Multi-omics integration for therapy response prediction in head and neck squamous cell carcinoma

Abstract

Head and neck squamous cell carcinoma (HNSCC) shows heterogeneous treatment responses. We developed an integrative multi-omics approach combining DNA methylation, gene expression, and clinical data to predict radiotherapy response. Using machine learning on a cohort of 150 HNSCC patients, we identified molecular signatures associated with treatment outcome. The integrated model achieved superior prediction accuracy compared to single-omics approaches, highlighting the value of multi-modal data integration for personalized treatment stratification.

Kristian Unger

Professor of AI assisted therapy decisions in oncology

Multi-omics integration and prediction