Original Article
Performance of Common Genetic Variants in Breast-Cancer Risk Models
N Engl J Med 2010; 362:986-993March 18, 2010
- Abstract
Background
Genomewide association studies have identified multiple genetic variants associated with breast cancer. The extent to which these variants add to existing risk-assessment models is unknown.
Methods
We used information on traditional risk factors and 10 common genetic variants associated with breast cancer in 5590 case subjects and 5998 control subjects, 50 to 79 years of age, from four U.S. cohort studies and one case–control study from Poland to fit models of the absolute risk of breast cancer. With the use of receiver-operating-characteristic curve analysis, we calculated the area under the curve (AUC) as a measure of discrimination. By definition, random classification of case and control subjects provides an AUC of 50%; perfect classification provides an AUC of 100%. We calculated the fraction of case subjects in quintiles of estimated absolute risk after the addition of genetic variants to the traditional risk model.
Results
The AUC for a risk model with age, study and entry year, and four traditional risk factors was 58.0%; with the addition of 10 genetic variants, the AUC was 61.8%. About half the case subjects (47.2%) were in the same quintile of risk as in a model without genetic variants; 32.5% were in a higher quintile, and 20.4% were in a lower quintile.
Conclusions
The inclusion of newly discovered genetic factors modestly improved the performance of risk models for breast cancer. The level of predicted breast-cancer risk among most women changed little after the addition of currently available genetic information.
Media in This Article
Figure 1
Five Models of Breast-Cancer Risk.Table 1
Baseline Data for the Subjects.Article Activity
- Article
Personalized medicine, the assignment of preventive measures or treatment interventions on the basis of individual characteristics, can result in better outcomes than the use of the same strategy for everyone. Recent changes in the U.S. Preventive Services Task Force guidelines1 for mammographic screening raise the question of whether recommendations about age at the onset of screening and the frequency of screening can be calibrated to an individual woman's risk of breast cancer. Clinicians already use guidelines in making decisions about assessments to identify carriers of rare BRCA1 and BRCA2 mutations, which confer very high risks of breast cancer and ovarian cancer.2 Single-nucleotide polymorphisms (SNPs) that have been shown to be associated with breast cancer in genomewide association studies3 are common but confer only small increases in risk. Whether these variants collectively enhance the identification of women at increased or reduced risk is unknown.
The Breast Cancer Risk Assessment Tool,4,5 commonly referred to as the Gail model, summarizes information about a woman's reproductive history, breast cancer in close relatives, and previous breast biopsies to estimate the probability of the development of breast cancer in subsequent years; the estimated breast-cancer risk based on the Gail model has been used for counseling, informing decisions about the use of tamoxifen,6 and determining sample size in randomized prevention trials.7 In this study, we empirically evaluated the contribution of a set of 10 newly established common genetic variants as an alternative to and as a supplement to the components of the Gail model in 5590 case subjects and 5998 control subjects, 50 to 79 years of age, included in the National Cancer Institute's Cancer Genetic Markers of Susceptibility multistage genomewide association study of breast cancer.8
Methods
Studies
Investigators from the Women's Health Initiative Observational Study,9 the American Cancer Society Cancer Prevention Study II Nutrition Cohort,10 the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (ClinicalTrials.gov number, NCT00002540),11 and the Nurses' Health Study12 collected information and specimens at baseline and clinical observations of the subjects for up to 15 years of follow-up. We also used data from the Polish Breast Cancer Study,13 a population-based case–control study in Warsaw and Lodz, Poland.
Subjects
All case subjects were women who had received a diagnosis of invasive breast cancer. Additional descriptions of the studies, including methods for case ascertainment and selection of control subjects, are included in Appendixes 1, 2, and 3 in the Supplementary Appendix, available with the full text of this article at NEJM.org.
