We investigated inflammatory markers such as the neutrophil-to-lymphocyte ratio (NLR) that may predict the response to anti-PD-1 (programmed cell death protein 1) antibody therapy. Data from 54 patients with non-small cell lung cancer (NSCLC) treated with anti-PD-1 antibodies were retrospectively analyzed. The NLR was assessed at baseline and 6 weeks after the start of treatment (post-treatment). Eighteen of 54 patients (33.3%) had objective responses to treatment. Older age, absence of brain metastasis, low post-treatment NLR (< 5), and immune-related adverse events were significantly associated with response. Patients with a high post-treatment NLR (≥ 5) had significantly shorter progression-free survival (PFS) than those with a low post-treatment NLR (median, 1.3 vs. 6.1 months, p < 0.001). Multivariate analysis demonstrated that high post-treatment NLR [hazard ratio (HR) 15.1, 95% confidence interval (CI) 1.5-50.1, p < 0.001], liver metastasis (HR 4.9, 95% CI 1.9-12.4, p = 0.001), and brain metastasis (HR 3.2, 95% CI 1.3-8.2, p = 0.013) were independent prognostic factors of shorter PFS. Overall survival (OS) was significantly different in patients with high and low post-treatment NLRs (median, 2.1 vs. 14.0 months, p < 0.001). A high post-treatment NLR remained an independent prognostic factor for OS in multivariate analysis (HR 3.9, 95% CI 1.6-9.2, p = 0.003). The NLR at 6 weeks after treatment initiation was a prognostic marker in patients with advanced NSCLC treated with anti-PD-1 antibody. Further studies are warranted to evaluate the role of the 6-week NLR as a predictor in anti-PD-1 antibody treatment.

PURPOSE Biomarkers on the basis of tumor-infiltrating lymphocytes (TIL) are potentially valuable in predicting the effectiveness of immune checkpoint inhibitors (ICI). However, clinical application remains challenging because of methodologic limitations and laborious process involved in spatial analysis of TIL distribution in whole-slide images (WSI). METHODS We have developed an artificial intelligence (AI)–powered WSI analyzer of TIL in the tumor microenvironment that can define three immune phenotypes (IPs): inflamed, immune-excluded, and immune-desert. These IPs were correlated with tumor response to ICI and survival in two independent cohorts of patients with advanced non–small-cell lung cancer (NSCLC). RESULTS Inflamed IP correlated with enrichment in local immune cytolytic activity, higher response rate, and prolonged progression-free survival compared with patients with immune-excluded or immune-desert phenotypes. At the WSI level, there was significant positive correlation between tumor proportion score (TPS) as determined by the AI model and control TPS analyzed by pathologists ( P < .001). Overall, 44.0% of tumors were inflamed, 37.1% were immune-excluded, and 18.9% were immune-desert. Incidence of inflamed IP in patients with programmed death ligand-1 TPS at < 1%, 1%-49%, and ≥ 50% was 31.7%, 42.5%, and 56.8%, respectively. Median progression-free survival and overall survival were, respectively, 4.1 months and 24.8 months with inflamed IP, 2.2 months and 14.0 months with immune-excluded IP, and 2.4 months and 10.6 months with immune-desert IP. CONCLUSION The AI-powered spatial analysis of TIL correlated with tumor response and progression-free survival of ICI in advanced NSCLC. This is potentially a supplementary biomarker to TPS as determined by a pathologist.

These results suggest that accurate assessment of HER2 status, including HER2 heterogeneity, is important in predicting trastuzumab responses and outcomes in patients with HER2-positive metastatic breast cancer.

Among patients with advanced NSCLC harboring WT EGFR, conventional chemotherapy, compared with first-generation EGFR TKI, was associated with improvement in PFS but not overall survival.