Abstract 558: Prognostic gene signature use in checkpoint inhibitor monotherapy for melanoma
Authors: Mariaelena Capone, Gabriele Madonna, Paolo Ascierto, Patrick Danaher, SuFey Ong, Sarah Warren, Joseph M. Beechem, Alessandra Cesano
Journal: Cancer Research (2018)

Abstract Introduction: The successful deployment of checkpoint inhibitors in cancer immunotherapy relies on the responsiveness of an individual's immune system for relief of that particular blockade in the cancer immunity cycle. As most patients fail to respond to immunotherapy, there is a need for biomarkers that can predict clinical benefit of the therapeutic by identifying the patient population most likely to respond. Gene expression signatures characterizing basal immune state within tumor have shown utility in retrospective analysis of clinical trials. This study utilizes tumor and PBMCs from patients with metastatic melanoma receiving either anti-CTLA-4 or anti-PD-1/PD-L1 to characterize local and peripheral patterns of gene expression associated with clinical benefit of therapy. The goal of this study is to evaluate existing gene signatures and develop novel signatures that predict response to checkpoint inhibitors in melanoma. Methods: Pretreatment biopsies from metastatic lesions of melanoma stage IV patients treated with ipilimumab (anti-CTLA4) or pembrolizumab (anti-PD-L1) were retrieved from the Department of Pathology archives. Cohorts were assembled of 30 patients receiving each drug, equally stratified between responders and nonresponders. RNA expression in tumor biopsies will be profiled with a pilot version of the NanoString® IO360 gene expression panel. In parallel, pre- and post-treatment PBMC from independent cohorts receiving either ipilimumab (anti-CTLA4) or pembrolizumab (anti-PD-1) were collected and profiled with the NanoString PanCancer Immune Panel (gene expression) and Immune Profiling Panel (protein expression). Results: Patterns of gene expression in the tumor biopsies and gene and protein expression in the PBMCs will be assessed for correlation to clinical outcome (PFS, OS, ORR). Specifically, the Tumor Inflammation Signature described by Ayers et al. (1), an investigational 18-gene signature of suppressed adaptive immune response that enriches for clinical response to pembrolizumab, will be assessed in these cohorts. Other patterns of gene and protein expression that correlate with response to immunotherapy or lack thereof will also be evaluated. Conclusion: Correlating patterns of gene expression from tumor with clinical response can lead to the development of biomarkers to better select patients who will respond to immunotherapy. It can also lead to identifying immune evasion. Gene and protein profiling from a readily accessed sample such as PBMC may give insights into early on-treatment signatures of efficacy. Utilization of a clinical grade platform such as the NanoString nCounter® may speed the development of diagnostic assays that can be used to predict and monitor patient response to immunotherapy. Reference: 1. Ayers et al., J Clin Invest 2017;127:2930. Citation Format: Mariaelena Capone, Gabriele Madonna, Paolo Ascierto, Patrick Danaher, SuFey Ong, Sarah Warren, Joseph M. Beechem, Alessandra Cesano. Prognostic gene signature use in checkpoint inhibitor monotherapy for melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 558.