how frequently we might have missed mutations in these 31 genes occurring at positions not covered by our assay, we compared our target regions to the sites of mutations identified in the The Cancer Genome Atlas (TCGA) whole-genome / whole-exome sequencing study. In TCGA (n=200 patients), 443 tier 1 somatic mutations were reported in the 68 genes included in our study. Only five of these 443 mutations (1%) were in regions that are not covered by our assay, and would thus have been missed in our analysis. This demonstrates our assay adequately covers those areas where mutations were found in TCGA. The mean number of non-synonymous mutations in our study was 3.70, compared to a mean of 5.24 recurrent tier 1 gene mutations (including silent changes) per whole exome in the TCGA cohort. 17 These findings, together with power analyses of the available genome-wide sequencing studies, suggest that our panel covered the majority driver genes that are recurrently mutated in AML, and particularly those mutated in >5% of patients. 18 Identification of mutually exclusive genetic alteration The MEGSA algorithm identified 540 different mutually exclusive gene signatures (MEGS) encompassing 3 to 8 different alterations (Supplemental Table 12). The nominally most significant MEGS (P=1.52x10-81 ) consisted of CBF rearrangements (considered as one group in this analysis), the translocations t(6;9)(p23;q34) and inv(3)(q21q26.2) / t(3;3)(q21;q26.2), KMT2A rearrangements., and NPM1, RUNX1, TP53 and biallelic CEBPA mutations. This MEGS was also the signature with the largest coverage (0.784; i.e., 78% of all patients had at least one of the alterations in the signature). 7

8 Associations between gene mutations and CR rate and RFS Associations between individual gene mutations and achievement of CR are reported in Supplemental Table 7. Overall, 364 of the 664 patients (55%) reached CR, including 60% of patients < 60 years and 48% of those aged 60 years. Patients with mutations in SRFS2, RUNX1, STAG2, ASXL1, TP53, U2AF1 and SF3B1 had significantly lower odds of achieving a CR, while patients with NPM1, FLT3-tyrosine kinase domain, DNMT3A and NRAS mutations had higher response rates in univariate analyses. To adjust for the complex associations between gene mutations and other known risk factors, we performed multivariate analyses and tested for interactions between risk factors and age group (Supplemental Table 9). In the resulting model, U2AF1 and SF3B1 mutations remained associated with a lower CR rate. Mutations in DNMT3A and RUNX1 showed significant interactions with age group, and both mutations were linked to lower CR rates only in patients younger than 60 years. NPM1, PTPN11, FLT3- tyrosine kinase domain and IDH2 mutations associated with higher odds of CR. Other factors associated with lower CR rates in the multivariate model were age 60 years, taml, an ECOG performance status of 2, higher leukocyte counts, and adverse cytogenetics according to the MRC classification. Univariate and multivariate analyses of factors associated with RFS are shown in Supplemental Tables 8 and 10, respectively. In univariate analyses, FLT3-ITD, DNMT3A, RUNX1 and TP53 mutations associated with shorter RFS, while bicebpa and, in trend, STAG2 mutations showed associations with longer RFS. In multivariate analyses, FLT3-ITD, DNMT3A, RUNX1 and TP53 mutations retained their significant association with shorter RFS. For this end point, no significant interactions between gene mutations and patient age were identified. Age 60 years, higher leukocyte counts, and adverse cytogenetics according to the MRC classification also predicted shorter RFS, while patients with favorable cytogenetics had longer RFS. 8

9 Overall, 36% of patients in our cohort underwent an allogeneic stem cell transplant, including 11% who received a transplant in first CR after their initial induction chemotherapy. A multivariate model for RFS where patients were censored at the time of allogeneic transplantation yielded results that were generally similar to the model without censoring (Supplemental Table 11). However, the unfavorable prognosis associated with mutated TP53 was particularly pronounced in younger patients (<60 years) if those receiving an allogeneic transplant were censored. Association between number of mutated genes and survival We found that patients with more than 5 mutated driver genes (n=56) had significantly shorter RFS and OS, compared to patients with fewer mutated genes (P=.003, respectively, Supplemental Figure 9). However, when the number of mutated genes was included as an additional variable in the multivariable models for RFS and OS, these associations were no longer significant (data not shown). 9

