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 inPloS one Vol. 15; no. 4; p. e0231953
Main Authors Espinosa Fernandez, Jose Rodrigo, Eckhardt, Bedrich L., Lee, Jangsoon, Lim, Bora, Pearson, Troy, Seitz, Rob S., Hout, David R., Schweitzer, Brock L., Nielsen, Tyler J., Lawrence, O. Rayne, Wang, Ying, Rao, Arvind, Ueno, Naoto T.
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 30.04.2020
Public Library of Science (PLoS)
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ISSN1932-6203
1932-6203
DOI10.1371/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.
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
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– name: 3 Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
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– name: 1 Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
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2020 Espinosa Fernandez et al 2020 Espinosa Fernandez et al
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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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Snippet The original algorithm that classified triple-negative breast cancer (TNBC) into six subtypes has recently been revised. The revised algorithm (TNBCtype-IM)...
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StartPage e0231953
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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