Pogosova‐Agadjanyan, Era L.

NPM1 mutations (NPM1+) are found in about 30% of adult AML. While NPM1+ are generally associated with favorable risk, prior studies have shown that NPM1+ older patients, defined as ≥ 65 years, have significantly worse outcomes. The updated European Leukemia Net 2022 (ELN22) now classifies NPM1+ AML patients with FLT3-ITD (ITD+) and MDS-associated mutations (MDSm+) as adverse-risk, while NPM1+/ITD-/MDSm+ patients remain favorable-risk unless they have an adverse-risk karyotype. A recent report (Chan et al., Blood Adv 2024) found MDSm+ conferred worse OS only in patients ≥ 60 years; however another report (Othman et al., Blood 2024) did not find MDSm+ were significantly associated with adverse outcomes in a larger cohort of NPM1+ patients. To further assess this question, we examined the mutational landscape in a large cohort of pre-treatment, diagnostic specimens from 456 patients enrolled in SWOG clinical trials (SWOG-9031, SWOG-9333, S0106, and S0112) and 126 patients from the UW/FHCC Hematopoietic Repository (Total N=582). Patients were treated with curative intent as per standard practice or clinical trials. Out of the 582 patients, 191 were NPM1+ (33%), of which 148 patients had cytogenetic and long-term outcome data. Kaplan-Meier and cumulative incidence estimates were estimated. C-statistics and AUC for Cox, cause-specific and logistic regression models for overall survival (OS), event-free survival (EFS), time to relapse (TTR, calculated in n=106 who achieved complete remission [CR], death in remission analyzed as a competing risk) and CR were calculated for age, ELN17, and ELN22, as well as ELN17 and ELN22 adjusting for age. ELN17 favorable-risk patients reclassified as adverse-risk (N=5 of 108) demonstrated significantly shorter EFS (HR=2.50, p=0.05) and CR rate (OR=0.20, p=0.024), although with similar OS (HR 1.30, p=0.66) and TTR (HR=1.20, p=0.86). ELN17 favorable-risk patients reclassified as ELN22 intermediate-risk (N=23 of 108) did not show a significant difference in OS (HR 1.47, p=0.20), EFS (HR 1.35, p=0.12), CR rate (OR 0.75, p=0.46), or TTR (HR=1.64, p=0.11) compared to ELN22 favorable-risk. For ELN17 intermediate-risk (N=40), 5 patients were reclassified as ELN22 adverse-risk (12.5%) and had significantly shorter EFS (HR 2.89, p=0.037) compared to ELN22 intermediate risk, although there was not a significant difference in CR rate (OR 0.31, p=0.23) or OS (HR 0.81, p=0.67). We next compared the prognostic significance of age relative to ELN risk classification. Age was associated with higher C-statistics (OS=0.65, TTR=0.57 and EFS=0.60) and AUC (CR=0.64) compared to both ELN17 (OS=0.56, TTR=0.54, EFS=0.54, CR=0.54) or ELN22 (OS=0.57, TTR=0.57, EFS=0.57, CR=0.58). The combination of age and ELN22 resulted in the highest C-statistics for OS, TTR, and EFS (OS=0.68, TTR=0.60, EFS=0.64). To further assess the impact of MDSm on risk-stratification of NPM1+ AML, we performed multivariable analyses adjusting for FLT3-ITD, MDSm, and age in patients without adverse-risk disease. These analyses showed MDSm did not have a significant association with OS when controlling for age or FLT3-ITD. We also compared ELN22 favorable-risk patients with and without MDSm (MDSm+ N=18; MDSm- N=61). Compared with the MDSm-, there was a trend toward an inferior OS (HR 1.85, p=0.054) for MDSm+ patients but not EFS (HR 1.38, p=0.3) or TTR (HR 1.2, p=0.64). When stratified by age (<65, N=53 vs. ≥65, N=26), MDSm+ in younger patients did not show a difference for OS (HR 0.96, p=0.96), EFS (HR 0.63, p=0.53) or TTR (HR 0.4, p=0.37). Older ELN22 favorable-risk patients (≥65) had similarly poor OS (MDSm- HR 3.33 and MDSm+ 3.27, respectively, p=0.001 for both) and EFS (MDSm- HR 3.11 and MDSm+ 2.39, p=0.001 and 0.01) compared to younger MDSm- patients. In our study, the ELN22 guidelines incrementally improve risk stratification for EFS in NPM1+ patients. However, age remains the most prognostic factor, and patients ≥ 65 years have similar adverse clinical outcomes, whether classified as favorable risk by ELN17, ELN22, or incorporating MDSm. Moreover, MDSm do not confer a worse prognosis in NPM1+ patients when accounting for age or FLT3-ITD status. These findings differ from a recent report by Chan et al. and are in keeping with the report by Othman et al. Therefore, further understanding of the underlying biology of age and its effect on NPM1+ AML is needed to improve outcomes for older patients.

