Dear Editor,
Acute Myeloid Leukemia (AML) is a heterogeneous hematological malignancy that most commonly presents in patients over the age of 60 (aged AML or aAML). aAML is associated with worse prognosis compared to younger adult AML patients [1]. The current molecular criteria considered for risk stratification (somatic mutations and cytogenetic abnormalities) were largely derived from molecular profiles of patients younger than 60 years of age [2]. Risk classifiers focused on aAML patients have been proposed [3, 4], but they only assessed selected gene mutations and/or did not include uniformly treated patients. While recent clinical trials with newly developed AML therapeutics [5, 6] might offer further insight into prognostication, comprehensive genomics data were not generated for further analyses. Thus, specific molecular determinants of clinical outcomes in aAML patients who are uniformly treated remain largely unknown.
To address this gap in knowledge, we performed whole exome sequencing (WES) on specimens collected from a clinically annotated aAML patient cohort enrolled in ECOG-ACRIN’s phase III clinical trial NCT00046930 [7] (Supplementary Table 1). These patients were uniformly managed and outcomes between experimental and placebo arms were not different, in trial results, and the patients from whom specimens were received (Supplementary Fig. 1). Centralized clinical and cytogenetics data were available. We assessed for recurrent somatic mutations in genes and cytogenetic events and performed association analyses to identify molecular events and clinical features predictive of clinical outcomes.
We first assessed the spectrum of somatic mutations in the patient samples in this study cohort. The cohort was characterized by 16 genes with recurrent oncogenic or likely oncogenic mutations (Supplementary Table 2), and each patient had an average of 3 oncogenic or likely oncogenic mutations (Fig. 1A, B). Most of the variants detected in these patients were variants of unknown significance (Supplementary Fig. 2). A subset of mutations were orthogonally validated using a custom targeted amplicon panel (Supplementary Fig. 3). This mutation rate was higher than that reported in patients younger than 60 (younger patients) in the BEAT AML [8] study cohort (validation cohort I; Wilcoxon test P = 1.44 × 10–29). We also observed a similarly increased mutation count in aAML patients compared to younger patients within validation cohort I (Wilcoxon test P = 9.48 × 10−4). This mutational increase might be due to age-related mutational processes where pre-leukemic cell clones would accumulate mutations prior to transformation into leukemic cells [9]. Consistently, we observed a significant enrichment of mutations in some known clonal hematopoiesis genes (ASXL1, TET2, SRSF2, and U2AF1) [9] (adjusted Fisher exact test P = 2.32 × 10−3, 3.73 × 10−2, 3.07 × 10−3, and 1.17 × 10−4; Fig. 1C; Supplementary Table 3). Clinical cytogenetics were available for a subset of patients from the study cohort (n = 166; Supplementary Table 4, Supplementary Fig. 4). When compared to younger patients in validation cohort I, we observed a significantly reduced frequency of MLL fusions and chromosome 16 inversions (adjusted P = 6.70 × 10−3 and 2.66 × 10−2 respectively; Fig. 1D; Supplementary Table 5).
When considering genes mutated in at least 5% of the patients independently (n = 199; Supplementary Fig. 5, Supplementary Tables 2 and 6) or with cytogenetics data (n = 166 patients; Fig. 1E; Supplementary Table 7), we found comparable patterns as to what has been reported for age unselected cohorts, including in validation cohort I [8]. A notable difference was mutual exclusivity between mutations in NPM1 and U2AF1 (FDR 2.96 × 10−5; previously reported in a retrospective analysis of other age-unselected cohorts [10]).
We next aimed to identify features (somatic and clinical features > 5%; n = 26; Supplementary Table 8) that were associated with overall survival (OS) in the study cohort. Complete molecular and outcomes data was available for 158 patients. Univariable analysis identified 13 distinct features that are associated with OS (P < 0.15; Supplementary Table 9). These 13 features were subsequently tested in recursive partitioning to identify patient subgroups with distinct outcomes. The terminal nodes of the model created 6 groups (G1–G6; Fig. 2A, B) based on five variables: Complex karyotype, mutations in TP53, FLT3-Internal Tandem Duplications (ITD), mutations in NPM1, and sex. We validated our findings in an independent aAML cohort (validation cohort II; Supplementary Tables 10A, B; Supplementary Fig. 6). Additionally, the six group classifier proved to be a better predictor of overall survival than ELN 2017 in the study cohort (ELN CPE = 0.624 and for the 6 group classifier CPE = 0.659 Supplementary Fig. 7A, B).
