@Article{cmes.2026.076798,
AUTHOR = {Thomas Kidu, Harini Kethar, Haben Gebrekidan, Haleem Farman, Ahmed Sedik, Walid El-Shafai, Jawad Khan},
TITLE = {Predicting Immunotherapy Outcomes in Colorectal Cancer Using Machine Learning and Multi-Omic Biomarkers: Development of a Real-Time Predictive Web Application},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {146},
YEAR = {2026},
NUMBER = {2},
PAGES = {0--0},
URL = {http://www.techscience.com/CMES/v146n2/66321},
ISSN = {1526-1506},
ABSTRACT = {Colorectal cancer is the third most diagnosed cancer worldwide, and immune checkpoint inhibitors have shown promising therapeutic outcomes in selected patient groups. This study performed a comprehensive analysis of multi-omics data from The Cancer Genome Atlas colorectal adenocarcinoma cohort (TCGA-COADREAD), accessed through cBioPortal, to develop machine learning models for predicting progression-free survival (PFS) following immunotherapy. The dataset included clinical variables, genomic alterations in Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS), B-Raf Proto-Oncogene (BRAF), and Neuroblastoma RAS Viral Oncogene Homolog (NRAS), microsatellite instability (MSI) status, tumor mutation burden (TMB), and expression of immune checkpoint genes. Kaplan–Meier analysis showed that KRAS mutations were significantly associated with reduced PFS, while BRAF and NRAS mutations had no significant impact. MSI-high tumors exhibited elevated TMB and increased immune checkpoint expression, reflecting their immunologically active phenotype. We developed both survival and classification models, with the Extra Trees classifier achieving the best performance (accuracy = 0.86, precision = 0.67, recall = 0.70, F1-score = 0.68, AUC = 0.84). These findings highlight the potential of combining genomic and immune biomarkers with machine learning to improve patient stratification and guide personalized immunotherapy decisions. An interactive web application was also developed to enable clinicians to input patient-specific molecular and clinical data and visualize individualized PFS predictions, supporting timely, data-driven treatment planning.},
DOI = {10.32604/cmes.2026.076798}
}