Multidimensional models incorporating genomic and microenvironment features likely outperform single-analyte tests to predict immunotherapy responseMulti-Omic Biomarkers May Improve Lung Cancer Treatment Predictions

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality worldwide. While immune checkpoint inhibitors (ICIs) targeting PD-1/PD-L1 and CTLA-4 have revolutionized the therapeutic landscape, only 20–30% of unselected patients achieve durable clinical benefits. Given the imperfect predictive value of traditional markers, such as PD-L1 expression and tumor mutational burden, there is an urgent need for multidimensional biomarkers to guide personalized immunotherapy. This review evaluates emerging predictive tools, with a specific focus on chemokine signatures and multi-omic (genomic, transcriptomic, proteomic, and metabolomic) biomarkers, including integrative models. By examining the biological rationale linking tumor microenvironment chemokine networks to antitumor immunity, we discuss recent advances in profiling that enable comprehensive predictive signatures. A comprehensive narrative literature search of PubMed and EMBASE (2015–2026) was performed to identify relevant peer-reviewed studies, clinical trials, and computational analyses. Evidence suggests that integrating chemokine profiles with multi-omic data holds significant promise for improving patient selection. Multidimensional models incorporating tumor genomics and immune microenvironment features are likely to outperform single-analyte tests in identifying ICI responders. Despite ongoing challenges, such as tumor heterogeneity, assay standardization, and data integration complexity, the development of liquid biopsies and advanced machine learning models offers a path toward robust, clinically applicable predictive nomograms, which are expected to refine immunotherapy decision-making and significantly improve clinical outcomes for patients with NSCLC.