Abstract P5-06-04: Classification of molecular subtypes of triple-negative breast cancer cell lines using two models

Abstract

Abstract Background: Triple-negative breast cancer (TNBC) is a heterogeneous group of tumors with collective poorer prognosis and clinical outcome than other breast cancer subtypes. Understanding and defining robust TNBC molecular subtypes is important for preclinical investigations of potential therapeutics or grant proposals. The original Vanderbilt algorithm (TNBCtype), published by Lehmann et al in 2011 (J Clin Invest, 121:2750), used an identified set of 2,188 genes to classify TNBC into six subtypes displaying unique expression and ontologies - basal-like 1 and 2 (BL1 and BL2), immunomodulatory (IM), mesenchymal (M), mesenchymal stem-like (MSL), and luminal androgen receptor (LAR) - and one unstable (UNS) subtype. In 2016, the same group proposed a refined algorithm (PLoS One 11:e0157368) that included just the BL1, BL2, M, and LAR subtypes, based on their new findings suggesting that the IM and MSL subtypes were derived from infiltrating lymphocytes and other stromal cell populations within the tumor. In order to improve the reproducibility of the TNBC subtyping panel and create a cost-effective clinical tool, a new 101-gene expression panel using the same gene expression data sets used for the original TNBCtype algorithm was proposed by Ring et al (BMC Cancer 16:143, 2016). In this study, we aimed to determine the robustness of TNBC subtyping in 28 known TNBC cell lines using both algorithms in order to identify concordant cell lines that could be considered the most suitable to be used in translational research. Methods: Publicly available gene data were used to classify 28 known TNBC cell lines using the original 2,188-gene algorithm and the reduced 101-gene model algorithm. Results: Of the 28 TNBC cell lines, 18 (64%) were concordant using both TNBC classification algorithms (Table); 5 of 6 cell lines classified as BL1 by the original TNBCtype model were concordant with the 101-gene model, as well as 6 of 8 BL2, 2 of 3 M, 2 of 4 MSL, and 3 of 3 LAR cell lines. Ten cell lines had changes in their molecular subtyping using the two different algorithms. Conclusions: We speculate that TNBC cell lines that have concordant molecular subtyping in both algorithms are the most stable for defining their molecular characteristics. Therefore, for drug development studies based on the Vanderbilt TNBC molecular subtyping, we recommend using these cell lines. We plan to correlate our findings with in vivo TNBC tumor animal models to identify the best molecularly stable cell lines to be considered for research use. TableCell LineTNBC Type / 101 GeneCell LineTNBC Type / 101 GeneCell LineTNBC Type / 101 GeneHCC2157BL1 / BL1HCC70BL2 / BL1MDAMB157MSL / MSLHCC1599BL1 / BL1HCC1806BL2 / BL2SUM159PTMSL / MSLHCC1937BL1 / BL1HDQP1BL2 / BL2MDAMB436BL2 / BL2HCC1143BL1 / BL1HCC1187UNS / NDMDAMB231UNS / MSLHCC3153BL1 / BL1DU4475UNS / BL1MDAMB453ND / LARMDAMB468BL1 / BL2BT549M / MSUM185PELAR / LARHCC38MSL / MCAL51M / BL1HCC2185LAR / LARSUM149PTBL2 / BL2CAL120M / MCAL148LAR / LARCAL851BL2 / BL1HS578TMSL / MHCC1395BL2 / BL2BT20BL2 / BL2 Citation Format: Espinosa Fernandez JR, Eckhardt BL, Lee J, Seitz RS, Hout DR, Ring BZ, Lim B, Ueno NT. Classification of molecular subtypes of triple-negative breast cancer cell lines using two models [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P5-06-04.