From mutational signatures to practice: Artificial intelligence-guided repurposing for blast crisis chronic myeloid leukemia.

Abstract

This article of discusses blast crisis chronic myeloid leukemia (CML), which is the most aggressive CML phase marked by rapid progression, substantial mutational complexity, and resistance to standard tyrosine kinase therapies. The methodology combines whole exome sequencing and machine learning to identify the molecular subtypes of blast crisis-CML and repurpose existing Food and Drug Administration-approved drugs on the basis of the Catalogue of Somatic Mutations in Cancer mutational patterns. In a pilot cohort (n = 7), three exploratory genomic clusters were identified: Breast cancer gene 2/tumor protein p53; isocitrate dehydrogenases (IDH) 1 and IDH 2 ten eleven translocation 2; and Janus kinase (JAK) 2/colony stimulating factor 3 receptor. The results present an opportunity to evaluate poly(ADP ribose) polymerase inhibitors (breast cancer gene 2/tumor protein p53), IDH inhibitors (IDH1/2 or ten eleven translocation 2), and JAK inhibitors (JAK2 or colony stimulating factor 3 receptor) as actionable therapeutics. Moreover, this e article presents as a strategic framework for mutation-targeted therapy targeting treatment-resistant leukemias, highlighting the potential of artificial intelligence-driven molecular stratification and uncovering clinically relevant therapeutic options for malignancies. However, limitations should be acknowledged, such as the limited cohort size and the necessity for validation in larger multicenter investigations. Prospective registries and trial enrollment should test signature-defined micro-cohorts with versioned and auditable reporting. These mappings are designed to provide hypotheses and depend on independent functional validation, prioritizing safety in combination methods with tyrosine kinase inhibitors, and allows for practical implementation in rapid turnaround environments.