Components of the Gail Model
We included components of the Gail model that were collected prospectively in the four cohort studies and retrospectively in the case–control study. These components were the number of first-degree relatives with a diagnosis of breast cancer, age at menarche, age at first live birth, and number of previous breast biopsies. Mammographic density and diagnosis of atypical hyperplasia, which have improved the performance of the Gail model in other studies,4,14 were not available for most studies and were not used in this analysis.
Genetic Variants and Genotyping
We analyzed genotypes in case subjects and control subjects for 10 SNPs with established associations with breast cancer that achieved genomewide significance in three published genomewide association studies.8,15 Some of the data used in our analysis were reported in earlier articles on the discovery of susceptibility alleles.8,16-18 Genotypes used in this analysis were described in a recent article about the Cancer Genetic Markers of Susceptibility study.8 Complete genotype data from a total of 5590 case subjects and 5998 control subjects were available for analysis after the exclusions described in Table 1 in the Supplementary Appendix.
Models
We fit a family of five logistic-regression models, including combinations of demographic factors (age, entry year, and cohort), the components of the Gail model, and genotypes for the 10 SNPs. The demographic model included demographic factors only; adjustment for age is described in Appendix 4 in the Supplementary Appendix. The nongenetic model added components of the Gail model to incorporate standard risk factors and reflect current clinical practice. Two models included demographic and genetic factors only: the genetic variant-count model included the number of risk-conferring variant alleles (one allele for heterozygotes and two alleles for homozygotes) in categories of fewer than 6, 7 to 8, 9 to 10, 11 to 12, and 13 or more; the genetic individual-variant model included separate effects of the 10 individual SNPs. The inclusive model combined demographic factors, components of the Gail model, and the 10 SNPs into a single model. Tables 1Table 1
Baseline Data for the Subjects., 2Table 2
Risk Factors Associated with Breast Cancer., and 3Table 3
Common Genetic Variants Associated with Breast Cancer. show the distribution of case subjects and control subjects according to cohort, age, and factors in the models. Except for age and year of study entry, all variables were fit as categorical terms.Interactions
We investigated whether interactions between components of the Gail model and genetic variants would improve the performance of the relevant models. To avoid complex issues involving model selection that are beyond the scope of this article, we evaluated nonmultiplicative effects using the Tukey 1-degree-of-freedom test applied to the interaction term between the risk scores based on the Gail model and those based on the genetic variants19,20 and a 16-degree-of-freedom test based on quintiles of the two scores. Further details are included in Appendix 5 in the Supplementary Appendix.
Estimates of Absolute Risk
We combined the risks from the logistic models, crude rates of breast cancer for each underlying cohort, and registry data to estimate the absolute risk of breast cancer per year, as described in Appendix 6 in the Supplementary Appendix. We used rates of breast cancer from U.S.21 and Polish22 registries, as appropriate.
Model Discrimination
Using receiver-operating-characteristic (ROC) curve analysis, we calculated the area under the curve (AUC), also known as a concordance (C) statistic, to assess discrimination. Each point on the ROC curve shows the effect of a rule for turning a risk estimate into a prediction of the development of breast cancer in a woman. The y axis of the ROC curve is the true positive rate or sensitivity (i.e., the proportion of women with breast cancer who were correctly predicted to have the disease). The x axis shows the false positive rate (the complement of specificity) (i.e., the proportion of women without breast cancer who were incorrectly predicted to have breast cancer). The area under the ROC curve, the AUC, measures how well the model discriminates between case subjects and control subjects. An ROC curve that corresponds to a random classification of case subjects and control subjects is a straight line with an AUC of 50%. An ROC curve that corresponds to perfect classification has an AUC of 100%. Figure 1Figure 1
Five Models of Breast-Cancer Risk. shows a modified ROC graph, in which the y axis is the true positive rate and the x axis is the false positive rate in the population instead of the rate among control subjects only.15 Comparison of Performance of Risk Models
To compare the performance of the models, we calculated the cross-classification of the assignment of case subjects into predicted risk bands of women with similar risk. The cutoff points were determined according to quintiles of estimated risk among case subjects and control subjects, weighted according to the inverse sampling fraction so as to apply the model to the underlying cohort (Appendix 7 in the Supplementary Appendix). We also used integrated discrimination improvement23 as a measure to compare models without setting cutoff points. This measure compares two models for the change in the difference in the average estimated annual risk of breast cancer between case subjects and control subjects20; thus, the integrated discrimination improvement will be large for a model that assigns a greater estimated risk to case subjects and a lesser estimated risk to control subjects. Details about the assessment of precision, testing of models, and extent of overfitting are included in Appendixes 8 and 9 in the Supplementary Appendix.
