Next-Generation Sequencing in Colorectal Cancer: Real-World Molecular Profiling and Clinical Correlations

Next-generation sequencing (NGS) has become a cornerstone of precision oncology in colorectal cancer (CRC), although its role in routine patient stratification remains incompletely defined. This retrospective single-centre study characterised the molecular landscape of clinically selected CRC patients undergoing routine NGS and explored associations between genomic alterations and clinicopathological features. 97 eligible patients who underwent targeted NGS using two validated sequencing platforms were selected. Demographic, clinicopathological, and molecular data were integrated, and associations were evaluated using descriptive statistics, exploratory association testing, principal component analysis, and multiple correspondence analysis. At least one reportable genetic alteration was identified in 91 patients (93.8%), comprising 198 alteration events across multiple cancer-related genes. The most frequently altered genes with pathogenic or likely pathogenic variants were TP53 (55.7%), KRAS (40.2%), PIK3CA (18.6%), and BRAF (11.5%), while microsatellite instability was detected in 19.1% of evaluable tumours. Exploratory analyses identified associations between selected molecular alterations and clinicopathological characteristics. However, these were generally modest, frequently limited by small subgroup sizes, and none survived Benjamini-Hochberg correction. Principal component analysis demonstrated substantial molecular heterogeneity without defining distinct clinicopathological subgroups. Routine targeted NGS provides detailed molecular characterisation of CRC and generates information relevant to biomarker-informed clinical decision-making in real-world practice. However, targeted panels alone were insufficient to establish robust molecular subgroups in this retrospective cohort. These findings highlight the biological complexity of CRC and support larger prospective studies incorporating broader molecular profiling to improve precision patient stratification and optimise personalised therapeutic strategies.