Abstract 5902: Integrative genomic analysis of checkpoint blockade in lung cancer: A multi-institution SU2C collaborative
Authors: Monica Arniella, Arvind Ravi, Chip Stewart, Sam Freeman, Mark Awad, Patrick Forde, Valsamo Anagnostou, Brian Henick, Jonathan Riess, Don Gibbons, Nathan Pennell, Vamsidhar Velcheti, Ignaty Leshchiner, Jaegil Kim, Subba Digumarthy, Mari Mino-Kenudson, John Heymach, Nir Hacohen, Naiyer Rizvi, Roy Herbst, Victor Velculescu, Julie Brahmer, Kurt Schalper, Pasi Jänne, Jedd Wolchok, Alice Shaw, Justin Gainor, Matthew Hellmann, Gad Getz
Journal: Cancer Research (2020)

Abstract PD-1/PD-L1 checkpoint blockade has been a landmark advance for many patients suffering from advanced non-small cell lung cancer (NSCLC). However, detailed biomarkers of response beyond tumor mutational burden (TMB) are still poorly understood. As part of the effort to elucidate these additional signatures, we describe our progress on the Stand Up to Cancer Lung (SU2C-Lung) cohort. Initial characterization of exomes recapitulates mutational and copy number profiles seen in The Cancer Genome Atlas (TCGA) project. To better define expression subtypes using RNA sequencing, we performed non-negative matrix factorization (NMF) across an aggregated set of publicly available NSCLC expression data (including adenocarcinoma, squamous cell carcinoma, and large cell neuroendocrine histologies), and demonstrate good concordance in the SU2C-Lung cohort between this expression-based classifier and clinically annotated histology. To gain further insight into how immune cell infiltrates vary across our cohort, we additionally tested two common deconvolution algorithms, EPIC and CIBERSORT. While these two methods agree for some prominent cell types, such as B cells and CD4 T cells, discrepancies in minor infiltrating components such as NK cells may suggest a limit to the inference of rare subpopulations from bulk sequencing data.Finally, we describe a novel approach for determining single-gene predictors of response. Using the method, which is based on comparison of top single transcriptional features identified from random bootstraps of the full cohort as compared to a set of background shuffles, we are able to show that we remain powered for discovery of RNA response biomarkers despite the typically burdensome toll of multiple hypothesis correction at genome wide scale. Acknowledgment: Supported by Stand Up To Cancer-American Cancer Society Lung Cancer Dream Team Translational Research Grant SU2C-AACR-DT17-15. Citation Format: Monica Arniella, Arvind Ravi, Chip Stewart, Sam Freeman, Mark Awad, Patrick Forde, Valsamo Anagnostou, Brian Henick, Jonathan Riess, Don Gibbons, Nathan Pennell, Vamsidhar Velcheti, Ignaty Leshchiner, Jaegil Kim, Subba Digumarthy, Mari Mino-Kenudson, John Heymach, Nir Hacohen, Naiyer Rizvi, Roy Herbst, Victor Velculescu, Julie Brahmer, Kurt Schalper, Pasi Jänne, Jedd Wolchok, Alice Shaw, Justin Gainor, Matthew Hellmann, Gad Getz. Integrative genomic analysis of checkpoint blockade in lung cancer: A multi-institution SU2C collaborative [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 5902.