10 SUPPLEMENTAL REFERENCES 1. Büchner T, Berdel WE, Haferlach C, et al. Age-related risk profile and chemotherapy dose response in acute myeloid leukemia: a study by the German Acute Myeloid Leukemia Cooperative Group. J Clin Oncol. 2009;27(1): Büchner T, Berdel WE, Schoch C, et al. Double induction containing either two courses or one course of high-dose cytarabine plus mitoxantrone and postremission therapy by either autologous stem-cell transplantation or by prolonged maintenance for acute myeloid leukemia. J Clin Oncol. 2006;24(16): Krug U, Berdel WE, Gale RP, et al. Increasing intensity of therapies assigned at diagnosis does not improve survival of adults with acute myeloid leukemia. Leukemia doi: /leu Braess J, Kreuzer K-A, Spiekermann K, et al. High Efficacy and Significantly Shortened Neutropenia Of Dose-Dense S-HAM As Compared To Standard Double Induction: First Results Of a Prospective Randomized Trial (AML-CG 2008). Blood. 2013;122(21):619 (abstract). 5. Bolger AM, Lohse M, Usadel B. Trimmomatic: A flexible trimmer for Illumina Sequence Data. Bioinformatics. 2014;30(15):btu Koboldt DC, Zhang Q, Larson DE, et al. VarScan 2: Somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Research. 2012;22(3): Ye K, Schulz MH, Long Q, Apweiler R, Ning Z. Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics. 2009;25(21): Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38(16):e164 e Genomes Project Consortium, Abecasis GR, Auton A, et al. An integrated map of genetic variation from 1,092 human genomes. Nature. 2012;491(7422): NCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2015;43(Database issue):d Forbes SA, Beare D, Gunasekaran P, et al. COSMIC: exploring the world's knowledge of somatic mutations in human cancer. Nucleic Acids Res. 2015;43(Database issue):d Schwarz JM, Rödelsperger C, Schuelke M, Seelow D. MutationTaster evaluates diseasecausing potential of sequence alterations. Nat Methods. 2010;7(8): Reinecke F, Satya RV, DiCarlo J. Quantitative analysis of differences in copy numbers using read depth obtained from PCR-enriched samples and controls. BMC Bioinformatics. 2015;16: Hua X, Hyland PL, Huang J, et al. MEGSA: A Powerful and Flexible Framework for Analyzing Mutual Exclusivity of Tumor Mutations. Am J Hum Genet. 2016;98(3): Cheson BD, Bennett JM, Kopecky KJ, et al. Revised recommendations of the International Working Group for Diagnosis, Standardization of Response Criteria, Treatment Outcomes, and Reporting Standards for Therapeutic Trials in Acute Myeloid Leukemia. J Clin Oncol. 2003;21(24): Grimwade D, Hills RK, Moorman AV, et al. Refinement of cytogenetic classification in acute myeloid leukemia: determination of prognostic significance of rare recurring chromosomal abnormalities among 5876 younger adult patients treated in the United Kingdom Medical Research Council trials. Blood. 2010;116(3): Cancer Genome Atlas Research Network. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med. 2013;368(22):

11 18. Lawrence MS, Stojanov P, Mermel CH, et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature. 2014;505(7484): Döhner H, Estey EH, Amadori S, et al. Diagnosis and management of acute myeloid leukemia in adults: recommendations from an international expert panel, on behalf of the European LeukemiaNet. Blood. 2010;115(3):

