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Objective: Anti-programmed death (PD)-1 therapy confers sustainable clinical benefits for patients with non-small-cell lung cancer (NSCLC), but only some patients respond to the treatment. Various clinical characteristics, including the PD-ligand 1 (PD-L1) level, are related to the anti-PD-1 response; however, none of these can independently serve as predictive biomarkers. Herein, we established a machine learning (ML)-based clinical decision support algorithm to predict the anti-PD-1 response by comprehensively combining the clinical information., Materials and Methods: We collected clinical data, including patient characteristics, mutations and laboratory findings, from the electronic medical records of 142 patients with NSCLC treated with anti-PD-1 therapy; these were analysed for the clinical outcome as the discovery set. Nineteen clinically meaningful features were used in supervised ML algorithms, including LightGBM, XGBoost, multilayer neural network, ridge regression and linear discriminant analysis, to predict anti-PD-1 responses. Based on each ML algorithm's prediction performance, the optimal ML was selected and validated in an independent validation set of PD-1 inhibitor-treated patients., Results: Several factors, including PD-L1 expression, tumour burden and neutrophil-to-lymphocyte ratio, could independently predict the anti-PD-1 response in the discovery set. ML platforms based on the LightGBM algorithm using 19 clinical features showed more significant prediction performance (area under the curve [AUC] 0.788) than on individual clinical features and traditional multivariate logistic regression (AUC 0.759)., Conclusion: Collectively, our LightGBM algorithm offers a clinical decision support model to predict the anti-PD-1 response in patients with NSCLC., (Copyright © 2021. Published by Elsevier Ltd.)

Background: The predictive value of immune monitoring with circulating CD8 + T lymphocytes for treatment response to programmed cell death protein 1 (PD-1) inhibitors has not been explored in non-small-cell lung cancer (NSCLC), prompting us to investigate whether dynamic changes in PD-1 + CD8 + T lymphocytes have predictive value for durable clinical benefit (DCB) and survival after PD-1 blockade., Methods: Patients with recurrent and/or metastatic NSCLC treated with PD-1 inhibitors were enrolled (discovery cohort; n = 94). Peripheral blood was obtained immediately before and after one cycle of treatment with PD-1 blockade. Phenotyping of circulating CD8 + T lymphocytes was conducted using multi-colour flow cytometry. Predictive values of dynamic changes in circulating PD-1 + CD8 + T lymphocytes during the first cycle were validated in an independent cohort (validation cohort; n = 54) of a prospective trial with a PD-1 inhibitor (NCT03486119)., Results: Circulating PD-1 + CD8 + T lymphocytes were enriched with effector/memory populations with elevated expression of activation- and exhaustion-related markers. Reduction in the frequency of PD-1 + cells among CD8 + T lymphocytes after one cycle of treatment was associated with a higher probability of DCB and superior survival outcomes in the discovery cohort. Similar results were obtained in the analysis of tumour antigen NY-ESO-1-specific CD8 + T lymphocytes and the validation cohort. Mechanistically, PD-1 molecule expression on CD8 + T lymphocytes suppresses the effector functions of tumour antigen-specific CD8 + T lymphocytes., Conclusions: Dynamic changes in circulating PD-1 + CD8 + T lymphocytes predict clinical, and survival benefit from PD-1 blockade treatment in NSCLC, providing a useful tool to identify patient subgroups who will optimally benefit from PD-1 inhibitors., Competing Interests: Conflict of interest statement None to declare., (Copyright © 2020 Elsevier Ltd. All rights reserved.)