Objective:

To evaluate emerging predictive tools for immunotherapy outcomes in NSCLC, focusing on chemokine signatures and multi-omic biomarkers.

Approach:

• Literature Search: A comprehensive narrative literature search of PubMed and EMBASE (2015–2026) was performed to identify relevant peer-reviewed studies, clinical trials, and computational analyses.

Key Findings:

• Only 20–30% of unselected NSCLC patients achieve durable responses to immune checkpoint inhibitors (ICIs).
• Tumor-derived chemokines, particularly pro-inflammatory Th1-type chemokines, are associated with favorable responses to ICIs, while immunosuppressive chemokines correlate with resistance.
• Multi-omic integration yields predictive models that outperform single biomarkers in identifying ICI responders.
• Specific genetic alterations in NSCLC tumors, such as co-mutations in STK11/LKB1 or KEAP1, influence the efficacy of checkpoint inhibitors.
• Emerging composite biomarkers and machine learning models improve prediction accuracy for immunotherapy benefits.

Interpretation:

Integrating chemokine profiles with multi-omic data holds promise for improving patient selection for immunotherapy in NSCLC.

Limitations:

• Challenges include tumor heterogeneity, assay standardization, and data integration complexity.
• Emerging models require prospective validation in clinical trials.

Conclusion:

Combining chemokine profiling with multi-omic biomarkers may refine patient selection for immunotherapy in NSCLC.