Signal translational efficiency between mRNA expression and antibody-based protein expression for breast cancer and its subtypes from cell lines to tissue

Aida Yazdanparast, Lang Li, Milan Radovich, Lijun Cheng

Research output: Contribution to journalEditorialpeer-review

Abstract

Background: Although gene transcripts and protein expression have been utilised to classify breast cancer subtypes, it is not clear whether the observed measurement of gene transcript abundance can predict its protein expression. Herein, we attempt to address gene transcript/protein associations using publically-available data on breast cancer tumour tissues and cell lines. Method: Correlation analysis between mRNAs and Reverse-phase protein arrays (RPPA) among 421 primary breast tumours and 33 breast cancer cell lines was conducted. Highly concordant proteins/genes were further analysed in different breast cancer subtypes. Results: The overall accordance of mRNA/RPPA correlation between cell lines and primary tissue is R2 = 0.71. Since most of these genes are well known drug targets, highly concordant gene/RPPA associations not only confirm that these gene transcripts can serve as biomarkers for their protein products in drug target selection, but also imply that breast cancer cell lines can serve as good models for primary breast cancer tumours.

Original languageEnglish (US)
JournalInternational Journal of Computational Biology and Drug Design
Volume11
Issue number1-2
DOIs
StatePublished - 2018

Keywords

  • Breast cancer
  • Cell lines
  • mRNA
  • Protein abundance
  • Reverse-phase protein array

ASJC Scopus subject areas

  • Computer Science Applications
  • Drug Discovery

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