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      Spatial profiling of cancer-associated fibroblasts of sporadic early onset colon cancer microenvironment

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          Abstract

          The incidence of sporadic early-onset colon cancer (EOCC) has increased worldwide. The molecular mechanisms in the tumor and the tumor microenvironment (TME) in EOCC are not fully understood. The aim of this study is to unravel unique spatial transcriptomic and proteomic profiles in tumor epithelial cells and cancer-associated fibroblasts (CAFs). Here, we divide the sporadic colon cancer tissue samples with transcriptomic data into patients diagnosed with EOCC (<50 yrs) and late-onset colon cancer (LOCC, ≥50 yrs) and then, analyze the data using CIBERSORTx deconvolution software. EOCC tumors are more enriched in CAFs with fibroblast associated protein positive expression (FAP(+)) than LOCC tumors. EOCC patients with higher FAP mRNA levels in CAFs have shorter OS (Log-rank test, p < 0.029). Spatial transcriptomic analysis of 112 areas of interest, using NanoString GeoMx digital spatial profiling, demonstrate that FAP(+) CAFs at the EOCC tumor invasive margin show a significant upregulation of WNT signaling and higher mRNA/protein levels of fibroblast growth factor 20 (FGF20). Tumor epithelial cells at tumor invasive margin of EOCC tumors neighboring FAP(+) CAFs show significantly higher mRNA/protein levels of fibroblast growth factor receptor (FGFR2) and PI3K/Akt signaling activation. NichNET analysis show a potential interaction between FGF20 and FGFFR2. The role of FGF20 in activating FGFR2/pFGFR2 and AKT/pAKT was validated in-vitro. In conclusion, we identify a unique FAP(+) CAF population that showed WNT signaling upregulation and increased FGF20 levels; while neighbor tumor cells show the upregulation/activation of FGFR2-PI3K/Akt signaling at the tumor invasive margin of EOCC tumors.

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          Most cited references56

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          Cancer Statistics, 2021

          Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths in the United States and compiles the most recent data on population-based cancer occurrence. Incidence data (through 2017) were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2018) were collected by the National Center for Health Statistics. In 2021, 1,898,160 new cancer cases and 608,570 cancer deaths are projected to occur in the United States. After increasing for most of the 20th century, the cancer death rate has fallen continuously from its peak in 1991 through 2018, for a total decline of 31%, because of reductions in smoking and improvements in early detection and treatment. This translates to 3.2 million fewer cancer deaths than would have occurred if peak rates had persisted. Long-term declines in mortality for the 4 leading cancers have halted for prostate cancer and slowed for breast and colorectal cancers, but accelerated for lung cancer, which accounted for almost one-half of the total mortality decline from 2014 to 2018. The pace of the annual decline in lung cancer mortality doubled from 3.1% during 2009 through 2013 to 5.5% during 2014 through 2018 in men, from 1.8% to 4.4% in women, and from 2.4% to 5% overall. This trend coincides with steady declines in incidence (2.2%-2.3%) but rapid gains in survival specifically for nonsmall cell lung cancer (NSCLC). For example, NSCLC 2-year relative survival increased from 34% for persons diagnosed during 2009 through 2010 to 42% during 2015 through 2016, including absolute increases of 5% to 6% for every stage of diagnosis; survival for small cell lung cancer remained at 14% to 15%. Improved treatment accelerated progress against lung cancer and drove a record drop in overall cancer mortality, despite slowing momentum for other common cancers.
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            QuPath: Open source software for digital pathology image analysis

            QuPath is new bioimage analysis software designed to meet the growing need for a user-friendly, extensible, open-source solution for digital pathology and whole slide image analysis. In addition to offering a comprehensive panel of tumor identification and high-throughput biomarker evaluation tools, QuPath provides researchers with powerful batch-processing and scripting functionality, and an extensible platform with which to develop and share new algorithms to analyze complex tissue images. Furthermore, QuPath’s flexible design makes it suitable for a wide range of additional image analysis applications across biomedical research.
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              Determining cell-type abundance and expression from bulk tissues with digital cytometry

              Single-cell RNA-seq (scRNA-seq) has emerged as a powerful technique for characterizing cellular heterogeneity, but it is currently impractical on large sample cohorts and cannot be applied to fixed specimens collected as part of routine clinical care. We previously developed an approach for digital cytometry, called CIBERSORT, that enables estimation of cell type abundances from bulk tissue transcriptomes. We now introduce CIBERSORTx, a machine learning method that extends this framework to infer cell-type-specific gene expression profiles without physical cell isolation. By minimizing platform-specific variation, CIBERSORTx also allows the use of scRNA-seq data for large-scale tissue dissection. We evaluated the utility of CIBERSORTx in multiple tumor types, including melanoma, where single-cell reference profiles were used to dissect bulk clinical specimens, revealing cell type-specific phenotypic states linked to distinct driver mutations and response to immune checkpoint blockade. We anticipate that digital cytometry will augment single-cell profiling efforts, enabling cost-effective, high-throughput tissue characterization without the need for antibodies, disaggregation, or viable cells.
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                Author and article information

                Contributors
                dave.hoon@providence.org
                Journal
                NPJ Precis Oncol
                NPJ Precis Oncol
                NPJ Precision Oncology
                Nature Publishing Group UK (London )
                2397-768X
                14 November 2023
                14 November 2023
                2023
                : 7
                : 118
                Affiliations
                [1 ]Department of Translational Molecular Medicine, Saint John’s Cancer Institute (SJCI), Providence Saint John’s Health Center (SJHC), ( https://ror.org/01gcc9p15) Santa Monica, CA 90404 USA
                [2 ]Department of Genome Sequencing Center, SJCI, Providence SJHC, ( https://ror.org/00rxpqe74) Santa Monica, CA 90404 USA
                [3 ]Department of Surgical Pathology, Providence SJHC, Santa Monica, CA 90404 USA
                [4 ]Department of Gastrointestinal and Hepatobiliary Surgery, Providence SJHC, Santa Monica, CA 90404 USA
                Author information
                http://orcid.org/0000-0001-9625-1127
                http://orcid.org/0000-0002-0689-1153
                http://orcid.org/0000-0003-1915-3683
                Article
                474
                10.1038/s41698-023-00474-w
                10645739
                37964075
                988ba468-f430-4947-b686-4be8c3660d37
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 30 April 2023
                : 24 October 2023
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                © Nature Publishing Group UK 2023

                cancer microenvironment
                cancer microenvironment

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