AbstractIntroductionAcute myeloid leukemia (AML) remains one of the deadliest hematopoietic malignancies. A better understanding of the molecular biology governing AML may lead to improved risk stratification and facilitate the development of novel therapies. Proteins are responsible for much of the biology of cells. Several studies have examined the global proteome in bulk mononuclear cells (MNCs) from AML specimens, which are comprised a heterogenous population of cells at various stages of differentiation.MethodsGiven the potential impact of the nonleukemic cells on protein expression profiles, we applied an integrative proteogenomic approach utilizing next‐generation sequencing and mass spectrometry‐based proteomics to identify novel protein biomarkers in unsorted MNCs and viable leukemic blasts (VLBs) isolated from blood and bone marrow specimens obtained at the time of AML diagnosis.ResultsWe identified significant differences in protein expression between VLBs and MNCs. Subsequent studies (N = 27) focused on proteomic profiling of VLBs that identified novel candidate biomarkers associated with mutational genotypes and clinical outcome, some of which were recapitulated in an independent cohort of patients. Using mass spectrometry, we also detected mutated protein products, some of which were predicted via in silico analyses to be potential neoantigens amenable to adoptive immunotherapy. As previously described, analyses comparing transcript and protein expression showed an overall modest correlation between mRNA and protein dataset, but enriching for genes associated with mutations significantly improved the protein–RNA correlation.ConclusionTogether, the results provide insight into the biology of VLBs and demonstrate the gains derived from examining the proteome in addition to genome and transcriptome.

AbstractSomatic mutations in isocitrate dehydrogenase (IDH) genes occur frequently in adult acute myeloid leukemia (AML) and less commonly in pediatric AML. The objective of this study was to describe the prevalence, mutational profile, and prognostic significance of IDH mutations in AML across age. Our cohort included 3141 patients aged between <1 month and 88 years treated on Children’s Cancer Group/Children’s Oncology Group (n = 1872), Southwest Oncology Group (n = 359), Eastern Cooperative Oncology Group (n = 397) trials, and in Beat AML (n = 333) and The Cancer Genome Atlas (n = 180) genomic characterization cohorts. We retrospectively analyzed patients in 4 age groups (age range, n): pediatric (0-17, 1744), adolescent/young adult (18-39, 444), intermediate-age (40-59, 640), older (≥60, 309). IDH mutations (IDHmut) were identified in 9.2% of the total cohort (n = 288; IDH1 [n = 123, 42.7%]; IDH2 [n = 165, 57.3%]) and were strongly correlated with increased age: 3.4% pediatric vs 21% older, P < .001. Outcomes were similar in IDHmut and IDH-wildtype (IDHWT) AML (event-free survival [EFS]: 35.6% vs 40.0%, P = .368; overall survival [OS]: 50.3% vs 55.4%, P = .196). IDH mutations frequently occurred with NPM1 (47.2%), DNMT3A (29.3%), and FLT3-internal tandem duplication (ITD) (22.4%) mutations. Patients with IDHmut AML with NPM1 mutation (IDHmut/NPM1mut) had significantly improved survival compared with the poor outcomes experienced by patients without (IDHmut/NPM1WT) (EFS: 55.1% vs 17.0%, P < .001; OS: 66.5% vs 35.2%, P < .001). DNTM3A or FLT3-ITD mutations in otherwise favorable IDHmut/NPM1mut AML led to inferior outcomes. Age group analysis demonstrated that IDH mutations did not abrogate the favorable prognostic impact of NPM1mut in patients aged <60 years; older patients had poor outcomes regardless of NPM1 status. These trials were registered at www.clinicaltrials.gov as #NCT00070174, #NCT00372593, #NCT01371981, #NCT00049517, and #NCT00085709.

