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. 2019 May 7;14(5):e0216400.
doi: 10.1371/journal.pone.0216400. eCollection 2019.

MicroRNAs as possible indicators of drug sensitivity in breast cancer cell lines

Katharina Uhr  1 , Wendy J C Prager-van der Smissen  1 , Anouk A J Heine  1 , Bahar Ozturk  1 , Marijn T M van Jaarsveld  1 , Antonius W M Boersma  1 , Agnes Jager  1 , Erik A C Wiemer  1 , Marcel Smid  1 , John A Foekens  1 , John W M Martens  1
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MicroRNAs as possible indicators of drug sensitivity in breast cancer cell lines

Katharina Uhr et al. PLoS One. .

Abstract

MicroRNAs (miRNAs) regulate gene expression post-transcriptionally. In this way they might influence whether a cell is sensitive or resistant to a certain drug. So far, only a limited number of relatively small scale studies comprising few cell lines and/or drugs have been performed. To obtain a broader view on miRNAs and their association with drug response, we investigated the expression levels of 411 miRNAs in relation to drug sensitivity in 36 breast cancer cell lines. For this purpose IC50 values of a drug screen involving 34 drugs were associated with miRNA expression data of the same breast cancer cell lines. Since molecular subtype of the breast cancer cell lines is considered a confounding factor in drug association studies, multivariate analysis taking subtype into account was performed on significant miRNA-drug associations which retained 13 associations. These associations consisted of 11 different miRNAs and eight different drugs (among which Paclitaxel, Docetaxel and Veliparib). The taxanes, Paclitaxel and Docetaxel, were the only drugs having miRNAs in common: hsa-miR-187-5p and hsa-miR-106a-3p indicative of drug resistance while Paclitaxel sensitivity alone associated with hsa-miR-556-5p. Tivantinib was associated with hsa-let-7d-5p and hsa-miR-18a-5p for sensitivity and hsa-miR-637 for resistance. Drug sensitivity was associated with hsa-let-7a-5p for Bortezomib, hsa-miR-135a-3p for JNJ-707 and hsa-miR-185-3p for Panobinostat. Drug resistance was associated with hsa-miR-182-5p for Veliparib and hsa-miR-629-5p for Tipifarnib. Pathway analysis for significant miRNAs was performed to reveal biological roles, aiding to find a potential mechanistic link for the observed associations with drug response. By doing so hsa-miR-187-5p was linked to the cell cycle G2-M checkpoint in line with this checkpoint being the target of taxanes. In conclusion, our study shows that miRNAs could potentially serve as biomarkers for intrinsic drug resistance and that pathway analyses can provide additional information in this context.

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Conflict of interest statement

I have read the journal's policy and the authors of this manuscript have the following competing interests: The drug screening referenced in this study was funded by Janssen Pharmaceutica (Beerse, Belgium) and has been described in detail earlier (Uhr et al., 2015). JWMM and JAF received for the referenced study funding from Janssen Pharmaceutica, representing a financial competing interest. AJ is collaborating with AbbVie (North Chicago, IL, USA) in clinical trials with the drug Veliparib and other drugs, not used in this study. AJ and JM received Veliparib to be used in this earlier study free of charge, representing a non-financial competing interest. Besides these detailed competing interests, AJ and JM have no further relevant declarations regarding these two commercial sources. KU, WJCPvdS, AAJH, BO, MS, EACW, AWMB and MTMvJ declare that they have no conflicts of interest. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. Overview of miRNAs associated with different drugs.
The correlation coefficient is given on the y-axis. Associations with drug resistance are given in orange; associations with drug sensitivity are given in blue. Bor = Bortezomib, JNJ = JNJ-707, Mit = Mitoxantrone, Pan = Panobinostat, Qui = Quisinostat, Ser = Serdemetan, Sir = Sirolimus, Sun = Sunitinib, Tip = Tipifarnib, Vel = Veliparib.
See this image and copyright information in PMC

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Grants and funding

This study was financially supported by the Daniel den Hoed Foundation, Rotterdam, the Netherlands (KU, JAF) URL: https://www.danieldenhoedstichting.nl/. Further funding sources were from the Cancer Genomics Netherlands – a grant from the Netherlands Organization of Scientific Research (NWO) (JWMM), URL: http://cancergenomics.nl/; a Dutch Cancer Society grant (EMCR 2007-3794) (EACW, AWMB, MTMvJ) URL: https://www.kwf.nl/pages/default.aspx. JAF received partial support from the H2020 ERC Advanced Grant #322737, URL: https://erc.europa.eu/funding/advanced-grants. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.