Improved prediction of PARP inhibitor response and identification of synergizing agents through use of a novel gene expression signature generation algorithm

Daniel J. McGrail, Curtis Chun Jen Lin, Jeannine Garnett, Qingxin Liu, Wei Mo, Hui Dai, Yiling Lu, Qinghua Yu, Zhenlin Ju, Jun Yin, Christopher P. Vellano, Bryan Hennessy, Gordon Mills, Shiaw Yih Lin

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Despite rapid advancement in generation of large-scale microarray gene expression datasets, robust multigene expression signatures that are capable of guiding the use of specific therapies have not been routinely implemented into clinical care. We have developed an iterative resampling analysis to predict sensitivity algorithm to generate gene expression sensitivity profiles that predict patient responses to specific therapies. The resultant signatures have a robust capacity to accurately predict drug sensitivity as well as the identification of synergistic combinations. Here, we apply this approach to predict response to PARP inhibitors, and show it can greatly outperforms current clinical biomarkers, including BRCA1/2 mutation status, accurately identifying PARP inhibitor-sensitive cancer cell lines, primary patient-derived tumor cells, and patient-derived xenografts. These signatures were also capable of predicting patient response, as shown by applying a cisplatin sensitivity signature to ovarian cancer patients. We additionally demonstrate how these drug-sensitivity signatures can be applied to identify novel synergizing agents to improve drug efficacy. Tailoring therapeutic interventions to improve patient prognosis is of utmost importance, and our drug sensitivity prediction signatures may prove highly beneficial for patient management.

Original languageEnglish (US)
Article number8
Journalnpj Systems Biology and Applications
Volume3
Issue number1
DOIs
StatePublished - Dec 1 2017
Externally publishedYes

Fingerprint

Transcriptome
Gene expression
Inhibitor
Gene Expression
Signature
Cells
Prediction
Drugs
Biomarkers
Microarrays
Pharmaceutical Preparations
Tumors
Predict
Therapy
Heterografts
Cisplatin
Ovarian Cancer
Prognosis
Cell
Resampling

ASJC Scopus subject areas

  • Computer Science Applications
  • Applied Mathematics
  • Modeling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Drug Discovery

Cite this

Improved prediction of PARP inhibitor response and identification of synergizing agents through use of a novel gene expression signature generation algorithm. / McGrail, Daniel J.; Lin, Curtis Chun Jen; Garnett, Jeannine; Liu, Qingxin; Mo, Wei; Dai, Hui; Lu, Yiling; Yu, Qinghua; Ju, Zhenlin; Yin, Jun; Vellano, Christopher P.; Hennessy, Bryan; Mills, Gordon; Lin, Shiaw Yih.

In: npj Systems Biology and Applications, Vol. 3, No. 1, 8, 01.12.2017.

Research output: Contribution to journalArticle

McGrail, Daniel J. ; Lin, Curtis Chun Jen ; Garnett, Jeannine ; Liu, Qingxin ; Mo, Wei ; Dai, Hui ; Lu, Yiling ; Yu, Qinghua ; Ju, Zhenlin ; Yin, Jun ; Vellano, Christopher P. ; Hennessy, Bryan ; Mills, Gordon ; Lin, Shiaw Yih. / Improved prediction of PARP inhibitor response and identification of synergizing agents through use of a novel gene expression signature generation algorithm. In: npj Systems Biology and Applications. 2017 ; Vol. 3, No. 1.
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