Results
The associations between each predictor and breast-cancer risk were consistent with published data, and they were similar in the different models (Table 2 in the Supplementary Appendix). Notably, the effect of a family history of breast cancer did not materially change after adjustment for SNPs. The risk of breast cancer among women who carried 13 or more of a maximum of 20 risk-conferring variant alleles (which occur in about 4% of the population) was nearly three times the risk among women who carry 6 or fewer variants (12%).
Table 3 in the Supplementary Appendix shows a comparison of how the models discriminated between women with breast cancer and those without the disease. The AUC for the demographic model was 53.4%. The addition of the SNP from the fibroblast growth factor receptor 2 gene (FGFR2) increased the AUC to 55.7%. The addition of four more SNPs (from Tox high-mobility group box family member 3 [TOX3], mitogen-activated protein kinase kinase kinase 1 [MAP3K1], chromosome 1p, and a RAD51-like 1 [RAD51L1]) increased the AUC further, to 57.9%. The AUC for the 7 SNPs described by Pharoah et al.15 was 58.6%, and the addition of the 3 others to incorporate all 10 SNPs listed in Table 3 in the Supplementary Appendix increased the AUC only to 59.7%. The discrimination in the simpler genetic variant-count model was somewhat less (58.9%).
Among the individual nongenetic factors, a history of breast biopsy yielded the greatest AUC (56.2%). The nongenetic model, with the use of all four components of the Gail model, showed an AUC of 58.0%. The inclusive model, with components of the Gail model and 10 SNPs, yielded an AUC of 61.8%, an increase of about 3.8 percentage points over the nongenetic model and 2.1 percentage points over the genetic individual-variant model. In short, the AUC for the genetic models was only slightly higher than the AUC for the nongenetic model, and the addition of the components from the Gail model increased the AUC by 2 more percentage points; thus, discrimination by means of the inclusive model is better than discrimination by means of either the genetic or nongenetic models. In parallel, integrated discrimination improvement, or the difference in the estimated annual risk of breast cancer between case subjects and control subjects, ranged from 0.015 to 0.022% in the inclusive model (Table 3 in the Supplementary Appendix). Our empirical analysis indicated a modest increment in discrimination by means of the inclusive model in relative and absolute terms, despite very low P values.
We also empirically examined the performance of high and low absolute risk (i.e., the performance of the models for women who might be singled out for more aggressive or less aggressive intervention than women at average risk). The genetic models yielded better discrimination than the nongenetic models among women at the lowest estimated risk (Figure 1B), but not among women at the highest estimated risk (Figure 1C). For women with a risk estimated from the inclusive model that was above the median in control subjects, the AUC was 58.8%, but it was only 56.6% for women with an estimated risk that was above the 80th percentile.
Table 4 in the Supplementary Appendix shows how the addition of genetic data affected classification of the case subjects with breast cancer. The percentages of all case subjects according to quintiles of absolute risk are shown for the inclusive and nongenetic models. The estimated annual risk was above 0.575% for substantially more case subjects with the inclusive model than with the nongenetic model (27.7% vs. 18.9%). Slightly fewer than half the case subjects (47.2%) were in the same category in both models. A total of 32.5% of the case subjects were in a higher-risk category with the inclusive model than with the nongenetic model, and 20.4% were in a lower category. The corresponding percentages for the control subjects were 26% and 28%. We found no evidence that a complex model involving interactions among Gail model components and SNPs performed materially better than the models described here (P>0.10).
