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Functional Assessment of BRCA1 variants using CRISPR-Mediated Base Editors
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BRCA1 Variant Assessment Using a Simple Analytic Assay.

Daniel M Kim1,2, Harriet E Feilotter1,2, Scott K Davey1,2,3

  • 1Department of Pathology and Molecular Medicine, Queen's University Cancer Research Institute, Queen's University, Kingston, ON, Canada.

The Journal of Applied Laboratory Medicine
|January 12, 2022
PubMed
Summary
This summary is machine-generated.

A new gene expression assay accurately predicts BRCA1 mutation status using the NanoString nCounter platform. This method, utilizing k-top scoring pairs (k-TSP), is a feasible and cost-effective approach for clinical validation.

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Area of Science:

  • Genomics
  • Molecular Diagnostics
  • Cancer Biomarkers

Background:

  • Previously developed a biological assay for predicting BRCA1 mutation status using gene expression profiles.
  • Original assay utilized whole genome microarrays and nearest shrunken centroids (NSC), which are difficult for clinical implementation due to time and cost.

Purpose of the Study:

  • Adapt the existing BRCA1 prediction assay for clinical use.
  • Implement the NanoString nCounter platform and k-top scoring pairs (k-TSP) method for a more targeted and cost-effective analysis.

Main Methods:

  • Adapted the BRCA1 prediction assay to the NanoString nCounter platform.
  • Utilized the k-top scoring pairs (k-TSP) method for data analysis.
  • Rebuilt a classifier using k-TSP on original microarray data, focusing on 10 gene pairs.

Main Results:

  • NanoString nCounter platform showed 93.8% agreement with microarray data.
  • The k-TSP classifier achieved 94.3% overall correct classification of BRCA1 status.
  • The new k-TSP classifier used only 10 gene pairs, compared to NSC's 43, and was 96.2% concordant with the original prediction.

Conclusions:

  • The k-TSP classifier accurately predicts BRCA1 status using NanoString nCounter data.
  • This adapted assay is feasible for initiating clinical validation.
  • The k-TSP method offers a more targeted and efficient approach for BRCA1 mutation status prediction.