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Clin Cancer Res. 2006 Jul 1;12(13):3896-901.

Accurate prediction of BRCA1 and BRCA2 heterozygous genotype using expression profiling after induced DNA damage.

Author information

  • 1Translational Cancer Genetics Team, The Institute of Cancer Research, Sutton, Surrey, United Kingdom. zsofia.kote-jarai@icr.ac.uk

Abstract

PURPOSE:

In this study, the differential gene expression changes following radiation-induced DNA damage in healthy cells from BRCA1/BRCA1 mutation carriers have been compared with controls using high-density microarray technology. We aimed to establish if BRCA1/BRCA2 mutation carriers could be distinguished from noncarriers based on expression profiling of normal cells.

EXPERIMENTAL DESIGN:

Short-term primary fibroblast cultures were established from skin biopsies from 10 BRCA1 and 10 BRCA2 mutation carriers and 10 controls, all of whom had previously had breast cancer. The cells were subjected to 15 Gy ionizing irradiation to induce DNA damage. RNA was extracted from all cell cultures, preirradiation and at 1 hour postirradiation. For expression profiling, 15 K spotted cDNA microarrays manufactured by the Cancer Research UK DNA Microarray Facility were used. Statistical feature selection was used with a support vector machine (SVM) classifier to determine the best feature set for predicting BRCA1 or BRCA2 heterozygous genotype. To investigate prediction accuracy, a nonprobabilistic classifier (SVM) and a probabilistic Gaussian process classifier were used.

RESULTS:

In the task of distinguishing BRCA1 and BRCA2 mutation carriers from noncarriers and from each other following radiation-induced DNA damage, the SVM achieved 90%, and the Gaussian process classifier achieved 100% accuracy. This effect could not be achieved without irradiation. In addition, the SVM identified a set of BRCA genotype predictor genes.

CONCLUSIONS:

We conclude that after irradiation-induced DNA damage, BRCA1 and BRCA2 mutation carrier cells have a distinctive expression phenotype, and this may have a future role in predicting genotypes, with application to clinical detection and classification of mutations.

PMID:
16818684
[PubMed - indexed for MEDLINE]
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