Integrative machine learning approach for forecasting lung cancer chemosensitivity: From algorithm to cell line validation.

ABSTRACT:

Background

Chemotherapy remains the primary treatment modality for patients with lung cancer; however, substantial inter-patient variability exists in responses to chemotherapeutic agents. Therefore, predicting individual responses is critical for optimizing treatment outcomes and improving patient prognosis.

Methods

This study developed a model to predict chemotherapy response in lung cancer patients by integrating multi-omics and clinical data from the Genomics of Drug Sensitivity in Cancer database, employing 45 machine learning algorithms. Data from the Gene Expression Omnibus database were utilized to validate the model. The impact of key genes on chemotherapy response was assessed in cell lines.

Results

A model combining random forest and support vector machine

SUBMITTER: Chen J 

PROVIDER: S-EPMC12329548 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature