scholarly journals Genome-wide association study identifies 25 known breast cancer susceptibility loci as risk factors for triple-negative breast cancer

2013 ◽  
Vol 35 (5) ◽  
pp. 1012-1019 ◽  
Author(s):  
Kristen S. Purrington ◽  
Susan Slager ◽  
Diana Eccles ◽  
Drakoulis Yannoukakos ◽  
Peter A. Fasching ◽  
...  
2015 ◽  
Vol 16 (6) ◽  
pp. 2231-2235 ◽  
Author(s):  
Samuel J Haryono ◽  
I Gusti Bagus Datasena ◽  
Wahyu Budi Santosa ◽  
Raymond Mulyarahardja ◽  
Kartika Sari

2016 ◽  
Vol 25 (15) ◽  
pp. 3361-3371 ◽  
Author(s):  
Mi-Ryung Han ◽  
Jirong Long ◽  
Ji-Yeob Choi ◽  
Siew-Kee Low ◽  
Sun-Seog Kweon ◽  
...  

Nature ◽  
2007 ◽  
Vol 447 (7148) ◽  
pp. 1087-1093 ◽  
Author(s):  
Douglas F. Easton ◽  
◽  
Karen A. Pooley ◽  
Alison M. Dunning ◽  
Paul D. P. Pharoah ◽  
...  

2010 ◽  
Vol 42 (6) ◽  
pp. 504-507 ◽  
Author(s):  
Clare Turnbull ◽  
◽  
Shahana Ahmed ◽  
Jonathan Morrison ◽  
David Pernet ◽  
...  

2019 ◽  
Author(s):  
Haoyu Zhang ◽  
Thomas U. Ahearn ◽  
Julie Lecarpentier ◽  
Daniel Barnes ◽  
Jonathan Beesley ◽  
...  

AbstractBreast cancer susceptibility variants frequently show heterogeneity in associations by tumor subtype. To identify novel loci, we performed a genome-wide association study (GWAS) including 133,384 breast cancer cases and 113,789 controls, plus 18,908 BRCA1 mutation carriers (9,414 with breast cancer) of European ancestry, using both standard and novel methodologies that account for underlying tumor heterogeneity by estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status and tumor grade. We identified 32 novel susceptibility loci (P<5.0×10-8), 15 of which showed evidence for associations with at least one tumor feature (false discovery rate <0.05). Five loci showed associations (P<0.05) in opposite directions between luminal- and non-luminal subtypes. In-silico analyses showed these five loci contained cell-specific enhancers that differed between normal luminal and basal mammary cells. The genetic correlations between five intrinsic-like subtypes ranged from 0.35 to 0.80. The proportion of genome-wide chip heritability explained by all known susceptibility loci was 37.6% for triple-negative and 54.2% for luminal A-like disease. These findings provide an improved understanding of genetic predisposition to breast cancer subtypes and will inform the development of subtype-specific polygenic risk scores.


2020 ◽  
Vol 21 (16) ◽  
pp. 5835
Author(s):  
Maria-Ancuta Jurj ◽  
Mihail Buse ◽  
Alina-Andreea Zimta ◽  
Angelo Paradiso ◽  
Schuyler S. Korban ◽  
...  

Genome-wide association studies (GWAS) are useful in assessing and analyzing either differences or variations in DNA sequences across the human genome to detect genetic risk factors of diseases prevalent within a target population under study. The ultimate goal of GWAS is to predict either disease risk or disease progression by identifying genetic risk factors. These risk factors will define the biological basis of disease susceptibility for the purposes of developing innovative, preventative, and therapeutic strategies. As single nucleotide polymorphisms (SNPs) are often used in GWAS, their relevance for triple negative breast cancer (TNBC) will be assessed in this review. Furthermore, as there are different levels and patterns of linkage disequilibrium (LD) present within different human subpopulations, a plausible strategy to evaluate known SNPs associated with incidence of breast cancer in ethnically different patient cohorts will be presented and discussed. Additionally, a description of GWAS for TNBC will be presented, involving various identified SNPs correlated with miRNA sites to determine their efficacies on either prognosis or progression of TNBC in patients. Although GWAS have identified multiple common breast cancer susceptibility variants that individually would result in minor risks, it is their combined effects that would likely result in major risks. Thus, one approach to quantify synergistic effects of such common variants is to utilize polygenic risk scores. Therefore, studies utilizing predictive risk scores (PRSs) based on known breast cancer susceptibility SNPs will be evaluated. Such PRSs are potentially useful in improving stratification for screening, particularly when combining family history, other risk factors, and risk prediction models. In conclusion, although interpretation of the results from GWAS remains a challenge, the use of SNPs associated with TNBC may elucidate and better contextualize these studies.


2011 ◽  
Vol 71 (19) ◽  
pp. 6240-6249 ◽  
Author(s):  
Kristen N. Stevens ◽  
Celine M. Vachon ◽  
Adam M. Lee ◽  
Susan Slager ◽  
Timothy Lesnick ◽  
...  

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