Key Points

  • Mutations in 7 genes independently predict OS in distinct cytogenetic groups of patients with AML aged ≥60 years and treated intensively.

  • We report and validate a simple genetic model to identify older patients with AML with very good, intermediate, or poor outcome with 7 + 3.

Abstract

To design a simple and reproducible classifier predicting the overall survival (OS) of patients with acute myeloid leukemia (AML) ≥60 years of age treated with 7 + 3, we sequenced 37 genes in 471 patients from the ALFA1200 (Acute Leukemia French Association) study (median age, 68 years). Mutation patterns and OS differed between the 84 patients with poor-risk cytogenetics and the 387 patients with good (n = 13), intermediate (n = 339), or unmeasured (n = 35) cytogenetic risk. TP53 (hazards ratio [HR], 2.49; P = .0003) and KRAS (HR, 3.60; P = .001) mutations independently worsened the OS of patients with poor-risk cytogenetics. In those without poor-risk cytogenetics, NPM1 (HR, 0.57; P = .0004), FLT3 internal tandem duplications with low (HR, 1.85; P = .0005) or high (HR, 3.51; P < 10−4) allelic ratio, DNMT3A (HR, 1.86; P < 10−4), NRAS (HR, 1.54; P = .019), and ASXL1 (HR, 1.89; P = .0003) mutations independently predicted OS. Combining cytogenetic risk and mutations in these 7 genes, 39.1% of patients could be assigned to a “go-go” tier with a 2-year OS of 66.1%, 7.6% to the “no-go” group (2-year OS 2.8%), and 3.3% of to the “slow-go” group (2-year OS of 39.1%; P < 10−5). Across 3 independent validation cohorts, 31.2% to 37.7% and 11.2% to 13.5% of patients were assigned to the go-go and the no-go tiers, respectively, with significant differences in OS between tiers in all 3 trial cohorts (HDF [Hauts-de-France], n = 141, P = .003; and SAL [Study Alliance Leukemia], n = 46; AMLSG [AML Study Group], n = 223, both P < 10−5). The ALFA decision tool is a simple, robust, and discriminant prognostic model for AML patients ≥60 years of age treated with intensive chemotherapy. This model can instruct the design of trials comparing the 7 + 3 standard of care with less intensive regimens.

Medscape Continuing Medical Education online

graphic

In support of improving patient care, this activity has been planned and implemented by Medscape, LLC and the American Society of Hematology. Medscape, LLC is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team.

Medscape, LLC designates this Journal-based CME activity for a maximum of 1.00 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Successful completion of this CME activity, which includes participation in the evaluation component, enables the participant to earn up to 1.0 MOC points in the American Board of Internal Medicine’s (ABIM) Maintenance of Certification (MOC) program. Participants will earn MOC points equivalent to the amount of CME credits claimed for the activity. It is the CME activity provider’s responsibility to submit participant completion information to ACCME for the purpose of granting ABIM MOC credit.

All other clinicians completing this activity will be issued a certificate of participation. To participate in this journal CME activity: (1) review the learning objectives and author disclosures; (2) study the education content; (3) take the post-test with a 75% minimum passing score and complete the evaluation at http://www.medscape.org/journal/blood; and (4) view/print certificate. For CME questions, see page 585.

Disclosures

Associate Editor Andrew Roberts, CME questions author Laurie Barclay, freelance writer and reviewer, Medscape, LLC, and the authors declare no competing financial interests.

Learning objectives

Upon completion of this activity, participants will be able to:

  • 1.

    Assess the oncogenetic predictors of short-term (remission) and long-term (overall survival; OS) benefit of intensive chemotherapy in patients aged at least 60 years who have acute myeloid leukemia, based on the ALFA1200 cytogenetic and gene sequencing study

  • 2.

    Evaluate the development and validation of a simple decision model accounting for cytogenetics and mutations that reproducibly identified patients aged at least 60 years who have acute myeloid leukemia and who had significant OS differences across multiple cohorts, based on the ALFA1200 cytogenetic and gene sequencing study

  • 3.

    Determine the clinical implications of a classification predicting OS of patients aged at least 60 years with acute myeloid leukemia treated with standard intensive 7+3 chemotherapy, based on the ALFA1200 cytogenetic and gene sequencing study

Release date: August 19, 2021; Expiration date: August 19, 2022

Introduction

Acute myeloid leukemia (AML) is mostly diagnosed in patients ≥60 years of age.1  Recent improvements in survival have been confined to younger adults with AML.2  Intensive chemotherapy, with or without allogeneic stem cell transplantation (HSCT), remains the standard of care of AML for all adults, including older, fit patients.3,4 

Recurrent cytogenetic and genetic lesions are key prognostic factors in patients with AML treated intensively, but the prognostic value of oncogenetic lesions has mostly been studied in younger adults.5-8  Yet, major interactions occur between age, oncogenetics, and treatment outcome.9-12  The genomic landscape of AML in older patients also differs from that in younger adults.9,13-16  After earlier studies focusing on the prognostic value of NPM1 orFLT3 mutations in older patients with AML,17-21  several studies, including those conducted by our group, have interrogated the prognostic value of a broader spectrum of recurrent genetic lesions in this population.22-24  However, none of these studies reproducibly identified subsets with patients with outcomes contrasting enough to guide upfront decisions between intensive chemotherapies and alternative investigational approaches.

In recent years, 7 + 3-based induction chemotherapy has been increasingly challenged by less intensive options, notably the combination of hypomethylating agents and venetoclax.16  To design future randomized studies of intensive and less intensive therapies in fit older patients with AML, specific decision tools must be developed to identity the minority of patients in whom 7 + 3 is unequivocally beneficial (“go-go”) or futile (“no-go”) among most older, fit patients with AML (“slow-go” group).

In this study, we leveraged the results of a 37-gene panel in 471 patients with AML aged ≥60 years and treated with intensive chemotherapy in the prospective, multicenter, ALFA1200 study (registered at www.clincialtrials.gov as #NCT01966497)24  to design a very simple 3-tier decision tool, which we validated in 3 independent cohorts.

Patients and methods

Patients

Patients aged ≥60 years with newly diagnosed AML (excluding acute promyelocytic leukemia and Philadelphia chromosome–positive AML), AML secondary to myelodysplastic syndromes (but not myeloproliferative neoplasms) or therapy-related AML (with ≥2 years remission) and an ECOG performance status ≤3, eligible for intensive chemotherapy, from 30 ALFA (Acute Leukemia French Association) centers were prospectively enrolled from September 2012 through June 2016 after providing informed consent. The study was conducted according to current ethics regulations, after approval of the study design by the French Ministry of Health Ethic Committee (“Comité Consultatif sur le Traitement de l'Information en Matière de Recherche dans le Domaine de la Santé”) and of data management by the National Commission for Information Technology and Liberties (“Commission Nationale de l'Informatique et des Libertés”).

We report the extended analysis of a 37-gene panel of the 471 ALFA1200 patients whose samples were sent at their enrollment to the ALFA Central Laboratory (Lille University Hospital, C.P.) for gene sequencing. Detailed information on the global cohort and CONSORT diagram and the full list of investigators have been published.24 

Treatment

Patients received a 7 + 3-based induction course including idarubicin (12 mg/m2 per day, days 1-3) and cytarabine as continuous infusions (200 mg/m2 per day, days 1-7). Patients who did not achieve complete remission (CR) or CR with incomplete platelet recovery (CRp)25  could receive a salvage course consisting of intermediate-dose cytarabine boluses (1.5 mg/m2 every 12 hours on days 1, 3, and 5, with dose reduction to 1 mg/m2 per 12 hours in patients aged ≥70, and further dose adaptation based on serum creatinine levels). Patients in CR/CRp received 2 such cytarabine bolus courses as consolidation. Details on HSCT eligibility have been published.24 

AML genetics

Cytogenetics including standard metaphase karyotyping and fluorescent in situ hybridization was performed locally and centrally reviewed by the ALFA cytogenetics reference laboratory (Versailles University Hospital; author C.T.) for the present study. Cytogenetic risk was stratified per current European LeukemiaNet (ELN) guidelines (supplemental Table 1, available on the Blood Web site). Molecular genetics was performed by targeted sequencing of a 37-gene panel (supplemental Table 2) on bone marrow or peripheral blood samples collected at inclusion. Technical details are provided in the supplemental Methods.

