Evaluating the Value of Genomic and Immune Features for Survival Prediction Beyond MSI in Colorectal Cancer

Abstract                                    Background  Traditional clinical predictors, such as tumor staging and pathological grading, are unable to completely express the inherent biological complexity and heterogeneity associated with colorectal cancer (CRC), which has led to an increased focus on the use of multi-omic data for developing better and personalized prognostic models by integrating genomics, transcriptional profiles, and clinical data. Additionally, CRC continues to be one of the leading causes of death from cancer worldwide. According to World Health Organization (WHO) reports, there are over 1.9 million new cases and almost 1 million annual deaths attributed to CRC (World Health Organization, 2024). Objective  The purpose of this study is to develop a predictive model for overall survival (OS) in patients with colorectal cancer (CRC) using microsatellite instability (MSI), mutation-based genomic features, and an exploratory gene expression profile of the immune system as potential predictors of prognosis. Ultimately, this study will contribute to improved risk classification in CRC patients and lead to more personalized and accurate cancer treatment options. Methods  TCGA Colon Adenocarcinoma (COAD) and Rectal Adenocarcinoma (READ) datasets containing clinical, somatic mutation, and RNA-sequencing information were accessed through the UCSC Xena Browser. The use of a single 12-character TCGA barcode per sample resulted in the creation of a pool of 511 primary tumors. Of those, 401 patients were alive at the time of analysis, and 110 patients died. The number of non-synonymous mutations divided by the total number of target regions was used as an estimate of Tumor Mutation Burden (TMB). Synonymous variants were excluded when calculating TMB. The standardized mean expression of 13 immune-related genes was used to create an Immune Score. Cox Proportional Hazards Regression was employed to model overall survival. Three models were created to evaluate how different factors affect overall survival. The first model included only the MSI MANTIS score. In addition to the MSI MANTIS score, the second model included 12 binary indicator variables representing commonly mutated colorectal cancer (CRC) genes, including but not limited to                   APC                   ,                   TP53                   ,                   KRAS                   , and                   BRAF                   . These mutation-based genomic features included both TMB and binary mutation indicators for commonly altered CRC genes. Lastly, the third model evaluated all previously mentioned factors along with an exploratory immune score. To assess each model's predictive ability, five-fold stratified cross-validation was performed. The C index, 3-year area under the curve (AUC), and 3-year accuracy measures were utilized to compare the models. Additional subgroup analyses utilizing the same methods described above were performed separately on the COAD and READ cohorts to determine if there were significant differences between tumor types. Results  All models had nearly random predictions with a C-index ranging from 0.43 to 0.51, a 3-year AUC ranging from 0.44 to 0.52, and an accuracy between 0.47 and 0.53. The best-performing model was the MSI-only model. The addition of the immune features worsened the performance of the models, while the mutation features improved the prediction ability but did not beat the MSI-only model. Conclusion  These findings suggest that genomic and immune features may not improve survival prediction beyond MSI in TCGA colorectal cancer.