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

Abed Agbarya1,2, Haitham Nasrallah3, Kamel Mhameed3

  • 1The Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa 3109601, Israel.

Life (Basel, Switzerland)
|July 28, 2026
PubMed

Insights

Social network analysis identified two distinct non-small cell lung cancer (NSCLC) genomic subgroups. The highly mutated cluster showed increased PD-L1 expression and tumor mutation burden, suggesting a more immunogenic tumor profile.

Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Non-small cell lung cancer (NSCLC) is characterized by significant genomic heterogeneity impacting treatment response and immunogenicity.
  • Understanding patient subgroups based on somatic mutation profiles is crucial for personalized medicine.
  • Social network analysis (SNA) offers a novel approach to explore complex genomic relationships.

Purpose of the Study:

  • To identify distinct subgroups of metastatic NSCLC patients using network-based modularity clustering based on somatic mutation profiles.
  • To compare identified subgroups regarding PD-L1 expression, tumor mutation burden (TMB), and clinical variables.
  • To assess the utility of mutation-based SNA for delineating biologically distinct NSCLC patient groups.

Main Methods:

  • Retrospective analysis of stage 4 NSCLC patient data (2022-2024) with actionable driver mutations.
  • Construction of a social network graph representing 129 patients based on shared somatic mutation profiles.
  • Application of network-based modularity clustering to identify distinct genomic subgroups.
  • Comparison of subgroups using PD-L1 expression levels, TMB, and multivariate logistic regression.

Main Results:

  • Two distinct genomic clusters were identified in NSCLC patients.
  • Cluster 2 (n=55) exhibited a higher prevalence of mutations including KRAS, TP53, BRAF, and STK11, compared to Cluster 1 (n=74).
  • Cluster 2 demonstrated significantly higher PD-L1 expression (29.8% vs. 13.7%, p=0.001) and TMB (7.8 vs. 5.8, p=0.021).
  • Both PD-L1 expression and TMB were significantly associated with cluster assignment in multivariate analysis (p < 0.05).

Conclusions:

  • Mutation-based SNA clustering effectively delineated two biologically distinct NSCLC patient subgroups.
  • The highly mutated subgroup displayed a more immunogenic phenotype characterized by higher PD-L1 expression and TMB.
  • This SNA approach provides a novel method for integrating genomic data, requiring prospective validation for clinical application.
Keywords:
actionable driver mutationsco-mutationsnon-small cell lung cancer (NSCLC)programmed death-ligand (PD-L1)social network analysis (SNA)tumor mutation burden (TMB)

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