Regularized Mixture Cure Models Identify a Gene Signature That Improves Risk Stratification within the Favorable-Risk Group in 2017 European Leukemianet (ELN) Classification of Acute Myeloid Leukemia (Alliance 152010)

Introduction: The 2017 ELN Favorable-risk group comprises specific recurrent cytogenetic and molecular alterations. Assignment to this group has important clinical implications, as allogeneic stem cell transplantation in first complete remission (CR) is not advised due to a relatively good outcome of patients (pts) receiving chemotherapy and transplant-associated mortality. However, not all Favorable-risk pts experience long-term survival and their outcomes are affected by co-occurring genetic features making recognition of pts who would most likely be cured of high importance. Thus, we used our regularized mixture cure model (MCM) that embeds false discovery rate control to identify prognostically relevant transcripts that can distinguish pts cured from pts susceptible to lower- or higher-risk of relapse.

Methods: We analyzed 312 pts <60 years with de novo AML classified in the 2017 ELN Favorable-risk group excluding APL who achieved a CR, had RNA-seq data available, and treated on one of 8 CALGB/Alliance frontline protocols with intensive 7+3-based induction chemotherapy. No pt received an allogeneic stem cell transplant in first CR. MCMs assume the population consists of 2 subgroups, cured and susceptible, thus there are 2 regression components, which permit identification of features associated with cure and/or latency of susceptible pts. 'Cured' is synonymous with attaining long-term relapse-free survival and thus pts have a survival probability of 1. We fit univariable MCMs to identify baseline demographic, clinical features, or selected gene mutations related to the probability of being cured and/or to the latency distribution (time to relapse). RNA-seq expression values were candidate covariates in a regularized MCM to identify a parsimonious list of transcripts associated with cure or latency. We then used 2017 ELN Favorable pts in an independent dataset (GSE37642) for validation.

Results: The estimated proportion cured (39.5%) was significant (P < 0.001) indicating that a MCM should be fit. Other than % of bone marrow blasts (P = 0.009) and % of blood blasts (P = 0.048), no other pretreatment features were significant in univariable MCMs. CEBPA^double and TET2 mutations were the only ones significantly associated with latency (P = 0.008 and P = 0.013, respectively), but neither was associated with cure (P = 0.881 and P = 0.507, respectively). Our regularized MCM identified 101 transcripts associated with cure and 71 transcripts associated with latency. Kaplan-Meier curves of cured vs susceptible pts as well as of those susceptible with lower vs higher risk of relapse or death were well separated. We mapped the 168 unique transcripts to GSE37642 Affymetrix HG-U133Plus2 GeneChip for validation and found that 65 cure-related probe sets and 57 latency probe sets mapped to 34 and 32 unique genes, respectively. The validation data yielded AUC=0.788 and C-statistic=0.887, indicating good predictive ability of the selected genes. As desired, the predicted cured group had a survival probability of 1 throughout the observation period (Fig 1), with good separation between predicted susceptibles in the lower- versus higher-risk groups (Fig 2). Cell death and survival was the top molecular/cellular function for both the cure and latency portions of the model with 22 and 20 transcripts involved, respectively, with only BRD2, HLA-A, HLA-B, NAIP, and TXNIP in common. Cell death and survival molecules unique to the latency portion of the model included CD300A, CRNDE, DDX39B, FAS, GPI, HLA-DRA, KLF13, LAT, let-7, LMO2, LSP1, NOTCH1, PLCG2, PSMB8, and U2AF1; those unique to the cure portion included ARID5B, BAG6, BMI1, CCL5, CDK7, DPPA4, ELOVL5, HLA-DRB1, IL18, INTS6, MAP4K4, mir-25, mir-491, NOMO1, NR1H3, RPS16, and TUBB.

Conclusion: Our regularized MCM identified important subsets of genes associated with cure and latency in the 2017 ELN Favorable-risk pts. Our results suggest that the Favorable-risk group includes distinct transcriptionally defined subgroups with different biological properties, which may be useful for refining this risk category by identification of pts who might be cured with chemotherapy alone. Given similar treatment with 7+3-based cytotoxic chemotherapy, the identified subgroups (cured, susceptible lower risk, susceptible higher risk) seem to have different sensitivities to 7+3-based therapy.

Support: R01LM013879, U10CA180821, U10CA180882, U24CA196171

Mims:Syndax: Membership on an entity's Board of Directors or advisory committees; Daiichi Sankyo: Other: Data Safety and Monitoring Board; Astellas: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Other: Data Safety and Monitoring Board; Servier: Membership on an entity's Board of Directors or advisory committees; AbbVie: Membership on an entity's Board of Directors or advisory committees; Genentech: Membership on an entity's Board of Directors or advisory committees; Zentalis: Membership on an entity's Board of Directors or advisory committees; Ryvu: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees. Stock:Jazz Pharmaceuticals: Honoraria; Kite: Honoraria; Kura Oncology: Honoraria; MorphoSys: Honoraria; Pfizer: Consultancy, Honoraria, Research Funding; Pluristem: Consultancy, Honoraria; Servier: Honoraria; Syndax: Consultancy, Honoraria; Newave Pharmaceuticals: Consultancy; Amgen: Honoraria; Agios: Honoraria. Byrd:Novartis: Consultancy, Honoraria; Kura Oncology, Inc: Consultancy; Janssen Pharmaceuticals, Inc.: Consultancy; AstraZeneca: Consultancy; Xencor, Inc: Research Funding; Pharmacyclics LLC: Honoraria, Research Funding; Syndax: Consultancy; TG Therapeutics: Honoraria; Vincerx Pharma: Current equity holder in publicly-traded company. Eisfeld:Karyopharm Therapeutics: Other: Spouse is current company employee.