An integrated bioinformatics analysis to dissect kinase dependency in triple negative breast cancer

Karen A. Ryall, Jihye Kim, Peter J. Klauck, Jimin Shin, Minjae Yoo, Anastasia Ionkina, Todd M. Pitts, John J. Tentler, Jennifer R. Diamond, S. Gail Eckhardt, Lynn E. Heasley, Jaewoo Kang, Aik-Choon Tan

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Background: Triple-Negative Breast Cancer (TNBC) is an aggressive disease with a poor prognosis. Clinically, TNBC patients have limited treatment options besides chemotherapy. The goal of this study was to determine the kinase dependency in TNBC cell lines and to predict compounds that could inhibit these kinases using integrative bioinformatics analysis. Results: We integrated publicly available gene expression data, high-throughput pharmacological profiling data, and quantitative in vitro kinase binding data to determine the kinase dependency in 12 TNBC cell lines. We employed Kinase Addiction Ranker (KAR), a novel bioinformatics approach, which integrated these data sources to dissect kinase dependency in TNBC cell lines. We then used the kinase dependency predicted by KAR for each TNBC cell line to query K-Map for compounds targeting these kinases. Wevalidated our predictions using published and new experimental data. Conclusions: In summary, we implemented an integrative bioinformatics analysis that determines kinase dependency in TNBC. Our analysis revealed candidate kinases as potential targets in TNBC for further pharmacological and biological studies.

Original languageEnglish
Article numberS2
JournalBMC Genomics
Volume16
Issue number12
DOIs
Publication statusPublished - 2015 Dec 9

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Triple Negative Breast Neoplasms
Computational Biology
Phosphotransferases
Cell Line
Pharmacology
Information Storage and Retrieval

Keywords

  • Bioinformatics
  • High-throughput screening
  • Kinase dependency
  • Triple-Negative Breast Cancer

ASJC Scopus subject areas

  • Biotechnology
  • Genetics

Cite this

Ryall, K. A., Kim, J., Klauck, P. J., Shin, J., Yoo, M., Ionkina, A., ... Tan, A-C. (2015). An integrated bioinformatics analysis to dissect kinase dependency in triple negative breast cancer. BMC Genomics, 16(12), [S2]. https://doi.org/10.1186/1471-2164-16-S12-S2

An integrated bioinformatics analysis to dissect kinase dependency in triple negative breast cancer. / Ryall, Karen A.; Kim, Jihye; Klauck, Peter J.; Shin, Jimin; Yoo, Minjae; Ionkina, Anastasia; Pitts, Todd M.; Tentler, John J.; Diamond, Jennifer R.; Eckhardt, S. Gail; Heasley, Lynn E.; Kang, Jaewoo; Tan, Aik-Choon.

In: BMC Genomics, Vol. 16, No. 12, S2, 09.12.2015.

Research output: Contribution to journalArticle

Ryall, KA, Kim, J, Klauck, PJ, Shin, J, Yoo, M, Ionkina, A, Pitts, TM, Tentler, JJ, Diamond, JR, Eckhardt, SG, Heasley, LE, Kang, J & Tan, A-C 2015, 'An integrated bioinformatics analysis to dissect kinase dependency in triple negative breast cancer', BMC Genomics, vol. 16, no. 12, S2. https://doi.org/10.1186/1471-2164-16-S12-S2
Ryall, Karen A. ; Kim, Jihye ; Klauck, Peter J. ; Shin, Jimin ; Yoo, Minjae ; Ionkina, Anastasia ; Pitts, Todd M. ; Tentler, John J. ; Diamond, Jennifer R. ; Eckhardt, S. Gail ; Heasley, Lynn E. ; Kang, Jaewoo ; Tan, Aik-Choon. / An integrated bioinformatics analysis to dissect kinase dependency in triple negative breast cancer. In: BMC Genomics. 2015 ; Vol. 16, No. 12.
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