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) |
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ISSN | 1932-6203 1932-6203 |
DOI | 10.1371/journal.pone.0231953 |
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Abstract | 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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AbstractList | 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. 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.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. |
Author | Nielsen, Tyler J. Eckhardt, Bedrich L. Lee, Jangsoon Wang, Ying Schweitzer, Brock L. Lim, Bora Rao, Arvind Hout, David R. Lawrence, O. Rayne Ueno, Naoto T. Seitz, Rob S. Espinosa Fernandez, Jose Rodrigo Pearson, Troy |
AuthorAffiliation | 1 Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America 3 Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America 2 Insight Genetics Incorporated, Nashville, Tennessee, United Sates of America University of Tennessee Health Science Center, UNITED STATES |
AuthorAffiliation_xml | – name: 2 Insight Genetics Incorporated, Nashville, Tennessee, United Sates of America – name: 3 Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America – name: University of Tennessee Health Science Center, UNITED STATES – name: 1 Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America |
Author_xml | – sequence: 1 givenname: Jose Rodrigo surname: Espinosa Fernandez fullname: Espinosa Fernandez, Jose Rodrigo – sequence: 2 givenname: Bedrich L. surname: Eckhardt fullname: Eckhardt, Bedrich L. – sequence: 3 givenname: Jangsoon surname: Lee fullname: Lee, Jangsoon – sequence: 4 givenname: Bora surname: Lim fullname: Lim, Bora – sequence: 5 givenname: Troy surname: Pearson fullname: Pearson, Troy – sequence: 6 givenname: Rob S. orcidid: 0000-0002-5806-2840 surname: Seitz fullname: Seitz, Rob S. – sequence: 7 givenname: David R. surname: Hout fullname: Hout, David R. – sequence: 8 givenname: Brock L. orcidid: 0000-0003-0178-5594 surname: Schweitzer fullname: Schweitzer, Brock L. – sequence: 9 givenname: Tyler J. orcidid: 0000-0002-9576-2838 surname: Nielsen fullname: Nielsen, Tyler J. – sequence: 10 givenname: O. Rayne surname: Lawrence fullname: Lawrence, O. Rayne – sequence: 11 givenname: Ying surname: Wang fullname: Wang, Ying – sequence: 12 givenname: Arvind surname: Rao fullname: Rao, Arvind – sequence: 13 givenname: Naoto T. orcidid: 0000-0002-0166-7275 surname: Ueno fullname: Ueno, Naoto T. |
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Cites_doi | 10.1371/journal.pone.0157368 10.1186/s13058-017-0855-0 10.1186/gb-2013-14-4-r36 10.1200/PO.17.00032 10.1093/bioinformatics/btp352 10.1172/JCI45014 10.1186/s12885-016-2198-0 10.1158/1078-0432.CCR-13-0799 |
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Copyright | 2020 Espinosa Fernandez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2020 Espinosa Fernandez et al 2020 Espinosa Fernandez et al |
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Notes | 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 |
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SubjectTerms | Algorithms Animals Biology and Life Sciences Biotechnology Breast cancer Cancer therapies Cell cycle Cell Line, Tumor Cell Transformation, Neoplastic Chemotherapy Classification Gene expression Humans Immunology Medical research Medicine and Health Sciences Mice Oncology Penicillin Physical Sciences Principal components analysis Research and Analysis Methods Translation Translational Research, Biomedical Triple Negative Breast Neoplasms - pathology Tumor cell lines Tumors Xenografts Xenotransplantation |
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Title | Identification of triple-negative breast cancer cell lines classified under the same molecular subtype using different molecular characterization techniques: Implications for translational research |
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