12 SUPPLEMENTAL TABLES AND FIGURES Supplemental Table 1: List of genes analyzed by targeted resequencing ADA entire CDS GATA1 exons 2,3 PTEN entire CDS ABCB1 entire CDS GATA2 exons 4-6 PTPN11 exons 3,13 ABCG2 entire CDS GATA3 exons 3-5 PTPRT entire CDS ASXL1 exon 12 HNRNPK entire CDS RAD21 entire CDS BCOR entire CDS HRAS exons 2,3 RUNX1 entire CDS BCORL1 entire CDS IDH1 exon 4 SETBP1 exon 4 BRAF exons 11,12,15 IDH2 exon 4 SF1 entire CDS CBL exons 8,9 IL7R exon 6 SF3A1 entire CDS CDA entire CDS JAK1 exons SF3B1 exons CDKN2A entire CDS JAK2 exons SMC1A entire CDS CEBPA entire CDS JAK3 entire CDS SMC3 entire CDS CSF3R exons KDM6A entire CDS SRSF2 exon 1 CSFR1 exons 7, 22 KMT2A exons 1,3,4,33 STAG2 entire CDS DAXX entire CDS KIT exons 8,9,11,17 TERC entire CDS DCK entire CDS KRAS exons 2,3 TERT exons 1,15 DCLK1 entire CDS MIR-142 entire CDS TET2 entire CDS DIS3 entire CDS MPL exon 10 TP53 entire CDS DNMT3A exons 7-23 MYD88 exons 3-5 U2AF1 exons 2,6 ETV6 entire CDS NOTCH1 exons 26-28,34 U2AF2 entire CDS EZH2 entire CDS NPM1 exons 10,11 WAC entire CDS FAM5C entire CDS NRAS exons 2,3 WT1 entire CDS FBXW7 exons 8-12 NT5C2 entire CDS ZRSR2 entire CDS FLT3 exons 13-16, 20 PHF6 entire CDS 12

13 Supplemental Table 2: Detailed list of genomic target regions See separate Excel file 13

14 Supplemental Table 3: Pretreatment characteristics according to clinical trial Variable Age, years Median Range Age group, no. (%) <60 years 60 years Total cohort N= AMLCG-1999 trial N= AMLCG-2008 trial N= (57) 218 (56) 158 (58) (43) 172 (44) 116 (42) Female gender, no. (%) 330 (50) 196 (50) 134 (49).75 de novo AML secondary AML therapy-related AML 570 (86) 59 (9) 35 (5) 343 (88) 29 (7) 18 (5) 227 (83) 30 (11) 17 (6).17 ECOG performance status, no. (%) < (21) 174 (46) 98 (26) 22 (6) 4 (1) 91 (34) 139 (51) 27 (10) 11 (4) 2 (1) WBC count, x10 9 /L <.001 Median Range , Hemoglobin, g/dl.68 Median Range Platelet count, x10 9 /L.01 Median Range Bone marrow blasts, % Median <.001 Range FAB category, no..12 M0 M1 M2 M4 M5 M6 M MRC cytogenetic risk category <.001 Favorable Intermediate Adverse 65 (10) 452 (70) 129 (20) 50 (13) 250 (65) 86 (22) 15 (6) 202 (78) 43 (17) Modified ELN classification #.12 Favorable Intermediate-I Intermediate-II Adverse 189 (29) 205 (32) 119 (18) 133 (21) 111 (29) 111 (29) 76 (20) 88 (23) 78 (30) 94 (36) 43 (17) 45 (17) NPM1, no. (%).04 Mutated Wild-type 221 (33) 442 (67) 117 (30) 273 (70) 104 (38) 170 (62) FLT3-ITD, no. (%).34 Present Absent 197 (30) 467 (70) 110 (28) 280 (72) 87 (32) 187 (68) CEBPA, no. (%) Double mutation Single mutation Wild-type 27 (4) 25 (4) 612 (92) 17 (4) 14 (4) 359 (92) 10 (4) 11 (4) 253 (93).86 # Modified ELN classification designates the ELN reporting system for genetic alterations as recommended by Döhner et al., 19 however only patients with double CEBPA mutations (and not those with single mutations) were classified as favorable. Abbreviations: ECOG, Eastern Cooperative Oncology Group; FAB, French-American-British classification; MRC, Medical Research Council; ELN, European LeukemiaNet; ITD, internal tandem duplication P.94 14

