Kamel Mhameed

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.

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.

Background: Non-small cell lung cancer (NSCLC) remains the leading cause of cancer-related mortality despite major advances in diagnostics and therapies. The prognosis remains poor, mostly due to late-stage presentation and molecular heterogeneity. Epidermal growth factor receptor (EGFR) mutations are common drivers of NSCLC. The development of EGFR tyrosine kinase inhibitors (TKIs) has significantly improved outcomes in patients with EGFR mutations. Variant allele frequency (VAF) is a quantitative genomic measure representing the proportion of sequencing reads harboring a given mutation. In NSCLC tissue, the EGFR mutation VAF reflects tumor clonality and intratumoral heterogeneity, and accumulating evidence suggests an association between EGFR VAF and response to EGFR-targeted TKIs. Methods: To address the limited synthesis of data on the relevance of EGFR mutation VAF in NSCLC, we conducted a narrative review of the literature using PubMed/MEDLINE and Embase databases and current clinical guidelines, synthesizing available evidence on EGFR VAF, including its biological, molecular, and therapeutic implications in EGFR-mutated disease. The review was structured in accordance with the SANRA (Scale for the Assessment of Narrative Review Articles) checklist. Results: EGFR VAF and on-treatment VAF dynamics are consistently associated with treatment response, progression-free survival, and overall survival in osimertinib-treated NSCLC. Baseline VAF enables risk stratification, early clearance kinetics predict durable benefit, and longitudinal VAF monitoring facilitates early detection of resistance. Importantly, the prognostic implications of VAF differ fundamentally between tissue-based and plasma-based measurements: high tissue VAF reflects clonal homogeneity and predicts favorable TKI response, whereas high plasma VAF indicates elevated tumor burden and is associated with inferior outcomes. In the second-line setting, the T790M/activating mutation ratio serves as a surrogate for resistance clonality and independently predicts osimertinib efficacy. Conclusions: EGFR VAF represents a promising dynamic molecular biomarker for treatment monitoring and precision decision-making in EGFR-mutated NSCLC.

Background/Objectives: Lung cancer remains the leading cause of cancer-related mortality globally. Approximately 45% of these tumors harbor oncogenic mutations that drive carcinogenesis and are amenable to targeted therapies. Other predictive biomarkers—e.g., PD-L1, TMB, and MSI—play a crucial role in patients’ management. This study aims to investigate the existence of mutation clusters (co-mutations) and evaluate the correlation of these clusters with various clinical and laboratory parameters. Methods: A retrospective study was conducted utilizing pathological samples from lung cancer patients harboring mutations in EGFR, KRAS, ALK, BRAF, MET, HER2, ROS1, NTRK, and NRG1. Data were collected from the Institute of Pathology at Carmel Medical Center between the years 2022 and 2024. Patients were stratified using a Two-Step Cluster Analysis algorithm based on actionable mutations and co-mutations. Heatmaps and dendrograms were generated to assess the correlation between these genomic clusters, clinical metrics, and predictive biomarkers. Results: The study cohort included 129 patients with actionable mutations. Five distinct clusters were identified: Clusters 1, 2, and 3 exhibited a high expression of STK11 and TP53 co-mutations alongside KRAS drivers (n = 38, n = 12, and n = 23, respectively). Clusters 4 and 5 demonstrated high expression of ALK alterations and tumor suppressor gene mutations (n = 31 and n = 25, respectively). Cluster comparisons demonstrated statistically significant differences between clusters regarding age, gender, PD-L1 expression, and tumor mutational burden. No significant associations were found regarding ethnicity or microsatellite instability status. Conclusions: By constructing clusters based on the aggregate of genomic alterations in patients with actionable mutations, it is possible to predict associations with distinct demographic and clinical characteristics. Future research should apply this analytical approach to larger cohorts to further characterize these subgroups and investigate potential correlations with therapeutic efficacy.

Background/Objectives: Lung cancer remains the leading cause of cancer-related mortality globally. Approximately 45% of these tumors harbor oncogenic mutations that drive carcinogenesis and are amenable to targeted therapies. Other predictive biomarkers— e.g., PD-L1, TMB, and MSI—play a crucial role in patients’ management. This study aims to investigate the existence of mutation clusters (co-mutations) and evaluate the correlation of these clusters with various clinical and laboratory parameters. Methods: A retrospective study was conducted utilizing pathological samples from lung cancer patients harboring mutations in EGFR, KRAS, ALK, BRAF, MET, HER2, ROS1, NTRK, and NRG1. Data were collected from the Institute of Pathology at Carmel Medical Center between the years 2022 and 2024. Patients were stratified using a Two-Step Cluster Analysis algorithm based on actionable mutations and co-mutations. Heatmaps and dendrograms were generated to assess the correlation between these genomic clusters, clinical metrics, and predictive biomarkers. Results: The study cohort included 129 patients with actionable mutations. Five distinct clusters were identified: Clusters 1,2, and 3 exhibited a high expression of STK11 and TP53 co-mutations alongside KRAS drivers (n=38, n=12 and n=23 respectively). Clusters 4 and 5 demonstrated high expression of ALK alterations and tumor suppressor gene mutations (n=31, n=25 respectively). Multivariate analysis demonstrated statistically significant differences between clusters regarding age, gender, PD-L1 expression, and Tumor Mutational Burden. No significant associations were found regarding ethnicity or Microsatellite Instability status. Conclusions: By constructing clusters based on the aggregate of genomic alterations in patients with actionable mutations, it is possible to predict associations with distinct demographic and clinical characteristics. Future research should apply this analytical approach to larger cohorts to further characterize these subgroups and investigate potential correlations with therapeutic efficacy.