Gene-network analysis predicts clinical response to immunotherapy in patients affected by NSCLC.

Objectives: Predictive biomarkers of response to immune checkpoint inhibitors (ICIs) have been extensively studied in non-small cell lung cancer (NSCLC) with controversial results. Recently, gene-network analysis emerged as a new tool to address tumor biology and behavior, representing a potential tool to evaluate response to therapies.

Methods: Clinical data and genetic profiles of 644 advanced NSCLCs were retrieved from cBioPortal and the Cancer Genome Atlas (TCGA); 243 ICI-treated NSCLCs were used to identify an immunotherapy response signatures via mutated gene network analysis and K-means unsupervised clustering. Signatures predictive values were tested in an external dataset of 242 cases and assessed versus a control group of 159 NSCLCs treated with standard chemotherapy.

Results: At least two mutations in the coding sequence of genes belonging to the chromatin remodelling pathway (A signature), and/or at least two mutations of genes involved in cell-to-cell signalling pathways (B signature), showed positive prediction in ICI-treated advanced NSCLC. Signatures performed best when combined for patients undergoing first-line immunotherapy, and for those receiving combined ICIs.

Conclusions: Alterations in genes related to chromatin remodelling complexes and cell-to-cell crosstalk may force dysfunctional immune evasion, explaining susceptibility to immunotherapy. Therefore, exploring mutated gene networks could be valuable for determining essential biological interactions, contributing to treatment personalization.