24 Background: Despite remarkable success, only a subset of cancer patients have shown benefit from the anti-PD1 25 therapy. Therefore, there is a growing need to identify predictive biomarkers and therapeutic combinations for 26 improving the clinical efficacy.
27Results: Based upon the hypothesis that aberrations of any gene that are close to MHC class I genes in the gene 28 network are likely to deregulate MHC I pathway and affect tumor response to anti-PD1, we developed a network 29 approach to infer genes, pathway, and potential therapeutic target genes associated with response to PD-1/PD-L1 30 checkpoint immunotherapies in cancer. Our approach successfully identified genes (e.g. B2M and PTEN) and 31 pathways (e.g. JAK/STAT and WNT) known to be associated with anti-PD1 response. Our prediction was further 32 validated by 5 CRISPR gene sets associated with tumor resistance to cytotoxic T cells. Our results also showed that 33 many cancer genes that act as hubs in the gene network may drive immune evasion through indirectly deregulating 34 the MHC I pathway. The integration analysis of transcriptomic data of the 34 TCGA cancer types and our prediction 35 reveals that MHC I-immunoregulations may be tissue-specific. The signature-based score, the MHC I association 36 immunoscore (MIAS), calculated by integration of our prediction and TCGA melanoma transcriptomic data also 37 showed a good correlation with patient response to anti-PD1 for 354 melanoma samples complied from 5 cohorts.
38In addition, most targets of the 36 compounds that have been tested in clinical trials or used for combination 39 treatments with anti-PD1 are in the top list of our prediction (AUC=0.833). Integration of drug target data with our 40 top prediction further identified compounds that were recently shown to enhance tumor response to anti-PD1, such 41 as inhibitors of GSK3B, CDK, and PTK2.
42Conclusion: Our approach is effective to identify candidate genes and pathways associated with response to anti-
43PD-1 therapy, and can also be employed for in silico screening of potential compounds to enhances the efficacy of 44 anti-PD1 agents against cancer. 45 46 47 48 50 Breakthroughs in cancer immunotherapies have opened a new front in the war against cancer [1]. Instead of 51 directly targeting cancer cells using specific inhibitors, immunotherapies stimulate and modulate the host's 52 immune system to eliminate cancer cells. Recently, immune checkpoint blockade (ICB), which enhances T-cell 53 activity by inhibiting immunosuppressive checkpoint molecules such as cytotoxic T-lymphocyte-associated antigen 54 4 (CTLA-4), programmed cell death 1 (PD-1), and programmed cell death protein ligand 1 (PD-L1), has produced 55remarkably durable responses in some cancer patients. Despite these successes, only a subset of cancer patients 56 benefits from these therapies, and rates of response vary widely among cancer types. Therefore, there is a growing 57 need to understand the mechanisms underlying this de novo resistance, to select predictive biomar...