A pan-cancer blueprint of the heterogeneous tumor microenvironment revealed by single-cell profiling
The stromal compartment of the tumor microenvironment consists of a heterogeneous set of tissue-resident and tumor-infiltrating cells, which are profoundly moulded by cancer cells. An outstanding question is to what extent this heterogeneity is similar between cancers affecting different organs. Her...
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Veröffentlicht in: | Cell research 2020-09, Vol.30 (9), p.745-762 |
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creator | Qian, Junbin Olbrecht, Siel Boeckx, Bram Vos, Hanne Laoui, Damya Etlioglu, Emre Wauters, Els Pomella, Valentina Verbandt, Sara Busschaert, Pieter Bassez, Ayse Franken, Amelie Bempt, Marlies Vanden Xiong, Jieyi Weynand, Birgit van Herck, Yannick Antoranz, Asier Bosisio, Francesca Maria Thienpont, Bernard Floris, Giuseppe Vergote, Ignace Smeets, Ann Tejpar, Sabine Lambrechts, Diether |
description | The stromal compartment of the tumor microenvironment consists of a heterogeneous set of tissue-resident and tumor-infiltrating cells, which are profoundly moulded by cancer cells. An outstanding question is to what extent this heterogeneity is similar between cancers affecting different organs. Here, we profile 233,591 single cells from patients with lung, colorectal, ovary and breast cancer (
n
= 36) and construct a pan-cancer blueprint of stromal cell heterogeneity using different single-cell RNA and protein-based technologies. We identify 68 stromal cell populations, of which 46 are shared between cancer types and 22 are unique. We also characterise each population phenotypically by highlighting its marker genes, transcription factors, metabolic activities and tissue-specific expression differences. Resident cell types are characterised by substantial tissue specificity, while tumor-infiltrating cell types are largely shared across cancer types. Finally, by applying the blueprint to melanoma tumors treated with checkpoint immunotherapy and identifying a naïve CD4
+
T-cell phenotype predictive of response to checkpoint immunotherapy, we illustrate how it can serve as a guide to interpret scRNA-seq data. In conclusion, by providing a comprehensive blueprint through an interactive web server, we generate the first panoramic view on the shared complexity of stromal cells in different cancers. |
doi_str_mv | 10.1038/s41422-020-0355-0 |
format | Article |
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n
= 36) and construct a pan-cancer blueprint of stromal cell heterogeneity using different single-cell RNA and protein-based technologies. We identify 68 stromal cell populations, of which 46 are shared between cancer types and 22 are unique. We also characterise each population phenotypically by highlighting its marker genes, transcription factors, metabolic activities and tissue-specific expression differences. Resident cell types are characterised by substantial tissue specificity, while tumor-infiltrating cell types are largely shared across cancer types. Finally, by applying the blueprint to melanoma tumors treated with checkpoint immunotherapy and identifying a naïve CD4
+
T-cell phenotype predictive of response to checkpoint immunotherapy, we illustrate how it can serve as a guide to interpret scRNA-seq data. In conclusion, by providing a comprehensive blueprint through an interactive web server, we generate the first panoramic view on the shared complexity of stromal cells in different cancers.</description><identifier>ISSN: 1001-0602</identifier><identifier>EISSN: 1748-7838</identifier><identifier>DOI: 10.1038/s41422-020-0355-0</identifier><identifier>PMID: 32561858</identifier><language>eng</language><publisher>Singapore: Springer Singapore</publisher><subject>38/1 ; 38/39 ; 45/62 ; 45/90 ; 45/91 ; 631/67/1857 ; 631/67/2329 ; 631/67/327 ; 631/67/580 ; Biomedical and Life Sciences ; Breast cancer ; Cancer ; CD4 antigen ; Cell Biology ; Heterogeneity ; Immunotherapy ; Internet ; Life Sciences ; Lymphocytes T ; Melanoma ; Organs ; Ovarian cancer ; Phenotypes ; Ribonucleic acid ; RNA ; Stromal cells ; Transcription factors ; Tumors</subject><ispartof>Cell research, 2020-09, Vol.30 (9), p.745-762</ispartof><rights>Center for Excellence in Molecular Cell Science, CAS 2020</rights><rights>Center for Excellence in Molecular Cell Science, CAS 2020.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c584t-3d5be1c8b21285269977e3b69afbbbd3d4a8773bf42088a91894a72cd823e2433</citedby><cites>FETCH-LOGICAL-c584t-3d5be1c8b21285269977e3b69afbbbd3d4a8773bf42088a91894a72cd823e2433</cites><orcidid>0000-0002-8772-6845 ; 0000-0003-4267-1307 ; 0000-0001-6145-045X ; 0000-0002-3429-302X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7608385/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7608385/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,882,27905,27906,41469,42538,51300,53772,53774</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32561858$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Qian, Junbin</creatorcontrib><creatorcontrib>Olbrecht, Siel</creatorcontrib><creatorcontrib>Boeckx, Bram</creatorcontrib><creatorcontrib>Vos, Hanne</creatorcontrib><creatorcontrib>Laoui, Damya</creatorcontrib><creatorcontrib>Etlioglu, Emre</creatorcontrib><creatorcontrib>Wauters, Els</creatorcontrib><creatorcontrib>Pomella, Valentina</creatorcontrib><creatorcontrib>Verbandt, Sara</creatorcontrib><creatorcontrib>Busschaert, Pieter</creatorcontrib><creatorcontrib>Bassez, Ayse</creatorcontrib><creatorcontrib>Franken, Amelie</creatorcontrib><creatorcontrib>Bempt, Marlies