Genotyping of FLT3 internal tandem duplications (FLT3-ITDs) was performed by fragment analysis, as previously published,26  and expressed as the ratio of ITD to wild-type. Because of technical limitations of our sequencing technology, the ASXL1 (NM_015338) c.1934dup mutational hotspot was investigated for all patients by fragment technique and confirmed by Sanger sequencing.27 CEBPA gene mutations were also sought with Sanger sequencing.28 

Knowledge bank predictions

Of the 100 variables required for the estimation of prognosis based on the multistage model proposed by Gerstung et al,29  28 were not available in our cohort, including 4 clinical (hemoglobin peripheral blasts and lactate dehydrogenase baseline values, and presence of splenomegaly) and 24 genetic variables (ATRX, BRAF, CBLB, CDKN2A, CREBBP, CUX1, EP300, FBXW7, GNAS, IKZF1, KDM5A, KDM6A, MLL2, MLL3, MLL5, MYC, NF1, PRPF40B, PTEN, RB1, SF1, SF3A1, SH2B3, and U2AF2). After imputation of missing data using the 1540-patient AMLSG (AML Study Group) knowledge bank (KB), 5-year prediction of overall survival was performed as previously described.30 

Validation cohorts

Information on the 3 validation cohorts is provided in the supplemental Methods, and their baseline characteristics are reported in supplemental Table 3.

Statistical analyses

Continuous and categorical variables are summarized with medians and ranges or numbers and percentages. For graphical displays of proportions, 95% confidence intervals (CIs) derived from binary logistic regressions are shown. Group comparisons of dichotomic variables and continuous variables are made with Fisher’s exact and Mann-Whitney tests, respectively. Overall (OS) and relapse-free (RFS) survivals are estimated with the Kaplan-Meier method from the data of trial inclusion until death or last contact (OS) or death, relapse, or last contact (RFS). Group differences for censored outcomes are calculated with log-rank tests.

Given the number of genes tested, variable selection for multivariate Cox models was based on Lasso (least absolute shrinkage and selection operator)-penalized regression, using the 1 standard error rule, with the R package glmnet.31  In datasets with a large number of variables, this method is more robust than conventional sequential methods (eg, backward selection) for selecting the variables leading to the model with optimal interpretability (ie, lower number of variables) and prediction accuracy.32  The final Cox model was inspected for interactions and for collinearity with variance inflation factors, retaining the conventional variance inflation factor threshold of 4 as indicative of unacceptable collinearity.33  Collinearity (ie, a strong correlation between predictors in a model) does not affect the overall predictive power of the model, but may lead to spurious estimations of an individual predictor’s contribution to the model. The proportional hazards assumption was verified by graphic inspection and testing of scaled Schoenfeld residuals.34  Harrell’s concordance indexes from Cox models were computed and tested with the R package survcomp.35  The C-index metric allows for global assessment of a Cox model prognostic performance, accounting for both occurrence and timing of events, where values of 0.5 and 1 indicate random and perfect predictions, respectively.36,37  Agreement between classifications was estimated with Cohen’s κ.38  All analyses were performed with R version 3.5.3 (www.R-project.org).

Results

Baseline characteristics of the study population

From September 2012 through June 2016, 509 patients were enrolled in the ALFA1200 study. The 471 (92%) patients whose samples were sent at enrollment for centralized genotyping constituted the study population. Median age was 68 years and 390 (82.8%) had clinically defined de novo AML. Median white blood cell (WBC) count was 5.3 × 109/L (range, 0.3 × 109/L to 546.6 × 109/L). Bone marrow blasts were ≥30% in 380 (80.7%) patients. Three hundred forty-one (72.4%) achieved CR/CRp after 1 or 2 courses (Table 1). Of 279 patients deemed eligible for transplant, 131 patients were transplant recipients, including 87 in first CR. With a median follow-up of 44.8 months, there were 207 relapses and 318 deaths, leading to a median RFS and OS of 14.8 and 21.2 months, respectively.

Table 1.

Characteristics of the study cohort

Variablen or median% or range
Sex   
 Female 204 43.3 
 Male 267 56.7 
Age, y 68 60-85 
ECOG performance status   
 0 205 43.5 
 1 200 42.5 
 2 52 11 
 3 1.9 
 NA 1.1 
HCT comorbidity index   
 0 354 75.2 
 1 108 22.9 
 NA 1.9 
Type of AML   
 De novo 390 82.8 
 Post–MDS 68 14.4 
 Treatment related 13 2.8 
 WBC, ×109/L 5.3 0.3-546.6 
Cytogenetic risk*   
 Good 13 2.8 
 Intermediate 339 72 
 Poor 84 17.8 
 NA 35 7.4 
ELN2017   
 Favorable 133 28.2 
 Intermediate 129 27.4 
 Adverse 195 41.4 
 NA 14 
 Follow-up, months (IQR) 44.8 43.0-49.9 
CR/CRp   
 After 1 course 311 66 
 After 2 courses 30 6.4 
 No 130 27.6 
Variablen or median% or range
Sex   
 Female 204 43.3 
 Male 267 56.7 
Age, y 68 60-85 
ECOG performance status   
 0 205 43.5 
 1 200 42.5 
 2 52 11 
 3 1.9 
 NA 1.1 
HCT comorbidity index   
 0 354 75.2 
 1 108 22.9 
 NA 1.9 
Type of AML   
 De novo 390 82.8 
 Post–MDS 68 14.4 
 Treatment related 13 2.8 
 WBC, ×109/L 5.3 0.3-546.6 
Cytogenetic risk*   
 Good 13 2.8 
 Intermediate 339 72 
 Poor 84 17.8 
 NA 35 7.4 
ELN2017   
 Favorable 133 28.2 
 Intermediate 129 27.4 
 Adverse 195 41.4 
 NA 14 
 Follow-up, months (IQR) 44.8 43.0-49.9 
CR/CRp   
 After 1 course 311 66 
 After 2 courses 30 6.4 
 No 130 27.6 

IQR, interquartile range; MDS, myelodysplastic syndrome; NA, not available.

*

Defined in supplemental Table 1, with details in supplemental Table 4.

Oncogenetic landscape

Cytogenetic risk was good, intermediate, poor, and not available in 13 (2.8%), 339 (72.0%), 84 (17.8%), and 35 (7.4%) patients, respectively. Details on cytogenetic groups are shown in supplemental Table 4. Among the 37 sequenced genes, 19 lesions in 17 genes were found in at least 5% of cases (supplemental Figure 1A). The most frequent lesions included DNTM3A (28.7%), NPM1 (27.0%), TET2 (21.0%), and FLT3-ITD (18.7%) mutations. The allele ratio (mutated/wild-type) of FLT3-ITDs was ≥0.5 in 21 of 88 (23.9%) patients with FLT3-ITD. The median number of mutations was 3 (range, 1-10). Only 290 patients (61.6%) could be assigned unambiguously to oncogene-defined subgroups as defined by Papaemmanuil et al in an all-ages cohort,7  the most frequent being NPM1, TP53-aneuploidy, and chromatin-spliceosome (supplemental Figure 1B). There were marked differences between the mutation spectrum of patients with poor-risk cytogenetics compared with good/intermediate risk cytogenetics, with signification overrepresentation of TP53, KRAS, SETBP1, ETV6, and CALR mutations (all P < .05) contrasting with a significant underrepresentation of NPM1, DNMT3A, FLT3-ITD, IDH2-R140, and IDH2-R172 mutations in patients with poor-risk cytogenetics (Figure 1A). ELN 2017 risk was favorable, intermediate, and adverse in 133 (28.2%), 129 (27.4%), and 195 (41.4%) patients, respectively, but remained unavailable because of missing cytogenetics and lack of a classifying mutation in 14 (3.0%) patients.

