Title |
Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression
|
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Published in |
Genome Biology, October 2016
|
DOI | 10.1186/s13059-016-1070-5 |
Pubmed ID | |
Authors |
Etienne Becht, Nicolas A. Giraldo, Laetitia Lacroix, Bénédicte Buttard, Nabila Elarouci, Florent Petitprez, Janick Selves, Pierre Laurent-Puig, Catherine Sautès-Fridman, Wolf H. Fridman, Aurélien de Reyniès |
Abstract |
We introduce the Microenvironment Cell Populations-counter (MCP-counter) method, which allows the robust quantification of the absolute abundance of eight immune and two stromal cell populations in heterogeneous tissues from transcriptomic data. We present in vitro mRNA mixture and ex vivo immunohistochemical data that quantitatively support the validity of our method's estimates. Additionally, we demonstrate that MCP-counter overcomes several limitations or weaknesses of previously proposed computational approaches. MCP-counter is applied to draw a global picture of immune infiltrates across human healthy tissues and non-hematopoietic human tumors and recapitulates microenvironment-based patient stratifications associated with overall survival in lung adenocarcinoma and colorectal and breast cancer. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 4 | 50% |
France | 2 | 25% |
Ireland | 1 | 13% |
Comoros | 1 | 13% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 4 | 50% |
Members of the public | 3 | 38% |
Science communicators (journalists, bloggers, editors) | 1 | 13% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Israel | 1 | <1% |
United States | 1 | <1% |
France | 1 | <1% |
Unknown | 755 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 156 | 21% |
Student > Ph. D. Student | 145 | 19% |
Student > Master | 76 | 10% |
Student > Bachelor | 54 | 7% |
Student > Doctoral Student | 35 | 5% |
Other | 97 | 13% |
Unknown | 195 | 26% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 196 | 26% |
Agricultural and Biological Sciences | 108 | 14% |
Medicine and Dentistry | 91 | 12% |
Immunology and Microbiology | 51 | 7% |
Computer Science | 25 | 3% |
Other | 58 | 8% |
Unknown | 229 | 30% |