Morrison, Carl Pabla, Sarabjot Conroy, Jeffrey Nesline, Mary Glenn, Sean Dressman, Devin Papanicolau-Sengos, Antonios Burgher, Blake Andreas, Jonathan Giamo, Vincent Qin, Moachun Wang, Yirong Lenzo, Felicia Omilian, Angela Bshara, Wiam Zibelman, Matthew Ghatalia, Pooja Dragnev, Konstantin Shirai, Keisuke Madden, Katherine Tafe, Laura Shah, Neel Kasuganti, Deepa de la Cruz-Merino, Luis Araujo, Isabel Saenger, Yvonne Bogardus, Margaret Villalona-Calero, Miguel Diaz, Zuanel Day, Roger Eisenberg, Marcia Anderson, Steven Puzanov, Igor Galluzzi, Lorenzo Gardner, Mark Ernstoff, Marc Additional file 1: of Predicting response to checkpoint inhibitors in melanoma beyond PD-L1 and mutational burden Table S1 Clinical characteristics of the melanoma cohort. Table S2 Survival analyses for studied biomarkers. Table S3 Comprehensive gene expression and mutational profile of melanoma cohort. Table S4 Over representation test results of categorical variables for gene expression clusters. Table S5 Over representation test results of gene ranks for gene expression clusters. Table S6 ORR for all biomarker groups studied. Table S7 Objective response rates for studied biomarkers. Table S8 Objective response counts for melanoma cohort. Table S9 ORR for training set response score groups. (XLSX 1378 kb) Pembrolizumab;Nivolumab;Ipilimumab;Algorithmic analysis;Inflamed;Borderline;Immune Desert 2018-05-09
    https://springernature.figshare.com/articles/dataset/Additional_file_1_of_Predicting_response_to_checkpoint_inhibitors_in_melanoma_beyond_PD-L1_and_mutational_burden/6243539
10.6084/m9.figshare.6243539.v1