The Indian cancer genome landscape: Pathogenic alterations and emerging therapeutic targets.

e15094 Background: Comprehensive genomic profiling (CGP) is essential for identifying actionable and prognostic biomarkers in oncology practice. This study investigates oncogenic alterations, key driver mutations, and co-mutation patterns in Indian cancer patients. By targeting hallmark pathways driving disease progression, it highlights the potential of combinatorial therapies over single-biomarker approaches to address cancer's complex evolution. Methods: A retrospective analysis was conducted on FFPE tumor samples from 3,740 Indian cancer patients across 37 malignancies. Comprehensive genomic profiling (CGP) was performed using next-generation sequencing (NGS) with whole exome sequencing and a custom-designed "Functional Cancer Exome" panel targeting 1,212 cancer-related genes. Variants were annotated using multiple databases, with functional and co-mutation analyses providing key insights into oncogenic mechanisms and mutation patterns. Results: A standard variant filtration strategy identified 2,795 pathogenic mutations, focusing on clinically relevant alterations across cancer types. The analysis highlights the prevalence of pathogenic mutations in 1075 pivotal genes, with TP53 (34.8%), KRAS (15.3%), and PIK3CA (10.3%) being most prevalent. When unique genes identified in this study were mapped onto hallmark pathways associated with cancer etiology, we arrived at 13 genes( MYC, IDH1, SOD1, CD36, EGFR, and GCH1 among others) that drive cross-talk across at least five distinct pathways, acting as master regulators of disease progression. We also looked at the functional categories of co-mutated genes compared with hallmark properties in table 1. Conclusions: One biomarker/gene and one drug combination may not work during disease evolution. The hub genes identified emphasize the need for combinatorial therapeutic approaches targeting multiple pathways to disrupt crosstalk driving disease progression. Future research trials integrating clinical validation will be essential to translate these therapeutic insights into effective personalized treatments. TSG Oncogenes Translocated Cancer Genes Kinases Development Marker Homeodomain TF Cytokine and Growth Factor TSG 59 0 1 4 2 1 12 1 Oncogenes 0 58 39 16 9 2 19 2 Translocated Cancer Genes 1 39 41 9 5 2 19 2 Kinases 4 16 9 57 12 0 0 2 Development Marker 2 9 5 12 36 0 0 3 Homeodomain 1 2 2 0 0 23 23 0 TF 12 19 19 0 0 23 102 0 Cytokine and Growth Factor 1 2 2 2 3 0 0 21