Open Access

ARTICLE

Predicting Immunotherapy Outcomes in Colorectal Cancer Using Machine Learning and Multi-Omic Biomarkers: Development of a Real-Time Predictive Web Application

Thomas Kidu¹, Harini Kethar², Haben Gebrekidan³, Haleem Farman⁴, Ahmed Sedik^4,5, Walid El-Shafai^6,7, Jawad Khan^8,*

1 Data Science, Department of Applied Mathematics and Statistics, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
2 Biomedical Engineering, Department of Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
3 Electrical Engineering, Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
4 Smart Systems Engineering Laboratory, College of Engineering, Prince Sultan University, Riyadh, Saudi Arabia
5 Department of Robotics and Intelligent Machines, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, Egypt
6 Automated Systems and Computing Lab (ASCL), Computer Science Department, Prince Sultan University, Riyadh, Saudi Arabia
7 Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
8 School of Computing, Gachon University, Seongnam, Republic of Korea

* Corresponding Author: Jawad Khan. Email:

(This article belongs to the Special Issue: Exploring the Impact of Artificial Intelligence on Healthcare: Insights into Data Management, Integration, and Ethical Considerations)

Computer Modeling in Engineering & Sciences 2026, 146(2), 41 https://doi.org/10.32604/cmes.2026.076798

Received 26 November 2025; Accepted 19 January 2026; Issue published 26 February 2026

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.

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