Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer.
Woosung ChungHye Hyeon EumHae-Ock LeeKyung-Min LeeHan-Byoel LeeKyu-Tae KimHan Suk RyuSangmin KimJeong Eon LeeYeon Hee ParkZhengyan KanWonshik HanWoong-Yang ParkPublished in: Nature communications (2017)
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.
Keyphrases
- single cell
- rna seq
- copy number
- high throughput
- induced apoptosis
- cell cycle arrest
- mitochondrial dna
- genome wide
- ejection fraction
- newly diagnosed
- bone marrow
- cell death
- endoplasmic reticulum stress
- dna methylation
- oxidative stress
- gene expression
- machine learning
- breast cancer risk
- big data
- young adults
- deep learning
- patient reported
- genetic diversity
- data analysis
- childhood cancer