Molecular determinants of response to PD-L1 blockade across tumor types

• Published in: Nature communications Vol. 12; no. 1; pp. 3969 - 11
• Main Authors: Banchereau, Romain, Leng, Ning, Zill, Oliver, Sokol, Ethan, Liu, Gengbo, Pavlick, Dean, Maund, Sophia, Liu, Li-Fen, Kadel, Edward, Baldwin, Nicole, Jhunjhunwala, Suchit, Nickles, Dorothee, Assaf, Zoe June, Bower, Daniel, Patil, Namrata, McCleland, Mark, Shames, David, Molinero, Luciana, Huseni, Mahrukh, Sanjabi, Shomyseh, Cummings, Craig, Mellman, Ira, Mariathasan, Sanjeev, Hegde, Priti, Powles, Thomas
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 25.06.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 45/23; 45/91; 631/67/1059; 631/67/580; 631/67/69; Bladder cancer; Cell cycle; Cyclin-dependent kinase 4; Deoxyribonucleic acid; DNA; DNA damage; DNA repair; Heterogeneity; Humanities and Social Sciences; Immune checkpoint inhibitors; Immunohistochemistry; Immunotherapy; Indication; Inhibitors; Kidney cancer; Learning algorithms; Lung cancer; Lung carcinoma; Machine learning; Monoclonal antibodies; multidisciplinary; Mutation; Non-small cell lung carcinoma; Patients; PD-1 protein; PD-L1 protein; Renal cell carcinoma; Ribonucleic acid; RNA; Science; Science (multidisciplinary); Signatures; Small cell lung carcinoma; Transcription; Tumors; Urothelial cancer; Urothelial carcinoma
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-021-24112-w

Immune checkpoint inhibitors targeting the PD-1/PD-L1 axis lead to durable clinical responses in subsets of cancer patients across multiple indications, including non-small cell lung cancer (NSCLC), urothelial carcinoma (UC) and renal cell carcinoma (RCC). Herein, we complement PD-L1 immunohistochemistry (IHC) and tumor mutation burden (TMB) with RNA-seq in 366 patients to identify unifying and indication-specific molecular profiles that can predict response to checkpoint blockade across these tumor types. Multiple machine learning approaches failed to identify a baseline transcriptional signature highly predictive of response across these indications. Signatures described previously for immune checkpoint inhibitors also failed to validate. At the pathway level, significant heterogeneity is observed between indications, in particular within the PD-L1 + tumors. mUC and NSCLC are molecularly aligned, with cell cycle and DNA damage repair genes associated with response in PD-L1- tumors. At the gene level, the CDK4/6 inhibitor CDKN2A is identified as a significant transcriptional correlate of response, highlighting the association of non-immune pathways to the outcome of checkpoint blockade. This cross-indication analysis reveals molecular heterogeneity between mUC, NSCLC and RCC tumors, suggesting that indication-specific molecular approaches should be prioritized to formulate treatment strategies. PD-L1 immune checkpoint inhibition has been used for several tumour types. Here, the authors use immunohistochemistry, tumour mutation burden and RNA-seq data from 366 patients with different indications to identify molecular signatures of response to atezolizumab and reveal pathway heterogeneity and the involvement of non-immune pathways.