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Updated: Jun 19, 2026
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Integrated Multi-Omics Approaches for Predicting Immune Checkpoint Inhibitor Response in NSCLC - Insights From
Eman Elayeh1, Shereen M Aleidi1,2, Orwa Aboud3,4,5
1Department of Biopharmaceutics and Clinical Pharmacy, School of Pharmacy, The University of Jordan, Amman, 11942, Jordan.
Background And Purpose:
Immune checkpoint inhibitors (ICIs) have improved outcomes in non-small cell lung cancer (NSCLC), yet durable benefit is limited to a subset of patients. Reliable predictive biomarkers are therefore essential. We reviewed genomic, proteomic, and metabolomic studies to evaluate how multi-omics integration advances prediction of ICI efficacy in NSCLC.
Methods:
A systematic search of PubMed, ClinicalTrials.gov, and Google Scholar was conducted on April 11, 2024, covering studies published from 2016 through January 2025, to identify omics-based biomarkers of ICI response in NSCLC. In total, 33 genomic, 9 proteomic, and 9 metabolomic studies met inclusion criteria. Each was evaluated using a standardized evidence rubric (0-14) assessing effect robustness, validation, cohort size, and clinical endpoint relevance.
Results:
Genomic predictors of poor response included EGFR and ALK/RET/ROS1 fusions, as well as KRAS co-mutations with STK11, KEAP1, or SMARCA4, all linked to immune-cold phenotypes with low tumor mutational burden (TMB) and poor T-cell infiltration. In contrast, KRAS/TP53 co-mutations, NOTCH family alterations, and BRAF V600E aligned with immune-hot signatures characterized by interferon signaling, PD-L1 upregulation, and cytotoxic T-cell infiltration. Proteomic studies consistently identified chemokines CXCL9 and CXCL10, apoptotic regulators (CASP8, FASLG), and checkpoint proteins (soluble PD-1, PD-L1, LAG-3) as predictive, while acute-phase proteins (SAA1/2, S100A8/9) correlated with resistance. Multi-analyte platforms such as PROphet demonstrated promising risk-stratification potential. Metabolomic profiling linked ICI benefit to higher baseline tryptophan, histidine, and short-chain fatty acids, while resistance was associated with increased 3-hydroxyanthranilic acid, pyruvate, and lipid metabolites indicating immunosuppressive IDO pathway activity.
Conclusion:
Multi-omics approaches converge on pathways governing antigenicity, interferon signaling, and immune-metabolic crosstalk. Although promising, most biomarkers require prospective validation in large, uniformly treated cohorts. Integrative strategies-particularly when combined with AI-driven analytics-hold potential to refine patient stratification and guide clinical use of ICIs in NSCLC.
