A novel nomogram could predict prognosis for multiple cancer patients with primary resistance to immune checkpoint inhibitors.

e14561 Background: Cancer patients with immune checkpoint inhibitors (ICIs) treatment often experience unique progression response patterns, accounting for the discordance of short-term and long-term benefits. Identifying the specific characteristics of different quality progression disease (PD) events can maximize the curative effect of ICIs and further improve the prognosis of the patients with immunotherapy. In this study, we established a neutrophil-to-lymphocyte ratio (NLR)-based nomogram model which could predict survival for NSCLC patients with primary resistance to ICIs. Methods: Univariate and multivariate Cox regression analysis were used to evaluate the ability of each parameter to predict overall survival (OS). The regression coefficients obtained in multivariate analysis were visualized in the form of a nomogram, thus a new nomogram and risk classification system were established. The discrimination ability of the new model was evaluated using area under the curve (AUC) in receiver operating characteristic curve (ROC) analysis. Survival curves were made by the Kaplan-Meier method and compared by the Log-rank test. Results: A total of 1235 patients with multiple cancer types who accepted at least one dose of ICIs treatment between 2015 and 2018 at Memorial Sloan Kettering Cancer Center were included in our study. Among them, 624 PD-patients (obtaining PD as their best response) were used to identify potential survival-related characteristics and build a nomogram model. Univariate and multivariate analysis demonstrated that pretreatment NLR, treatment lines and Eastern Cooperative Oncology Group performance status scores were independent prognostic factors and were included in the predictive model. The ROC curves implied the good discrimination ability of the predictive model with the AUC value for 1-year OS was 0.756. And, the calibration curves showed that the predicted value of the 1-year survival rate by the nomogram was in good agreement with the actual observed value of it. Based on the nomogram, we divided PD-patients into high-, med- and low-risk subgroups. The OS of cancer patients in three risk groups were accurately differentiated either in all PD-patients or PD-NSCLC-patients, but not in the patients who responded to ICIs. Importantly, the prognosis of the low-risk PD-patients was significantly better compared with the patients in med- and low-risk groups (P < 0.001). Moreover, in the PD-patients with NSCLC, similar results were also found that patients had a worse OS in the high-risk group (P < 0.001). Interestingly, in contrast to the results in PD-patients, a comparable OS between high-, med- and low-risk groups based on our model was verified in the patients who responded to ICIs. Conclusions: The pretreatment NLR-based nomogram could predict the prognosis for pan-cancer patients with primary resistance to ICIs treatment.