scholarly journals Gene-Panel Sequencing and the Prediction of Breast-Cancer Risk

2015 ◽  
Vol 372 (23) ◽  
pp. 2243-2257 ◽  
Author(s):  
Douglas F. Easton ◽  
Paul D.P. Pharoah ◽  
Antonis C. Antoniou ◽  
Marc Tischkowitz ◽  
Sean V. Tavtigian ◽  
...  
2017 ◽  
Vol 24 (10) ◽  
pp. 3060-3066 ◽  
Author(s):  
Erin O’Leary ◽  
Daniela Iacoboni ◽  
Jennifer Holle ◽  
Scott T. Michalski ◽  
Edward D. Esplin ◽  
...  

Impact ◽  
2020 ◽  
Vol 2020 (7) ◽  
pp. 12-15
Author(s):  
Peter Devilee ◽  
Marjanka Schmidt

"Breast cancer affects more than 360,000 women per year in the EU and causes more than 90,000 deaths. Identification of women at high risk of the disease can lead to early detection or disease prevention through intensive screening, therapeutic and/or lifestyle preventive measures, or prophylactic surgery. Breast cancer risk is determined by a combination of genetic and lifestyle risk factors. The advent of next generation sequencing has opened the opportunity for testing in many disease genes, and diagnostic gene panel testing is being introduced in many EU countries. However, the cancer risks associated with most variants in most genes are unknown. This leads to a major problem in appropriate counselling and management of women undergoing panel testing. The BRIDGES and B-CAST projects are jointly building a knowledge base that will allow identification of women at high-risk of specific subtypes of breast cancer, through comprehensive evaluation of DNA variants in known and suspected breast cancer genes. The effort exploits the huge resources established through the Breast Cancer Association Consortium (BCAC) and ENIGMA (Evidence-based Network for the Interpretation of Germline Mutant Alleles). Existing datasets will be expanded by sequencing all known breast cancer susceptibility genes in >100,000 breast cancer cases and controls from population-based studies. Risk factor and tumour genome data have been collected for 10,000 cases. Jointly, the data will allow us to generate a comprehensive risk model with unprecedented discriminative power, that can provide personalised risk estimates. We will develop online tools to aid the interpretation of gene variants and provide risk estimates in a user-friendly format, to help genetic counsellors and patients worldwide to make informed clinical decisions for risk management. We will evaluate the acceptability and utility of comprehensive gene panel testing in the clinical genetics context."


2016 ◽  
Vol 53 (5) ◽  
pp. 298-309 ◽  
Author(s):  
Douglas F Easton ◽  
Fabienne Lesueur ◽  
Brennan Decker ◽  
Kyriaki Michailidou ◽  
Jun Li ◽  
...  

2021 ◽  
Author(s):  
D. Gareth R. Evans ◽  
Elke M. van Veen ◽  
Elaine F. Harkness ◽  
Adam R. Brentnall ◽  
Susan M. Astley ◽  
...  

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Melissa C. Southey ◽  
James G. Dowty ◽  
Moeen Riaz ◽  
Jason A. Steen ◽  
Anne-Laure Renault ◽  
...  

AbstractPopulation-based estimates of breast cancer risk for carriers of pathogenic variants identified by gene-panel testing are urgently required. Most prior research has been based on women selected for high-risk features and more data is needed to make inference about breast cancer risk for women unselected for family history, an important consideration of population screening. We tested 1464 women diagnosed with breast cancer and 862 age-matched controls participating in the Australian Breast Cancer Family Study (ABCFS), and 6549 healthy, older Australian women enroled in the ASPirin in Reducing Events in the Elderly (ASPREE) study for rare germline variants using a 24-gene-panel. Odds ratios (ORs) were estimated using unconditional logistic regression adjusted for age and other potential confounders. We identified pathogenic variants in 11.1% of the ABCFS cases, 3.7% of the ABCFS controls and 2.2% of the ASPREE (control) participants. The estimated breast cancer OR [95% confidence interval] was 5.3 [2.1–16.2] for BRCA1, 4.0 [1.9–9.1] for BRCA2, 3.4 [1.4–8.4] for ATM and 4.3 [1.0–17.0] for PALB2. Our findings provide a population-based perspective to gene-panel testing for breast cancer predisposition and opportunities to improve predictors for identifying women who carry pathogenic variants in breast cancer predisposition genes.


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