Background: Somatic mutations in the IDH genes are common in acute myeloid leukemia (AML). Mutations occur at active site arginine residues in IDH1 (R132) and IDH2 (R140, R172). IDH inhibitors, ivosidenib (IDH1) and enasidenib (IDH2), have shown improved clinical outcomes in patients with relapsed/refractory IDH-mutant AML and are approved for use in this setting, while investigations in combination with chemotherapy are underway in de novo AML. The prognostic significance of IDH mutations remains controversial. We hypothesize that refining our understanding of IDH-mutated AML will contribute to risk-adapted treatment strategies, including optimal use of IDH-targeted agents. The objective of our study was to identify characteristics that affect outcome in de novo IDH-mutated AML across the age spectrum utilizing a large cohort of patients enrolled on several pediatric and adult trials. Methods: The total cohort (N=3588) included patients age <1 month - 88 years. Pediatric and young adult (YA) patients were treated on recent CCG/COG AML trials (CCG2961, COG 03P1, 0531, 1031, N=2231) and YA/adult patients were treated on SWOG S0106 (N=396), ECOG E1900 AML trials (N=398), Beat AML (N=363), or included in the TCGA biology study (N=200; outcomes not evaluated for this group). Patients were analyzed according to three defined age groups: "younger" 0-29 years of age (N=2401), "intermediate" 30-59 years of age (N=819) and "older" 60 years of age and above (N=319). Data regarding clinical characteristics and treatment outcomes were collected and evaluated according to the standard practices of each cooperative or research groups for the respective studies. Mutational profiling was performed by targeted sequencing for patient samples from COG trials, Beat-AML, ECOG, and SWOG; whole-genome or whole-exome sequencing for the TCGA cohort. Prevalence of IDH mutations, co-occurring mutations, and outcomes (5-year event-free survival [EFS]) were analyzed across the defined age groups. Results: The prevalence of IDH mutations among the entire cohort was 8.6% (N=276). Analysis according to mutation type demonstrated that IDH2 mutations comprised 57% (N=158) and IDH1 mutations 43% (N=118). The prevalence of IDH mutations was strongly correlated with increased age (Fig 1A); according to the age-defined cohorts was 4.0% (N=82) in younger, 15.2% (N=126) in intermediate, and 20.3% (N=65) in older patients (p<0.001). The co-occurring mutational profile demonstrated that IDH mutations commonly co-occurred with NPM1,DNMT3A, and FLT3-ITD mutations (Fig 1B). Among IDH-mutant patients, 48% (N=132) harbored dual mutations (IDHmut/NPM1mut); 11.6% (N=32) triple mutations (IDHmut/NPM1mut/DNMT3Amut), the latter particularly prevalent in intermediate-aged adults (N=27/32, 85%). Analysis of the cohort with outcome data (N=2824) demonstrated that IDHmut and IDH-WT patients experienced similar EFS of 39% vs. 40%, respectively (p=0.723). Given the favorable prognostic significance of NPM1, we subsequently analyzed outcomes of this dual mutant group and found that NPM1 mutations retained favorable prognostic impact in the context of IDH mutant patients (Fig 2A). Notably, this effect was age-dependent, as those with IDHmut/NPM1mut had a favorable EFS compared to IDHmut/NPM1-WT for younger (76% vs. 24%, p<0.001) and intermediate patients (43% vs. 15%, p=0.001, Fig 2B) but this favorable effect was lost in older patients (29% vs. 34%, p=0.456, Fig 2C). Among intermediate age patients, outcome was further