Discussion
In our study involving 5590 case subjects with breast cancer and 5998 control subjects, the addition of information on 10 genetic variants to a standard clinical breast-cancer risk model predicted the risk of breast cancer only slightly better than the clinical model alone. In the inclusive model, the ROC curve was above 60% of the possible AUC. That is, about 60% of the time, a randomly selected patient with breast cancer had a higher estimated risk than the risk for a randomly selected woman in whom breast cancer did not develop during the follow-up period. By contrast, a single dichotomous risk factor detected in 60% of case subjects and 40% of control subjects (odds ratio, 2.25; AUC, 60%) would discriminate about as well as our model by the AUC criterion. We saw similarly modest improvement using measures based on the change in estimated risk. Although our data suggest that the SNP-only model predicted risk slightly better than the Gail model, the nongenetic clinical variables are available at essentially no cost, whereas the costs of obtaining genetic information are likely to be substantial.
We have evaluated risk-prediction models as we would any other clinical test. Our results are presented in terms of absolute risks, which are easily translated into positive and negative predictive values in considering who will benefit from a clinical test, including a risk model.24-26 Our findings were based on a large number of case subjects and control subjects, drawn from four prospective cohorts and one population-based case–control study. Our inclusive model incorporated information on components of the Gail model and genotypes of newly established SNPs; this information is not often included together in the same study. Our analysis also had technical advantages over other risk-modeling efforts: we fitted the effects of age and cohort so as not to give the models credit for fit based on demographic factors; we were able to assess the effect of adding SNPs on estimated risk and on changes in risk category for case subjects and for a hypothetical population.
There were some unavoidable weaknesses of this study and its data sources, particularly in evaluating risk for clinical use. For example, we pooled data from four U.S. cohort studies and a case–control study from Poland; these studies had different designs and enrollment characteristics. We included only women of European ancestry in the empirical analysis, and we did not consider subtypes of breast cancer. As a group, the study participants are not representative of any specific population. Screening practice may vary within and among studies. Because of self-selection for participation in the studies, we expect that the average risks in the U.S. studies will be different from each other and from the U.S. average. Similarly, in a clinical context, we cannot expect the risk for an individual woman to be the same as the average risk for a population. Our estimates of the performance of the risk models may be slightly higher than can be expected in typical clinical settings because we report results with minor overfitting. Given the complexity of the model with 4 Gail model components and 10 SNPs, we chose to fit each factor without constraints but did not attempt to evaluate hundreds of interactions among the factors or with age and cohort. Our simple interaction models gave no suggestion of improved performance from interactions between genetic and Gail model components; more sophisticated modeling may improve the performance of these factors in predicting breast-cancer risk. Finally, comparisons of models are slightly unfavorable to the Gail model because we could not include the history of atypical hyperplasia and information on mammographic density. Although they are not routinely incorporated into the Gail model, these factors may improve the performance of future versions of the model.14
Our analysis indicates that the genetic variants we studied provide modest improvements in discrimination and prediction models, whether measured as the AUC, as a discrimination index, or as a change in position in broad bands of risk, such as might be used in clinical settings. We see little evidence of benefit from including genetic variants at the extremes of high and low risk, categories in which further stratification might be most valuable. This empirical demonstration of the potential benefit of adding SNP data to breast-cancer risk models based on individual data is generally consistent with theoretical predictions15,27 that use published estimates of effect.
Because statistics such as the AUC and integrated discrimination improvement do not provide a readily intuitive sense of the clinical usefulness of these models, we focused much of our discussion on the degree of incremental improvement associated with adding the genetic variants. As in diabetes28 and cardiovascular disease,29 the addition of the common SNPs added little to the predictive value of the clinical models. On the basis of theoretical models, Gail30 has shown that increases in the AUC similar to those observed here are not sufficiently large to improve meaningfully the identification of women who might benefit from tamoxifen prophylaxis or assignment of screening mammography.
