L Paré1, T Pascual1, E Seguí1, C Teixidó2, M Gonzalez-Cao3, P Galván1, A Rodríguez1, B González4, M Cuatrecasas4, E Pineda1, A Torné5, G Crespo6, S Martin-Algarra7, E Pérez-Ruiz8, Ò Reig1, M Viladot1, C Font1, B Adamo1, M Vidal1, L Gaba1, M Muñoz1, I Victoria1, G Ruiz1, N Viñolas1, B Mellado1, J Maurel1, J Garcia-Corbacho1, M Á Molina-Vila9, M Juan10, J M Llovet11, N Reguart1, A Arance1, A Prat12. 1. Translational Genomic and Targeted Therapeutics in Solid Tumors, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Department of Medical Oncology, Hospital Clínic of Barcelona, Barcelona, Spain. 2. Translational Genomic and Targeted Therapeutics in Solid Tumors, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Pathology Service, Hospital Clínic of Barcelona, Barcelona, Spain. 3. Quironsalud Group, Dr. Rosell Oncology Institute (IOR), Dexeus University Hospital, Barcelona, Spain. 4. Pathology Service, Hospital Clínic of Barcelona, Barcelona, Spain. 5. Gynecology Service, Hospital Clínic of Barcelona, Barcelona, Spain. 6. Department of Medical Oncology, Hospital Universitario de Burgos, Burgos, Spain. 7. Department of Medical Oncology, Clínica Universitaria de Navarra, Pamplona, Spain. 8. Department of Medical Oncology, Hospital Costa del Sol REDISSEC, Marbella, Spain. 9. Pangaea Oncology, Laboratory of Molecular Biology, Quirón-Dexeus University Institute, Barcelona, Spain. 10. Immunology Department, Hospital Clinic of Barcelona, Barcelona, Spain. 11. BCLC Group, Translational Research Lab in Hepatic Oncology, IDIBAPS, Hospital Clínic, CIBERehd, Barcelona, Universitat de Barcelona; Barcelona, Spain; Mount Sinai Liver Cancer Program, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, USA; Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain. 12. Translational Genomic and Targeted Therapeutics in Solid Tumors, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Department of Medical Oncology, Hospital Clínic of Barcelona, Barcelona, Spain. Electronic address: alprat@clinic.cat.
Abstract
Background: We hypothesized that the abundance of PD1 mRNA in tumor samples might explain the differences in overall response rates (ORR) observed following anti-PD1 monotherapy across cancer types. Patients and methods: RNASeqv2 data from 10 078 tumor samples representing 34 different cancer types was analyzed from TCGA. Eighteen immune-related gene signatures and 547 immune-related genes, including PD1, were explored. Correlations between each gene/signature and ORRs reported in the literature following anti-PD1 monotherapy were calculated. To translate the in silico findings to the clinical setting, we analyzed the expression of PD1 mRNA using the nCounter platform in 773 formalin-fixed paraffin embedded (FFPE) tumor samples across 17 cancer types. To test the direct relationship between PD1 mRNA, PDL1 immunohistochemistry (IHC), stromal tumor-infiltrating lymphocytes (sTILs) and ORR, we evaluated an independent FFPE-based dataset of 117 patients with advanced disease treated with anti-PD1 monotherapy. Results: In pan-cancer TCGA, PD1 mRNA expression was found strongly correlated (r > 0.80) with CD8 T-cell genes and signatures and the proportion of PD1 mRNA-high tumors (80th percentile) within a given cancer type was variable (0%-84%). Strikingly, the PD1-high proportions across cancer types were found strongly correlated (r = 0.91) with the ORR following anti-PD1 monotherapy reported in the literature. Lower correlations were found with other immune-related genes/signatures, including PDL1. Using the same population-based cutoff (80th percentile), similar proportions of PD1-high disease in a given cancer type were identified in our in-house 773 tumor dataset as compared with TCGA. Finally, the pre-established PD1 mRNA FFPE-based cutoff was found significantly associated with anti-PD1 response in 117 patients with advanced disease (PD1-high 51.5%, PD1-intermediate 26.6% and PD1-low 15.0%; odds ratio between PD1-high and PD1-intermediate/low = 8.31; P < 0.001). In this same dataset, PDL1 tumor expression by IHC or percentage of sTILs was not found associated with response. Conclusions: Our study provides a clinically applicable assay that links PD1 mRNA abundance, activated CD8 T-cells and anti-PD1 efficacy.
Background: We hypothesized that the abundance of PD1 mRNA in tumor samples might explain the differences in overall response rates (ORR) observed following anti-PD1 monotherapy across cancer types. Patients and methods: RNASeqv2 data from 10 078 tumor samples representing 34 different cancer types was analyzed from TCGA. Eighteen immune-related gene signatures and 547 immune-related genes, including PD1, were explored. Correlations between each gene/signature and ORRs reported in the literature following anti-PD1 monotherapy were calculated. To translate the in silico findings to the clinical setting, we analyzed the expression of PD1 mRNA using the nCounter platform in 773 formalin-fixed paraffin embedded (FFPE) tumor samples across 17 cancer types. To test the direct relationship between PD1 mRNA, PDL1 immunohistochemistry (IHC), stromal tumor-infiltrating lymphocytes (sTILs) and ORR, we evaluated an independent FFPE-based dataset of 117 patients with advanced disease treated with anti-PD1 monotherapy. Results: In pan-cancer TCGA, PD1 mRNA expression was found strongly correlated (r > 0.80) with CD8 T-cell genes and signatures and the proportion of PD1 mRNA-high tumors (80th percentile) within a given cancer type was variable (0%-84%). Strikingly, the PD1-high proportions across cancer types were found strongly correlated (r = 0.91) with the ORR following anti-PD1 monotherapy reported in the literature. Lower correlations were found with other immune-related genes/signatures, including PDL1. Using the same population-based cutoff (80th percentile), similar proportions of PD1-high disease in a given cancer type were identified in our in-house 773 tumor dataset as compared with TCGA. Finally, the pre-established PD1 mRNA FFPE-based cutoff was found significantly associated with anti-PD1 response in 117 patients with advanced disease (PD1-high 51.5%, PD1-intermediate 26.6% and PD1-low 15.0%; odds ratio between PD1-high and PD1-intermediate/low = 8.31; P < 0.001). In this same dataset, PDL1tumor expression by IHC or percentage of sTILs was not found associated with response. Conclusions: Our study provides a clinically applicable assay that links PD1 mRNA abundance, activated CD8 T-cells and anti-PD1 efficacy.
Authors: Maria Gonzalez-Cao; Santiago Viteri; Niki Karachaliou; Andres Aguilar; Juan Jose García-Mosquera; Rafael Rosell Journal: Transl Lung Cancer Res Date: 2018-12
Authors: Ignacio Isola; David F Moreno; Esther Moga; Mari-Pau Mena; Natalia Tovar; Luis Gerardo Rodríguez-Lobato; Aina Oliver-Caldés; M Carmen Salgado; Fara Brasó-Maristany; Jordi Yagüe; M Teresa Cibeira; Aleix Prat; Laura Rosiñol; Joan Bladé; Carlos Fernández de Larrea Journal: Ann Hematol Date: 2021-08-31 Impact factor: 3.673