Distilling Multi-Modal Genomic Knowledge for Drug Response Prediction
Shuang Ge, Shuqing Sun, Huan Xu, Zhixiang Ren · 2024
In clinical practice, the accurate assessment of patient response to drugs is crucial for personalized treatment. Research has demonstrated that alterations in genomic profiles can significantly influence the efficacy of cancer therapies. Studies of cancer drug sensitivity have the potential to predict heterogeneous cell line responses to various drugs, facilitate drug screening processes, and identify new biomarkers for sensitive populations[3]. Unlike traditional documentation of patient drug responses, cancer pharmacogenomics research has established a comprehensive database of in vitro cultured cell lines, encompassing genomic data across multiple modalities, including gene expression, mutation, copy number variation, and methylation patterns[1]. Recent investigations have confirmed that incorporating information from diverse genomic modalities can yield a wide range of biological insights that enhance the predictive modeling capabilities of drug responses[2]. However, access to paired genomic data remains challenging within practical clinical settings, which limits the application of high-performance multi-modal models.