Gene-Mutation-Based Algorithm for Prediction of Treatment Response in Colorectal Cancer Patients

• Published in: Cancers Vol. 14; no. 8; p. 2045
• Main Authors: Johnson, Heather, El-Schich, Zahra, Ali, Amjad, Zhang, Xuhui, Simoulis, Athanasios, Wingren, Anette Gjörloff, Persson, Jenny L.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 18.04.2022; MDPI
• Subjects: algorithm; Algorithms; Biomarkers; biomedical laboratory science; biomedicinsk laboratorievetenskap; Cancer; Clinical Genetics; Colorectal cancer; colorectal cancer biomarkers; colorectal cancer metastasis; colorectal cancer progression; Colorectal carcinoma; Computer Systems; datorteknik; Decision making; gene mutations; Kinases; klinisk genetik; KRAS; Learning algorithms; Machine learning; Metastases; Mortality; Mutation; Patients; Tumors; KRAS; gene mutations; colorectal cancer metastasis; colorectal cancer biomarkers; algorithm; colorectal cancer progression
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers14082045

Purpose: Despite the high mortality of metastatic colorectal cancer (mCRC), no new biomarker tools are available for predicting treatment response. We developed gene-mutation-based algorithms as a biomarker classifier to predict treatment response with better precision than the current predictive factors. Methods: Random forest machine learning (ML) was applied to identify the candidate algorithms using the MSK Cohort (n = 471) as a training set and validated in the TCGA Cohort (n = 221). Logistic regression, progression-free survival (PFS), and univariate/multivariate Cox proportional hazard analyses were performed and the performance of the candidate algorithms was compared with the established risk parameters. Results: A novel 7-Gene Algorithm based on mutation profiles of seven KRAS-associated genes was identified. The algorithm was able to distinguish non-progressed (responder) vs. progressed (non-responder) patients with AUC of 0.97 and had predictive power for PFS with a hazard ratio (HR) of 16.9 (p < 0.001) in the MSK cohort. The predictive power of this algorithm for PFS was more pronounced in mCRC (HR = 16.9, p < 0.001, n = 388). Similarly, in the TCGA validation cohort, the algorithm had AUC of 0.98 and a significant predictive power for PFS (p < 0.001). Conclusion: The novel 7-Gene Algorithm can be further developed as a biomarker model for prediction of treatment response in mCRC patients to improve personalized therapies.