Clinical Report: Combining Chemokine Profiles and Multi-Omics Biomarkers for Predicting Immunotherapy Outcomes in NSCLC

Overview

This narrative review evaluates the integration of chemokine signatures and multi-omic biomarkers to enhance predictive accuracy for immunotherapy outcomes in non-small cell lung cancer (NSCLC).

Background

Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related mortality, with immune checkpoint inhibitors (ICIs) providing new treatment options. However, the limited response rates of 20-30% indicate a need for more effective predictive biomarkers. This review focuses on emerging multidimensional biomarkers that could refine patient selection for immunotherapy.

Data Highlights

This review synthesizes findings from various studies, emphasizing the integration of chemokine profiles with multi-omic data to improve predictive models for immunotherapy outcomes.

Key Findings

• Only 20-30% of NSCLC patients achieve durable responses to immune checkpoint inhibitors.
• Pro-inflammatory Th1-type chemokines are associated with favorable responses to immunotherapy, while immunosuppressive chemokines correlate with resistance.
• Multi-omic integration yields predictive models that outperform single biomarkers in identifying immunotherapy responders.
• Specific genetic alterations in NSCLC tumors can influence the efficacy of checkpoint inhibitors.
• Emerging composite biomarkers and machine learning models show improved accuracy in predicting immunotherapy benefits.
• Combining chemokine profiling with multi-omic biomarkers may enhance patient selection for immunotherapy.

Clinical Implications

The integration of chemokine profiles and multi-omic biomarkers could lead to more accurate patient selection for immunotherapy in NSCLC.

Conclusion

The review discusses the combination of chemokine and multi-omic biomarkers to refine immunotherapy decision-making in NSCLC.

Related Resources & Content

1. Frontiers in Immunology, 2026 -- Multi-omics biomarkers for predicting resistance, hyperprogression, and immune-related toxicity during PD-1/PD-L1 therapy in lung cancer
2. Frontiers in Immunology, 2026 -- Multi-omics analysis of kidney renal cell carcinoma in silico with preliminary in vivo validation
3. asco ai in oncology, 2026 -- Multimodal Model Uses Pathology Data to Predict Immunotherapy Response in NSCLC
4. asco ai in oncology, 2026 -- Improved Immunotherapy Response Prediction in NSCLC With Deep-Learning Radiomic Biomarker
5. ASCO Issues Updated Guidelines for Stage IV NSCLC With and Without Driver Alterations - The ASCO Post, 2024
6. PD-L1 And TMB Testing Of Patients With Lung Cancer For Immunooncology Therapies - CAP
7. Prediction accuracy of biomarkers for response to immune checkpoint inhibitors in advanced non-small cell lung cancer: A systematic review and meta-analysis. | Journal of Clinical Oncology
8. ASCO Issues Updated Guidelines for Stage IV NSCLC With and Without Driver Alterations - The ASCO Post
9. PD-L1 And TMB Testing Of Patients With Lung Cancer For Immunooncology Therapies - CAP
10. Prediction accuracy of biomarkers for response to immune checkpoint inhibitors in advanced non-small cell lung cancer: A systematic review and meta-analysis. | Journal of Clinical Oncology