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 enabl...

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Vydáno v:	Briefings in bioinformatics Ročník 22; číslo 6
Hlavní autoři:	Maeser, Danielle, Gruener, Robert F, Huang, Rong Stephanie
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England Oxford University Press 05.11.2021
Témata:	Antineoplastic Agents - therapeutic use Biomarkers, Tumor - metabolism Cell Line, Tumor Humans Problem Solving Protocol Software biomarker identification drug discovery drug response prediction
ISSN:	1467-5463, 1477-4054, 1477-4054
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	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.
AbstractList	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. 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.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.
Author	Maeser, Danielle Gruener, Robert F Huang, Rong Stephanie
Author_xml	– sequence: 1 givenname: Danielle surname: Maeser fullname: Maeser, Danielle organization: Department of Bioinformatics & Computational Biology, University of Minnesota, Minneapolis, MN 55455, USA – sequence: 2 givenname: Robert F surname: Gruener fullname: Gruener, Robert F organization: Ben May Department for Cancer Research, University of Chicago, Chicago, IL 60637, USA – sequence: 3 givenname: Rong Stephanie orcidid: 0000-0002-9862-0368 surname: Huang fullname: Huang, Rong Stephanie organization: Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/34260682$$D View this record in MEDLINE/PubMed
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Copyright	The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com. The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2021
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PublicationDate_xml	– month: 11 year: 2021 text: 2021-11-05 day: 05
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PublicationTitle	Briefings in bioinformatics
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References	Geeleher (2021110815063688000_ref11) 2014; 15 2021110815063688000_ref16 Koti (2021110815063688000_ref21) 2013; 13 Ling (2021110815063688000_ref1) 2018; 191 Office of Cancer Genomics (2021110815063688000_ref15) Lin (2021110815063688000_ref18) 2012; 7 NCI DREAM Community (2021110815063688000_ref4) 2014; 32 Schätzle (2021110815063688000_ref20) 2020; 16 Jang (2021110815063688000_ref5) 2014 Geeleher (2021110815063688000_ref13) 2014; 9 2021110815063688000_ref17 Gruener (2021110815063688000_ref14) 2021; 13 Seashore-Ludlow (2021110815063688000_ref2) 2015; 5 The Cancer Genome Atlas Research Network (2021110815063688000_ref22) 2013; 45 Azuaje (2021110815063688000_ref6) 2017; 18 Geeleher (2021110815063688000_ref10) 2016; 17 Ali (2021110815063688000_ref7) 2019; 11 Adam (2021110815063688000_ref8) 2020; 4 Colaprico (2021110815063688000_ref23) 2016; 44 Güvenç Paltun (2021110815063688000_ref9) 2021; 22 Geeleher (2021110815063688000_ref12) 2017; 27 Cule (2021110815063688000_ref19) 2021 Iorio (2021110815063688000_ref3) 2016; 166
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Snippet	Cell line drug screening datasets can be utilized for a range of different drug discovery applications from drug biomarker discovery to building translational...
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SubjectTerms	Antineoplastic Agents - therapeutic use Biomarkers, Tumor - metabolism Cell Line, Tumor Humans Problem Solving Protocol Software
Title	oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data
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