A machine learned classifier that uses gene expression data to accurately predict estrogen receptor status.

ABSTRACT:

Background

Selecting the appropriate treatment for breast cancer requires accurately determining the estrogen receptor (ER) status of the tumor. However, the standard for determining this status, immunohistochemical analysis of formalin-fixed paraffin embedded samples, suffers from numerous technical and reproducibility issues. Assessment of ER-status based on RNA expression can provide more objective, quantitative and reproducible test results.

Methods

To learn a parsimonious RNA-based classifier of hormone receptor status, we applied a machine learning tool to a training dataset of gene expression microarray data obtained from 176 frozen breast tumors, whose ER-status was determined by applying ASCO-CAP guidelines to standardized immunohistochemical testing of formalin fix

SUBMITTER: Bastani M 

PROVIDER: S-EPMC3846850 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature