Prediction of biomarkers and therapeutic combinations for anti-PD-1 immunotherapy using the global gene network association

• Published in: Nature communications Vol. 13; no. 1; p. 42
• Main Authors: Wu, Chia-Chin, Wang, Y. Alan, Livingston, J. Andrew, Zhang, Jianhua, Futreal, P. Andrew
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 10.01.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 38; 45; 631/114/2401; 631/114/2413; 631/114/2785; 631/67/1059/2325; Antineoplastic Agents - pharmacology; Biomarkers; Cancer; Clinical trials; Combined Modality Therapy - methods; CRISPR; Cyclin-Dependent Kinase 4; Cytotoxicity; Focal Adhesion Kinase 1; Gene expression; Gene mapping; Gene Regulatory Networks; Genes; Glycogen Synthase Kinase 3 beta; Histocompatibility Antigens Class I; Humanities and Social Sciences; Humans; Immune Checkpoint Inhibitors - pharmacology; Immunoregulation; Immunotherapy; Immunotherapy - methods; Inhibitors; Integration; Lymphocytes; Lymphocytes T; Major histocompatibility complex; Melanoma; Melanoma - therapy; multidisciplinary; Patients; PD-1 protein; Precision Medicine; Predictions; Programmed Cell Death 1 Receptor; Science; Science (multidisciplinary); T-Lymphocytes, Cytotoxic - metabolism; Therapeutic targets; Tumors
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-021-27651-4

Owing to a lack of response to the anti-PD1 therapy for most cancer patients, we develop a network approach to infer genes, pathways, and potential therapeutic combinations that are associated with tumor response to anti-PD1. Here, our prediction identifies genes and pathways known to be associated with anti-PD1, and is further validated by 6 CRISPR gene sets associated with tumor resistance to cytotoxic T cells and targets of the 36 compounds that have been tested in clinical trials for combination treatments with anti-PD1. Integration of our top prediction and TCGA data identifies hundreds of genes whose expression and genetic alterations that could affect response to anti-PD1 in each TCGA cancer type, and the comparison of these genes across cancer types reveals that the tumor immunoregulation associated with response to anti-PD1 would be tissue-specific. In addition, the integration identifies the gene signature to calculate the MHC I association immunoscore (MIAS) that shows a good correlation with patient response to anti-PD1 for 411 melanoma samples complied from 6 cohorts. Furthermore, mapping drug target data to the top genes in our association prediction identifies inhibitors that could potentially enhance tumor response to anti-PD1, such as inhibitors of the encoded proteins of  CDK4 , GSK3B , and PTK2 . A lot of cancer patients are not responsive to anti-PD1 therapy. Here, the authors develop a network approach to identify genes, pathways and potential therapeutic combinations and develop an MHC-I gene immunoscore associated with tumour response to anti-PD1.