Mountain View
biomedical and healthCAre Data Discovery Index Ecosystem
help Advanced Search
Title: A phenotype-based model for rational selection of novel targeted therapies in treating aggressive breast cancer      
dateReleased:
06-01-2011
description:
Treating unselected cancer patients with new drugs dilutes proof of efficacy when only a fraction of patients respond to therapy. We conducted a meta-analysis on eight primary breast cancer microarray datasets representing diverse breast cancer phenotypes. We present a high-throughput protocol which incorporates drug sensitivity signatures to guide preclinical testing for effective therapeutic agents. Specifically, we focus on drug classes currently undergoing early phase clinical testing. Our genomic and experimental results suggest that the majority of basal-like breast cancers should respond to inhibitors of the phosphatidylinositol-3-kinase pathway, and that a relatively low toxicity histone deacetylase inhibitor, valproic acid, may target aggressive breast cancers. For a subset of drugs, prediction of sensitivity associates with tumor recurrence, suggesting clinical relevance. Preclinical studies using both cell lines and patient tumors grown in 3-dimensional in vitro and orthotopic in vivo preclinical models provide an efficient and highly relevant assessment of drug sensitivity in tumor phenotypes, and validate our genomic analyses. Together, our results show that high-throughput transcriptional profiling can significantly impact drug selection for breast cancer patients. Pre-identification of patient response may not only improve therapeutic response rates, it can also assist in quickly identifying the optimal inclusion criteria for clinical trials. Our model facilitates personalized drug therapy for cancer patients and may be generalized for study of drug efficacy in other diseases. Breast cancer pleural effusion samples from triple negative patients. Compared samples that are computationally predicted to be sensitive to valproic acid and those that are not predicted to be sensitive.
privacy:
not applicable
aggregation:
instance of dataset
ID:
E-GEOD-18331
refinement:
raw
alternateIdentifiers:
18331
keywords:
functional genomics
dateModified:
06-13-2011
availability:
available
types:
gene expression
name:
Homo sapiens
ID:
A-AFFY-44
name:
Affymetrix GeneChip Human Genome U133 Plus 2.0 [HG-U133_Plus_2]
accessURL: https://www.ebi.ac.uk/arrayexpress/files/E-GEOD-18331/E-GEOD-18331.raw.1.zip
storedIn:
ArrayExpress
qualifier:
gzip compressed
format:
TXT
accessType:
download
authentication:
none
authorization:
none
accessURL: https://www.ebi.ac.uk/arrayexpress/files/E-GEOD-18331/E-GEOD-18331.processed.1.zip
storedIn:
ArrayExpress
qualifier:
gzip compressed
format:
TXT
accessType:
download
authentication:
none
authorization:
none
accessURL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE18331
storedIn:
Gene Expression Omnibus
qualifier:
not compressed
format:
HTML
accessType:
landing page
primary:
true
authentication:
none
authorization:
none
abbreviation:
EBI
homePage: http://www.ebi.ac.uk/
ID:
SCR:004727
name:
European Bioinformatics Institute
homePage: https://www.ebi.ac.uk/arrayexpress/
ID:
SCR:002964
name:
ArrayExpress
Similar Datasets

Feedback?

If you are having problems using our tools, or if you would just like to send us some feedback, please post your questions on GitHub.