Title |
Quantifying tumor-infiltrating immune cells from transcriptomics data
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---|---|
Published in |
Cancer Immunology, Immunotherapy, March 2018
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DOI | 10.1007/s00262-018-2150-z |
Pubmed ID | |
Authors |
Francesca Finotello, Zlatko Trajanoski |
Abstract |
By exerting pro- and anti-tumorigenic actions, tumor-infiltrating immune cells can profoundly influence tumor progression, as well as the success of anti-cancer therapies. Therefore, the quantification of tumor-infiltrating immune cells holds the promise to unveil the multi-faceted role of the immune system in human cancers and its involvement in tumor escape mechanisms and response to therapy. Tumor-infiltrating immune cells can be quantified from RNA sequencing data of human tumors using bioinformatics approaches. In this review, we describe state-of-the-art computational methods for the quantification of immune cells from transcriptomics data and discuss the open challenges that must be addressed to accurately quantify immune infiltrates from RNA sequencing data of human bulk tumors. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 6 | 20% |
Germany | 3 | 10% |
United Kingdom | 2 | 7% |
France | 2 | 7% |
Brazil | 1 | 3% |
Russia | 1 | 3% |
Israel | 1 | 3% |
Ireland | 1 | 3% |
Spain | 1 | 3% |
Other | 2 | 7% |
Unknown | 10 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 21 | 70% |
Members of the public | 7 | 23% |
Practitioners (doctors, other healthcare professionals) | 2 | 7% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 422 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 100 | 24% |
Student > Ph. D. Student | 75 | 18% |
Student > Master | 45 | 11% |
Student > Postgraduate | 26 | 6% |
Student > Bachelor | 26 | 6% |
Other | 50 | 12% |
Unknown | 100 | 24% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 121 | 29% |
Agricultural and Biological Sciences | 52 | 12% |
Medicine and Dentistry | 40 | 9% |
Immunology and Microbiology | 31 | 7% |
Computer Science | 24 | 6% |
Other | 38 | 9% |
Unknown | 116 | 27% |