Machine Learning–Driven Integration of Cancer Cell Phenotypes Predicts Cisplatin Sensitivity

TL;DR

This study uses machine learning to predict how sensitive cancer cells are to cisplatin, a common chemotherapy drug, by analyzing gene expression patterns.

Contribution

A novel machine learning model called CSP26G was developed to predict cisplatin sensitivity using gene expression data.

Findings

The CSP26G model accurately predicted cisplatin sensitivity in resistant and sensitive cancer cell lines.

The model classified lung cancer patients into cisplatin-sensitive and resistant groups, with the sensitive group showing longer survival.

CSP26G also predicted responsiveness to other DNA-damaging agents, showing broader applicability.

Abstract

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.

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 26 genes (CSP26G) model was developed.

Cisplatin‐resistant A549CR cells experimentally confirmed the external validity of the CSP26G model. The model also classified patients with non‐small cell lung cancer in The Cancer Genome Atlas (TCGA) clinical database into cisplatin‐sensitive and cisplatin‐resistant groups. The predicted sensitive group showed significantly longer survival than the predicted resistant group. Furthermore, CSP26G predicts not only cisplatin efficacy but also responsiveness to other DNA‐damaging agents.

These findings indicate that the sensitivity prediction model constructed through the integration of DEG and machine learning analyses can forecast drug sensitivity, thereby contributing to the advancement of effective and personalized precision medicine in classical chemotherapies.

The CSP26G model, a predictor of cisplatin sensitivity, was developed using machine learning to integrate cancer cell phenotypes. This model accurately predicted sensitivity in vitro and the prognosis of patients with non‐small cell lung cancer.

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