Social Network Clustering Analysis for Detection 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 NSCLC patients who underwent surgery between 2022 and 2024 was 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 independently predicted cluster assignment (p<0.05). Mutation-based SNA clustering successfully delineated biologically distinct subgroups of NSCLC patients. The highly mutated cluster displayed increased immunogenicity, reflected in elevated PD-L1 and TMB levels. This method offers a novel integrative approach that requires prospective validation before clinical implementation.