AI Article Synopsis

  • Researchers developed a new biomarker classifier using gene mutations to predict treatment response in metastatic colorectal cancer (mCRC) more accurately than existing methods.
  • The 7-Gene Algorithm, based on mutations in seven KRAS-associated genes, showed high predictive effectiveness, distinguishing between patients who responded to treatment and those who did not, with significant results in both training and validation cohorts.
  • This new algorithm could enhance personalized treatment strategies for mCRC patients, potentially leading to better outcomes.

Article Abstract

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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9030299PMC
http://dx.doi.org/10.3390/cancers14082045DOI Listing

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