Clinical Scorecard: Combining Chemokine Profiles and Multi-Omics Biomarkers for Predicting Immunotherapy Outcomes in Non-Small Cell Lung Cancer: An In-Depth Narrative Review

At a Glance

Key Highlights

• Only 20–30% of NSCLC patients achieve durable responses to immune checkpoint inhibitors.
• Pro-inflammatory chemokines are associated with favorable responses, while immunosuppressive chemokines correlate with resistance.
• Multi-omic integration yields predictive models that outperform single biomarkers.
• Specific genetic alterations influence the efficacy of checkpoint inhibitors.
• Emerging composite biomarkers and machine learning models enhance prediction accuracy for immunotherapy benefits.

Guideline-Based Recommendations

Diagnosis

• Utilize PD-L1 expression and tumor mutational burden (TMB) as current biomarkers for NSCLC immunotherapy.

Management

• Consider multi-omic approaches to refine patient selection for immunotherapy.

Monitoring & Follow-up

• Implement real-time monitoring of tumor and immune dynamics through liquid biopsies.

Risks

• Acknowledge the limitations of PD-L1 and TMB as predictive markers due to intratumoral heterogeneity and analytical challenges.

Patient & Prescribing Data

Patients with advanced NSCLC.

Integration of chemokine profiles with multi-omic data is promising for improving patient selection for immunotherapy.

Clinical Best Practices

• Incorporate multidimensional biomarkers in clinical decision-making for NSCLC immunotherapy.
• Focus on the biological rationale linking tumor microenvironment chemokine networks to antitumor immunity.

Related Resources & Content

• PubMed
• EMBASE