Machine Learning-Driven Integration of Cancer Cell Phenotypes Predicts Cisplatin Sensitivity.

ABSTRACT:

Background

Precision medicine has personalized anticancer therapies and has been considered standard practice. Although current cancer genomic profiling tests are powerful tools to predict the efficacy of molecular targeted drugs or immune checkpoint inhibitors, they are not readily applicable for classical anticancer agents. In this study, we report a novel concept of phenotype-based classification using machine learning analysis of gene expression patterns to predict the effectiveness of anticancer agents.

Methods

Hierarchical clustering of IC₅₀ values distinguished cisplatin-sensitive and resistant cell lines. Differentially expressed gene (DEG) analysis and SHAP value-based machine learning identified 26 key genes, and the cisplatin sensitivity predictor using

SUBMITTER: Ujiie H 

PROVIDER: S-EPMC12631745 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature