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      Cell of origin alters myeloid-mediated immunosuppression in lung adenocarcinoma

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          SUMMARY

          Solid carcinomas are often highly heterogenous cancers, arising from multiple epithelial cells of origin. Yet, how the cell of origin influences the response of the tumor microenvironment is poorly understood. Lung adenocarcinoma (LUAD) arises in the distal alveolar epithelium which is populated primarily by alveolar epithelial type I (AT1) and type II (AT2) cells. It has been previously reported that Gramd2 + AT1 cells can give rise to a histologically-defined LUAD that is distinct in pathology and transcriptomic identity from that arising from Sftpc + AT2 cells 1, 2 . To determine how cells of origin influence the tumor immune microenvironment (TIME) landscape, we comprehensively characterized transcriptomic, molecular, and cellular states within the TIME of Gramd2 + AT1 and Sftpc + AT2-derived LUAD using KRAS G12D oncogenic driver mouse models. Myeloid cells within the Gramd2 + AT1-derived LUAD TIME were increased, specifically, immunoreactive monocytes and tumor associated macrophages (TAMs). In contrast, the Sftpc + AT2 LUAD TIME was enriched for Arginase-1 + myeloid derived suppressor cells (MDSC) and TAMs expressing profiles suggestive of immunosuppressive function. Validation of immune infiltration was performed using flow cytometry, and intercellular interaction analysis between the cells of origin and major myeloid cell populations indicated that cell-type specific markers SFTPD in AT2 cells and CAV1 in AT1 cells mediated unique interactions with myeloid cells of the differential immunosuppressive states within each cell of origin mouse model. Taken together, Gramd2 + AT1-derived LUAD presents with an anti-tumor, immunoreactive TIME, while the TIME of Sftpc + AT2-derived LUAD has hallmarks of immunosuppression. This study suggests that LUAD cell of origin influences the composition and suppression status of the TIME landscape and may hold critical implications for patient response to immunotherapy.

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          Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

          This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.
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              Fast, sensitive, and accurate integration of single cell data with Harmony

              The emerging diversity of single cell RNAseq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies. Here, real biological differences are interspersed with technical differences. We present Harmony, an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms. We show that Harmony requires dramatically fewer computational resources. It is the only currently available algorithm that makes the integration of ~106 cells feasible on a personal computer. We apply Harmony to PBMCs from datasets with large experimental differences, 5 studies of pancreatic islet cells, mouse embryogenesis datasets, and cross-modality spatial integration.
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                Author and article information

                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                2692-8205
                23 June 2024
                : 2024.06.19.599651
                Affiliations
                [1 ]Department of Integrative Translational Sciences, Beckman Research Institute, City of Hope, Duarte, CA USA 91010
                [2 ]Department of Surgery, University of Southern California, Los Angeles, CA USA 90089
                [3 ]Department of Translational Genomics, University of Southern California, Los Angeles, CA USA 90089
                [4 ]Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA USA 90089
                [5 ]Division of Thoracic Surgery, Department of Surgery, City of Hope National Medical Center, City of Hope, Duarte, CA USA 91010.
                [6 ]Department of Pathology, City of Hope National Medical Center, City of Hope, Duarte, CA USA 91010
                [7 ]Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of California, San Diego, La Jolla, CA USA 92093.
                [8 ]Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA USA 90089.
                Author notes
                [+]

                Equal contribution

                [*]

                Shared contribution

                [** ]Corresponding author contact information: Crystal Marconett, Ph.D., Department of Integrative Translational Sciences, Beckman Research Institute, City of Hope, Duarte CA 91010, USA. cmarconett@ 123456coh.org
                Author information
                http://orcid.org/0000-0002-4744-8350
                http://orcid.org/0000-0002-8463-7126
                Article
                10.1101/2024.06.19.599651
                11213232
                38948812
                fbf1bdaf-7858-4f3e-baab-42f9180def0f

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.

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                Article

                single nucleus rna sequencing,tumor immune microenvironment,myeloid-derived suppressor cells,tumor-associated macrophages

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