Robert F. Gruener
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Robert F. Gruener has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Rong Stephanie Huang
Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455, USA
2 shared publications - Alexander L. Ling
Harvey Cushing Neuro-Oncology Laboratories, Department of Neurosurgery and Center for Tumors of the Nervous System, Mass General Brigham Cancer Institute and Harvard Medical School, Boston, MA 02115, USA
1 shared publication - Danielle Maeser
Department of Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455, USA
1 shared publication - Geoffrey L. Greene
Ben May Department for Cancer Research, University of Chicago, Chicago, USA
1 shared publication - Gladys Morrison
Committee for Clinical Pharmacology and Pharmacogenomics, University of Chicago, Chicago, IL 60637, USA
1 shared publication - Paul Geeleher
Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
1 shared publication
Latest Publications
- Top Cited Papers
- 15 July 2021
- Published by Oxford University Press (OUP) in Briefings in Bioinformatics
- Vol. 22 (6)
oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data
Cell line drug screening datasets can be utilized for a range of different drug discovery applications from drug biomarker discovery to building translational models of drug response. Previously, we described three separate methodologies to (1) correct for general levels of drug sensitivity to enable drug-specific biomarker discovery, (2) predict clinical drug response in patients and (3) associate these predictions with clinical features to perform in vivo drug biomarker discovery. Here, we unite and update these methodologies into one R package (oncoPredict) to facilitate the development and adoption of these tools. This new OncoPredict R package can be applied to various in vitro and in vivo contexts for drug and biomarker discovery.
- 15 July 2021
- Published by Oxford University Press (OUP) in Briefings in Bioinformatics
- Vol. 22 (6)
Top Cited PapersoncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data
Cell line drug screening datasets can be utilized for a range of different drug discovery applications from drug biomarker discovery to building translational models of drug response. Previously, we described three separate methodologies to (1) correct for general levels of drug sensitivity to enable drug-specific biomarker discovery, (2) predict clinical drug response in patients and (3) associate these predictions with clinical features to perform in vivo drug biomarker discovery. Here, we unite and update these methodologies into one R package (oncoPredict) to facilitate the development and adoption of these tools. This new OncoPredict R package can be applied to various in vitro and in vivo contexts for drug and biomarker discovery.
- Open Access
Facilitating Drug Discovery in Breast Cancer by Virtually Screening Patients Using In Vitro Drug Response Modeling
(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.
Open AccessFacilitating Drug Discovery in Breast Cancer by Virtually Screening Patients Using In Vitro Drug Response Modeling
(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.