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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.

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Inactivation of the adenomatous polyposis coli (APC) is common across many cancer types and serves as a critical initiating event in most sporadic colorectal cancers (CRC). APC-deficiency activates WNT signaling which remains an elusive target for cancer therapy, prompting us to apply the synthetic essentiality framework to identify druggable vulnerabilities for APC-deficient cancers. Tryptophan 2,3-dioxygenase 2 (TDO2) was identified as a synthetic essential effector of APC-deficient CRC. Mechanistically, APC-deficiency results in TCF4/ B-catenin-mediated upregulation of TDO2 gene transcription. TDO2 in turn activates the Kyn-AhR pathway which increases glycolysis to drive anabolic cancer cell growth and CXCL5 secretion to recruit macrophages into the tumor microenvironment. Therapeutically, APC-deficient CRC models were susceptible to TDO2 depletion or pharmacological inhibition which impaired cancer cell proliferation and enhanced anti-tumor immune profiles. Thus, APC-deficiency activates a TCF4-TDO2-AhR-CXCL5 circuit that impacts multiple cancer hallmarks via autonomous and non-autonomous mechanisms, and illuminates a genotype-specific vulnerability in CRC.

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Long non-coding RNAs (lncRNAs) represent a class of versatile molecules that exhibit the potential to regulate gene expression at various levels, namely transcriptional, post-transcriptional and epigenetic, thereby influencing critical cellular processes such as proliferation, apoptosis, invasion and drug resistance. The lncRNA H19, among the earliest identified within this category, has emerged as a significant participant in the pathogenesis of a multitude of both malignant and benign gynecological diseases. An escalating body of evidence indicates a functionally pertinent network of lncRNA H19 coexpression linked with the extracellular matrix architecture and immune microenvironment during cancer progression. This association may provide insightful leads for the selection of innovative diagnostic biomarkers and assist in the delineation of potent pharmaceutical targets for gynecological oncology. The present comprehensive review presented a synthesis of the expression profiles and multifaceted implications of lncRNA H19 across a spectrum of gynecological pathologies. Contents1. Introduction 2. Overview of lncRNA 3. Multifaceted role of lncRNA H19 4. lncRNA H19 and gynecologic malignancies 5. lncRNA H19 and benign gynecological diseases 6. Effects of lncRNA H19 on immunity 7. Conclusion

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...