Inflammatory-Molecular Clusters as Predictors of Immunotherapy Response in Advanced Non-Small-Cell Lung Cancer

Area of Science:

• Oncology
• Immunology
• Biomarker Discovery

Background:

• Immunotherapy has improved outcomes for advanced non-small-cell lung cancer (NSCLC), but individual biomarker predictive value is limited.
• Systemic inflammatory indices from routine blood tests may offer a broader view of host-tumor immunobiology.

Purpose of the Study:

• To investigate the combined predictive value of systemic inflammatory indices, PD-L1 expression, and molecular alterations for immune checkpoint inhibitor (ICI) response in NSCLC.
• To stratify patients into distinct inflammatory-molecular clusters to identify subgroups with differential treatment outcomes.

Main Methods:

• Retrospective study of 298 stage IIIB-IV NSCLC patients treated with ICIs.
• Collected baseline neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), SII, PD-L1 expression, and molecular alterations (EGFR, KRAS, ALK, TP53).
• Evaluated associations with objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) using Kaplan-Meier and Cox analyses.

Main Results:

• Four distinct inflammatory-molecular clusters showed significantly different outcomes (p < 0.001).
• Low NLR and high PD-L1 expression (Cluster A) correlated with the highest ORR (41%), longest PFS (13.0 months), and OS (22.5 months).
• High NLR, low PD-L1 (<1%), and EGFR mutation independently predicted shorter PFS. A combined model showed superior predictive performance (AUC 0.82).

Conclusions:

• Integrating systemic inflammatory indices with PD-L1 and molecular status identifies clinically meaningful NSCLC subgroups with distinct immunotherapy outcomes.
• This multidimensional approach enhances prediction of ICI response and patient stratification, especially where extensive molecular profiling is limited.