What question did this study set out to answer?

The aim is to evaluate patient selection strategies based on pathogenic germline variants in lung cancer.

March 21, 2026Open Access

520P Enriching for pathogenic germline variants (PGVs) in lung cancer (LC): A systematic review of patient selection strategies

Key Points

The aim is to evaluate patient selection strategies based on pathogenic germline variants in lung cancer.
Utilized Two-Step Cluster Analysis algorithm for patient stratification
Generated heatmaps and dendrograms to analyze genomic clusters
Examined associations with clinical metrics and protein biomarkers
Included a cohort of 129 patients with actionable mutations
Identified five distinct genomic clusters with varying patient distributions
Clusters 1-3 showed high expression of STK11 and TP53 co-mutations with KRAS drivers
Clusters 4-5 had high expression of ALK alterations and tumor suppressor mutations
Multivariate analysis revealed significant differences in clinical characteristics across clusters

Abstract

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 protein biomarkers. Results:The study cohort included 129 patients with actionable mutations.Five distinct clusters were identified with the following distribution: Cluster 1 (n=38), Cluster 2 (n=12), Cluster 3 (n=23), Cluster 4 (n=31), and Cluster 5 (n=25).Analysis of heatmaps and dendrograms revealed that Clusters 1-3 exhibited a high expression of STK11 and TP53 co-mutations alongside KRAS drivers.Conversely, Clusters 4-5 demonstrated high expression of ALK alterations and tumor suppressor gene mutations.Multivariate analysis demonstrated statistically significant differences between clusters regarding age, sex, PD-L1 expression, and Tumor Mutational Burden (TMB).No significant associations were found regarding ethnicity or Microsatellite Instability (MSI) 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.

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