Although the Gail model and SNP-inclusive models may help to identify groups of women who have an increased risk of breast cancer for trials of interventions, none of the models in our set of data accurately predicted the development of breast cancer. Our results indicate that the recent identification of common genetic variants does not herald the arrival of personalized prevention of breast cancer in most women. Even with the addition of these common variants, breast-cancer risk models are not yet able to identify women at reduced or elevated risk in a clinically useful way.
Supported in part by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics of the National Cancer Institute and by grants from the National Institutes of Health (N01WH22110, P01 CA87969, P01 CA53996, R01 CA49449, U01CA098233, and U01CA098710).
Disclosure forms provided by the authors are available with the full text of this article at NEJM.org.
We thank Prisca Fall-Keita and David Check for assistance in preparation of an earlier version of the manuscript and the late Dr. Eugenia Calle for her contributions in developing the American Cancer Society cohort.
Source Information
From the Division of Cancer Epidemiology and Genetics (S.W., P.H., M.G.-C., L.A.B., M.E.S., R. Pfeiffer, K.B.J., G.D.T., R.N.H., M.H.G., S.J.C.) and the Early Detection Research Group, Division of Cancer Prevention (C.D.B.), National Cancer Institute, Bethesda, MD; the Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle (R. Prentice, C.K.); the Department of Epidemiology, American Cancer Society, Atlanta (H.S.F., W.R.D., M.J.T.); the Institute for Health Research, Kaiser Permanente, Denver (H.S.F.); INSERM, Lyon, France (D.G.C.); the Departments of Epidemiology (D.G.C., S.E.H., P.K., D.J.H.) and Biostatistics (B.R.), Harvard School of Public Health; and Channing Laboratory, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School (S.E.H., B.R., D.J.H.) — all in Boston; the Department of Cancer Epidemiology and Prevention, Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw, Poland (J.L.); the Division of Medical Oncology and Hematology, Los Angeles Biomedical Research Institute at Harbor–UCLA Medical Center, Torrance, CA (R.C.); Ohio State University, Columbus (R.D.J.); and Information Management Services, Silver Spring, MD (D.W.B., P.H.).
Address reprint requests to Dr. Wacholder at the Division of Cancer Epidemiology and Genetics, National Cancer Institute, 6120 Executive Blvd., EPS 5050, MSC-7244, Bethesda, MD 20892, or at wacholds@mail.nih.gov.
- References
References
1
Screening for breast cancer: US. Preventive Services Task Force recommendation statement. Ann Intern Med 2009;151:716-726
Web of Science | Medline2
ACOG practice bulletin no103: hereditary breast and ovarian cancer syndrome. Obstet Gynecol 2009;113:957-966
Medline3
Manolio TA ,Brooks LD ,Collins FSA . A HapMap harvest of insights into the genetics of common disease. J Clin Invest 2008;118:1590-1605
CrossRef | Web of Science | Medline4
Gail MH ,Brinton LA ,Byar DP , et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst 1989;81:1879-1886
CrossRef | Web of Science | Medline5
Gail MH ,Benichou J . Validation studies on a model for breast cancer risk. J Natl Cancer Inst 1994;86:573-575[Erratum, J Natl Cancer Inst 1994;86:803.]
CrossRef | Web of Science | Medline6
Gail MH ,Costantino JP ,Bryant J , et al. Weighing the risks and benefits of tamoxifen treatment for preventing breast cancer. J Natl Cancer Inst 1999;91:1829-1846[Erratum, J Natl Cancer Inst 2000;92:275.]