Figure 1.

Differences in mutation pattern and outcome according to cytogenetic risk. (A) Mutation pattern in patients with favorable/intermediate risk cytogenetics (n = 352) vs adverse cytogenetics (n = 84). P values from Fisher’s exact test. (B) Rates of CR/CRp at 2 courses in patients with good/intermediate (n = 352), poor (n = 84), or missing (n = 35) cytogenetics with 95% CIs. P values from Fisher’s exact test. OS (C) and RFS (D) according to cytogenetic risk. P values from log-rank tests.

Figure 1.

Differences in mutation pattern and outcome according to cytogenetic risk. (A) Mutation pattern in patients with favorable/intermediate risk cytogenetics (n = 352) vs adverse cytogenetics (n = 84). P values from Fisher’s exact test. (B) Rates of CR/CRp at 2 courses in patients with good/intermediate (n = 352), poor (n = 84), or missing (n = 35) cytogenetics with 95% CIs. P values from Fisher’s exact test. OS (C) and RFS (D) according to cytogenetic risk. P values from log-rank tests.

Impact of cytogenetics on outcome

Two hundred and sixty-eight of 352 (76.1%) patients with good or intermediate cytogenetic risk achieved CR/CRp after 1 or 2 courses, compared with 47 of 84 (56.0%) patients with poor-risk cytogenetics (P = 0.0004; Figure 1B). OS and RFS were also markedly different between cytogenetic subgroups, with median OS and RFS of 25.0 and 16.8 months in patients with good/intermediate-risk cytogenetics compared with 9.5 and 7.1 months in patients with poor-risk cytogenetics, respectively (P < 10−4 and P = .0005, respectively; Figure 1C-D). These major differences in mutation spectrum, short- and long-term outcome between cytogenetic groups prompted us to investigate the prognosis of gene mutations in an oncogenetic hierarchy stemming from cytogenetic groups. The 35 patients with missing cytogenetics had a CR rate of 74.3%, median OS and RFS of 31.0 and 19.3 months, without significant difference from patients with favorable/intermediate cytogenetics (CR, P = .84; OS, P = .60; RFS P = .99). We therefore grouped patients into poor (n = 84) and non–poor (favorable/intermediate/missing; n = 387) cytogenetic risk for all further analyses. Of note, although among those 387 patients without poor-risk cytogenetics there was a trend toward better outcome in patients with normal karyotype than in those with intermediate risk aneuploid karyotype (supplemental Figure 2), further cytogenetic stratification beyond the dichotomic poor-risk vs other classifiers did not affect our multivariate survival analyses (data not shown).

Molecular predictors of CR in cytogenetic groups

In univariate analysis, mutations in NPM1, IDH2-R140, and DNMT3A were associated with higher CR/CRp rates after 1 or 2 courses, and mutations in TET2, NRAS, RUNX1, ASXL1, SETBP1, and ETV6 to lower CR/CRp rates in the 387 patients with non–adverse cytogenetics (all P < .05; supplemental Figure 3A). In the 84 patients with adverse cytogenetics, no single gene mutation significantly influenced CR/CRp rate (supplemental Figure 3B). We next performed multivariable logistic regression accounting for all gene mutations and clinical covariates (age, sex, secondary AML, and WBC count) in each of the 2 cytogenetic strata. After variable selection by penalized regression, NPM1 mutations predicted a high CR/CRp rate, whereas mutations in NRAS, SETBP1, RUNX1, and ASXL1 retained significant detrimental impact on CR/CRp, independent of high WBC count in patients without poor-risk cytogenetics, although only higher WBC count impacted CR/CRp rate in those with poor-risk cytogenetics (Table 2).

Table 2.

Multivariate logistic regression for CR/CRp achievement according to cytogenetic risk

VariableOdds ratio95% CIP
Non–poor cytogenetics (n = 387)    
 Log (WBC) 0.69 0.58-0.82 <.0001 
 NPM1 mutation 2.25 1.15-4.51 .02 
 NRAS mutation 0.46 0.23-0.91 .02 
 SETBP1 mutation 0.16 0.02-0.87 .04 
 RUNX1 mutation 0.43 0.23-0.81 .009 
 ASXL1 mutation 0.52 0.28-0.98 .04 
Poor risk cytogenetics (n = 84)    
 Log (WBC) 0.61 0.41-0.87 .009 
VariableOdds ratio95% CIP
Non–poor cytogenetics (n = 387)    
 Log (WBC) 0.69 0.58-0.82 <.0001 
 NPM1 mutation 2.25 1.15-4.51 .02 
 NRAS mutation 0.46 0.23-0.91 .02 
 SETBP1 mutation 0.16 0.02-0.87 .04 
 RUNX1 mutation 0.43 0.23-0.81 .009 
 ASXL1 mutation 0.52 0.28-0.98 .04 
Poor risk cytogenetics (n = 84)    
 Log (WBC) 0.61 0.41-0.87 .009 

Univariate prognostic impact of gene mutations in cytogenetic groups

We next studied the prognostic value of gene mutations on OS in patients according to their cytogenetic risk. In patients with non–poor cytogenetics (n = 387), NPM1 mutations were associated with a lower hazards ratio (HR) for death in univariate analysis (HR, 0.69; 95% CI, 0.52-0.92; P = .011), whereas mutations in DNMT3A, NRAS, ASXL1, RUNX1, PHF6, CSF3R, SETBP1, and ETV6 all conferred a higher risk of death (all P < .05, Figure 2A). In univariate analysis, only the adverse prognostic impact of high allelic ratio (≥0.5) FLT3-ITDs (HR, 2.04; 95% CI, 1.23-3.39; P = .006), but not of low allelic ratio (HR, 1.31; 95% CI, 0.95-1.82; P = .09) reached statistical significance. In patients with poor-risk cytogenetics (n = 84), both TP53 (HR, 2.31; 95% CI, 1.43-3.73; P = .0006) and KRAS (HR, 3.01; 95% CI, 1.42-6.39; P = .004) mutations further worsened OS in univariate analyses (Figure 2B).

Figure 2.

HRs of death according to gene mutations. (A) Patients with non–poor cytogenetic risk (n = 387). (B) Patients with poor cytogenetic risk (n = 84). Error bars indicate 95% CI. P values are from univariate Cox models.

Figure 2.

HRs of death according to gene mutations. (A) Patients with non–poor cytogenetic risk (n = 387). (B) Patients with poor cytogenetic risk (n = 84). Error bars indicate 95% CI. P values are from univariate Cox models.

Multivariate prognostic impact of gene mutations in cytogenetic groups

In the 387 patients with non–poor cytogenetics, the presence of an NPM1 mutation predicted prolonged OS (HR, 0.57; 95% CI, 0.41-0.77; P = .0004), whereas FLT3-ITDs with low (HR, 1.85; 95% CI, 1.31-2.62; P = .0005) or high allelic ratio (HR, 3.51; 95% CI, 2.03-6.08; P < 10−4); DNMT3A (HR, 1.86; 95% CI, 1.40-2.47; P < 10−4), NRAS (HR, 1.54; 95% CI, 1.07-2.20; P = .019), or ASXL1 (HR, 1.89; 95% CI, 1.34-2.67; P = .0003) mutations independently predicted a shorter OS in a multivariate Cox model after variable selection by Lasso-penalized regression (Table 3). The range of HRs in this model allowed for designing a simple score (−1 point for an NPM1 mutation, +1 point each for FLT3-ITD low allele ratio, DNMT3A, NRAS, and ASLX1, and 2 points for FLT3-ITD high allele ratio) that could regroup patients with non–poor cytogenetics into 4 distinct risk categories with 12-month OS estimates ranging from 96.2% (95% CI, 89.0-100.0) to 43.5% (95% CI, 31.3-60.5; supplemental Table 5; supplemental Figure 4).

