Title |
Cancer expression quantitative trait loci (eQTLs) can be determined from heterogeneous tumor gene expression data by modeling variation in tumor purity
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Published in |
Genome Biology, September 2018
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DOI | 10.1186/s13059-018-1507-0 |
Pubmed ID | |
Authors |
Paul Geeleher, Aritro Nath, Fan Wang, Zhenyu Zhang, Alvaro N. Barbeira, Jessica Fessler, Robert L. Grossman, Cathal Seoighe, R. Stephanie Huang |
Abstract |
Expression quantitative trait loci (eQTLs) identified using tumor gene expression data could affect gene expression in cancer cells, tumor-associated normal cells, or both. Here, we have demonstrated a method to identify eQTLs affecting expression in cancer cells by modeling the statistical interaction between genotype and tumor purity. Only one third of breast cancer risk variants, identified as eQTLs from a conventional analysis, could be confidently attributed to cancer cells. The remaining variants could affect cells of the tumor microenvironment, such as immune cells and fibroblasts. Deconvolution of tumor eQTLs will help determine how inherited polymorphisms influence cancer risk, development, and treatment response. |
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Geographical breakdown
Country | Count | As % |
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United States | 7 | 28% |
United Kingdom | 4 | 16% |
Norway | 2 | 8% |
Germany | 2 | 8% |
India | 1 | 4% |
Unknown | 9 | 36% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 12 | 48% |
Scientists | 11 | 44% |
Practitioners (doctors, other healthcare professionals) | 1 | 4% |
Science communicators (journalists, bloggers, editors) | 1 | 4% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 71 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 18 | 25% |
Researcher | 10 | 14% |
Other | 4 | 6% |
Student > Doctoral Student | 4 | 6% |
Student > Master | 4 | 6% |
Other | 8 | 11% |
Unknown | 23 | 32% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 15 | 21% |
Agricultural and Biological Sciences | 10 | 14% |
Computer Science | 6 | 8% |
Medicine and Dentistry | 5 | 7% |
Engineering | 3 | 4% |
Other | 7 | 10% |
Unknown | 25 | 35% |