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MicroRNA expression signatures for the prediction of BRCA1/2 mutation-associated hereditary breast cancer in paraffin-embedded formalin-fixed breast tumors. | LitMetric

AI Article Synopsis

  • - The study focuses on identifying germline mutations in the BRCA1 and BRCA2 genes in breast cancer patients, which is important for determining treatment and management, but only 25% of those tested are positive for these mutations, highlighting the need for additional diagnostic tools.
  • - Researchers analyzed 124 breast cancer samples, using microarray expression profiling to assess the levels of 829 miRNAs and developed a 35-miRNA classifier that accurately predicts BRCA1/2 mutation status with high precision (95% for training and 92% for testing sets).
  • - A logistic regression model utilizing six specific miRNAs also successfully distinguished between mutation carriers and noncarriers with 92% accuracy, suggesting that miRNA signatures could help refine

Article Abstract

Screening for germline mutations in breast cancer-associated genes BRCA1 and BRCA2 is indicated for patients with breast cancer from high-risk breast cancer families and influences both treatment options and clinical management. However, only 25% of selected patients test positive for BRCA1/2 mutation, indicating that additional diagnostic biomarkers are necessary. We analyzed 124 formalin-fixed paraffin-embedded (FFPE) tumor samples from patients with hereditary (104) and sporadic (20) invasive breast cancer, divided into two series (A and B). Microarray expression profiling of 829 human miRNAs was performed on 76 samples (Series A), and bioinformatics tool Prophet was used to develop and test a microarray classifier. Samples were stratified into a training set (n = 38) for microarray classifier generation and a test set (n = 38) for signature validation. A 35-miRNA microarray classifier was generated for the prediction of BRCA1/2 mutation status with a reported 95% (95% CI = 0.88-1.0) and 92% (95% CI: 0.84-1.0) accuracy in the training and the test set, respectively. Differential expression of 12 miRNAs between BRCA1/2 mutation carriers versus noncarriers was validated by qPCR in an independent tumor series B (n = 48). Logistic regression model based on the expression of six miRNAs (miR-142-3p, miR-505*, miR-1248, miR-181a-2*, miR-25* and miR-340*) discriminated between tumors from BRCA1/2 mutation carriers and noncarriers with 92% (95% CI: 0.84-0.99) accuracy. In conclusion, we identified miRNA expression signatures predictive of BRCA1/2 mutation status in routinely available FFPE breast tumor samples, which may be useful to complement current patient selection criteria for gene testing by identifying individuals with high likelihood of being BRCA1/2 mutation carriers.

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Source
http://dx.doi.org/10.1002/ijc.29021DOI Listing

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