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Molecular determinants of drug response in TNBC cell lines

  • Nathan M. Merrill
  • Eric J. Lachacz
  • Nathalie M. Vandecan
  • Peter J. Ulintz
  • Liwei Bao
  • John P. Lloyd
  • Joel A. Yates
  • Aki Morikawa
  • Sofia D. MerajverEmail author
  • Matthew B. SoellnerEmail author
Preclinical study

Abstract

Purpose

There is a need for biomarkers of drug efficacy for targeted therapies in triple-negative breast cancer (TNBC). As a step toward this, we identify multi-omic molecular determinants of anti-TNBC efficacy in cell lines for a panel of oncology drugs.

Methods

Using 23 TNBC cell lines, drug sensitivity scores (DSS3) were determined using a panel of investigational drugs and drugs approved for other indications. Molecular readouts were generated for each cell line using RNA sequencing, RNA targeted panels, DNA sequencing, and functional proteomics. DSS3 values were correlated with molecular readouts using a FDR-corrected significance cutoff of p* < 0.05 and yielded molecular determinant panels that predict anti-TNBC efficacy.

Results

Six molecular determinant panels were obtained from 12 drugs we prioritized based on their efficacy. Determinant panels were largely devoid of DNA mutations of the targeted pathway. Molecular determinants were obtained by correlating DSS3 with molecular readouts. We found that co-inhibiting molecular correlate pathways leads to robust synergy across many cell lines.

Conclusions

These findings demonstrate an integrated method to identify biomarkers of drug efficacy in TNBC where DNA predictions correlate poorly with drug response. Our work outlines a framework for the identification of novel molecular determinants and optimal companion drugs for combination therapy based on these correlates.

Keywords

Triple-negative breast cancer Molecular determinants Combination therapy Sequencing Functional proteomics 

Notes

Acknowledgements

We kindly thank Rork Kuick (University of Michigan) for his insight on statistical analysis and careful editing of this manuscript. We thank the MD Anderson RPPA core for protein analysis, TIGEM in Naples, Italy for RNA sequencing, and the University of Michigan sequencing core for DNA sequencing.

Author contributions

Study concept and design: M.B.S., N.M.M. Acquisition of data: N.M.M., N.M.V., E.J.L., L.W.B. Drafting the manuscript: N.M.M., J.A.Y., M.B.S., S.D.M. Analysis and interpretation of data: N.M.M., M.B.S., P.J.L, J.P.L., S.D.M. Experimentation: N.M.M., N.M.V., E.J.L., L.W.B. Statistical analysis: N.M.M., P.U.L., J.P.L. Administrative, technical, or material support: J.A.Y., A.M. Study supervision: M.B.S., S.D.M. All authors reviewed and approved the final version of the manuscript.

Funding information

This research was supported by the National Institutes of Health (1R21CA218498 to M.B.S. and S.D.M.), the Breast Cancer Research Foundation (to S.D.M.), Tempting Tables (to S.D.M.), The Rose Run (to S.D.M.), and the Kathy Bruk Pearce Research Fund of the University of Michigan Rogel Cancer Center (to M.B.S.).

Compliance with ethical standards

Conflicts of interest

All authors declare that they have no conflicts of interest related to this work.

Research involving human and animal participants

This article does not contain any studies with human participants or animals performed by any of the authors.

Supplementary material

10549_2019_5473_MOESM1_ESM.docx (15 kb)
Supplementary material 1 (DOCX 14 kb)
10549_2019_5473_MOESM2_ESM.eps (656 kb)
Supplementary material 2 Candidate molecular correlates in TNBC cell lines. Examples of compounds with significant correlates include: A. temsirolimus and B. docetaxel. All molecular marker readouts have a FDR-adjusted p*-value < 0.05. Top bars depict DSS3, with dark green representing the most sensitive lines and white representing the least sensitive. Orange labeled readouts are derived from DNA variants. Black labeled readouts are derived from targeted RNA expression levels (EPS 656 kb)
10549_2019_5473_MOESM3_ESM.eps (2.4 mb)
Supplementary material 3 Strong agreement between nanostring and RNA sequencing results for Copanlisib (EPS 2418 kb)
10549_2019_5473_MOESM4_ESM.docx (16 kb)
Supplementary material 4 Cell culture conditions. When comparing the Nanostring readouts with RNA sequencing, we observed strong correlations for A. IL7R, B. ERBB2, C. KIF2C, D. CD164, E. PRR15L. F. CACNB3, G. MYCL, H. EPHA2, I. PIK3CD, J. IGFBP4, and K. IRAK2. We observed poor correlations for L. FOSL1 (DOCX 314 kb)
10549_2019_5473_MOESM5_ESM.pdf (312 kb)
Supplementary material 5 Table showing IC50 values, hill-slopes, max response, dose range, and DSS3 values in individual cell lines. (PDF 312 kb)
10549_2019_5473_MOESM6_ESM.pdf (26 kb)
Supplementary material 6 Individual correlating DNA non-synonymous mutations in the molecular correlate panel. Genes marked with “*” indicate a roll-up of mutations was used for correlations. SIFT, Polyphen2, MutationTaster, MutationAssessor, and FATHMM were used to generate functional predictions. (PDF 25 kb)
10549_2019_5473_MOESM7_ESM.xlsx (304 kb)
Supplementary material 7 (XLSX 304 kb)
10549_2019_5473_MOESM8_ESM.xlsx (68 kb)
Supplementary material 8 (XLSX 67 kb)
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Supplementary material 9 (XLSX 12 kb)
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Supplementary material 10 (XLSX 953 kb)
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Supplementary material 11 (CSV 4768 kb)
10549_2019_5473_MOESM12_ESM.xlsx (109 kb)
Supplementary material 12 (XLSX 109 kb)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Internal MedicineUniversity of MichiganAnn ArborUSA

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