Our recursive partitioning analysis identified a novel group of patients solely consisting of females lacking complex cytogenetics, NPM1 mutations and FLT3-ITDs (G2; Supplementary Fig. 8). The survival probability of G2 (0.66) was not different from that of a known non-M3 good risk AML patients harboring NPM1 mutations [2] (OS probability 0.5; G1 in Fig. 2A; Supplementary Fig. 9). The survival probability of G2 was significantly better than patient groups (G4–G6; Fig. 2A, B) characterized by features previously identified to associate with poor clinical outcomes [2]. G2 patients had a higher incidence of achievement of complete remission compared to patients in G3 (Chi squared test P = 0.042; Supplementary Figure 10A), consistent with superior response to treatment. Furthermore, the survival probability of G2 was better than that of a group of males with the same genetic background (G3; Fig. 2A, B; Supplementary Fig. 10B).
The novel better risk group identified (G2) re-classified most female patients in this group from poor or intermediate ELN 2017 risk to good risk classification (Fig. 2C). Both G2 and G3 harbored poor and good risk molecular features. Some trends were observed suggesting differences in the frequencies of molecular events between G2 and G3, however, they were not significantly different between the groups (proportional test; P > 0.05; Fig. 2D and Supplementary Table 11). Furthermore, G2 and G3 did not have different mutation burdens (Supplementary Fig. 11; Wilcoxon rank-sum test P = 0.099), which may be the result of the small numbers of patients identified in each group. Nonetheless, this lack of difference in mutation burden suggests the possibility that there was no difference in DNA damage repair or chemotherapy response mechanisms that could contribute to differences in disease biology associated with the distinct clinical outcomes observed [11].
AML is more prevalent in males at any age, and it has already been reported that female pediatric and young adult AML patients had a better prognosis than males from the same age range [12]. We assessed for the potential applicability of survival differences between G2 and G3 patient groups to all adult AML patients over the age of 18. We analyzed outcomes in an AML cohort of adults younger than 60 years of age enrolled in ECOG-ACRIN clinical trial NCT00049517 (Supplementary Table 12) [13]. Applying our risk classifier did not identify a significant survival difference between men and women without complex cytogenetics, NPM1 mutations, and FLT3-ITDs (Supplementary Fig. 12) in younger patients. This finding suggests that the novel risk group classification is specifically relevant to aAML patients.
We created two risk groups by visually comparing the Kaplan–Meier curves of the 6 terminal nodes (Fig. 2B). The low risk group included the two subgroups G1 and G2 (hazard ratios < 1) and the high risk group included the other four subgroups (hazard ratios > 1). We validated this separation using validation cohort II. Using our risk stratification, low risk patients had significantly better OS in both cohorts (log-rank tests P < 0.001; Fig. 2E and Supplementary Fig. 13). Furthermore, the groups were better associated with OS than standard ELN 2017 classification in validation cohort II (CPE for ELN 0.594 and for the new classifier 0.615).
Sex differences have been recognized in cancer incidence and outcomes, and may be an important factor in personalized treatment approaches. Previous publications have reported that AML female patients have overall better outcomes than male AML patients [12], however, this difference was not identified in NCT00046930 [12] (Supplementary Fig. 14), and analyses did not integrate genomics data. To our knowledge, this is the first report of integrative analysis between clinical and molecular events in aAML patients that has identified a classification in which sex serves as a risk predictor. The novel risk group identified reclassified a sub-group of female patients into a good risk category (Fig. 2C), which has implications for treatment selection in these cases [2]. Similarly, univariate (Supplementary Table 13) followed by recursive partitioning analysis also identified sex as a classifying parameter for the achievement of complete remission (Supplementary Fig. 15) in the study cohort. Our findings may be specific to the reported cohorts and require a larger study for confirmation. Future studies could improve upon the proposed risk classifier identified further by incorporating gene expression data, functional features such as BH3 profiling given growing interest in targeting apoptotic pathways in leukemia [14], as well as other laboratory values (e.g., serum LDH, albumin, or extreme leukocytosis). Intensive combination chemotherapy remains an upfront treatment option for fit aAML patients (NCCN 2022 guidelines), however, since upfront treatment options for aAML patients are evolving [15], independent assessments of the applicability of this risk classifier to emerging therapeutic approaches, such as Venetoclax combinations [15], will be required. If confirmed, this new risk assessment approach could inform age-appropriate risk stratification when evaluating the role of intensive combination chemotherapy induction treatment for aAML patients.
