Integrative Genomic and Clinical Profiling of Colorectal Cancer Liver Metastases to Guide Personalized Surgery and Liver Transplantation

<h3>Abstract</h3> <h3>Background/Aim:</h3> Colorectal cancer liver metastases (CRLM) remain a leading cause of cancer-related mortality. Although hepatic resection is the only established curative option, recurrence exceeds 60%, underscoring substantial biologic heterogeneity. Liver transplantation (LT) has re-emerged for highly selected patients with unresectable CRLM, but optimal biologic selection criteria remain undefined. This study integrates genomic and clinical data to develop a biologically grounded framework for surgical and transplant decision-making. <h3>Materials and Methods:</h3> The Memorial Sloan Kettering 2017 metastatic colorectal cancer cohort was analyzed using cBioPortal-formatted clinical, genomic, and survival data. The study included patients with liver-only metastatic presentation. Genomic variables comprised <i>KRAS, NRAS, BRAF, TP53, APC, PIK3CA, SMAD4</i> alterations, tumor mutational burden, microsatellite instability, and copy-number alteration burden. Survival was assessed using Kaplan–Meier and Cox models. A predefined molecular-risk classification stratified patients into low (RAS/RAF wild-type, <i>SMAD4</i>-intact), intermediate (isolated <i>KRAS</i> mutation), and high risk (<i>NRAS, BRAF</i>, or <i>SMAD4</i> alterations). Machine-learning models predicted 24-month mortality. <h3>Results:</h3> Among 503 patients, molecular-risk groups demonstrated distinct survival (5-year OS: 74.5%, 56.6%, and 49.8%; <i>p</i>&lt;0.001). Intermediate- and high-risk groups were independently associated with worse survival (HR=1.71 and 2.60, respectively). Integrated clinicogenomic models modestly improved predictive performance (AUROC 0.697), with <i>BRAF</i> mutation, tumor sidedness, and age as key contributors. Inclusion of metastasectomy status increased AUROC but reflected post-treatment bias. <h3>Conclusion:</h3> Molecular-risk stratification and integrated modeling identify clinically meaningful prognostic groups in CRLM. These findings support incorporation of genomic profiling into precision surgical and transplant evaluation, while emphasizing the need for prospective validation.