Evolving non-invasive biomarkers in NSCLC immunotherapy: integrating liquid biopsy and multi-omics profiling for precision oncology

Abstract

The therapeutic landscape for advanced non-small cell lung cancer (NSCLC) has been transformed by immune checkpoint inhibitors (ICIs), yet significant response heterogeneity necessitates robust, dynamic predictive biomarkers. Conventional tissue-based markers, such as PD-L1 expression and tumor mutational burden (TMB), are hindered by their invasive nature and inability to capture the dynamic tumor-host immune interplay. This review synthesizes the paradigm shift toward a dynamic, multi-parametric framework for precision immuno-oncology. We highlight the clinical utility of the liquid biopsy toolbox—including circulating tumor DNA (ctDNA) for molecular residual disease (MRD) monitoring, circulating tumor cells (CTCs), and extracellular vesicles (EVs) in reflecting systemic immune status. Furthermore, we explore biological insights from multi-omics profiling, covering genomic drivers of resistance (e.g., STK11/KEAP1), immunometabolic crosstalk, and systemic inflammatory indicators like the NLR/PLR ratio. The potential of radiomics and pathomics to extract spatial signatures via artificial intelligence (AI) is discussed to address whole-tumor heterogeneity. Finally, we emphasize the integration of these disparate data streams through multimodal AI and Explainable AI (XAI) to construct high-fidelity predictive models. This integrated approach aims to overcome standardization hurdles and enable personalized, adaptive management in NSCLC immunotherapy.