Table 3.

Multivariate Cox models for OS in patients according to cytogenetic risk

VariableHR95% CIP
Non–poor risk cytogenetics (n = 387)    
 NPM1 mutation 0.57 0.41-0.77 .0004 
 FLT3-ITD low ratio 1.85 1.31-2.62 .0005 
 FLT3-ITD high ratio 3.51 2.03-6.08 <.0001 
 NRAS mutation 1.54 1.07-2.20 .019 
 ASXL1 mutation 1.89 1.34-2.67 .0003 
 DNMT3A mutation 1.86 1.40-2.47 <.0001 
Poor risk cytogenetics (n = 84)    
 KRAS mutation 3.60 1.68-7.72 .001 
 TP53 mutation 2.49 1.53-4.04 .0003 
VariableHR95% CIP
Non–poor risk cytogenetics (n = 387)    
 NPM1 mutation 0.57 0.41-0.77 .0004 
 FLT3-ITD low ratio 1.85 1.31-2.62 .0005 
 FLT3-ITD high ratio 3.51 2.03-6.08 <.0001 
 NRAS mutation 1.54 1.07-2.20 .019 
 ASXL1 mutation 1.89 1.34-2.67 .0003 
 DNMT3A mutation 1.86 1.40-2.47 <.0001 
Poor risk cytogenetics (n = 84)    
 KRAS mutation 3.60 1.68-7.72 .001 
 TP53 mutation 2.49 1.53-4.04 .0003 

After Lasso-penalized regression in the 84 patients with poor-risk cytogenetics, TP53 (HR, 2.49; 95% CI, 1.53-4.04; P = .0003) and KRAS (HR, 3.60; 95% CI, 1.68-7.72; P = .001) mutations independently predicted worse OS (Table 3). Patients with poor-risk genetics without TP53 or KRAS mutation had a 12-month OS of 58.3% (95% CI, 45.9-74.1) vs 19.4% (95% CI, 10.0-37.8) for those with either mutation (log-rank test, P = 2 × 10−5; supplemental Figure 4; supplemental Table 5).

Development of the ALFA molecular decision tool

We empirically designed a 3-tier oncogenetic decision model assigning patients with 2-year OS estimates >60% to the go-go group, those with 2-year OS estimates <10% to the no-go group, and all others to the slow-go tier. Patients with non–poor cytogenetics and either NPM1 mutation with at most 1 mutation among FLT3-ITD low allelic ratio, DNMT3A, ASXL1, or NRAS mutations or those with NPM1, FLT3-ITD, DNMT3A, ASXL1, or NRAS, all wild-type (n = 184; 39.1%, ie, groups A and B in supplemental Table 5), were assigned to the very favorable go-go group. Conversely, patients with adverse risk cytogenetics and either a mutation in KRAS or TP53 (n = 36; 7.6%, ie, group F in supplemental Table 5) were assigned to the no-go group, and the remaining 251 patients (53.3%) were assigned to the slow-go group (Table 4). Two-year OS estimates were 66.1% (95% CI, 59.5-73.3), 39.1% (95% CI, 33.5-45.7), and 2.8% (95% CI, 0.4-19.2) in the go-go, slow-go, and no-go groups, respectively (overall log-rank test; P < 10−5; Figure 3A). Censoring OS at the time of HSCT in first CR did not affect those results (supplemental Figure 5). CR rates were 84.2% (95% CI, 78.0-89.0), 66.5% (95% CI, 60.3-72.3), and 52.3% (95% CI, 35.7-69.2) in the go-go, slow-go, and no-go groups, respectively (overall Fisher’s exact test, P < 10−5). RFS also markedly differed between these tiers, with 2-year estimates of 49.7% (95% CI, 42.4-58.2), 30.2% (95% CI, 24.0-38.1), and 5.3% (95% CI, 0.8-35.5), respectively (overall log-rank test, P < 10−5; Figure 3B).

Table 4.

Repartition and outcome per ALFA decision tool tier in the ALFA1200 training cohort and validation cohorts

Training cohortValidation cohorts
ALFA1200 (n = 471)AMLSG (n = 223)HDF (n = 141)SAL (n = 466)
n (%)2-y OS (95% CI)Pn (%)2-y OS (95% CI)Pn (%)2-y OS (95% CI )Pn (%)2-y OS (95% CI)P
Go-go* 184 (39.1) 66.1% (59.5-73.3) <10−5 84 (37.7) 44.8 (35.3-56.9) .0006 44 (31.2) 43.4 (30.7-61.4) .06 171 (36.7) 35.5 (28.9-43.5) .02 
Slow-go 251 (53.3) 39.1% (33.5-45.7) Ref. 113 (50.7) 21.9 (15.4-31.2) Ref. 78 (55.3) 29.9 (21.2-42.2) Ref. 243 (52.1) 28.2 (20.1-31.2) Ref. 
No-go 36 (7.6) 2.8% (0.4-19.2) <10−5 26 (11.6) 3.8 (0.5-26.2) 3 × 10−5 19 (13.5) 10.5 (2.8-39.1) .01 52 (11.2) 2.0 (0.2-13.9) <10−5 
Overall log-rank test   <10−5   <10−5   .003   <10−5 
Training cohortValidation cohorts
ALFA1200 (n = 471)AMLSG (n = 223)HDF (n = 141)SAL (n = 466)
n (%)2-y OS (95% CI)Pn (%)2-y OS (95% CI)Pn (%)2-y OS (95% CI )Pn (%)2-y OS (95% CI)P
Go-go* 184 (39.1) 66.1% (59.5-73.3) <10−5 84 (37.7) 44.8 (35.3-56.9) .0006 44 (31.2) 43.4 (30.7-61.4) .06 171 (36.7) 35.5 (28.9-43.5) .02 
Slow-go 251 (53.3) 39.1% (33.5-45.7) Ref. 113 (50.7) 21.9 (15.4-31.2) Ref. 78 (55.3) 29.9 (21.2-42.2) Ref. 243 (52.1) 28.2 (20.1-31.2) Ref. 
No-go 36 (7.6) 2.8% (0.4-19.2) <10−5 26 (11.6) 3.8 (0.5-26.2) 3 × 10−5 19 (13.5) 10.5 (2.8-39.1) .01 52 (11.2) 2.0 (0.2-13.9) <10−5 
Overall log-rank test   <10−5   <10−5   .003   <10−5 

P values by overall log-rank test and from pairwise log-rank tests considering the slow-go group as reference (Ref.)

*

Go-go tier: non–poor cytogenetics, NPM1 mutated and at most 1 mutation among FLT3-ITD low, DNMT3A, ASXL1, or NRAS OR non–poor cytogenetics and NPM1, FLT3-ITD, DNMT3A, ASXL1, and NRAS all wild-type; no-go tier: poor-risk cytogenetic with KRAS and/or TP53 mutation; slow-go: all others.

Figure 3.

Outcome of the ALFA1200 cohort according to the ALFA decision tool. Overall survival (A) and relapse-free survival (B) according to ALFA decision tiers. P values from log-rank tests.

Figure 3.

Outcome of the ALFA1200 cohort according to the ALFA decision tool. Overall survival (A) and relapse-free survival (B) according to ALFA decision tiers. P values from log-rank tests.

