Facilitating drug discovery in breast cancer by virtually screening patients using in vitro drug response modeling

Robert F. Gruener, Alexander Ling, Ya Fang Chang, Gladys Morrison, Paul Geeleher, Geoffrey L. Greene, R. Stephanie Huang

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


(1) Background: Drug imputation methods often aim to translate in vitro drug response to in vivo drug efficacy predictions. While commonly used in retrospective analyses, our aim is to investigate the use of drug prediction methods for the generation of novel drug discovery hypotheses. Triple-negative breast cancer (TNBC) is a severe clinical challenge in need of new therapies. (2) Methods: We used an established machine learning approach to build models of drug response based on cell line transcriptome data, which we then applied to patient tumor data to obtain predicted sensitivity scores for hundreds of drugs in over 1000 breast cancer patients. We then examined the relationships between predicted drug response and patient clinical features. (3) Results: Our analysis recapitulated several suspected vulnerabilities in TNBC and identified a number of compounds-of-interest. AZD-1775, a Wee1 inhibitor, was predicted to have preferential activity in TNBC ( p < 2.2 × 10 -16) and its efficacy was highly associated with TP53 mutations ( p = 1.2 × 10 -46). We validated these findings using independent cell line screening data and pathway analysis. Additionally, co-administration of AZD-1775 with standard-of-care paclitaxel was able to inhibit tumor growth ( p < 0.05) and increase survival ( p < 0.01) in a xenograft mouse model of TNBC. (4) Conclusions: Overall, this study provides a framework to turn any cancer transcriptomic dataset into a dataset for drug discovery. Using this framework, one can quickly generate meaningful drug discovery hypotheses for a cancer population of interest.

Original languageEnglish (US)
Article number885
Pages (from-to)1-16
Number of pages16
Issue number4
StatePublished - Feb 2021

Bibliographical note

Funding Information:
Acknowledgments: the compound AZD-1775 was supplied by Astra-Zeneca through the NIH Cancer Therapy Evaluation Program (CTEP) program.

Funding Information:
Funding: This work was supported by a NIH/NCI grant R01CA204856.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.


  • Breast cancer
  • Drug discovery
  • Drug imputation
  • Transcriptome
  • Wee1


Dive into the research topics of 'Facilitating drug discovery in breast cancer by virtually screening patients using in vitro drug response modeling'. Together they form a unique fingerprint.

Cite this