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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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Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Singapore 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7608385/
https://www.ncbi.nlm.nih.gov/pubmed/32561858
http://dx.doi.org/10.1038/s41422-020-0355-0
Descripción
Sumario: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.