Abstract

To achieve therapeutic goals, many cancer chemotherapeutics are used at doses close to their maximally tolerated doses. Thus, it may be expected that when therapies are combined at therapeutic doses, toxicity profiles may change. In many ways, prediction of synergistic toxicities for drug combinations is similar to predicting synergistic efficacy, and is dependent upon building hypotheses from molecular mechanisms of drug toxicity. The key objective of this initiative was to generate and make publicly available key high-content data sets for mechanistic hypothesis generation as it pertains to a unique toxicity profile of a drug pair for several anticancer drug combinations. The expectation is that tissue-based genomic information that are derived from target tissues will also facilitate the generation and testing of mechanistic hypotheses. The view is that availability of these data sets for bioinformaticians and other scientists will contribute to analysis of these data and evaluation of the approach.

Design Type(s)

drug design objective • stimulus or stress design • microarray data analysis objective

Measurement Type(s)

response to drug

Technology Type(s)

microarray

Factor Type(s)

animal body part • experimental condition

Sample Characteristic(s)

Rattus norvegicus • bone marrow • heart • liver • kidney • skin of body

Machine-accessible metadata file describing the reported data (ISA-Tab format)

Details

Title
Transcriptomic profiles of tissues from rats treated with anticancer drug combinations
Author
Davis, Myrtle 1 ; Knight, Elaine 2 ; Eldridge, Sandy R 2 ; Li, Jianying 3 ; Bushel, Pierre R 4 

 Toxicology and Pharmacology Branch, National Cancer Institute, Division of Cancer Treatment and Diagnosis, Bethesda, USA (GRID:grid.48336.3a) (ISNI:0000 0004 1936 8075) ; Present address: Discovery Toxicology, Bristol-Myers Squibb, Princeton, NJ, 08540, USA., (GRID:grid.419971.3) 
 Toxicology and Pharmacology Branch, National Cancer Institute, Division of Cancer Treatment and Diagnosis, Bethesda, USA (GRID:grid.48336.3a) (ISNI:0000 0004 1936 8075) 
 Kelly Government Solutions, Research Triangle Park, NC, USA (GRID:grid.48336.3a) ; Integrative Bioinformatics Group, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA (GRID:grid.280664.e) (ISNI:0000 0001 2110 5790) ; Microarray and Genome Informatics Group, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA (GRID:grid.280664.e) (ISNI:0000 0001 2110 5790) 
 Microarray and Genome Informatics Group, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA (GRID:grid.280664.e) (ISNI:0000 0001 2110 5790) ; Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, Division of Intramural Research, NC, USA (GRID:grid.280664.e) (ISNI:0000 0001 2110 5790) 
Publication year
2019
Publication date
Jan 2019
Publisher
Nature Publishing Group
e-ISSN
20524463
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2342366087
Copyright
This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.