Data availability
De-identified patient-level clinical and molecular data from NCT00046930 will be deposited into the National Institutes of Health’s NCTN/NCORP Data Archive (https://nctn-data-archive.nci.nih.gov) 6 months after publication. De-identified patient-level next generation sequencing data files will be deposited into the National Cancer Institute’s Cancer Data Service Portal (https://datacommons.cancer.gov/repository/cancer-data-service) 6 months after publication. Clinical and molecular data from validation cohort II are available upon request from carroll2@mail.med.upenn.edu. This manuscript utilized data from Dataset NCT00049517-D2 from the NCTN/NCORP Data Archive of the National Cancer Institute’s (NCI’s) National Clinical Trials Network (NCTN). Data were originally collected from clinical trial NCT00049517 entitled “Combination Chemotherapy With or Without Monoclonal Antibody Therapy in Treating Patients With AML Leukemia”. All analyses and conclusions in this manuscript are the sole responsibility of the authors and do not necessarily reflect the opinions or views of the clinical trial investigators, the NCTN, the NCORP or the NCI. During the review process, requested data files were available upon request to editorial staff and reviewers through direct invitations from the corresponding author (fg5q@virginia.edu) to a confidential Box folder.
Change history
06 July 2023
A Correction to this paper has been published: https://doi.org/10.1038/s41408-023-00862-2
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Acknowledgements
The authors thank the following funding sources: UVA Cancer Center through the NCI Cancer Center Support Grant P30 CA44579, the University of Virginia, funding from the American Society of Hematology (ASHAMFDP-20121) under the ASH-AMFDP partnership with The Robert Wood Johnson Foundation, Leukemia Research Foundation Young Investigator award, and U10 5U10CA180827 subaward to FGB. Partial support UL1 TR001866 from the National Center for Advancing Translational Sciences, National Institutes of Health (NIH) Clinical and Translational Science Award (CTSA) program to FR. TB was supported by the William C. and Joyce C. O’Neil Charitable Trust, Memorial Sloan Kettering Single Cell Sequencing Initiative. Evans foundation grant to AMM. The authors thank the following service providers: Next generation sequencing services were provided by the New York Genome Center and Weill Cornell Medicine Genomics Resources Core Facility. Flow sorting services were provided by the Memorial Sloan Kettering Cancer Center’s Flow Cytometry Core Facility. Computational resources and technical support were provided by the Weill Cornell Medicine Applied Bioinformatics core, the Memorial Sloan Kettering Cancer Center Bioinformatics core, and the School of Medicine Research Computing group at The University of Virginia. Daniel Gildea is currently affiliated with MedStar Georgetown University Hospital, Maria Kleppe is currently affiliated with Imago Biosciences, Inc., Tak Lee is currently affiliated with The Rockefeller University, H. Moses Murdock is currently affiliated with Harvard Medical School/Brigham and Women’s Hospital, Franck Rapaport is currently affiliated with Sanofi, Caroline Sheridan is currently affiliated with Immunai, and Martin S. Tallman is currently affiliated with Northwestern University Feinberg School of Medicine and the Robert H. Lurie Comprehensive Cancer Center. This study was conducted by the ECOG-ACRIN Cancer Research Group (Peter J. O’Dwyer, MD and Mitchell D. Schnall, MD, PhD, Group Co-Chairs). The study was supported by the National Cancer Institute of the National Institutes of Health under the following award numbers: CA180827, CA180820, CA233290, CA189859, CA233321, CA233270, U10CA180820, U10CA180794, UG1CA189859, and UG1CA233290. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or ECOG-ACRIN, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. government.
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Conceived the study: FEG-B, MK, FR, AMM, and RL. Clinical trial and project development participation: TB, MC, LDC, HF, CEM, EP, GJR, ZS, and MST. Specimen processing and management: TL, CS, LW, EP, MK, and FEG-B. Data management: FR, YN, and FEG-B. Performed bench experiments and assays: SH, CS, and FEG-B. Performed data analysis: FR., KS, YN, DH, and FEG-B. Provided clinical data: ZS, HMM, MC, and EP. Clinical annotation of specimens: AT, EP, YZ, and FEG-B. Performed data and results interpretation: FR, KS, YN, MG, MK, and FEG-B. Generated figures: FR, KS, YN, and FEG-B. Wrote manuscript: FR, KS, YN, DG, and FEG-B. Reviewed results, edited the manuscript, and approved the final version of the manuscript: all authors.
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Rapaport, F., Seier, K., Neelamraju, Y. et al. Integrative analysis identifies an older female-linked AML patient group with better risk in ECOG-ACRIN Cancer Research Group’s clinical trial E3999. Blood Cancer J. 12, 137 (2022). https://doi.org/10.1038/s41408-022-00736-z
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DOI: https://doi.org/10.1038/s41408-022-00736-z
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