Vanden</creatorcontrib><creatorcontrib>Xiong, Jieyi</creatorcontrib><creatorcontrib>Weynand, Birgit</creatorcontrib><creatorcontrib>van Herck, Yannick</creatorcontrib><creatorcontrib>Antoranz, Asier</creatorcontrib><creatorcontrib>Bosisio, Francesca Maria</creatorcontrib><creatorcontrib>Thienpont, Bernard</creatorcontrib><creatorcontrib>Floris, Giuseppe</creatorcontrib><creatorcontrib>Vergote, Ignace</creatorcontrib><creatorcontrib>Smeets, Ann</creatorcontrib><creatorcontrib>Tejpar, Sabine</creatorcontrib><creatorcontrib>Lambrechts, Diether</creatorcontrib><title>A pan-cancer blueprint of the heterogeneous tumor microenvironment revealed by single-cell profiling</title><title>Cell research</title><addtitle>Cell Res</addtitle><addtitle>Cell Res</addtitle><description>The stromal compartment of the tumor microenvironment consists of a heterogeneous set of tissue-resident and tumor-infiltrating cells, which are profoundly moulded by cancer cells. An outstanding question is to what extent this heterogeneity is similar between cancers affecting different organs. Here, we profile 233,591 single cells from patients with lung, colorectal, ovary and breast cancer (
n
= 36) and construct a pan-cancer blueprint of stromal cell heterogeneity using different single-cell RNA and protein-based technologies. We identify 68 stromal cell populations, of which 46 are shared between cancer types and 22 are unique. We also characterise each population phenotypically by highlighting its marker genes, transcription factors, metabolic activities and tissue-specific expression differences. Resident cell types are characterised by substantial tissue specificity, while tumor-infiltrating cell types are largely shared across cancer types. Finally, by applying the blueprint to melanoma tumors treated with checkpoint immunotherapy and identifying a naïve CD4
+
T-cell phenotype predictive of response to checkpoint immunotherapy, we illustrate how it can serve as a guide to interpret scRNA-seq data. 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research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Qian, Junbin</au><au>Olbrecht, Siel</au><au>Boeckx, Bram</au><au>Vos, Hanne</au><au>Laoui, Damya</au><au>Etlioglu, Emre</au><au>Wauters, Els</au><au>Pomella, Valentina</au><au>Verbandt, Sara</au><au>Busschaert, Pieter</au><au>Bassez, Ayse</au><au>Franken, Amelie</au><au>Bempt, Marlies Vanden</au><au>Xiong, Jieyi</au><au>Weynand, Birgit</au><au>van Herck, Yannick</au><au>Antoranz, Asier</au><au>Bosisio, Francesca Maria</au><au>Thienpont, Bernard</au><au>Floris, Giuseppe</au><au>Vergote, Ignace</au><au>Smeets, Ann</au><au>Tejpar, Sabine</au><au>Lambrechts, Diether</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A pan-cancer blueprint of the heterogeneous tumor microenvironment revealed by single-cell profiling</atitle><jtitle>Cell research</jtitle><stitle>Cell Res</stitle><addtitle>Cell Res</addtitle><date>2020-09-01</date><risdate>2020</risdate><volume>30</volume><issue>9</issue><spage>745</spage><epage>762</epage><pages>745-762</pages><issn>1001-0602</issn><eissn>1748-7838</eissn><abstract>The stromal compartment of the tumor microenvironment consists of a heterogeneous set of tissue-resident and tumor-infiltrating cells, which are profoundly moulded by cancer cells. An outstanding question is to what extent this heterogeneity is similar between cancers affecting different organs. Here, we profile 233,591 single cells from patients with lung, colorectal, ovary and breast cancer (
n
= 36) and construct a pan-cancer blueprint of stromal cell heterogeneity using different single-cell RNA and protein-based technologies. We identify 68 stromal cell populations, of which 46 are shared between cancer types and 22 are unique. We also characterise each population phenotypically by highlighting its marker genes, transcription factors, metabolic activities and tissue-specific expression differences. Resident cell types are characterised by substantial tissue specificity, while tumor-infiltrating cell types are largely shared across cancer types. Finally, by applying the blueprint to melanoma tumors treated with checkpoint immunotherapy and identifying a naïve CD4
+
T-cell phenotype predictive of response to checkpoint immunotherapy, we illustrate how it can serve as a guide to interpret scRNA-seq data. In conclusion, by providing a comprehensive blueprint through an interactive web server, we generate the first panoramic view on the shared complexity of stromal cells in different cancers.</abstract><cop>Singapore</cop><pub>Springer Singapore</pub><pmid>32561858</pmid><doi>10.1038/s41422-020-0355-0</doi><tpages>18</tpages><orcidid>https://orcid.org/0000-0002-8772-6845</orcidid><orcidid>https://orcid.org/0000-0003-4267-1307</orcidid><orcidid>https://orcid.org/0000-0001-6145-045X</orcidid><orcidid>https://orcid.org/0000-0002-3429-302X</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | 38/1 38/39 45/62 45/90 45/91 631/67/1857 631/67/2329 631/67/327 631/67/580 Biomedical and Life Sciences Breast cancer Cancer CD4 antigen Cell Biology Heterogeneity Immunotherapy Internet Life Sciences Lymphocytes T Melanoma Organs Ovarian cancer Phenotypes Ribonucleic acid RNA Stromal cells Transcription factors Tumors |
title | A pan-cancer blueprint of the heterogeneous tumor microenvironment revealed by single-cell profiling |
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