Comparison with alternative decision tools

To benchmark our ALFA decision tool, we ranked patients according to their 5-year survival predicted by the KB approach.29,30  The 184 patients with best predicted outcome were assigned to a KB go-go group, and the 36 with poorest KB predictions were assigned to the KB no-go group. Agreement between ALFA and the KB decision tools was moderate (Cohens κ, 0.47; 95% CI, 0.40-0.55; supplemental Table 6). This KB-guided decision tool also discriminated among patients with different outcomes (supplemental Figure 6). However, its concordance index for OS was 0.73 (95% CI, 0.68-0.77), comparable with that of the ALFA decision tool (0.72; 95% CI, 0.68-0.77; P = .58). Thus, the KB approach did not supersede the simpler ALFA decision tool. Similarly, in the 457 patients with evaluable ELN 2017 risk, the concordance index of the ALFA decision tool for OS was still 0.72 (95% CI, 0.63-0.72), compared with only 0.63 (95% CI, 0.59-0.68) for the ELN 2017 risk classification (P = 2 × 10−5; supplemental Table 6).

External validation of the ALFA decision tool

We finally performed external validation of the ALFA decision tool in 3 distinct cohorts of 141 to 466 older patients treated intensively with older accrual dates and longer follow-up, resulting in lower median OS durations (supplemental Table 3). The proportion of patients assigned to the go-go group ranged from 31.2% to 37.7% and that of no-go patients from 11.2% to 13.5%. Statistically significant differences in OS were seen between decision tiers in all 3 cohorts (overall log-rank tests: HDF [Hauts-de-France], P = .003 and AMLSG [AML Study Group] and SAL [Study Alliance Leukemia], both P < 10−5; Figure 4), and to a lesser extent on RFS (supplemental Figure 7).

Figure 4.

External validation of the ALFA decision tool. OS according to ALFA decision tiers: (A) 141 patients accrued to the Hauts-De-France registry (HDF cohort); (B) 466 patients accrued to Study Alliance Leukemia trials (SAL cohort); and (C) 233 patients ≥60 years or older excluding t(15;17) cases in the AML Study Group public dataset7,29  (AMLSG cohort). Characteristics of these cohorts are provided in supplemental Table 3. P values from log-rank tests.

Figure 4.

External validation of the ALFA decision tool. OS according to ALFA decision tiers: (A) 141 patients accrued to the Hauts-De-France registry (HDF cohort); (B) 466 patients accrued to Study Alliance Leukemia trials (SAL cohort); and (C) 233 patients ≥60 years or older excluding t(15;17) cases in the AML Study Group public dataset7,29  (AMLSG cohort). Characteristics of these cohorts are provided in supplemental Table 3. P values from log-rank tests.

Discussion

This study relies on cytogenetics and targeted sequencing of 37 genes in a uniformly treated, prospective cohort of 471 patients aged ≥60 years, with newly diagnosed AML. We identified oncogenetic predictors of short-term (remission) and long-term (OS) benefit of intensive chemotherapy. We developed and validated a simple decision model that accounted for cytogenetics and mutations in 7 genes (NPM1, FLT3-ITD, DNMT3A, NRAS, ASXL1, KRAS, and TP53) that reproducibly identified patients with significant differences in overall survival across multiple cohorts.

The results of intensive chemotherapy in older adults with newly diagnosed AML have long remained disappointing, and physicians are increasingly turning to alternative options, such as the combination of azacitidine and venetoclax, especially for unfit patients.16  Fitness for chemotherapy has 2 orthogonal dimensions: one is related to the patients’ condition and the other to the disease risk. The characteristics of our cohort, including most patients with no comorbidity and good performance status, resulting in relatively low early death rate (6.8% at day 30), enabled us to carefully study the role of disease-related factors on chemotherapy outcome. Our cohort including a similar proportion of de novo AML and poor-risk cytogenetics (∼20%) compared with other populations of older AML patients treated intensively (supplemental Table 3).39,40  This reflects the accepted notion that treatment decision can be delayed until obtaining the results of cytogenetics in older patients with AML.41,42 

In keeping with many previous reports,17,22  our results stress the prominent role of poor-risk cytogenetics, mostly complex karyotype and related chromosomal imbalances (deletion 5q/monosomy 5 or 7 [del5q/-5-7], and so on) in predicting induction failure and shorter overall survival. Conventional karyotyping failure remains an obstacle to accurate patient stratification. In our series, the 7.4% of patients in this situation had similar outcome as patients with non–poor cytogenetics, enabling us to group them. Routine use of molecular cytogenetic tools in this context will overcome this limitation.43,44 

Among patients with non–poor cytogenetics, we identified mutations in NRAS, SETBP1, RUNX1, and ASXL1 to independently predict lower CR rate. These findings differ from other published series,23,45  perhaps owing to the assessment after 2 cycles in the present series. Of note, some of these genes (SETBP1 and RUNX1) did not harbor prognostic impact in our analysis of OS. Though this could be because of their overlap with other frequently mutated genes such as ASXL1, it raises the possibility that salvage therapies, the details of which were not available in our cohort, could have attenuated their prognostic impact beyond primary induction failure.

In univariate analysis, we found shorter OS among patients without poor risk cytogenetics in those harboring mutations in DNMT3A, NRAS, ASXL1, RUNX1, PHF6, CSF3R, SETBP1, and ETV6, and in poor-risk cytogenetics AMLs with mutations in TP53 or KRAS. Most of these gene mutations have already been associated with poorer outcome in AML cohorts treated intensively.6,11,13,23,45 

The prognostic value of DNTM3A mutations remains debated,11,26,46  and we found similar prognosis between R882 hotspot substitutions and other mutations (data not shown). DNTM3A, but also TET2 or ASXL1 (DTA) are often preleukemic hits in AML.47-50  Based on VAFs, most of these mutations appeared as ancestral mutations in our cohort, and our cohort was not powered to assign a different prognostic value to secondary, as opposed to ancestral, DTA lesions (data not shown).

In our univariate analysis, only a high allele ratio of FLT3-ITD, as defined by ELN consensus (ratio, ≥0.5),3  was associated with shorter OS. However, variable selection based on penalized Lasso regression uncovered a lesser, yet significant prognostic value to lower FLT3-ITD clones in multivariate analysis. This finding stresses both the need to perform stringent variable selection when considering large genotyping datasets31  and the challenges in using allele ratio of FLT3-ITDs for prognostic assignment,51  most of which were overcome here by centralized evaluation. These results confirm the longstanding notion that FLT3-ITD is also a high-risk lesion in older patients with AML.19  Of 74 patients with mutated NPM1 without FLT3-ITD, 48 (64.9%) had at least 1 mutation in DNMT3A, NRAS, or ASXL1, which had independent poor prognostic value in our final Cox model. The variable incidence of these adverse co-mutations in different cohorts has most likely contributed to the divergent reports on the prognostic role of NPM1 mutations in older patients with AML.20-22 

Progresses in chemotherapy-free regimens have challenged the role of intensive therapies in other heme malignancies. Decision tools have thus been developed to segregate patients for whom intensive treatment should not be questioned (go-go) from those where it should be carefully considered (slow-go ) or readily discarded (no-go).52  Combining cytogenetics and the mutational status of 7 genes, we could design and validate such a 3-tier decision tool. With a C-index for OS in the higher range of reported values across genetic classifiers in various AML cohorts,29,30,53  the resulting model was more discriminative than ELN 2017 risk stratification, whose limits in this context have already been reported,22,24  and comparable to the more cumbersome KB approach, which takes 100 variables as input, including important clinical variables, such as age and WBC count, compared with only 8 for our decision tool, and has yet to be fully validated in patients >60 years or age.29,30 

Across all tested cohorts, the ALFA decision tool identified 30% to 35% of patients with superior outcome, including a 2-year OS of 66.1% in the more recent ALFA1200 trial. These go-go patients should be considered candidates for intensive chemotherapies, and future trials should aim at improving its results (eg, through the addition of novel therapies, or intervening early using measurable residual disease).54-56  Importantly, this go-go group could not be solely identified on the basis of NPM1 and FLT3-ITD status, let alone the presence of core-binding factor fusions, which are rarely seen at this age.

