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An integrated model of the transcriptome of HER2-positive breast cancer
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(2013) An integrated model of the transcriptome of HER2-positive breast cancer. PLoS One, Volume 8 (Number 11). Article number e79298. doi:10.1371/journal.pone.0079298
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Official URL: http://dx.doi.org/10.1371/journal.pone.0079298
Abstract
Our goal in these analyses was to use genomic features from a test set of primary breast tumors to build an integrated transcriptome landscape model that makes relevant hypothetical predictions about the biological and/or clinical behavior of HER2-positive breast cancer. We interrogated RNA-Seq data from benign breast lesions, ER+, triple negative, and HER2-positive tumors to identify 685 differentially expressed genes, 102 alternatively spliced genes, and 303 genes that expressed single nucleotide sequence variants (eSNVs) that were associated with the HER2-positive tumors in our survey panel. These features were integrated into a transcriptome landscape model that identified 12 highly interconnected genomic modules, each of which represents a cellular processes pathway that appears to define the genomic architecture of the HER2-positive tumors in our test set. The generality of the model was confirmed by the observation that several key pathways were enriched in HER2-positive TCGA breast tumors. The ability of this model to make relevant predictions about the biology of breast cancer cells was established by the observation that integrin signaling was linked to lapatinib sensitivity in vitro and strongly associated with risk of relapse in the NCCTG N9831 adjuvant trastuzumab clinical trial dataset. Additional modules from the HER2 transcriptome model, including ubiquitin-mediated proteolysis, TGF-beta signaling, RHO-family GTPase signaling, and M-phase progression, were linked to response to lapatinib and paclitaxel in vitro and/or risk of relapse in the N9831 dataset. These data indicate that an integrated transcriptome landscape model derived from a test set of HER2-positive breast tumors has potential for predicting outcome and for identifying novel potential therapeutic strategies for this breast cancer subtype.
| Item Type: | Journal Article | ||||||||
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| Subjects: | Q Science > QH Natural history > QH426 Genetics R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer) |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||||
| Library of Congress Subject Headings (LCSH): | Breast -- Cancer -- Genetic aspects | ||||||||
| Journal or Publication Title: | PLoS One | ||||||||
| Publisher: | Public Library of Science | ||||||||
| ISSN: | 1932-6203 | ||||||||
| Official Date: | 1 November 2013 | ||||||||
| Dates: |
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| Volume: | Volume 8 | ||||||||
| Number: | Number 11 | ||||||||
| Number of Pages: | 21 | ||||||||
| Article Number: | Article number e79298 | ||||||||
| DOI: | 10.1371/journal.pone.0079298 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access | ||||||||
| Funder: | 26.2 With Donna Foundation, Eveleigh Family Foundation (EFF), Carmichael Family, Mayo Foundation, National Cancer Institute (U.S.) (NCI) | ||||||||
| Grant number: | CA129949 (NCI), CA155129 (NCI) |
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