stratified by DNMT3A status, and IDHmut/NPM1mut/DNMT3Amut patients experienced an inferior EFS of 25% vs. 57% in IDHmut/NPMmut (p=0.003, Fig 2D). Conclusion: Analysis of this large patient cohort provides the most comprehensive description of IDH mutations in AML across the age spectrum. We confirm age-associated prevalence of IDH mutations and frequent co-occurrence with NPM1 mutation in all ages and in further combination with DNMT3A mutation in intermediate-aged adults. We definitively demonstrate that IDH mutation status is not an independent prognostic determinant of outcome in any age group. Co-occurrence of NPM1 and IDH mutations favorably impacts outcome in patients < 60 years of age with AML, particularly in the absence of DNMT3A mutation. Our data support that IDH inhibitors may be of particular interest in older adults and in patients <60 according to co-occurring NPM1 and DNMT3A mutations. Disclosures Othus: Marck: Consultancy; Daiichi Sankyo: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees; Glycomimetics: Membership on an entity's Board of Directors or advisory committees. Radich:Jazz: Consultancy; Novartis Pharmaceuticals Corporation: Consultancy, Research Funding; Amgen: Consultancy; Bristol-Myers Squibb: Consultancy. Abdel-Wahab:Merck: Consultancy; Janssen: Consultancy; Envisagenics Inc.: Current equity holder in private company; H3 Biomedicine Inc.: Consultancy, Research Funding. Tallman:UpToDate: Patents & Royalties; ADC Therapeutics: Research Funding; BioSight: Membership on an entity's Board of Directors or advisory committees, Research Funding; Glycomimetics: Research Funding; Rafael: Research Funding; Amgen: Research Funding; Bioline rx: Membership on an entity's Board of Directors or advisory committees; Daiichi-Sankyo: Membership on an entity's Board of Directors or advisory committees; KAHR: Membership on an entity's Board of Directors or advisory committees; Rigel: Membership on an entity's Board of Directors or advisory committees; Delta Fly Pharma: Membership on an entity's Board of Directors or advisory committees; Oncolyze: Membership on an entity's Board of Directors or advisory committees; Jazz Pharma: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Abbvie: Research Funding; Cellerant: Research Funding; Orsenix: Research Funding. Atallah:Jazz: Consultancy; Genentech: Consultancy; Abbvie: Consultancy; Takeda: Consultancy, Research Funding; Pfizer: Consultancy; Novartis Pharmaceutical Corporation: Consultancy. Luger:Daiichi-Sankyo: Honoraria; Pfizer: Honoraria; Bristol-Myers Squibb: Honoraria; Acceleron: Honoraria; Agios: Honoraria; Loxo Oncology: Honoraria; Onconova: Research Funding; Kura: Research Funding; Biosight: Research Funding; Ariad: Research Funding; Hoffman La Roche: Research Funding. Levine:Novartis: Consultancy; Loxo: Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees; Morphosys: Consultancy; Astellas: Consultancy; Janssen: Consultancy; Prelude Therapeutics: Research Funding; Amgen: Honoraria; Gilead: Honoraria; Lilly: Consultancy, Honoraria; Qiagen: Current equity holder in publicly-traded company, Membership on an entity's Board of Directors or advisory committees; Imago: Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees; C4 Therapeutics: Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees; Isoplexis: Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Research Funding; Roche: Consultancy, Honoraria, Research Funding. Cooper:Celgene: Other: Spouse was an employee of Celgene (through August 2019).