High-accuracy prediction of colorectal cancer chemotherapy efficacy using machine learning applied to gene expression data

Soukaina Amniouel; Mohsin Saleet Jafri

doi:10.3389/fphys.2023.1272206

High-accuracy prediction of colorectal cancer chemotherapy efficacy using machine learning applied to gene expression data

Front Physiol. 2024 Jan 18:14:1272206. doi: 10.3389/fphys.2023.1272206. eCollection 2023.

Authors

Soukaina Amniouel¹, Mohsin Saleet Jafri^{1

2}

Affiliations

¹ School of Systems Biology, George Mason University, Fairfax, VA, United States.
² Center for Biomedical Engineering and Technology, University of Maryland School of Medicine, Baltimore, MD, United States.

Abstract

Introduction: FOLFOX and FOLFIRI chemotherapy are considered standard first-line treatment options for colorectal cancer (CRC). However, the criteria for selecting the appropriate treatments have not been thoroughly analyzed. Methods: A newly developed machine learning model was applied on several gene expression data from the public repository GEO database to identify molecular signatures predictive of efficacy of 5-FU based combination chemotherapy (FOLFOX and FOLFIRI) in patients with CRC. The model was trained using 5-fold cross validation and multiple feature selection methods including LASSO and VarSelRF methods. Random Forest and support vector machine classifiers were applied to evaluate the performance of the models. Results and Discussion: For the CRC GEO dataset samples from patients who received either FOLFOX or FOLFIRI, validation and test sets were >90% correctly classified (accuracy), with specificity and sensitivity ranging between 85%-95%. In the datasets used from the GEO database, 28.6% of patients who failed the treatment therapy they received are predicted to benefit from the alternative treatment. Analysis of the gene signature suggests the mechanistic difference between colorectal cancers that respond and those that do not respond to FOLFOX and FOLFIRI. Application of this machine learning approach could lead to improvements in treatment outcomes for patients with CRC and other cancers after additional appropriate clinical validation.

Keywords: FOLFIRI; FOLFOX; chemoresistance; colorectal cancer; feature selection; gene expression; machine learning.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was supported by the National Science Foundation (United States) Grant No. 2116886. MJ has received funding of NCI SBIR grant through a subcontract from the company Pathodynamics that is exploring clinical applications. The authors declare that this study received funding from Pathodynamics. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.