Breast cancer risk stratification in women of screening age: Incremental effects of adding mammographic density, polygenic risk, and a gene panel
Tài liệu tham khảo
Eccles, 2013, Critical research gaps and translational priorities for the successful prevention and treatment of breast cancer, Breast Cancer Res, 15, R92, 10.1186/bcr3493
Michailidou, 2015, Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer, Nat Genet, 47, 373, 10.1038/ng.3242
Michailidou, 2017, Association analysis identifies 65 new breast cancer risk loci, Nature, 551, 92, 10.1038/nature24284
Kapoor, 2015, Multigene panel testing detects equal rates of pathogenic BRCA1/2 mutations and has a higher diagnostic yield compared to limited BRCA1/2 analysis alone in patients at risk for hereditary breast cancer, Ann Surg Oncol, 22, 3282, 10.1245/s10434-015-4754-2
Thompson, 2016, Panel testing for familial breast cancer: calibrating the tension between research and clinical care, J Clin Oncol, 34, 1455, 10.1200/JCO.2015.63.7454
Evans, 2017, The impact of a panel of 18 SNPs on breast cancer risk in women attending a UK familial screening clinic: a case-control study, J Med Genet, 54, 111, 10.1136/jmedgenet-2016-104125
Tyrer, 2005, A breast cancer prediction model incorporating familial and personal risk factors. Stat Med. 2004;23(7):1111-1130. Published correction appears in, Stat Med, 24, 156, 10.1002/sim.1913
Gail, 1989, Projecting individualized probabilities of developing breast cancer for white females who are being examined annually, J Natl Cancer Inst, 81, 1879, 10.1093/jnci/81.24.1879
Carver, 2021, CanRisk tool-a web interface for the prediction of breast and ovarian cancer risk and the likelihood of carrying genetic pathogenic variants, Cancer Epidemiol Biomarkers Prev, 30, 469, 10.1158/1055-9965.EPI-20-1319
Warwick, 2014, Mammographic breast density refines Tyrer-Cuzick estimates of breast cancer risk in high-risk women: findings from the placebo arm of the International Breast Cancer Intervention Study I, Breast Cancer Res, 16, 451, 10.1186/s13058-014-0451-5
Brentnall, 2015, Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort, Breast Cancer Res, 17, 147, 10.1186/s13058-015-0653-5
Brentnall, 2020, A case-control evaluation of 143 single nucleotide polymorphisms for breast cancer risk stratification with classical factors and mammographic density, Int J Cancer, 146, 2122, 10.1002/ijc.32541
Dite, 2016, Breast cancer risk prediction using clinical models and 77 independent risk-associated SNPs for women aged under 50 years: Australian Breast Cancer Family Registry, Cancer Epidemiol Biomarkers Prev, 25, 359, 10.1158/1055-9965.EPI-15-0838
Vachon, 2015, The contributions of breast density and common genetic variation to breast cancer risk, J Natl Cancer Inst, 107, 10.1093/jnci/dju397
Mavaddat, 2019, Polygenic risk scores for prediction of breast cancer and breast cancer subtypes, Am J Hum Genet, 104, 21, 10.1016/j.ajhg.2018.11.002
Evans, 2016
van Veen, 2018, Use of single-nucleotide polymorphisms and mammographic density plus classic risk factors for breast cancer risk prediction, JAMA Oncol, 4, 476, 10.1001/jamaoncol.2017.4881
McIntosh
Turnbull, 2010, Genome-wide association study identifies five new breast cancer susceptibility loci, Nat Genet, 42, 504, 10.1038/ng.586
Dorling, 2021, Breast cancer risk genes - association analysis in more than 113,000 women, N Engl J Med, 384, 428, 10.1056/NEJMoa1913948
Richards, 2015, Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology, Genet Med, 17, 405, 10.1038/gim.2015.30
Evans, 2017, Pathology update to the Manchester Scoring System based on testing in over 4000 families, J Med Genet, 54, 674, 10.1136/jmedgenet-2017-104584
Evans, 2020, Breast cancer in neurofibromatosis 1: survival and risk of contralateral breast cancer in a five country cohort study. Genet Med. 2020;22(2):398-406. Published correction appears in, Genet Med, 22, 242, 10.1038/s41436-019-0671-2
Hu, 2021, A population-based study of genes previously implicated in breast cancer, N Engl J Med, 384, 440, 10.1056/NEJMoa2005936
Li, 2019, Prevalence of BRCA1 and BRCA2 pathogenic variants in a large, unselected breast cancer cohort, Int J Cancer, 144, 1195, 10.1002/ijc.31841
Manchanda, 2020, Economic evaluation of population-based BRCA1/BRCA2 mutation testing across multiple countries and health systems, Cancers (Basel), 12, 1929, 10.3390/cancers12071929
Petridis, 2019, Frequency of pathogenic germline variants in BRCA1, BRCA2, PALB2, CHEK2 and TP53 in ductal carcinoma in situ diagnosed in women under the age of 50 years, Breast Cancer Res, 21, 58, 10.1186/s13058-019-1143-y
McWilliams, 2020, Risk stratified breast cancer screening: UK healthcare policy decision-making stakeholders’ views on a low-risk breast screening pathway, BMC Cancer, 20, 680, 10.1186/s12885-020-07158-9
Evans, 2019, Breast cancer pathology and stage are better predicted by risk stratification models that include mammographic density and common genetic variants, Breast Cancer Res Treat, 176, 141, 10.1007/s10549-019-05210-2
Astley, 2018, A comparison of five methods of measuring mammographic density: a case-control study, Breast Cancer Res, 20, 10, 10.1186/s13058-018-0932-z
Cecchini, 2012, Baseline mammographic breast density and the risk of invasive breast cancer in postmenopausal women participating in the NSABP study of tamoxifen and raloxifene (STAR), Cancer Prev Res (Phila), 5, 1321, 10.1158/1940-6207.CAPR-12-0273
Ionescu, 2019, Prediction of reader estimates of mammographic density using convolutional neural networks, J Med Imaging (Bellingham), 6
Haji Maghsoudi, 2021, Deep-LIBRA: an artificial-intelligence method for robust quantification of breast density with independent validation in breast cancer risk assessment, Med Image Anal, 73, 10.1016/j.media.2021.102138
Kallenberg, 2016, Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring, IEEE Trans Med Imaging, 35, 1322, 10.1109/TMI.2016.2532122
Smith, 2016, The contribution of whole gene deletions and large rearrangements to the mutation spectrum in inherited tumor predisposing syndromes, Hum Mutat, 37, 250, 10.1002/humu.22938
Evans, 2022, The importance of ethnicity: are breast cancer polygenic risk scores ready for women who are not of White European origin?, Int J Cancer, 150, 73, 10.1002/ijc.33782