Conversely, the ALFA decision tool consistently identified ∼10% of no-go patients with very poor short-term survival. Importantly, this group was not only defined by poor-risk cytogenetics, and the short OS of these high-risk patients contrasted with a seemingly acceptable CR/CRp rate of 52.3%. These findings illustrate the need for integrated cytogenetic and genetic risk stratification and stress that CR achievement, per se, may not always translate into prolonged survival in older patients with AML, raising a question about its role as a clinical trial end point in this population. For this minor subset of no-go AMLs, despite adequate performance status and lack of comorbidities, it may be considered ethical to evaluate investigational agents in trials without an intensive reference arm. The remaining 50% to 55% of patients constitute the slow-go group, where one may consider randomizing current intensive regimens vs promising, less intensive combinations.16  Long-term survival data with these less intensive therapies should be inspected stratifying according to the proposed decision tool to strengthen this hypothesis, accounting for the important differences in age and comorbidities of cohorts so far treated with intensive chemotherapy vs hypomethylating agents: venetoclax combinations.57,58  Our decision tool was robust to censoring at HSCT in first remission, a strategy increasingly accessible to older patients with AML.59 

Finally, the ALFA decision tool identified significant differences in OS in 3 independent validation cohorts, although the overall lower OS of these cohorts buffered the survival differences between tiers. This likely reflects the fact that patients were mostly accrued to these cohorts before 2010 and thus had longer follow-up,17,60,61  but also stresses the progress made in the supportive care of these patients over the recent years.62 

Overall, our ALFA decision model relies on a limited number of lesions, all part of the core oncogenes sequenced in AML cohorts, enabling cross comparison of different treatment approaches currently explored in nonrandomized trials.63-65  Incorporation of additional genetic and nongenetic biomarkers of chemosensitivity may in the future help further refine prognostic assessment in larger cohorts of older patients with AML before treatment is initiated.66  With the acceleration of sequencing turn-around time in most centers, and the increasing acceptance of delayed treatment initiation in older patients with AML,41,42  our simple, reproducible, and discriminant decision tool has the potential to first instruct the design of future clinical trials in fit patients with AML, and then guide frontline treatment decisions in routine practice.

Acknowledgments

The authors thank the ALFA1200 investigators Frédérique Hilaire, Rebecca Grapin, Anne Solenn Debrie, Imen Chouikh, and Lydia Amghar from the ALFA coordination office for data collection; Julie Lejeune and Sylvie Chevret from the Department of Medical Bio-informatics (Hôpital Saint-Louis, Assistance Publique–Hôpitaux de Paris, Paris, France) for data management; Christophe Roumier and the Tumor Bank of the Lille University Hospital (certification NF 96900-2014/65453-1) for handling, conditioning, and storing patient samples; and Sandrine Geffroy and Maxime Bucci for help with sequencing experiments.

This study was funded by the French National Cancer Institute and French Health Ministry (PHRC 2007/1911 and PRTK TRANSLA10-060).

Authorship

Contribution: R.I. designed the study, performed analyses, and drafted the manuscript; C.G. and H.D. were the principal investigators and coordinators of the ALFA1200 study; C.B., T.B., C.P., E.L., J.-B.M., L.A., D.L., J.V.M., L. Gastaud, E.R., P.R., X.T., S.C., T.C., and N.B. accrued patients for the ALFA1200 study; E.F., A.M.-R., O.N., and O.B. did the sequencing analyses of the ALFA1200 study under the supervision of C.P. and N.D.; C.B., C.R., H.S., L. Goursaud, and K.-J.W. accrued patients to the validation cohorts; C. Thiede, C.P., and N.D. supervised molecular analyses of the validation cohorts; C. Terré centrally reviewed cytogenetics of the ALFA1200 study; K.C.-L., R.I., C.G., H.D., and N.D. controlled the database; and all authors reviewed the manuscript and approved its final version.

Conflict-of-interest disclosure: The authors have no competing financial interests.

Correspondence: Raphael Itzykson, Service Hématologie Adultes, Hôpital Saint-Louis, 1 Av Claude Vellefaux, F-75010 Paris; e-mail: raphael.itzykson@aphp.fr.

Presented in part at the 60th annual meeting of the American Society of Hematology, San Diego, CA, 30 November to 4 December 2018.

An analysis limited to only a few genes of the discovery cohort has been reported.24 

The original data will be made available in response to e-mail request to the corresponding author (raphael.itzykson@aphp.fr).

The online version of this article contains a data supplement.

There is a Blood Commentary on this article in this issue.

The publication costs of this article were defrayed in part by page charge payment. Therefore, and solely to indicate this fact, this article is hereby marked ”advertisement” in accordance with 18 USC section 1734.

