Integration of gene expression and alternative splicing enhances IGHV mutation status and survival risk prediction in chronic lymphocytic leukemia

Abstract

Abstract The immunoglobulin heavy chain variable region (IGHV) mutation status is a key prognostic marker in chronic lymphocytic leukemia (CLL), shaping disease progression and therapeutic response. Current IGHV testing relies on molecular genomic analyses and requires specific laboratory tests, limiting its accessibility. Here, we developed an unbiased computational approach to accurately predict IGHV mutation status based on gene expression and alternative splicing profiles derived from transcriptome data. We showed that predicted IGHV status had a superior predictive power for overall and failure-free survival compared to conventional IGHV testing methods. Moreover, we identified significant novel associations between key transcriptomic features and genetic lesions, indicating potential but unexplored roles in CLL pathogenesis. Our results thus highlight a novel strategy for integrating molecular features to improve CLL prognosis, underscoring the prognostic value of RNA alternative splicing in CLL biology.