Tumor-infiltrating natural killer cell profiling for therapeutic stratification in smokers with resectable non-small cell lung cancer

Objective: Neoadjuvant chemoimmunotherapy has improved outcomes in resectable non-small cell lung cancer (NSCLC), yet its real-world implementation is often challenged by surgical delays, cancellations, immune-related fibrosis, and increased postoperative complications. Smokers with NSCLC, despite being at higher risk for postoperative complications, have enhanced responses to neoadjuvant chemoimmunotherapy. This study aimed to identify smoking-associated immune cell determinants that could inform treatment strategies. Methods: Single-cell RNA sequencing was performed on 61 lung samples from non-smokers, smokers, and patients with chronic obstructive pulmonary disease (COPD), as well as 8 invasive lung adenocarcinomas, to define smoking-related immune signatures. Using RNA sequencing data from 102 resected NSCLC and 24 NSCLC patients treated with neoadjuvant chemoimmunotherapy, we conducted in silico deconvolution to evaluate associations between cellular composition and clinical outcomes. Results: Among 140 lung cellular phenotypes, two natural killer (NK) cell subsets were strongly associated with smoking and COPD severity. “Stress-responsive NK (NKSR)” cells exhibited immature features and cytokine-responsive signatures. “Exhausted NK (NKExh)” cells showed mature features and elevated multiple immune checkpoint expression (PDCD1, TIGIT, and LAG3). Abundant intratumoral NKSR cells were associated with significantly improved survival after surgery, particularly in current smokers. Conversely, tumors with low NKSR and high NKExh cell profiles were associated with better responses to neoadjuvant chemoimmunotherapy. Conclusions: Tumor-infiltrating NK cell phenotyping may aid in therapeutic stratification in smokers with NSCLC. NKSR cell preservation predicts favorable outcomes with upfront surgery, while NKExh cell enrichment indicates greater benefit from neoadjuvant chemoimmunotherapy. These profiles may help optimize treatment by balancing therapeutic benefit and risk.