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Updated: Jan 14, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Polygenic risk scores for breast cancer: Progress, challenges, and clinical integration
Jerry S Lanchbury1, Holly J Pederson2
1Salt Lake City, UT, USA.
Abstract:
In a previous commentary, "An apparent quandary: adoption of polygenics and gene panels for personalized breast cancer risk stratification," we highlighted the exceptional progress that had been made uncovering the polygenic risk component underpinning breast cancer susceptibility and drew attention to the critical challenges inherent in translating these advancements into measurable improvements in clinical care. Among the barriers to adoption discussed were the inherent biases in polygenic risk scores (PRS) developed in populations of largely European descent, the unpreparedness of professional societies for adoption, and the lack of education of current healthcare professionals and the cadres of those emerging from training. In this update, we focus on the substantial research advances that have been made and the cultural and practical transitions that have begun to position the field for delivering personalized preventive breast cancer services to all women. It is our perception that the discussion has moved from questions of establishing scientific validity to a focus on the development and application of practical operational solutions to better stratify risk. Additional issues raised are concerns over misuse, the composition of the single nucleotide polymorphism (SNPs) panels used and adjustment for non-European populations. High-risk breast clinics will benefit dramatically from models that incorporate genome-wide susceptibility genetics. Improved risk estimation may aid in patients' decisions about preventive medication and in clinical decisions concerning the need for and frequency of MRI screening, and, in exceptional cases, may even help patients at hereditary risk with surgical decision-making.
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