Prediction of breast cancer risk based on flow-variant analysis of circulating peripheral blood B cells

Mahrukh M. Syeda, Kinnari Upadhyay, Johnny Loke, Alexander Pearlman, Susan Klugman, Yongzhao Shao, Harry Ostrer

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Purpose:Identifying women at high risk for breast cancer can trigger a personal program of annual mammograms and magnetic resonance imaging scans for early detection, prophylactic surgery, or chemoprevention to reduce the risk of cancer. Yet, current strategies to identify high-risk mutations based on sequencing panels of genes have significant false-positive and false-negative results, suggesting the need for alternative approaches.Methods:Flow-variant assays (FVAs) that assess the effects of mutations in the double-strand break (DSB) repair genetic pathway in lymphoblastoid cells in response to treatment with radiomimetic agents were assessed for sensitivity, specificity, and accuracy both alone and as part of a logistic regression classification score. In turn, these assays were validated in circulating B cells and applied to individuals with personal and/or family history of breast and/or ovarian cancer.Results:A three-FVA classification score based on logistic regression had 95% accuracy. Individuals from a breast cancer-positive cohort with affected family members had high-risk FVA classification scores.Conclusion:Application of a classification score based on multiple FVAs could represent an alternative to panel sequencing for identifying women at high risk for cancer.

Original languageEnglish (US)
Pages (from-to)1071-1077
Number of pages7
JournalGenetics in Medicine
Issue number9
StatePublished - Sep 1 2017


  • breast cancer
  • functional genomics
  • genetic testing
  • high risk
  • panel sequencing

ASJC Scopus subject areas

  • Genetics(clinical)


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