15 Supplemental Table 4A: List of 2395 sequence variants categorized as leukemia driver mutations See separate Excel file Supplemental Table 4B: List of 993 regions with copy number variations as detected by the QUANDICO algorithm See separate Excel file Supplemental Table 5A: Associations between gene mutations and clinical patient characteristics See separate Excel file Supplemental Table 5B: Associations between gene mutations and cytogenetic subgroups See separate Excel file Supplemental Table 5C: Pair-wise associations between gene mutations See separate Excel file 15

16 Supplemental Table 6: Gene mutations and overall survival Abbreviations: 95% CI, 95% confidence interval; TKD, tyrosine kinase domain; PTD, partial tandem duplication, ITD, internal tandem duplication Footnotes: Hazard rations and P values are from a univariate Cox proportional hazards model stratified for trial arm. bicebpa indicates double, and mocebpa singe CEBPA mutations. 16

17 Supplemental Table 7: Gene mutations and complete remission rate Abbreviations: 95% CI, 95% confidence interval; TKD, tyrosine kinase domain; ITD, internal tandem duplication; PTD, partial tandem duplication Footnotes: Odds ratios and P values are from a logistic regression model adjusting for trial arm. bicebpa indicates double, and mocebpa singe CEBPA mutations. 17

18 Supplemental Table 8: Gene mutations and relapse-free survival Abbreviations: 95% CI, 95% confidence interval; TKD, tyrosine kinase domain; PTD, partial tandem duplication; ITD, internal tandem duplication Footnotes: Hazard rations and P values are from a univariate Cox proportional hazards model stratified for trial arm. bicebpa indicates double, and mocebpa singe CEBPA mutations. 18

19 Supplemental Table 9: Multivariate analysis for achievement of complete remission Footnotes: 629 patients were included in the multivariate model, while 35 patients (5%) were excluded due to missing data. Gene mutations were included in the multivariate model if they were detected in at least 20 patients and had a univariate P value for CR, not adjusted for multiple comparisons, of.10. The model was adjusted for trial arm. No variable selection or elimination technique was applied, and all variables were retained in the final model. Tests for interaction with age group (<60 years vs. 60 years) were performed for each variable. * The P value for the interaction between DNMT3A mutation status and age group was.014. # The P value for the interaction between RUNX1 mutation status and age group was.017. Abbreviations: 95% CI, 95% confidence interval; ECOG, Eastern Cooperative Oncology Group; MRC, Medical Research Council; ITD, internal tandem duplication; TKD, tyrosine kinase domain 19

20 Supplemental Table 10: Multivariate analysis for relapse-free survival Footnotes: 355 patients who achieved CR were included in the multivariate model, while 9 patients (2%) were excluded due to missing data. Gene mutations were included in the multivariate model if they were detected in at least 20 patients and had a univariate P value for RFS, not adjusted for multiple comparisons, of.10. The model was stratified for trial arm. No variable selection or elimination technique was applied, and all variables were retained in the final model. Tests for interaction with age group (<60 years vs. 60 years) were performed for each variable, but no significant interactions were detected. Abbreviations: 95% CI, 95% confidence interval; ECOG, Eastern Cooperative Oncology Group; MRC, Medical Research Council; ITD, internal tandem duplication 20

21 Supplemental Table 11: Multivariate analysis for relapse-free survival, with censoring at the time of allogeneic transplantation Footnotes: 355 patients who achieved CR were included in the multivariate model, while 9 patients (2%) were excluded due to missing data. Gene mutations were included in the multivariate models if they were detected in at least 20 patients and had a univariate P value for RFS, not adjusted for multiple comparisons, of.10. The model was stratified for trial arm. No variable selection or elimination technique was applied, and all variables were retained in the final model. Tests for interaction with age group (<60 years vs. 60 years) were performed for each variable. * The P value for the interaction between TP53 mutation status and age group was.016. Abbreviations: 95% CI, 95% confidence interval; ECOG, Eastern Cooperative Oncology Group; MRC, Medical Research Council; ITD, internal tandem duplication 21

