Penalized Bayesian forward continuation ratio model with application to high-dimensional data with discrete survival outcomes.

While time-to-event data are often continuous, there are several instances where discrete survival data, which are inherently ordinal, may be available or are more appropriate or useful. Several discrete survival models exist, but the forward continuation ratio model with a complementary log-log link has a survival interpretation and is closely related to the Cox proportional hazards model, despite being an ordinal model. This model has previously been implemented in the high-dimensional setting using the ordinal generalized monotone incremental forward stagewise algorithm. Here, we propose a Bayesian penalized forward continuation ratio model with a complementary log-log link and explore different priors to perform variable selection and regularization. Through simulations, we show that our Bayesian model outperformed the existing frequentist method in terms of variable selection performance, and that a 10% prior inclusion probability performed better than 1% or 50%. We also illustrate our model on a publicly available acute myeloid leukemia dataset to identify genomic features associated with discrete survival. We identified nine features that map to ten unique genes, five of which have been previously associated with leukemia in the literature. In conclusion, our proposed Bayesian model is flexible, allows simultaneous variable selection and uncertainty quantification, and performed well in simulation studies and application to real data.

Identifying long-term survivors and those at higher or lower risk of relapse among patients with cytogenetically normal acute myeloid leukemia using a high-dimensional mixture cure model.

Patients with cytogenetically normal acute myeloid leukemia (CN-AML) may harbor prognostically relevant gene mutations and thus be categorized into one of the three 2022 European LeukemiaNet (ELN) genetic-risk groups. Nevertheless, there remains heterogeneity with respect to relapse-free survival (RFS) within these genetic-risk groups. Our training set included 306 adults on Alliance for Clinical Trials in Oncology studies with de novo CN-AML aged < 60 years who achieved a complete remission and for whom centrally reviewed cytogenetics, RNA-sequencing, and gene mutation data from diagnostic samples were available (Alliance trial A152010). To overcome deficiencies of the Cox proportional hazards model when long-term survivors are present, we developed a penalized semi-parametric mixture cure model (MCM) to predict RFS where RNA-sequencing data comprised the predictor space. To validate model performance, we employed an independent test set from the German Acute Myeloid Leukemia Cooperative Group (AMLCG) consisting of 40 de novo CN-AML patients aged < 60 years who achieved a complete remission and had RNA-sequencing of their pre-treatment sample. For the training set, there was a significant non-zero cure fraction (p = 0.019) with 28.5% of patients estimated to be cured. Our MCM included 112 genes associated with cure, or long-term RFS, and 87 genes associated with latency, or shorter-term time-to-relapse. The area under the curve and C-statistic were respectively, 0.947 and 0.783 for our training set and 0.837 and 0.718 for our test set. We identified a novel, prognostically relevant molecular signature in CN-AML, which allows identification of patient subgroups independent of 2022 ELN genetic-risk groups.Trial registration Data from companion studies CALGB 8461, 9665 and 20202 (trials registered at www.clinicaltrials.gov as, respectively, NCT00048958, NCT00899223, and NCT00900224) were obtained from Alliance for Clinical Trials in Oncology under data sharing study A152010. Data from the AMLCG 2008 trial was registered at www.clinicaltrials.gov as NCT01382147.

Beat-AML 2024 ELN-Refined Risk Stratification for Older Adults with Newly Diagnosed AML Given Lower-Intensity Therapy.

While the 2022 European LeukemiaNet (ELN) acute myeloid leukemia (AML) risk classification reliably predicts outcomes in younger patients treated with intensive chemotherapy, it is unclear whether it applies to adults ≥ 60 years treated with lower-intensity treatment (LIT). We aimed to test the prognostic impact of ELN risk in patients with newly diagnosed (ND) AML ≥ 60 years given LIT and to further refine risk stratification for these patients. A total of 595 patients were included: 11% had favorable-risk, 11% had intermediate-risk, and 78% had adverse-risk AML as defined by ELN. ELN risk was prognostic for overall survival (OS) (P<0.001) but did not stratify favorable-risk from intermediate-risk (P=0.71). Within adverse-risk AML, the impact of additional molecular abnormalities was further evaluated. Multivariable analysis was performed on a training set (N=316) and identified IDH2 mutation as an independent favorable prognostic factor, and KRAS, MLL2, and TP53 mutations as unfavorable (P<0.05). A "mutation-score" was calculated for each combination of these mutations, assigning adverse-risk patients into two risk groups: -1 to 0 points ("Beat-AML-intermediate") vs 1+ points ("Beat-AML-adverse"). In the final refined risk classification, the ELN favorable- and intermediate-risk groups were combined into a newly defined "Beat-AML-favorable-risk", in addition to mutation scoring within the ELN adverse-risk. This approach redefines risk for older ND AML and proposes refined Beat-AML-favorable- (22%), Beat-AML-intermediate- (41%), and Beat-AML-adverse-risk (37%) groups with improved discrimination for OS (2-year OS: 48% vs 33% vs 11%, respectively, P<0.001; C-index: 0.60 vs 0.55 for ELN), providing patients and providers additional information for treatment decision-making.