Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer
By Woosung Chung, Hye Hyeon Eum, Hae-Ock Lee, Kyung-min Lee, Han-Byoel Lee, Kyu‐Tae Kim, H. Ryu, Sangmin Kim, J. Lee, Yeon-Hee Park and 3 more. Nature Communications, 2017
876 citations recorded by the source corpus.
Single-cell transcriptome profiling of tumour tissue isolates allows the characterization of heterogeneous tumour cells along with neighbouring stromal and immune cells. Here we adopt this powerful approach to breast cancer and analyse 515 cells from 11 patients. Inferred copy number variations from the single-cell RNA-seq data separate carcinoma cells from non-cancer cells. At a single-cell resolution, carcinoma cells display common signatures within the tumour as well as intratumoral heterogeneity regarding breast cancer subtype and crucial cancer-related pathways. Most of the non-cancer cells are immune cells, with three distinct clusters of T lymphocytes, B lymphocytes and macrophages. T lymphocytes and macrophages both display immunosuppressive characteristics: T cells with a regulatory or an exhausted phenotype and macrophages with an M2 phenotype. These results illustrate that the breast cancer transcriptome has a wide range of intratumoral heterogeneity, which is shaped by the tumour cells and immune cells in the surrounding microenvironment. Genetic heterogeneity in breast cancer has been demonstrated at a single-cell resolution with high levels of genome coverage. Here, the authors perform transcriptome analysis of 515 single cells from 11 patients and define core gene expression signatures for subtype-specific single breast cancer cells and tumour-infiltrating immune cells.
Subfields: Cancer Research, Immunology, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/70d4a4965b6ce3eb986a883e8251bac49c4aa344
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Citations in the corpus
- T Cell Dysfunction in Cancer (2018, background): “Recent work describes the use of these technologies to obtain an in-depth characterization of tumor ecosystems in different cancer types, including breast cancer (Chung et al., 2017), RCC (Chevrier et al., 2017), NSCLC (Lavin et al., 2017), and HCC (Zheng et al., 2017).”
- SCENIC: Single-cell regulatory network inference and clustering (2017, neutral): “57, 73 , Figure 3 and 6a ).”
- Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation (2018, methodology): “BRCA Single Cell RNA expression (Chung et al., 2017) GEO: GSE75688”
- Single-cell analysis reveals fibroblast clusters linked to immunotherapy resistance in cancer. (2020, background): “In recent years, scRNA-seq appeared as a revolutionary technic for addressing intra-tumor cellular complexity (27,30,34-44).”
- The history and advances in cancer immunotherapy: understanding the characteristics of tumor-infiltrating immune cells and their therapeutic implications (2020, background): “TAMs harbor highly plastic phenotypes and can be induced to polarize toward classically activated (M1) or alternatively activated (M2) phenotypes to exert immunostimulatory or immunosuppressive functions in tumor immunity.207,208 M1 macrophages exhibit a proinflammatory phenotype with tumor-killing ”