Literature DB >> 34260682

oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data.

Danielle Maeser1, Robert F Gruener2, Rong Stephanie Huang3.   

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.
© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  biomarker identification; drug discovery; drug response prediction

Mesh:

Substances:

Year:  2021        PMID: 34260682      PMCID: PMC8574972          DOI: 10.1093/bib/bbab260

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   13.994


  19 in total

Review 1.  More than fishing for a cure: The promises and pitfalls of high throughput cancer cell line screens.

Authors:  Alexander Ling; Robert F Gruener; Jessica Fessler; R Stephanie Huang
Journal:  Pharmacol Ther       Date:  2018-06-25       Impact factor: 12.310

2.  Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset.

Authors:  Brinton Seashore-Ludlow; Matthew G Rees; Jaime H Cheah; Murat Cokol; Edmund V Price; Matthew E Coletti; Victor Jones; Nicole E Bodycombe; Christian K Soule; Joshua Gould; Benjamin Alexander; Ava Li; Philip Montgomery; Mathias J Wawer; Nurdan Kuru; Joanne D Kotz; C Suk-Yee Hon; Benito Munoz; Ted Liefeld; Vlado Dančík; Joshua A Bittker; Michelle Palmer; James E Bradner; Alykhan F Shamji; Paul A Clemons; Stuart L Schreiber
Journal:  Cancer Discov       Date:  2015-10-19       Impact factor: 39.397

3.  Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies.

Authors:  Paul Geeleher; Zhenyu Zhang; Fan Wang; Robert F Gruener; Aritro Nath; Gladys Morrison; Steven Bhutra; Robert L Grossman; R Stephanie Huang
Journal:  Genome Res       Date:  2017-08-28       Impact factor: 9.043

Review 4.  Machine learning and feature selection for drug response prediction in precision oncology applications.

Authors:  Mehreen Ali; Tero Aittokallio
Journal:  Biophys Rev       Date:  2018-08-10

Review 5.  Machine learning approaches to drug response prediction: challenges and recent progress.

Authors:  George Adam; Ladislav Rampášek; Zhaleh Safikhani; Petr Smirnov; Benjamin Haibe-Kains; Anna Goldenberg
Journal:  NPJ Precis Oncol       Date:  2020-06-15

6.  Methodological challenges in translational drug response modeling in cancer: A systematic analysis with FORESEE.

Authors:  Lisa-Katrin Schätzle; Ali Hadizadeh Esfahani; Andreas Schuppert
Journal:  PLoS Comput Biol       Date:  2020-04-20       Impact factor: 4.475

7.  Facilitating Drug Discovery in Breast Cancer by Virtually Screening Patients Using In Vitro Drug Response Modeling.

Authors:  Robert F Gruener; Alexander Ling; Ya-Fang Chang; Gladys Morrison; Paul Geeleher; Geoffrey L Greene; R Stephanie Huang
Journal:  Cancers (Basel)       Date:  2021-02-20       Impact factor: 6.639

8.  pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels.

Authors:  Paul Geeleher; Nancy Cox; R Stephanie Huang
Journal:  PLoS One       Date:  2014-09-17       Impact factor: 3.240

9.  Cancer biomarker discovery is improved by accounting for variability in general levels of drug sensitivity in pre-clinical models.

Authors:  Paul Geeleher; Nancy J Cox; R Stephanie Huang
Journal:  Genome Biol       Date:  2016-09-21       Impact factor: 13.583

Review 10.  Computational models for predicting drug responses in cancer research.

Authors:  Francisco Azuaje
Journal:  Brief Bioinform       Date:  2017-09-01       Impact factor: 11.622

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Journal:  J Oncol       Date:  2022-06-20       Impact factor: 4.501

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Journal:  Genes (Basel)       Date:  2022-06-18       Impact factor: 4.141

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6.  Establishment of a Cell Necroptosis Index to Predict Prognosis and Drug Sensitivity for Patients With Triple-Negative Breast Cancer.

Authors:  Jindong Xie; Wenwen Tian; Yuhui Tang; Yutian Zou; Shaoquan Zheng; Linyu Wu; Yan Zeng; Song Wu; Xinhua Xie; Xiaoming Xie
Journal:  Front Mol Biosci       Date:  2022-05-05

7.  Patient-Level DNA Damage Repair Pathway Profiles and Anti-Tumor Immunity for Gastric Cancer.

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Journal:  Front Cell Dev Biol       Date:  2022-01-20

9.  Construction and Validation of a Novel Pyroptosis-Related Four-lncRNA Prognostic Signature Related to Gastric Cancer and Immune Infiltration.

Authors:  Zhengguang Wang; Lei Cao; Sitong Zhou; Jin Lyu; Yang Gao; Ronghua Yang
Journal:  Front Immunol       Date:  2022-03-22       Impact factor: 7.561

10.  A 10-Gene Signature Identified by Machine Learning for Predicting the Response to Transarterial Chemoembolization in Patients with Hepatocellular Carcinoma.

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Journal:  J Oncol       Date:  2022-01-24       Impact factor: 4.375

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