Social Network Clustering Analysis for Detection of Associated Genetic Co-Mutations in Patients with Actionable Driver Mutations in NSCLC.

Non-small cell lung cancer (NSCLC) exhibits genomic heterogeneity that affects tumor immunogenicity and PD-L1 expression. Patient clustering based on shared mutational profiles using social network analysis (SNA) has been narrowly explored. The study aimed to identify subgroups of NSCLC patients with similar somatic mutation profiles using network-based modularity clustering, and to compare these groups with respect to PD-L1 expression, Tumor mutation burden (TMB), and clinical variables. Data of patients with stage 4 (metastatic) NSCLC, whose tumor tissue samples were collected between 2022 and 2024, were analyzed. This retrospective study included NSCLC patients harboring actionable driver mutations in genes such as EGFR, KRAS, ALK, BRAF, MET. A social network of 129 patients was constructed. Two distinct genomic clusters were identified. Cluster 2 (n = 55) showed a higher prevalence of KRAS, TP53, BRAF, STK11 and additional mutations, while cluster 1 (n = 74) displayed a limited number of driver mutations. Cluster 2 had significantly higher PD-L1 expression (29.8% vs. 13.7%, p = 0.001) and higher TMB (7.8 vs. 5.8, p = 0.021). In multivariate logistic regression, both PD-L1 and TMB were associated with cluster assignment (p < 0.05). Mutation-based SNA clustering delineated two biologically distinct subgroups of NSCLC patients. The highly mutated cluster displayed higher PD-L1 expression and TMB, a profile consistent with a more immunogenic phenotype. This method offers a novel integrative approach that requires prospective validation before clinical implementation.