Improved somatic mutation profiling in small-cell lung cancer using RNA-sequencing and the T2T-CHM13 genome reference

Comprehensive identification of somatic mutations (SMs) is essential for understanding the molecular landscape of small cell lung cancer (SCLC). While DNA-sequencing remains a prevalent method for SMs detection, RNA-sequencing (RNA-seq) offers a powerful complementary strategy by focusing on mutations in actively transcribed regions. Despite its potential, the utility of RNA-seq for somatic variant discovery in SCLC remains underexplored. Here, we present a robust RNA-seq analysis pipeline that integrates a two-pass HISAT2 alignment with somatic variant calling using MuTect2 from the Genome Analysis Toolkit (GATK). Variant annotation using COSMIC and dbSNP databases revealed a predominance of G > A and C > T transitions, consistent with mutational signature of tobacco exposure and oxidative stress. Our analysis identified six key oncogenes (COL4A1, FN1, AKT1, TRAF4, MYC, PTK2) implicated in SCLC signaling pathways, alongside 259 previously unreported high-impact mutations. Leveraging the complete T2T-CHM13 reference genome improved variant resolution, capturing more SNPs, MNPs, and Indels compared to GRCh38. Integration with CancerVar tool prioritized putative driver genes, including UBC, TSC1, ADGRL1, and ATN1. Notably, these driver mutations showed a negative association with the SCLC-inflamed subtype, which is characterized by increased immune infiltration and prolonged survival. Our findings highlight the untapped potential of RNA-seq for somatic mutation profiling in SCLC and unveil novel molecular targets with implications for patient stratification, immunotherapy, and precision treatment strategies.