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 algorithms exhibited superior performance in both the training and validation sets. Furthermore, patients in the sensitive group demonstrated longer overall survival compared to those in the resistant group. TMED4 and DYNLRB1 genes were identified as pivotal features in the model and exhibited higher expression levels in the chemotherapy-resistant group. SiRNA-mediated knockdown of gene expression enhanced the chemosensitivity of lung cancer cell lines to chemotherapeutic agents.

Conclusions: This study successfully developed a high-performance machine learning model for predicting chemotherapy response in lung cancer and elucidated a strong correlation between TMED4 and DYNLRB1 gene expression and chemotherapy resistance. We further provide a user-friendly web server (available at https://smuonco.shinyapps.io/LC-DrugPortal/) to enable clinical utilization of our model, promoting personalized chemotherapy selection for lung cancer patients.

Keywords: Cell Line Validation; Chemosensitivity; Lung Cancer; Machine Learning; Prediction.

Graphical abstract

Fig. 1

Schematic overview of the development and validation of machine learning models for predicting chemotherapy drug sensitivity in lung cancer patients, and the exploration of the predictive value of TMED4 and DYNLRB1 genes in lung cancer chemotherapy response.

Fig. 2

Evaluation of chemotherapy drug sensitivity prediction models. (A) Pearson correlation coefficients (r values) of the models in the training set, validation set, and their average. (B) R values of prediction models for the top 10 chemotherapy drugs based on the average of training and validation sets. (C) Comparison of r values between the newly developed model and the pRRophetic model for predicting the same drugs. (D) Log10(p) values of IC50 differences between predicted drug-sensitive and drug-resistant groups across multiple datasets, as determined by the Wilcoxon rank-sum test. (E-I) Kaplan-Meier survival curves for paclitaxel, methotrexate, and cisplatin, comparing overall survival between predicted drug-sensitive and drug-resistant groups across various datasets.

Fig. 3

TMED4 expression in lung cancer cell lines and its impact on chemotherapy drug sensitivity. (A) TMED4 expression levels in drug-resistant and drug-sensitive cells for various lung cancer chemotherapy drugs. (B) Relative expression levels of TMED4 in five lung cancer cell lines (A549, H1299, H460, HCC827, and PC9) treated with three distinct siRNA sequences. (C) Dose-response curves for five chemotherapeutic agents (5-fluorouracil, cisplatin, gemcitabine, etoposide, and epirubicin) in TMED4 knockdown lung cancer cell lines.

Fig. 4

DYNLRB1 expression in lung cancer cell lines and its impact on chemotherapy drug sensitivity.cancer cell. (A) DYNLRB1 expression levels in drug-resistant and drug-sensitive cells for various lung cancer chemotherapeutic agents. (B) Relative expression levels of DYNLRB1 in five lung cancer cell lines (A549, H1299, H460, HCC827, and PC9) treated with three distinct siRNA sequences. (C) Dose-response curves for five chemotherapeutic agents (5-fluorouracil, cisplatin, gemcitabine, etoposide, and epirubicin) in DYNLRB1 knockdown lung cancer cell lines.