Abstract LB-401: A proteomics-based biomarker discovery platform for predicting clinical response to immune checkpoint inhibitor therapy in non-small cell lung cancer
Authors: Michal Harel, Irena Khononov, Eran Issler, Coren Lahav, Ella Fremder, Eyal Jacob, Nili Dahan, Ziv Raviv, Avishag Shkedy, Reema Jacob, Adam Dicker, Haim Bar, Ofer Sharon, Yuval Shaked
Journal: Cancer Research (2020)

Abstract Immune checkpoint inhibitors (ICIs) have changed the treatment paradigm for non-small cell lung cancer (NSCLC) due to their unprecedented rates of durable response. However, their therapeutic efficacy is limited to a minority of NSCLC patients. Thus, there is a great need for reliable biomarkers to predict outcome. Our previous studies have identified pro-tumorigenic, host-mediated responses to cancer treatment modalities including chemotherapy, radiation and targeted drugs. In this study, we characterized the host-mediated response to ICI therapy, and investigated its potential to predict clinical outcome in NSCLC. In preclinical experiments, Lewis lung carcinoma (LLC) cells exhibited enhanced migratory and invasive properties in vitro upon exposure to plasma from tumor-free, anti-PD-1-treated mice. Additionally, in comparison to control arms, an increased mortality rate was observed in mice intravenously injected with LLC cells that had been pre-conditioned with plasma from anti-PD-1-treated mice. These findings suggest that anti-PD-1 treatment induces a systemic host response that potentially promotes tumor aggressiveness. To identify host response-based biomarkers that predict clinical outcome of ICI therapy, plasma samples were obtained from NSCLC patients at baseline and 4 weeks after a single dose of anti-PD-1 therapy. Proteomic profiling of plasma samples was performed using ELISA-based protein arrays. We then used support vector machine (SVM) algorithm to identify a proteomic signature that is predictive of clinical response to treatment. The cohort consisted of 87 NSCLC patients, who were divided into a training set (n=33) and an independent validation set (n=54). Our classifier identified a three-protein signature that accurately distinguishes between responders and non-responders, with an area under the curve (AUC) of 0.89 [confidence interval (CI): 0.76 - 1.0; p-value: 4.11E-05] in the training set and 0.72 [CI: 0.55 - 0.89; p-value: 0.01] in the validation set. Pathway enrichment analysis of proteomic profiles of non-responders revealed multiple pathways directly related to cancer and lung cancer specifically, as well as pathways associated with immunosuppression. Our study demonstrates the potential clinical utility of analyzing the host response to ICI therapy, in particular for the discovery of novel predictive biomarkers for NSCLC patient stratification. Citation Format: Michal Harel, Irena Khononov, Eran Issler, Coren Lahav, Ella Fremder, Eyal Jacob, Nili Dahan, Ziv Raviv, Avishag Shkedy, Reema Jacob, Adam Dicker, Haim Bar, Ofer Sharon, Yuval Shaked. A proteomics-based biomarker discovery platform for predicting clinical response to immune checkpoint inhibitor therapy in non-small cell lung cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-401.