Title |
Transfer of clinically relevant gene expression signatures in breast cancer: from Affymetrix microarray to Illumina RNA-Sequencing technology
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Published in |
BMC Genomics, November 2014
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DOI | 10.1186/1471-2164-15-1008 |
Pubmed ID | |
Authors |
Debora Fumagalli, Alexis Blanchet-Cohen, David Brown, Christine Desmedt, David Gacquer, Stefan Michiels, Françoise Rothé, Samira Majjaj, Roberto Salgado, Denis Larsimont, Michail Ignatiadis, Marion Maetens, Martine Piccart, Vincent Detours, Christos Sotiriou, Benjamin Haibe-Kains |
Abstract |
Microarrays have revolutionized breast cancer (BC) research by enabling studies of gene expression on a transcriptome-wide scale. Recently, RNA-Sequencing (RNA-Seq) has emerged as an alternative for precise readouts of the transcriptome. To date, no study has compared the ability of the two technologies to quantify clinically relevant individual genes and microarray-derived gene expression signatures (GES) in a set of BC samples encompassing the known molecular BC's subtypes. To accomplish this, the RNA from 57 BCs representing the four main molecular subtypes (triple negative, HER2 positive, luminal A, luminal B), was profiled with Affymetrix HG-U133 Plus 2.0 chips and sequenced using the Illumina HiSeq 2000 platform. The correlations of three clinically relevant BC genes, six molecular subtype classifiers, and a selection of 21 GES were evaluated. |
X Demographics
Geographical breakdown
Country | Count | As % |
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India | 2 | 20% |
United States | 2 | 20% |
Qatar | 1 | 10% |
United Kingdom | 1 | 10% |
Germany | 1 | 10% |
France | 1 | 10% |
Unknown | 2 | 20% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 7 | 70% |
Scientists | 3 | 30% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Germany | 2 | 1% |
United States | 2 | 1% |
France | 1 | <1% |
Denmark | 1 | <1% |
Ukraine | 1 | <1% |
Unknown | 147 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 44 | 29% |
Student > Ph. D. Student | 30 | 19% |
Student > Master | 19 | 12% |
Student > Doctoral Student | 11 | 7% |
Student > Bachelor | 10 | 6% |
Other | 24 | 16% |
Unknown | 16 | 10% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 43 | 28% |
Agricultural and Biological Sciences | 35 | 23% |
Medicine and Dentistry | 27 | 18% |
Computer Science | 13 | 8% |
Engineering | 6 | 4% |
Other | 12 | 8% |
Unknown | 18 | 12% |