Dynamic Breast Cancer Risk Management Through Multi-Factor Genetic Testing
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Solution Overview
Problem
Current breast cancer screening and treatment methods rely heavily on incomplete and inaccurate self-reported family history, leading to inconsistent and inefficient resource allocation, over- and under-screening, and missed early detection opportunities.
Innovation Solution
A method that analyzes sequenced genetic data for multiple genes, including BRCA1, BRCA2, PALB2, ATM, and CHEK2, and calculates polygenic risk scores to provide personalized preventive care and screening recommendations tailored to individual risk profiles, integrating genetic data with electronic health records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-factor genetic testing is implemented, then measurement precision of breast cancer risk is improved, but device complexity increases
Solution Approach 1:
The genetic testing system is segmented into distinct functional modules: DNA extraction module, sequencing module, variant analysis module (checking for pathogenic variants in BRCA1, BRCA2, PALB2, ATM, CHEK2 genes), and polygenic risk scoring module. Each module processes specific aspects of genetic analysis independently, improving measurement precision while managing system complexity through modular design.
Solution Approach 2:
The system changes multiple parameters simultaneously: it tests for specific pathogenic variants (qualitative parameter) and calculates polygenic risk scores (quantitative parameter). By integrating both variant presence/absence and risk score thresholds, the system achieves comprehensive risk assessment with improved precision across different risk levels.
2Reliability
If comprehensive genetic testing is performed, then reliability of risk assessment is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary filtering by first checking for high-penetrance pathogenic variants in key genes (BRCA1, BRCA2, PALB2, ATM, CHEK2). Only after this initial screening does it proceed to calculate polygenic risk scores. This preliminary action ensures reliable risk assessment by prioritizing the most significant genetic factors, reducing unnecessary computational time for low-risk individuals.
Solution Approach 2:
The system uses feedback mechanisms where polygenic risk score calculations are conditioned on the absence of pathogenic variants. If pathogenic variants are detected, the system immediately classifies the patient as high-risk without requiring polygenic score calculation. This feedback loop optimizes time usage by avoiding redundant computations while maintaining reliable assessment.
3Manufacturing precision
If polygenic risk scoring is calculated, then manufacturing precision of risk classification is improved, but use of energy increases
Solution Approach 1:
The system applies local quality by calculating polygenic risk scores only for specific gene regions and variants relevant to breast cancer risk. Rather than analyzing the entire genome, it focuses computational energy on predetermined genetic loci and polygenic markers, achieving precise risk classification while minimizing energy consumption through targeted analysis.
Solution Approach 2:
The system performs partial action by calculating polygenic risk scores only when pathogenic variants are absent. For individuals with detected pathogenic variants, the system stops at the variant detection stage and classifies them as high-risk without requiring full polygenic scoring. This partial approach maintains classification precision for the most critical cases while reducing overall computational energy usage.
Data Source
AI summary
Various embodiments disclosed relate to a method of preventive care for breast cancer. A method may include analyzing sequenced genetic data originating from the patient to determine whether the patient has a qualifying variant in any of genes BRCA1, BRCA2, PALB2, ATM, and CHEK2, wherein the qualifying variant can include one or more pathogenic variants, variants of uncertain significance (VUS), or combinations thereof. In an event that the patient does not have a qualifying variant in any of genes BRCA1, BRCA2, PALB2, ATM, and CHEK2, the method can include calculating a polygenic risk score (PRS) of the patient. If the patient has a PRS lower than a predetermined threshold, the method can include classifying the patient as low risk for breast cancer.