A multi-task domain-adapted model to predict chemotherapy response from mutations in recurrently altered cancer genes.

ABSTRACT: Next-generation sequencing (NGS) is increasingly utilized in oncological practice; however, only a minority of patients benefit from targeted therapy. Developing drug response prediction (DRP) models is important for the "untargetable" majority. Prior DRP models typically use whole-transcriptome and whole-exome sequencing data, which are clinically unavailable. We aim to develop a DRP model toward the repurposing of chemotherapy, requiring only information from clinical-grade NGS (cNGS) panels of restricted gene sets. Data sparsity and limited patient drug response information make this challenging. We firstly show that existing DRPs perform equally with whole-exome versus cNGS (∼300 genes) data. Drug IDentifier (DruID) is then described, a DRP model for restricted gene sets using transfer

SUBMITTER: Jayagopal A 

PROVIDER: S-EPMC11952854 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature