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Abstract Communications between tumor cells and surrounding immune cells help shape the tumor immunity continuum. Recent breakthroughs in high-throughput technologies as well as computational algorithms had reported many important tumor-immune cell (TIC) communications, which were scattered in thousands of published studies and impeded systematical characterization of the TIC communications across cancer. Here, a comprehensive database, TICCom, was developed to model TIC communications, containing 739 experimentally-validated or manually-curated interactions collected from more than 3,000 literatures as well as 4,537,709 predicted interactions inferred via six computational algorithms by reanalyzing 32 scRNA-seq datasets and bulk RNA-seq data across 25 cancer types. The communications between tumor cells and 14 types of immune cells were characterized, and the involved ligand-receptor interactions were further integrated. 14190 human and 3650 mouse integrated ligand-receptor interactions with supplemented corresponding function information were also stored in the TICCom database. Our database would serve as a valuable resource for investigating TIC communications.

Summary: The typical genomic feature of acute myeloid leukemia (AML) M3 subtype is the fusion event of PML/RARα, and ATRA/ATO-based combination therapy is current standard treatment regimen for M3 subtype. Here, a machine-learning model based on expressions of PML/RARα targets was developed to identify M3 patients by analyzing 1228 AML patients. Our model exhibited high accuracy. To enable more non-M3 AML patients to potentially benefit from ATRA/ATO therapy, M3-like patients were further identified. We found that M3-like patients had strong GMP features, including the expression patterns of M3 subtype marker genes, the proportion of myeloid progenitor cells, and deconvolution of AML constituent cell populations. M3-like patients exhibited distinct genomic features, low immune activity and better clinical survival. The initiative identification of patients similar to M3 subtype may help to identify more patients that would benefit from ATO/ATRA treatment and deepen our understanding of the molecular mechanism of AML pathogenesis.

LncRNA-lncRNA immune cooperative regulations are identified by integrating multi-omics data across 20 cancer types and the roles of immune cooperative lncRNAs in tumor-immune functions are explored.

Abstract Background Long noncoding RNAs (lncRNAs) are emerging as critical regulatory elements and play fundamental roles in the biology of various cancers. However, we are still lack of knowledge about their expression patterns and functions in human colorectal cancer (CRC). Methods Differentially expressed lncRNAs in CRC were identified by bioinformatics screen and the level of MIR22HG in CRC and control tissues were determined by qRT-PCR. Cell viability and migration capacities were examined by MTT and transwell assay. Mouse model was used to examine the function and rational immunotherapy of MIR22HG in vivo. Results We systematically investigated the expression pattern of lncRNAs and revealed MIR22HG acts as a tumor suppressor in CRC. The expression of MIR22HG was significantly decreased in CRC, which was mainly driven by copy number deletion. Reduced expression of MIR22HG was significantly associated with poor overall survival. Silencing of MIR22HG promoted cell survival, proliferation and tumor metastasis in vitro and in vivo. Mechanistically, MIR22HG exerts its tumor suppressive activity by competitively interacting with SMAD2 and modulating the activity of TGFβ pathway. Decreased MIR22HG promoted the epithelial-mesenchymal transition in CRC. Importantly, we found that MIR22HG expression is significantly correlated with CD8A and overexpression of MIR22HG triggers T cell infiltration, enhancing the clinical benefits of immunotherapy. Conclusion MIR22HG acts as a tumor suppressor in CRC. Our data provide mechanistic insights into the regulation of MIR22HG in TGFβ pathway and facilitates immunotherapy in cancer.