Lung cancer is the leading cause of cancer-related death worldwide. Inactivation of tumor suppressor genes (TSGs) promotes lung cancer malignant progression. Here, we take advantage of the clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9-mediated somatic gene knockout in a mouse model to identify bona fide TSGs. From individual knockout of 55 potential TSGs, we identify five genes, including, ,, , and, whose knockout significantly promotes lung tumorigenesis. These candidate genes are frequently down-regulated in human lung cancer specimens and significantly associated with survival in patients with lung cancer. Through crossing the conditional knockout allele to the mouse model, we further find that deletion dramatically promotes lung cancer progression. The tumor-promotive effect of knockout in vivo is mainly mediated through an increase of the EZH2 level, which up-regulates the H3K27me3 level. Moreover, the -knockout lung tumors are preferentially sensitive to EZH2 inhibitor treatment. Collectively, our study provides a systematic screening of TSGs in vivo and identifies UTX as an important epigenetic regulator in lung tumorigenesis.

Lung cancer is the leading cause of cancer-related deaths worldwide. Tumor suppressor genes remain to be systemically identified for lung cancer. Through the genome-wide screening of tumor-suppressive transcription factors, we demonstrate here that GATA4 functions as an essential tumor suppressor in lung cancer in vitro and in vivo. Ectopic GATA4 expression results in lung cancer cell senescence. Mechanistically, GATA4 upregulates multiple miRNAs targeting TGFB2 mRNA and causes ensuing WNT7B downregulation and eventually triggers cell senescence. Decreased GATA4 level in clinical specimens negatively correlates with WNT7B or TGF-β2 level and is significantly associated with poor prognosis. TGFBR1 inhibitors show synergy with existing therapeutics in treating GATA4-deficient lung cancers in genetically engineered mouse model as well as patient-derived xenograft (PDX) mouse models. Collectively, our work demonstrates that GATA4 functions as a tumor suppressor in lung cancer and targeting the TGF-β signaling provides a potential way for the treatment of GATA4-deficient lung cancer.

Small molecular PD-1 inhibitors are lacking in current immunooncology clinic. PD-1/PD-L1 antibody inhibitors currently approved for clinical usage block interaction between PD-L1 and PD-1 to enhance cytotoxicity of CD8 + cytotoxic T lymphocyte (CTL). Whether other steps along the PD-1 signaling pathway can be targeted remains to be determined. Here, we report that methylene blue (MB), an FDA-approved chemical for treating methemoglobinemia, potently inhibits PD-1 signaling. MB enhances the cytotoxicity, activation, cell proliferation, and cytokine-secreting activity of CTL inhibited by PD-1. Mechanistically, MB blocks interaction between Y248-phosphorylated immunoreceptor tyrosinebased switch motif (ITSM) of human PD-1 and SHP2. MB enables activated CTL to shrink PD-L1 expressing tumor allografts and autochthonous lung cancers in a transgenic mouse model. MB also effectively counteracts the PD-1 signaling on human T cells isolated from peripheral blood of healthy donors. Thus, we identify an FDA-approved chemical capable of potently inhibiting the function of PD-1. Equally important, our work sheds light on a novel strategy to develop inhibitors targeting PD-1 signaling axis.

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With emergence of blockchain technologies and the associated cryptocurrencies, such as Bitcoin, understanding network dynamics behind Blockchain graphs has become a rapidly evolving research direction. Unlike other financial networks, such as stock and currency trading, blockchain based cryptocurrencies have the entire transaction graph accessible to the public (i.e., all transactions can be downloaded and analyzed). A natural question is then to ask whether the dynamics of the transaction graph impacts the price of the underlying cryptocurrency. We show that standard graph features such as degree distribution of the transaction graph may not be sufficient to capture network dynamics and its potential impact on fluctuations of Bitcoin price. In contrast, the new graph associated topological features computed using the tools of persistent homology, are found to exhibit a high utility for predicting Bitcoin price dynamics. Using the proposed persistent homology-based techniques, we offer a new elegant, easily extendable and computationally light approach for graph representation learning on Blockchain.