REFERENCES

1.
Döhner
H
,
Weisdorf
DJ
,
Bloomfield
CD.
Acute Myeloid Leukemia
.
N Engl J Med.
2015
;
373
(
12
):
1136
-
1152
.
2.
Juliusson
G
,
Lazarevic
V
,
Hörstedt
AS
,
Hagberg
O
,
Höglund
M
;
Swedish Acute Leukemia Registry Group
.
Acute myeloid leukemia in the real world: why population-based registries are needed
.
Blood.
2012
;
119
(
17
):
3890
-
3899
.
3.
Döhner
H
,
Estey
E
,
Grimwade
D
, et al
.
Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel
.
Blood.
2017
;
129
(
4
):
424
-
447
.
4.
Dombret
H
,
Itzykson
R.
How and when to decide between epigenetic therapy and chemotherapy in patients with AML
.
Hematology (Am Soc Hematol Educ Program).
2017
;
2017
(
1
):
45
-
53
.
5.
Grimwade
D
,
Walker
H
,
Harrison
G
, et al;
Medical Research Council Adult Leukemia Working Party
.
The predictive value of hierarchical cytogenetic classification in older adults with acute myeloid leukemia (AML): analysis of 1065 patients entered into the United Kingdom Medical Research Council AML11 trial
.
Blood.
2001
;
98
(
5
):
1312
-
1320
.
6.
Patel
JP
,
Gönen
M
,
Figueroa
ME
, et al
.
Prognostic relevance of integrated genetic profiling in acute myeloid leukemia
.
N Engl J Med.
2012
;
366
(
12
):
1079
-
1089
.
7.
Papaemmanuil
E
,
Gerstung
M
,
Bullinger
L
, et al
.
Genomic Classification and Prognosis in Acute Myeloid Leukemia
.
N Engl J Med.
2016
;
374
(
23
):
2209
-
2221
.
8.
Ley
TJ
,
Miller
C
,
Ding
L
, et al;
Cancer Genome Atlas Research Network
.
Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia
.
N Engl J Med.
2013
;
368
(
22
):
2059
-
2074
.
9.
Appelbaum
FR
,
Gundacker
H
,
Head
DR
, et al
.
Age and acute myeloid leukemia
.
Blood.
2006
;
107
(
9
):
3481
-
3485
.
10.
Pastore
F
,
Dufour
A
,
Benthaus
T
, et al
.
Combined molecular and clinical prognostic index for relapse and survival in cytogenetically normal acute myeloid leukemia
.
J Clin Oncol.
2014
;
32
(
15
):
1586
-
1594
.
11.
Metzeler
KH
,
Herold
T
,
Rothenberg-Thurley
M
, et al;
AMLCG Study Group
.
Spectrum and prognostic relevance of driver gene mutations in acute myeloid leukemia
.
Blood.
2016
;
128
(
5
):
686
-
698
.
12.
Straube
J
,
Ling
VY
,
Hill
GR
,
Lane
SW.
The impact of age, NPM1mut, and FLT3ITD allelic ratio in patients with acute myeloid leukemia
.
Blood.
2018
;
131
(
10
):
1148
-
1153
.
13.
Tsai
CH
,
Hou
HA
,
Tang
JL
, et al
.
Genetic alterations and their clinical implications in older patients with acute myeloid leukemia
.
Leukemia.
2016
;
30
(
7
):
1485
-
1492
.
14.
Silva
P
,
Neumann
M
,
Schroeder
MP
, et al
.
Acute myeloid leukemia in the elderly is characterized by a distinct genetic and epigenetic landscape
.
Leukemia.
2017
;
31
(
7
):
1640
-
1644
.
15.
Lindsley
RC
,
Mar
BG
,
Mazzola
E
, et al
.
Acute myeloid leukemia ontogeny is defined by distinct somatic mutations
.
Blood.
2015
;
125
(
9
):
1367
-
1376
.
16.
DiNardo
CD
,
Jonas
BA
,
Pullarkat
V
, et al
.
Azacitidine and Venetoclax in Previously Untreated Acute Myeloid Leukemia
.
N Engl J Med.
2020
;
383
(
7
):
617
-
629
.
17.
Röllig
C
,
Thiede
C
,
Gramatzki
M
, et al;
Study Alliance Leukemia
.
A novel prognostic model in elderly patients with acute myeloid leukemia: results of 909 patients entered into the prospective AML96 trial
.
Blood.
2010
;
116
(
6
):
971
-
978
.
18.
Becker
H
,
Marcucci
G
,
Maharry
K
, et al
.
Favorable prognostic impact of NPM1 mutations in older patients with cytogenetically normal de novo acute myeloid leukemia and associated gene- and microRNA-expression signatures: a Cancer and Leukemia Group B study
.
J Clin Oncol.
2010
;
28
(
4
):
596
-
604
.
19.
Whitman
SP
,
Maharry
K
,
Radmacher
MD
, et al
.
FLT3 internal tandem duplication associates with adverse outcome and gene- and microRNA-expression signatures in patients 60 years of age or older with primary cytogenetically normal acute myeloid leukemia: a Cancer and Leukemia Group B study
.
Blood.
2010
;
116
(
18
):
3622
-
3626
.
20.
Ostronoff
F
,
Othus
M
,
Lazenby
M
, et al
.
Prognostic significance of NPM1 mutations in the absence of FLT3-internal tandem duplication in older patients with acute myeloid leukemia: a SWOG and UK National Cancer Research Institute/Medical Research Council report [published correction appears in J Clin Oncol. 2015;33(15):1715]
.
J Clin Oncol.
2015
;
33
(
10
):
1157
-
1164
.
21.
Dickson
GJ
,
Bustraan
S
,
Hills
RK
, et al
.
The value of molecular stratification for CEBPA(DM) and NPM1(MUT) FLT3(WT) genotypes in older patients with acute myeloid leukaemia
.
Br J Haematol.
2016
;
172
(
4
):
573
-
580
.
22.
Prassek
VV
,
Rothenberg-Thurley
M
,
Sauerland
MC
, et al
.
Genetics of acute myeloid leukemia in the elderly: mutation spectrum and clinical impact in intensively treated patients aged 75 years or older
.
Haematologica.
2018
;
103
(
11
):
1853
-
1861
.
23.
Eisfeld
AK
,
Kohlschmidt
J
,
Mrózek
K
, et al
.
Mutation patterns identify adult patients with de novo acute myeloid leukemia aged 60 years or older who respond favorably to standard chemotherapy: an analysis of Alliance studies
.
Leukemia.
2018
;
32
(
6
):
1338
-
1348
.
24.
Gardin
C
,
Pautas
C
,
Fournier
E
, et al
.
Added prognostic value of secondary AML-like gene mutations in ELN intermediate-risk older AML: ALFA-1200 study results
.
Blood Adv.
2020
;
4
(
9
):
1942
-
1949
.
25.
Sievers
EL
,
Larson
RA
,
Stadtmauer
EA
, et al;
Mylotarg Study Group
.
Efficacy and safety of gemtuzumab ozogamicin in patients with CD33-positive acute myeloid leukemia in first relapse
.
J Clin Oncol.
2001
;
19
(
13
):
3244
-
3254
.
26.
Renneville
A
,
Boissel
N
,
Nibourel
O
, et al
.
Prognostic significance of DNA methyltransferase 3A mutations in cytogenetically normal acute myeloid leukemia: a study by the Acute Leukemia French Association
.
Leukemia.
2012
;
26
(
6
):
1247
-
1254
.
27.
Gelsi-Boyer
V
,
Trouplin
V
,
Adélaïde
J
, et al
.
Mutations of polycomb-associated gene ASXL1 in myelodysplastic syndromes and chronic myelomonocytic leukaemia
.
Br J Haematol.
2009
;
145
(
6
):
788
-
800
.
28.
Preudhomme
C
,
Sagot
C
,
Boissel
N
, et al;
ALFA Group
.
Favorable prognostic significance of CEBPA mutations in patients with de novo acute myeloid leukemia: a study from the Acute Leukemia French Association (ALFA)
.
Blood.
2002
;
100
(
8
):
2717
-
2723
.
29.
Gerstung
M
,
Papaemmanuil
E
,
Martincorena
I
, et al
.
Precision oncology for acute myeloid leukemia using a knowledge bank approach
.
Nat Genet.
2017
;
49
(
3
):
332
-
340
.
30.
Fenwarth
L
,
Thomas
X
,
de Botton
S
, et al
.
A personalized approach to guide allogeneic stem cell transplantation in younger adults with acute myeloid leukemia
.
Blood.
2021
;
137
(
4
):
524
-
532
.
31.
Friedman
J
,
Hastie
T
,
Tibshirani
R.
Regularization Paths for Generalized Linear Models via Coordinate Descent
.
J Stat Softw.
2010
;
33
(
1
):
1
-
22
.
32.
Tibshirani
R.
Regression Shrinkage and Selection via the Lasso
.
J R Stat Soc B.
1996
;
58
(
1
):
267
-
288
.
33.
Obrien
RM.
A Caution Regarding Rules of Thumb for Variance Inflation Factors
.
Qual Quant.
