Identification of triple-negative breast cancer cell lines classified under the same molecular subtype using different molecular characterization techniques: Implications for translational research

Jose Rodrigo Espinosa Fernandez, Bedrich L Eckhardt, Jangsoon Lee, Bora Lim, Troy Pearson, Rob S Seitz, David R Hout, Brock L Schweitzer, Tyler J Nielsen, O Rayne Lawrence, Ying Wang, Arvind Rao and Naoto T Ueno

PLOS ONE, 2020, vol. 15, issue 4, 1-8

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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0231953

DOI: 10.1371/journal.pone.0231953

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