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

Table 1 Genomic and biological distinctions across machine learning-derived clusters

Cluster	Enriched genes	Pathogenic mechanism	Sample count
Cluster 1	BRCA2, TP53, EGFR	Genomic instability	2
Cluster 2	TET2, DNMT3A, IDH1/2	Epigenetic deregulation	2
Cluster 3	JAK2, CBL, CSF3R	Cytokine signaling	3

Table 2 Catalogue of Somatic Mutations in Cancer mutational signatures predominant in each genomic cluster of blast crisis chronic myeloid leukemia

Cluster	Dominant Catalogue of Somatic Mutations in Cancer signatures
Cluster 1	Signature 3 (breast cancer susceptibility gene-deficiency), signature 5 (aging)
Cluster 2	Signature 1 (5-methylcytosine deamination), signature 2 (apolipoprotein B mRNA editing enzyme catalytic polypeptide)
Cluster 3	Signature 13 (polymerase error), signature 18 (reactive oxygen species-related)

Table 3 Artificial intelligence-prioritized therapies linked to genomic features in each blast crisis chronic myeloid leukemia cluster

Cluster	Targeted therapies
Cluster 1	Olaparib, erlotinib, nutlin-3
Cluster 2	Ivosidenib, azacitidine, decitabine
Cluster 3	Ruxolitinib, colony-stimulating factor 1 receptor inhibitors, N-acetylcysteine

Table 4 Blast crisis chronic myeloid leukemia clusters integrating mutation biology and therapeutic guidance

Cluster	Key mutations	Catalogue of Somatic Mutations in Cancer signatures	Processes	Drugs
Cluster 1	Breast cancer susceptibility gene 2, TP53	S3, S5	Homologous recombination deficiency	Olaparib, nutlin-3
Cluster 2	Isocitrate dehydrogenase 1/2, ten-eleven translocation 2	S1, S2	Methylation shift	Ivosidenib, azacitidine
Cluster 3	Janus kinase 2, colony-stimulating factor 3 receptor	S13, S18	Inflammation, reactive oxygen species	Ruxolitinib, N-acetylcysteine