Omics and artificial intelligence integration for stratifying blast crisis CML using COSMIC signatures and pan-cancer...

Although chronic-phase chronic myeloid leukemia (CP-CML) is treatable and nearly curable in about 50% of patients, accelerated-phase chronic myeloid leukemia (AP-CML) shows concerning drug resistance, while blast crisis chronic myeloid leukemia (BC-CML) is highly lethal. Advances in whole exome sequencing (WES) reveal pan-cancer mutations in BC-CML, supporting mutation-guided therapies beyond Breakpoint cluster region-Abelson. Artificial intelligence (AI) and machine learning (ML) enable genomic stratification and drug repurposing, addressing overlooked actionable mutations.

To stratify BC-CML into molecular subtypes using WES, ML, and AI for precision drug repurposing.

Included 123 CML patients (111 CP-CML, 5 AP-CML, 7 BC-CML). WES identified pan-cancer mutations. Variants annotated via Ensembl Variant Effect Predictor and Catalogue of Somatic Mutations in Cancer (COSMIC). ML (principal component analysis, K-means) stratified BC-CML. COSMIC signatures and PanDrugs prioritized drugs. Analysis of variance/Kruskal-Wallis validated differences (P < 0.05).

In this exploratory, hypothesis-generating study of BC-CML patients (n = 7), we detected over 2500 somatic mutations. ML identified three BC-CML clusters: (1) Cluster 1 [breast cancer susceptibility gene 2 (BRCA2), TP53]; (2) Cluster 2 [isocitrate dehydrogenase (IDH) 1/2, ten-eleven translocation 2]; and (3) Cluster 3 [Janus kinase (JAK) 2, colony-stimulating factor 3 receptor], with distinct COSMIC signatures. Therapies: (1) Polyadenosine-diphosphate-ribose polymerase inhibitors (olaparib); (2) IDH inhibitors (ivosidenib); and (3) JAK inhibitors (ruxolitinib). Mutational burden, signatures, and targets varied significantly across clusters, supporting precision stratification.

This WES-AI-ML framework provides mutation-guided therapies for BC-CML, enabling real-time stratification and Food and Drug Administration-approved drug repurposing. While this exploratory study is limited by its small sample size (n = 7), it establishes a methodological framework for precision oncology stratification that warrants validation in larger, multi-center cohorts.