Exploratory plasma ctDNA genomic biomarkers identified by whole-exome sequencing and a novel bioinformatics pipeline in advanced driver-negative NSCLC.

Abstract

e20540 Background: Immune checkpoint inhibitors (ICIs) have improved outcomes in advanced non-small-cell lung cancer (NSCLC), but predictive biomarkers remain suboptimal. Blood-based tumour mutational burden (bTMB) captures part of the signal, yet its clinical performance is inconsistent. Whole-exome sequencing (WES) of plasma-derived ctDNA, analysed through an automated, AI-enabled platform (AIRGenomics), may reveal broader genomic patterns associated with response or resistance to immunotherapy. Methods: We conducted a prospective observational study of 37 patients with advanced NSCLC without EGFR, ALK or ROS1 alterations treated in first line with pembrolizumab alone or chemo-immunotherapy. Baseline plasma ctDNA was analysed by WES and processed with the AIRGenomics platform (Nextflow-based pipeline for QC, alignment, somatic/germline calling, CNV and annotation) including an AI-based pathogenicity model. bTMB was calculated as somatic mutations/Mb. Unsupervised clustering was performed according to PD-L1 status and progression-free survival (PFS). Survival was assessed with Kaplan–Meier and Cox models. Results: Median bTMB was 12.31 mut/Mb. Higher bTMB was associated with tumours with PD-L1≥50% and adenocarcinomas but not with overall survival (OS) or PFS and showed limited discrimination for response (AUC 0.328). Cluster analysis by PD-L1/PFS identified recurrently altered genes (including KMT2C, CEP89 and TPSB2 ). Univariable survival analysis revealed 11 genes associated in mutated status with worse OS and PFS; among them, CYP4F2 (OS wild-type median not reached vs. mutated 9 months; p=0.011), ARSD (OS wild-type median not reached vs. mutated 13 months; p=0.017) and TPSB2 (OS wild-type 24 months vs. mutated 1,5 months; p=0.007) were selected for multivariable modelling. In the Cox model, CYP4F2 (HR=2,846; IC 95%: 1,102–7,352; p=0,031) and TPSB2 (HR=3,089; IC95%: 1,053–9,060; p=0,040) were independently biomarkers associated with shorter OS (χ² =13,128; p=0.004), and CYP4F2 (HR=3,167; IC95%: 1,384–7,244; p=0,006) remained an independent predictor of shorter PFS (χ² =11.116; p=0.011). Conclusions: This proof-of-concept study demonstrates that WES of ctDNA processed through the AIRGenomics platform is viable in real-world cases of advanced NSCLC treated with immunotherapy, detecting new potential candidate genes and pathways like predictive biomarkers, such as CYP4F2, ARSD , and TPSB2 . Multivariate Cox model of the CYP4F2, TPSB2, and ARSD genes. Gene HR (OS) IC95% (OS) p (OS) HR (PFS) IC95% (PFS) p (PFS) CYP4F2 2,846 1,102–7,352 0,031 3,167 1,384–7,244 0,006 TPSB2 3,089 1,053–9,060 0,040 2,073 0,693–6,201 0,192 ARSD 2,609 0,997–6,827 0,051 1,707 0,752–3,877 0,201