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- Mavaddat, Nasim, et al.
(författare)
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Prediction of Breast Cancer Risk Based on Profiling With Common Genetic Variants
- 2015
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Ingår i: Journal of the National Cancer Institute. - : Oxford University Press (OUP). - 1460-2105 .- 0027-8874. ; 107:5, s. 036-036
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Tidskriftsartikel (refereegranskat)abstract
- Background: Data for multiple common susceptibility alleles for breast cancer may be combined to identify women at different levels of breast cancer risk. Such stratification could guide preventive and screening strategies. However, empirical evidence for genetic risk stratification is lacking. Methods: We investigated the value of using 77 breast cancer-associated single nucleotide polymorphisms (SNPs) for risk stratification, in a study of 33 673 breast cancer cases and 33 381 control women of European origin. We tested all possible pair-wise multiplicative interactions and constructed a 77-SNP polygenic risk score (PRS) for breast cancer overall and by estrogen receptor (ER) status. Absolute risks of breast cancer by PRS were derived from relative risk estimates and UK incidence and mortality rates. Results: There was no strong evidence for departure from a multiplicative model for any SNP pair. Women in the highest 1% of the PRS had a three-fold increased risk of developing breast cancer compared with women in the middle quintile (odds ratio [OR] = 3.36, 95% confidence interval [CI] = 2.95 to 3.83). The ORs for ER-positive and ER-negative disease were 3.73 (95% CI = 3.24 to 4.30) and 2.80 (95% CI = 2.26 to 3.46), respectively. Lifetime risk of breast cancer for women in the lowest and highest quintiles of the PRS were 5.2% and 16.6% for a woman without family history, and 8.6% and 24.4% for a woman with a first-degree family history of breast cancer. Conclusions: The PRS stratifies breast cancer risk in women both with and without a family history of breast cancer. The observed level of risk discrimination could inform targeted screening and prevention strategies. Further discrimination may be achievable through combining the PRS with lifestyle/environmental factors, although these were not considered in this report.
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| 2. |
- Watkins, Johnathan, et al.
(författare)
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Genomic Complexity Profiling Reveals That HORMAD1 Overexpression Contributes to Homologous Recombination Deficiency in Triple-Negative Breast Cancers
- 2015
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Ingår i: Cancer Discovery. - 2159-8274 .- 2159-8290. ; 5:5, s. 488-505
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Tidskriftsartikel (refereegranskat)abstract
- Triple-negative breast cancers (TNBC) are characterized by a wide spectrum of genomic alterations, some of which might be caused by defects in DNA repair processes such as homologous recombination (HR). Despite this understanding, associating particular patterns of genomic instability with response to therapy has been challenging. Here, we show that allelic-imbalanced copy-number aberrations (AiCNA) are more prevalent in TNBCs that respond to platinum-based chemotherapy, thus providing a candidate predictive biomarker for this disease. Furthermore, we show that a high level of AiCNA is linked with elevated expression of a meiosis-associated gene, HORMAD1. Elevated HORMAD1 expression suppresses RAD51-dependent HR and drives the use of alternative forms of DNA repair, the generation of AiCNAs, as well as sensitizing cancer cells to HR-targeting therapies. Our data therefore provide a mechanistic association between HORMAD1 expression, a specific pattern of genomic instability, and an association with response to platinum-based chemotherapy in TNBC. SIGNIFICANCE: Previous studies have shown correlation between mutational "scars" and sensitivity to platinums extending beyond associations with BRCA1/2 mutation, but do not elucidate the mechanism. Here, a novel allele-specific copy-number characterization of genome instability identifies and functionally validates the inappropriate expression of the meiotic gene HORMAD1 as a driver of HR deficiency in TNBC, acting to induce allelic imbalance and moderate platinum and PARP inhibitor sensitivity with implications for the use of such "scars" and expression of meiotic genes as predictive biomarkers.
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