22 Supplemental Table 12: Results of mutually exclusive gene set analysis (MEGSA) See separate Excel file 22

23 Supplemental Figure 1 Supplemental Figure 1: Schematic representation of the algorithm used for classification of sequence variants. NHLBI ESP6500 denotes the National Heart, Lung and Blood Institute 6500 exomes sequencing project; COSMIC, the catalogue of Somatic Mutations in Cancer; and dbsnp, the National Center for Biotechnology Information Short Genetic Variations database. 10,11 Data from the 1000 genomes project were also used to identify germline polymorphisms. 9

24 Supplemental Figure 2 Supplemental Figure 2: Histograms comparing the frequencies of driver gene mutations in patient aged <60 years (n=376) and in patients aged 60 years (n=288). Mutations are ordered according to their overall frequency in all 664 patients. Colors represent the functional category assigned to each driver gene. Symbols: * P<.05; ** P<.01; *** P<.001 by Fisher s exact test, adjusted for multiple testing using the Benjamini-Hochberg procedure.

25 Supplemental Figure 3 Supplemental Figure 3: Histogram showing the number of gene mutations per patient in the entire cohort of 664 AML patients. The median number of mutations per sample was 4 (red line; range, 0 10).

26 Supplemental Figure 4 Supplemental Figure 4: Histogram showing the variant allele frequencies of (A) variants known to represent germline polymorphisms and (B) variants classified as known or putative AML driver mutations. Variant allele frequencies were adjusted for local copy number, and insertions / deletions of 15 bp were excluded. The vertical, red dashed line marks a variant allele frequency (VAF) of 50%, the expected VAF of a heterozygous variant present in all cells in the specimen (e.g., a heterozygous germline polymorphism).

27 Supplemental Figure 5 Supplemental Figure 5: Individual gene mutations and overall survival in 664 adult AML patients. (A) Kaplan-Meier plots for OS for patients with mutated NPM1 (n=221), FLT3-ITD (n=197), the combined NPM1 mutated / FLT3-ITD negative genotype (n=109), or mutated CEBPA (25 patients with single and 27 with double mutations), compared to patients without these alterations. The red curve represents patients with, and the black curve patients without the indicated mutation or genotype. For CEBPA, the red curve represents patients with double mutations, the blue curve patients with single mutations, and the black curve patients with wild-type CEBPA. (B) Kaplan-Meier plots for additional gene mutations that were found in at least 20 patients and that showed a significant (P<.05) association with OS (DNMT3A, n=209; RUNX1, n=102; ASXL1, n=71; SRSF2, n=65; TP53, n=63; BCOR, n=45; U2AF1, n=27; and SF3B1, n=22). P values were calculated from a univariate Cox proportional hazards model stratified for trial arm, using the Wald test.

28 Supplemental Figure 6 Supplemental Figure 6: (A) OS for MRC intermediate-risk patients aged <60 years or (B) aged 60 years, stratified according to the combination of NPM1 mutation and FLT3-ITD status. P values were calculated from Cox proportional hazards models using the Wald test. Abbreviations: mut, mutated; wt, wild-type; neg, negative.

29 Supplemental Figure 7 Supplemental Figure 7: Overall survival of (A) patients aged <60 years and (B) patients aged 60 years with MRC intermediate-risk cytogenetics according to the combined NPM1, FLT3-ITD, DNMT3A, and RUNX1 mutation status.

30 Supplemental Figure 8 Supplemental Figure 8: (A) Variant allele frequencies (VAFs) for single nucleotide variants (SNVs) and for insertions / deletions (InDels) of increasing length. (B) Per-gene comparison of VAFs for SNVs and InDels <15 base pairs, for genes in which both types of mutations were observed 10 times.

31 Supplemental Figure 9 Supplemental Figure 9: (A) RFS and (B) OS according to the number of mutated genes per patient.