| Abstrakt: |
Background: We previously developed a biological assay to accurately predict BRCA1 (BRCA1 DNA repair associated) mutation status, based on gene expression profiles of Epstein–Barr virus-transformed lymphoblastoid cell lines. The original work was done using whole genome expression microarrays, and nearest shrunken centroids analysis. While these approaches are appropriate for model building, they are difficult to implement clinically, where more targeted testing and analysis are required for time and cost savings. Methods: Here, we describe adaptation of the original predictor to use the NanoString nCounter platform for testing, with analysis based on the k-top scoring pairs (k-TSP) method. Results: Assessing gene expression using the nCounter platform on a set of lymphoblastoid cell lines yielded 93.8% agreement with the microarray-derived data, and 87.5% overall correct classification of BRCA1 carriers and controls. Using the original gene expression microarray data used to develop our predictor with nearest shrunken centroids, we rebuilt a classifier based on the k-TSP method. This classifier relies on the relative expression of 10 pairs of genes, compared to the original 43 identified by nearest shrunken centroids (NSC), and was 96.2% concordant with the original training set prediction, with a 94.3% overall correct classification of BRCA1 carriers and controls. Conclusions: The k-TSP classifier was shown to accurately predict BRCA1 status using data generated on the nCounter platform and is feasible for initiating a clinical validation. [ABSTRACT FROM AUTHOR] |
