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      Pan-cancer analysis connects tumor matrisome to immune response

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          Abstract

          Recent sequencing efforts unveil genomic landscapes of tumor microenvironment. A key compartment in this niche is the extracellular matrix (ECM) and its related components – matrisome. Yet, little is known about the extent to which matrisome pattern is conserved in progressive tumors across diverse cancer types. Using integrative genomic approaches, we conducted multi-platform assessment of a measure of deregulated matrisome associated with tumor progression, termed as tumor matrisome index (TMI), in over 30,000 patient-derived samples. Combined quantitative analyses of genomics and proteomics reveal that TMI is closely associated with mutational load, tumor pathology, and predicts survival across different malignancies. Interestingly, we observed an enrichment of specific tumor-infiltrating immune cell populations, along with signatures predictive of resistance to immune checkpoint blockade immunotherapy, and clinically targetable immune checkpoints in TMI high tumors. B7-H3 emerged as a particularly promising target for anti-tumor immunity in these tumors. Here, we show that matrisomal abnormalities could represent a potential clinically useful biomarker for prognostication and prediction of immunotherapy response.

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          Most cited references22

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          The blockade of immune checkpoints in cancer immunotherapy.

          Among the most promising approaches to activating therapeutic antitumour immunity is the blockade of immune checkpoints. Immune checkpoints refer to a plethora of inhibitory pathways hardwired into the immune system that are crucial for maintaining self-tolerance and modulating the duration and amplitude of physiological immune responses in peripheral tissues in order to minimize collateral tissue damage. It is now clear that tumours co-opt certain immune-checkpoint pathways as a major mechanism of immune resistance, particularly against T cells that are specific for tumour antigens. Because many of the immune checkpoints are initiated by ligand-receptor interactions, they can be readily blocked by antibodies or modulated by recombinant forms of ligands or receptors. Cytotoxic T-lymphocyte-associated antigen 4 (CTLA4) antibodies were the first of this class of immunotherapeutics to achieve US Food and Drug Administration (FDA) approval. Preliminary clinical findings with blockers of additional immune-checkpoint proteins, such as programmed cell death protein 1 (PD1), indicate broad and diverse opportunities to enhance antitumour immunity with the potential to produce durable clinical responses.
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            Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin.

            Recent genomic analyses of pathologically defined tumor types identify "within-a-tissue" disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head and neck, and a subset of bladder cancers coalesced into one subtype typified by TP53 alterations, TP63 amplifications, and high expression of immune and proliferation pathway genes. Of note, bladder cancers split into three pan-cancer subtypes. The multiplatform classification, while correlated with tissue-of-origin, provides independent information for predicting clinical outcomes. All data sets are available for data-mining from a unified resource to support further biological discoveries and insights into novel therapeutic strategies. Copyright © 2014 Elsevier Inc. All rights reserved.
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              Epithelial-mesenchymal transition spectrum quantification and its efficacy in deciphering survival and drug responses of cancer patients

              Epithelial-mesenchymal transition (EMT) is a reversible and dynamic process hypothesized to be co-opted by carcinoma during invasion and metastasis. Yet, there is still no quantitative measure to assess the interplay between EMT and cancer progression. Here, we derived a method for universal EMT scoring from cancer-specific transcriptomic EMT signatures of ovarian, breast, bladder, lung, colorectal and gastric cancers. We show that EMT scoring exhibits good correlation with previously published, cancer-specific EMT signatures. This universal and quantitative EMT scoring was used to establish an EMT spectrum across various cancers, with good correlation noted between cell lines and tumours. We show correlations between EMT and poorer disease-free survival in ovarian and colorectal, but not breast, carcinomas, despite previous notions. Importantly, we found distinct responses between epithelial- and mesenchymal-like ovarian cancers to therapeutic regimes administered with or without paclitaxelin vivo and demonstrated that mesenchymal-like tumours do not always show resistance to chemotherapy. EMT scoring is thus a promising, versatile tool for the objective and systematic investigation of EMT roles and dynamics in cancer progression, treatment response and survival.
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                Author and article information

                Contributors
                darren.lim.w.t@singhealth.com.sg
                ctlim@nus.edu.sg
                Journal
                NPJ Precis Oncol
                NPJ Precis Oncol
                NPJ Precision Oncology
                Nature Publishing Group UK (London )
                2397-768X
                22 May 2019
                22 May 2019
                2019
                : 3
                : 15
                Affiliations
                [1 ]ISNI 0000 0001 2180 6431, GRID grid.4280.e, NUS Graduate School for Integrative Sciences & Engineering, , National University of Singapore, ; Singapore, Singapore
                [2 ]ISNI 0000 0001 2180 6431, GRID grid.4280.e, Department of Biomedical Engineering, , National University of Singapore, ; Singapore, Singapore
                [3 ]ISNI 0000 0004 0620 9745, GRID grid.410724.4, Division of Radiation Oncology, , National Cancer Centre Singapore, ; Singapore, Singapore
                [4 ]ISNI 0000 0004 0620 9745, GRID grid.410724.4, Division of Medical Sciences, , National Cancer Centre Singapore, ; Singapore, Singapore
                [5 ]ISNI 0000 0004 0385 0924, GRID grid.428397.3, Oncology Academic Clinical Programme, , Duke-NUS Medical School, ; Singapore, Singapore
                [6 ]ISNI 0000 0000 9486 5048, GRID grid.163555.1, Department of Anatomical Pathology, , Singapore General Hospital, ; Singapore, Singapore
                [7 ]ISNI 0000 0004 0637 0221, GRID grid.185448.4, Institute of Molecular and Cell Biology, , Agency for Science, Technology and Research, ; Singapore, Singapore
                [8 ]Sysmex Asia Pacific Pte Ltd, Singapore, Singapore
                [9 ]ISNI 0000 0004 0620 9745, GRID grid.410724.4, Division of Medical Oncology, , National Cancer Centre Singapore, ; Singapore, Singapore
                [10 ]ISNI 0000 0004 0385 0924, GRID grid.428397.3, Office of Clinical Sciences, , Duke-NUS Medical School, ; Singapore, Singapore
                [11 ]ISNI 0000 0001 2180 6431, GRID grid.4280.e, Mechanobiology Institute, , National University of Singapore, ; Singapore, Singapore
                [12 ]ISNI 0000 0001 2180 6431, GRID grid.4280.e, Institute for Health Innovation and Technology, , National University of Singapore, ; Singapore, Singapore
                Author information
                http://orcid.org/0000-0003-1752-7039
                http://orcid.org/0000-0002-1648-1473
                http://orcid.org/0000-0003-4019-9782
                Article
                87
                10.1038/s41698-019-0087-0
                6531473
                31123708
                78997e96-a18b-4c3a-9d1a-77bf3d144f29
                © The Author(s) 2019

                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
                : 19 November 2018
                : 10 April 2019
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                cancer genomics,predictive markers
                cancer genomics, predictive markers

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