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Background Accessible biomarkers are needed for immunotherapy in advanced non-small-cell lung cancer (NSCLC). We previously described a multivariate risk prediction model, the iSEND, which categorises advanced NSCLC patients treated with nivolumab into Good, Intermediate or Poor groups. This model was developed by using only clinical and analytical variables (sex, ECOG-performance status, neutrophil-to-lymphocyte ratio [NLR] and post-treatment delta NLR). Methods An international database of 439 patients who received post-platinum PD-1/L1 monotherapies was collected for validation. Performance of the iSEND to different PD-L1 groups was compared by using time-dependent positive predictive value (PPV) for their mortality events. Results Median follow-up was 18.2 months (95% CI: 15.9–19.6). The overall survival of the iSEND Good (HR = 0.31, 95% CI: 0.22–0.43, p < 0.0001) was superior to the iSEND Poor. Time-dependent PPV for mortality of iSEND Poor was superior to PD-L1 = 0% group at 12 (75 vs. 53%, p = 0.01) and 18 months (85 vs. 46%, p = 0.03). However, female gender did not independently associate with better outcome in the validation cohort. Conclusion The iSEND model is associated with the outcome of post-platinum PD-1/L1 monotherapy in advanced NSCLC patients. The iSEND Poor demonstrated a superior performance to PD-L1 = 0% in negative prognostication. Prospective investigation and modelling with other significant parameters in a larger cohort are warranted.

Introduction: Presently, programmed death ligand 1 is the most commonly used biomarker to predict response to immune checkpoint inhibitors (ICIs) in NSCLC. Owing to its several limitations, there is continuous search for more precise and reliable markers. Frameshift mutations by insertion or deletion (fsindels) are suggested to induce more immunogenic tumor-specific neoantigens, conferring better response to ICIs. Positive correlation of fsindels with ICI response has been studied in melanoma and renal cell carcinoma. We investigated the implication of fsindels in the clinical outcomes and immune landscape of patients with NSCLC treated with ICIs. Methods: We utilized The Cancer Genome Atlas data set to analyze tumor mutational burden, neoantigen burden, and immune landscape in relation to fsindel status. In addition, utilizing the clinical data from 122 patients treated with ICIs, we evaluated the influence of fsindels on disease response rates and survival outcomes. Results: A positive correlation between fsindel burden and tumor mutational burden and activated CD4/CD8 T-cell infiltration was shown. Presence of fsindels was also associated with significant prolongation of progression-free survival in patients treated with ICIs (median 6.2 versus 2.7 months [p ¼ 0.01]). In addition, significant differences in the overall response rates (26% versus 12% [p ¼ 0.04]) and disease control rates (68% versus 48% [p ¼ 0.02]) were observed in patients with fsindels. Conclusion: Our findings suggest that fsindels may have a predictive role for ICI response in NSCLC.