Abstract
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.
Highlights
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
The efficacy of antiPD-1therapy depends on the presentation of neoantigens by major histocompatibility complex (MHC) class I molecules on the surface of cancer cells for surveillance by cytotoxic CD8+ T cells[22]
We hypothesized that aberrations of any gene that are close to MHC class I genes in the gene network are likely to deregulate the MHC class I antigen processing and presentation pathway and affect tumor response to anti-PD1
Summary
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. Immune checkpoint blockade (ICB), which enhances T-cell activity by inhibiting immunosuppressive checkpoint molecules such as cytotoxic T-lymphocyteassociated antigen 4 (CTLA-4), programmed cell death 1 (PD-1), and programmed cell death protein ligand 1 (PD-L1), has produced durable responses in some cancer patients Despite these successes, only a subset of cancer patients benefits from these therapies, and rates of response vary widely among cancer types. A considerable number of patients with a high tumor mutation burden (TMB) have poor responses, and a subset of patients with low TMB can respond to ICB Both patient cohort studies[4,5,6] and genetically engineered mouse models[7] have shown that cancer cells can utilize their genetic and epigenetic aberrations to influence various aspects of the immune landscape, such as recruitment of immunosuppressive cells into the tumor microenvironment, stimulation of tumor resistance to T-cell attack, and deregulation of immune checkpoint molecule expression. The network approaches can help to elucidate the genes/pathways associated with ICB responses
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