Identification of triple-negative breast cancer cell lines classified under the same molecular subtype using different molecular characterization techniques: Implications for translational research
The original algorithm that classified triple-negative breast cancer (TNBC) into six subtypes has recently been revised. The revised algorithm (TNBCtype-IM) classifies TNBC into five subtypes and a modifier based on immunological (IM) signatures. The molecular signature may differ between cancer cel...
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Published in | PloS one Vol. 15; no. 4; p. e0231953 |
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Main Authors | , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Public Library of Science
30.04.2020
Public Library of Science (PLoS) |
Subjects | |
Online Access | Get full text |
ISSN | 1932-6203 1932-6203 |
DOI | 10.1371/journal.pone.0231953 |
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Summary: | The original algorithm that classified triple-negative breast cancer (TNBC) into six subtypes has recently been revised. The revised algorithm (TNBCtype-IM) classifies TNBC into five subtypes and a modifier based on immunological (IM) signatures. The molecular signature may differ between cancer cells in vitro and their respective tumor xenografts. We identified cell lines with concordant molecular subtypes regardless of classification algorithm or analysis of cells in vitro or in vivo, to establish a panel of clinically relevant molecularly stable TNBC models for translational research. Gene expression data were used to classify TNBC cell lines using the original and the revised algorithms. Tumor xenografts were established from 17 cell lines and subjected to gene expression profiling with the original 2188-gene algorithm TNBCtype and the revised 101-gene algorithm TNBCtype-IM. A total of six cell lines (SUM149PT (BL2), HCC1806 (BL2), SUM149PT (BL2), BT549 (M), MDA-MB-453 (LAR), and HCC2157 (BL1)) maintained their subtype classification between in vitro and tumor xenograft analyses across both algorithms. For TNBC molecular classification-guided translational research, we recommend using these TNBC cell lines with stable molecular subtypes. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Current address: Olivia Newton-John Cancer Research Institute and School of Cancer Medicine La Trobe University, Heidelberg, Australia Competing Interests: The authors of this paper have read the journal’s policy and the authors of this paper have the following competing interests: NTU has contracted research with Insight Genetics Inc. (http://www.insightgenetics.com/). RSS, DRH, BLS, TJN, ORL are all paid employees of Insight Genetics, Inc. This does not alter our adherence to PLOS ONE policies on sharing data and materials. The authors would like to declare the following patents/patent applications associated with this research: Insight Genetics Inc. has taken an exclusive license to a patent application for the analysis method to categorize TNBCType patients into various subtypes. The patent application is as follows: [US14/358,330]. Gene expression datasets can be uploaded for classification by TNBCType at http://cbc.mc.vanderbilt.edu/tnbc/. TNBCType-IM is a proprietary algorithm of Insight Genetics Inc. Current address: Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America |
ISSN: | 1932-6203 1932-6203 |
DOI: | 10.1371/journal.pone.0231953 |