CrossRef | Web of Science | Medline7
Costantino JP ,Gail MH ,Pee D , et al. Validation studies for models projecting the risk of invasive and total breast cancer incidence. J Natl Cancer Inst 1999;91:1541-1548
CrossRef | Web of Science | Medline8
Thomas G ,Jacobs KB ,Kraft P , et al. A multistage genome-wide association study in breast cancer identifies two new risk alleles at 1p11.2 and 14q24.1 (RAD51L1). Nat Genet 2009;41:579-584
CrossRef | Web of Science | Medline9
Langer RD ,White E ,Lewis CE ,Kotchen JM ,Hendrix SL ,Trevisan M . The Women's Health Initiative Observational Study: baseline characteristics of participants and reliability of baseline measures. Ann Epidemiol 2003;13:Suppl:S107-S121
CrossRef | Web of Science | Medline10
Calle EE ,Rodriguez C ,Jacobs EJ , et al. The American Cancer Society Cancer Prevention Study II Nutrition Cohort: rationale, study design, and baseline characteristics. Cancer 2002;94:2490-2501
CrossRef | Web of Science | Medline11
Hayes RB ,Reding D ,Kopp W , et al. Etiologic and early marker studies in the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. Control Clin Trials 2000;21:Suppl:349S-355S
CrossRef | Medline12
Willett WC ,Stampfer MJ ,Colditz GA ,Rosner BA ,Hennekens CH ,Speizer FE . Moderate alcohol consumption and the risk of breast cancer. N Engl J Med 1987;316:1174-1180
Full Text | Web of Science | Medline13
Garcia-Closas M ,Egan KM ,Newcomb PA , et al. Polymorphisms in DNA double-strand break repair genes and risk of breast cancer: two population-based studies in USA and Poland, and meta-analyses. Hum Genet 2006;119:376-388
CrossRef | Web of Science | Medline14
Chen JB ,Pee D ,Ayyagari R , et al. Projecting absolute invasive breast cancer risk in white women with a model that includes mammographic density. J Natl Cancer Inst 2006;98:1215-1226
CrossRef | Web of Science | Medline15
Pharoah PDP ,Antoniou AC ,Easton DF ,Ponder BAJ . Polygenes, risk prediction, and targeted prevention of breast cancer. N Engl J Med 2008;358:2796-2803
Full Text | Web of Science | Medline16
Ahmed S ,Thomas G ,Ghoussaini M , et al. Newly discovered breast cancer susceptibility loci on 3p24 and 17q23.2. Nat Genet 2009;41:585-590
CrossRef | Web of Science | Medline17
Easton DF ,Pooley KA ,Dunning AM , et al. Genome-wide association study identifies novel breast cancer susceptibility loci. Nature 2007;447:1087-1093
CrossRef | Web of Science | Medline18
Hunter DJ ,Kraft P ,Jacobs KB , et al. A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat Genet 2007;39:870-874
CrossRef | Web of Science | Medline19
Tukey JW . One degree of freedom for non-additivity. Biometrics 1949;5:232-242
CrossRef | Web of Science20
Chatterjee N ,Kalaylioglu Z ,Moslehi R ,Peters U ,Wacholder S . Powerful multilocus tests of genetic association in the presence of gene-gene and gene-environment interactions. Am J Hum Genet 2006;79:1002-1016
CrossRef | Web of Science | Medline21
Surveillance Epidemiology and End Results (SEER) program. SEER 17 incidence and mortality, 2000-2003, with Kaposi sarcoma and mesothelioma. Bethesda, MD: National Cancer Institute, 2007. (Accessed February 11, 2010, at http://www.seer.cancer.gov/seerstat.)
22
Number of new cancer cases by site and age groups. The Maria Skłodowska-Curie Memorial Cancer Center, Department of Epidemiology and Cancer Prevention. (Accessed February 11, 2010, at http://epid.coi.waw.pl/krn/english/liczba_zach_rozp/default.asp.)