2007
;
41
(
5
):
673
-
690
.
34.
Grambsch
PM
,
Therneau
TM.
Proportional Hazards Tests and Diagnostics Based on Weighted Residuals
.
Biometrika.
1994
;
81
(
3
):
515
-
526
.
35.
Schröder
MS
,
Culhane
AC
,
Quackenbush
J
,
Haibe-Kains
B.
survcomp: an R/Bioconductor package for performance assessment and comparison of survival models
.
Bioinformatics.
2011
;
27
(
22
):
3206
-
3208
.
36.
Harrell
FE
Jr
,
Lee
KL
,
Mark
DB.
Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors
.
Stat Med.
1996
;
15
(
4
):
361
-
387
.
10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4
37.
Pencina
MJ
,
DAgostino
RB.
Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation
.
Stat Med.
2004
;
23
(
13
):
2109
-
2123
.
38.
Cohen
J.
A Coefficient of Agreement for Nominal Scales
.
Educ Psychol Meas.
1960
;
20
(
1
):
37
-
46
.
39.
Schlenk
RF
,
Fröhling
S
,
Hartmann
F
, et al;
AML Study Group Ulm
.
Phase III study of all-trans retinoic acid in previously untreated patients 61 years or older with acute myeloid leukemia
.
Leukemia.
2004
;
18
(
11
):
1798
-
1803
.
40.
Stölzel
F
,
Pfirrmann
M
,
Aulitzky
WE
, et al;
Study Alliance Leukemia
.
Risk stratification using a new prognostic score for patients with secondary acute myeloid leukemia: results of the prospective AML96 trial
.
Leukemia.
2011
;
25
(
3
):
420
-
428
.
41.
Bertoli
S
,
Bérard
E
,
Huguet
F
, et al
.
Time from diagnosis to intensive chemotherapy initiation does not adversely impact the outcome of patients with acute myeloid leukemia
.
Blood.
2013
;
121
(
14
):
2618
-
2626
.
42.
Röllig
C
,
Kramer
M
,
Schliemann
C
, et al
.
Does time from diagnosis to treatment affect the prognosis of patients with newly diagnosed acute myeloid leukemia?
Blood.
2020
;
136
(
7
):
823
-
830
.
43.
Vosberg
S
,
Herold
T
,
Hartmann
L
, et al
.
Close correlation of copy number aberrations detected by next-generation sequencing with results from routine cytogenetics in acute myeloid leukemia
.
Genes Chromosomes Cancer.
2016
;
55
(
7
):
553
-
567
.
44.
Renneville
A
,
Abdelali
RB
,
Chevret
S
, et al
.
Clinical impact of gene mutations and lesions detected by SNP-array karyotyping in acute myeloid leukemia patients in the context of gemtuzumab ozogamicin treatment: results of the ALFA-0701 trial
.
Oncotarget.
2014
;
5
(
4
):
916
-
932
.
45.
Österroos
A
,
Björklund
M
,
Eriksson
A
, et al
.
Integrated transcriptomic and genomic analysis improves prediction of complete remission and survival in elderly patients with acute myeloid leukemia
.
Blood Cancer J.
2020
;
10
(
6
):
67
.
46.
Gaidzik
VI
,
Schlenk
RF
,
Paschka
P
, et al
.
Clinical impact of DNMT3A mutations in younger adult patients with acute myeloid leukemia: results of the AML Study Group (AMLSG)
.
Blood.
2013
;
121
(
23
):
4769
-
4777
.
47.
Rothenberg-Thurley
M
,
Amler
S
,
Goerlich
D
, et al
.
Persistence of pre-leukemic clones during first remission and risk of relapse in acute myeloid leukemia
.
Leukemia.
2018
;
32
(
7
):
1598
-
1608
.
48.
Morita
K
,
Kantarjian
HM
,
Wang
F
, et al
.
Clearance of Somatic Mutations at Remission and the Risk of Relapse in Acute Myeloid Leukemia
.
J Clin Oncol.
2018
;
36
(
18
):
1788
-
1797
.
49.
Jongen-Lavrencic
M
,
Grob
T
,
Hanekamp
D
, et al
.
Molecular Minimal Residual Disease in Acute Myeloid Leukemia
.
N Engl J Med.
2018
;
378
(
13
):
1189
-
1199
.
50.
Miles
LA
,
Bowman
RL
,
Merlinsky
TR
, et al
.
Single-cell mutation analysis of clonal evolution in myeloid malignancies
.
Nature.
2020
;
587
(
7834
):
477
-
482
.
51.
Daver
N
,
Schlenk
RF
,
Russell
NH
,
Levis
MJ.
Targeting FLT3 mutations in AML: review of current knowledge and evidence
.
Leukemia.
2019
;
33
(
2
):
299
-
312
.
52.
Ghia
P
,
Hallek
M.
Management of chronic lymphocytic leukemia
.
Haematologica.
2014
;
99
(
6
):
965
-
972
.
53.
Wang
M
,
Lindberg
J
,
Klevebring
D
, et al
.
Validation of risk stratification models in acute myeloid leukemia using sequencing-based molecular profiling
.
Leukemia.
2017
;
31
(
10
):
2029
-
2036
.
54.
Stone
RM
,
Mandrekar
SJ
,
Sanford
BL
, et al
.
Midostaurin plus Chemotherapy for Acute Myeloid Leukemia with a FLT3 Mutation
.
N Engl J Med.
2017
;
377
(
5
):
454
-
464
.
55.
Stein
EM
,
DiNardo
CD
,
Fathi
AT
, et al
.
Ivosidenib or enasidenib combined with intensive chemotherapy in patients with newly diagnosed AML: a phase 1 study
.
Blood.
2021
;
137
(
13
):
1792
-
1803
.
56.
Cortes
JE
,
Douglas Smith
B
,
Wang
ES
, et al
.
Glasdegib in combination with cytarabine and daunorubicin in patients with AML or high-risk MDS: Phase 2 study results
.
Am J Hematol.
2018
;
93
(
11
):
1301
-
1310
.
57.
DiNardo
CD
,
Tiong
IS
,
Quaglieri
A
, et al
.
Molecular patterns of response and treatment failure after frontline venetoclax combinations in older patients with AML
.
Blood.
2020
;
135
(
11
):
791
-
803
.
58.
Maiti
A
,
Qiao
W
,
Sasaki
K
, et al
.
Venetoclax with decitabine vs intensive chemotherapy in acute myeloid leukemia: A propensity score matched analysis stratified by risk of treatment-related mortality
.
Am J Hematol.
2021
;
96
(
3
):
282
-
291
.
59.
Del Galy
AS
,
Marouf
A
,
Raffoux
E
, et al
.
Allogeneic hematopoietic stem cell transplantation in elderly patients with acute myeloid leukemia or myelodysplastic syndromes: myth and reality
.
Leukemia.
2021
;
35
(
1
):
225
-
228
.
60.
Fröhling
S
,
Schlenk
RF
,
Kayser
S
, et al;
German-Austrian AML Study Group
.
Cytogenetics and age are major determinants of outcome in intensively treated acute myeloid leukemia patients older than 60 years: results from AMLSG trial AML HD98-B
.
Blood.
2006
;
108
(
10
):
3280
-
3288
.
61.
Schaich
M
,
Parmentier
S
,
Kramer
M
, et al
.
High-dose cytarabine consolidation with or without additional amsacrine and mitoxantrone in acute myeloid leukemia: results of the prospective randomized AML2003 trial
.
J Clin Oncol.
2013
;
31
(
17
):
2094
-
2102
.
62.
Burnett
AK
,
Hills
RK
,
Russell
N.
Twenty five years of UK trials in acute myeloid leukaemia: what have we learned?
Br J Haematol.
2020
;
188
(
1
):
86
-
100
.
63.
DiNardo
CD
,
Pratz
KW
,
Letai
A
, et al
.
Safety and preliminary efficacy of venetoclax with decitabine or azacitidine in elderly patients with previously untreated acute myeloid leukaemia: a non-randomised, open-label, phase 1b study
.
Lancet Oncol.
2018
;
19
(
2
):
216
-
228
.
64.
Lancet
JE
,
Uy
GL
,
Cortes
JE
, et al
.
CPX-351 (cytarabine and daunorubicin) Liposome for Injection Versus Conventional Cytarabine Plus Daunorubicin in Older Patients With Newly Diagnosed Secondary Acute Myeloid Leukemia
.
J Clin Oncol.
2018
;
36
(
26
):
2684
-
2692
.
65.
Wei
AH
,
Strickland
SA
Jr
,
Hou
JZ
, et al
.
Venetoclax Combined With Low-Dose Cytarabine for Previously Untreated Patients With Acute Myeloid Leukemia: Results From a Phase Ib/II Study
.
J Clin Oncol.
2019
;
37
(
15
):
1277
-
1284
.
66.
Vo
TT
,
Ryan
J
,
Carrasco
R
, et al
.
Relative mitochondrial priming of myeloblasts and normal HSCs determines chemotherapeutic success in AML
.
Cell.
2012
;
151
(
2
):
344
-
355
.

Supplemental data