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Published on: October 11, 2018
Predicting Triple-Negative Breast Cancer Subtype Using Multiple Single Nucleotide Polymorphisms for Breast Cancer
Lothar Häberle1,2, Alexander Hein1, Matthias Rübner1
1Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Identifying specific genetic markers, or single nucleotide polymorphisms (SNPs), can improve the prediction of triple-negative breast cancer. This genetic information, combined with clinical factors, aids in optimizing screening for targeted therapies.
Area of Science:
- Oncology
- Genetics
- Biomarker Discovery
Background:
- Triple-negative breast cancer (TNBC) research is expanding, necessitating efficient screening for targeted therapies.
- Current screening for TNBC is time-consuming due to molecular subtyping and biomarker assessment.
- Germline genotypes could predict TNBC molecular subtypes early, optimizing treatment planning and screening costs.
Purpose of the Study:
- To identify single nucleotide polymorphisms (SNPs) associated with breast cancer risk that can predict triple negativity.
- To evaluate the predictive value of these SNPs in combination with clinical factors.
Main Methods:
- A cross-sectional study of 1271 invasive breast cancer patients.
- Genotyping of 76 validated breast cancer risk SNPs.
- Logistic regression and variable selection techniques to identify predictive SNPs alongside age and BMI.
- Cross-validation to determine the most accurate prediction model.
Main Results:
- The SNP rs10069690 (TERT, CLPTM1L) was significantly associated with triple negativity.
- Four SNPs (rs10069690, RAD51B, CCND1, FGFR2) improved triple-negative prediction when added to clinical predictors.
- The Area Under the Curve (AUC) increased from 0.618 to 0.625 with the addition of SNPs.
- Age at diagnosis was the strongest predictor overall.
Conclusions:
- Adding breast cancer risk-associated SNPs to prediction models based on age and BMI enhances TNBC prediction.
- These findings can potentially be used for prescreening in TNBC molecular therapy studies.
