Abstract 633: Application of a novel analytical metric that quantifies copy number alterations in low-coverage, genome-wide sequencing of cell-free DNA to monitor and differentiate response to immunotherapy in cancer patients

Abstract

Abstract Inhibitors of the PD-1/PD-L1/CTLA4 immune checkpoint pathway have transformed the cancer treatment paradigm. It has been observed that even some patients with advanced, refractory malignancies achieve durable responses; however, only a minority of patients benefit, demonstrating the importance of developing new biomarkers to predict patient outcome. The most commonly used biomarkers for predicting response to checkpoint inhibitors are the expression of PD-1/PD-L1, microsatellite instability, and tumor mutational burden. While these markers have been shown to have varying degrees of predictive power, they have not been used to monitor and differentiate response during treatment and require invasive procedures. Interrogating cell-free DNA (cfDNA) isolated from plasma (liquid biopsy) provides a promising noninvasive method for monitoring response. We describe the use of low-coverage (~0.3X) genome-wide sequencing of cfDNA, validated extensively for detecting chromosomal abnormalities in non-invasive prenatal testing (NIPT) and previously shown to enable incidental detection of occult maternal malignancies, to detect tumor-specific copy number alterations; and the development of a new metric–the genome instability number (GIN)–to monitor response to these drugs. Using a series of more than 450 plasma aliquots taken from more than 75 patients throughout their treatment, we demonstrate how the GIN can be used to discriminate clinical response from progression, differentiate progression from pseudoprogression, and identify hyperprogressive disease, as early as 4-6 weeks after treatment initiation. Finally, we provide evidence for a delayed therapeutic response to checkpoint inhibitors relative to targeted therapies. While this study is still ongoing, these initial data provide proof of concept for using this method for monitoring treatment outcome in cancer patients receiving immunotherapy. Citation Format: Taylor J. Jensen, Aaron M. Goodman, Christopher K. Ellison, Shumei Kato, Gregory A. Daniels, Lisa Tran, Prachi Nakashe, Erin McCarthy, Amin R. Mazloom, Graham McLennan, Daniel S. Grosu, Mathias Ehrich, Razelle Kurzrock. Application of a novel analytical metric that quantifies copy number alterations in low-coverage, genome-wide sequencing of cell-free DNA to monitor and differentiate response to immunotherapy in cancer patients [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 633.