23
Pencina MJ ,D'Agostino RB ,D'Agostino RB Jr ,Vasan RS . Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 2008;27:157-72, 207
CrossRef | Web of Science | Medline24
Schiffman M ,Castle PE ,Jeronimo J ,Rodriguez AC ,Wacholder S . Human papillomavirus and cervical cancer. Lancet 2007;370:890-907
CrossRef | Web of Science | Medline25
Kraft P ,Wacholder S ,Cornelis MC , et al. Beyond odds ratios -- communicating disease risk based on genetic profiles. Nat Rev Genet 2009;10:264-269
CrossRef | Web of Science | Medline26
Katki HA ,Wacholder S ,Solomon D ,Castle PE ,Schiffman M . Risk estimation for the next generation of prevention programmes for cervical cancer. Lancet Oncol 2009;10:1022-1023
CrossRef | Web of Science | Medline27
Gail MH . Discriminatory accuracy from single-nucleotide polymorphisms in models to predict breast cancer risk. J Natl Cancer Inst 2008;100:1037-1041
CrossRef | Web of Science | Medline28
Lyssenko V ,Jonsson A ,Almgren P , et al. Clinical risk factors, DNA variants, and the development of type 2 diabetes. N Engl J Med 2008;359:2220-2232
Full Text | Web of Science | Medline29
Kathiresan S ,Melander O ,Anevski D , et al. Polymorphisms associated with cholesterol and risk of cardiovascular events. N Engl J Med 2008;358:1240-1249
Full Text | Web of Science | Medline30
Gail MH . Value of adding single-nucleotide polymorphism genotypes to a breast cancer risk model. J Natl Cancer Inst 2009;101:959-963
CrossRef | Web of Science | Medline
- Citing Articles (72)
Citing Articles
1
B. Mukherjee, J. Ahn, S. B. Gruber, N. Chatterjee. (2012) Testing Gene-Environment Interaction in Large-Scale Case-Control Association Studies: Possible Choices and Comparisons. American Journal of Epidemiology 175:3, 177-190
CrossRef2
S Harlid, M I L Ivarsson, S Butt, E Grzybowska, J E Eyfjörd, P Lenner, A Försti, K Hemminki, J Manjer, J Dillner, J Carlson. (2012) Combined effect of low-penetrant SNPs on breast cancer risk. British Journal of Cancer 106:2, 389-396
CrossRef3
Mattias Johansson, Benny Holmström, Sally R. Hinchliffe, Anders Bergh, Ulf-Håkan Stenman, Göran Hallmans, Fredrik Wiklund, Pär Stattin. (2012) Combining 33 genetic variants with prostate-specific antigen for prediction of prostate cancer: Longitudinal study. International Journal of Cancer 130:1, 129-137
CrossRef4
Mladen Tzvetkov, Nicolas von Ahsen. (2012) Pharmacogenetic screening for drug therapy. Pathology1
CrossRef5
Aiko Sueta, Hidemi Ito, Takakazu Kawase, Kaoru Hirose, Satoyo Hosono, Yasushi Yatabe, Kazuo Tajima, Hideo Tanaka, Hiroji Iwata, Hirotaka Iwase, Keitaro Matsuo. (2011) A genetic risk predictor for breast cancer using a combination of low-penetrance polymorphisms in a Japanese population. Breast Cancer Research and Treatment
CrossRef6
Elizabeth A. Camp, Stanley J. Robboy, Elizabeth E. Hatch, Linda T. Titus-Ernstoff, Arthur L. Herbst, William Strohsnitter, Raymond H. Kaufman, Ervin Adam. (2011) Mammography Screening Behaviors of Women Exposed Prenatally to Diethylstilbestrol. Journal of Women's Health111208112224002
CrossRef7
Matthew P. Banegas, Mitchell H. Gail, Andrea LaCroix, Beti Thompson, Maria Elena Martinez, Jean Wactawski-Wende, Esther M. John, F. Allan Hubbell, Shagufta Yasmeen, Hormuzd A. Katki. (2011) Evaluating breast cancer risk projections for Hispanic women. Breast Cancer Research and Treatment
CrossRef8
Andrew J. Vickers, Hans Lilja. (2011) We Need a Better Marker for Prostate Cancer. How About Renaming PSA?. Urology
CrossRef9
Yi Su, Li-Juan Chen, Jian-Rong He, Xue-Jiao Yuan, Yu-Ling Cen, Feng-Xi Su, Lu-Ying Tang, Ai-Hua Zhang, Wei-Qing Chen, Ying Lin, Shen-Ming Wang, Ze-Fang Ren. (2011) Urinary rubidium in breast cancers. Clinica Chimica Acta 412:23-24, 2305-2309
CrossRef10
Kathleen F. Kerr, Margaret S. Pepe. (2011) Joint Modeling, Covariate Adjustment, and Interaction. Epidemiology 22:6, 805-812
CrossRef11
Nobuyasu Yoshimoto, Takeshi Nishiyama, Tatsuya Toyama, Satoru Takahashi, Norio Shiraki, Hiroshi Sugiura, Yumi Endo, Mai Iwasa, Yoshitaka Fujii, Hiroko Yamashita. (2011) Genetic and environmental predictors, endogenous hormones and growth factors, and risk of estrogen receptor-positive breast cancer in Japanese women. Cancer Science 102:11, 2065-2072
CrossRef12
Michael L. Maitland, Richard L. Schilsky. (2011) Clinical trials in the era of personalized oncology. CA: A Cancer Journal for Clinicians 61:6, 365-381
CrossRef13
Catherine Meads, Ikhlaaq Ahmed, Richard D. Riley. (2011) A systematic review of breast cancer incidence risk prediction models with meta-analysis of their performance. Breast Cancer Research and Treatment
CrossRef14
L. Jostins, J. C. Barrett. (2011) Genetic risk prediction in complex disease. Human Molecular Genetics 20:R2, R182-R188
CrossRef15
María Soler Artigas, Daan W Loth, Louise V Wain, Sina A Gharib, Ma'en Obeidat, Wenbo Tang, Guangju Zhai, Jing Hua Zhao, Albert Vernon Smith, Jennifer E Huffman, Eva Albrecht, Catherine M Jackson, David M Evans, Gemma Cadby, Myriam Fornage, Ani Manichaikul, Lorna M Lopez, Toby Johnson, Melinda C Aldrich, Thor Aspelund, Inês Barroso, Harry Campbell, Patricia A Cassano, David J Couper, Gudny Eiriksdottir, Nora Franceschini, Melissa Garcia, Christian Gieger, Gauti Kjartan Gislason, Ivica Grkovic, Christopher J Hammond, Dana B Hancock, Tamara B Harris, Adaikalavan Ramasamy, Susan R Heckbert, Markku Heliövaara, Georg Homuth, Pirro G Hysi, Alan L James, Stipan Jankovic, Bonnie R Joubert, Stefan Karrasch, Norman Klopp, Beate Koch, Stephen B Kritchevsky, Lenore J Launer, Yongmei Liu, Laura R Loehr, Kurt Lohman, Ruth J F Loos, Thomas Lumley, Khalid A Al Balushi, Wei Q Ang, R Graham Barr, John Beilby, John D Blakey, Mladen Boban, Vesna Boraska, Jonas Brisman, John R Britton, Guy G Brusselle, Cyrus Cooper, Ivan Curjuric, Santosh Dahgam, Ian J Deary, Shah Ebrahim, Mark Eijgelsheim, Clyde Francks, Darya Gaysina, Raquel Granell, Xiangjun Gu, John L Hankinson, Rebecca Hardy, Sarah E Harris, John Henderson, Amanda Henry, Aroon D Hingorani, Albert Hofman, Patrick G Holt, Jennie Hui, Michael L Hunter, Medea Imboden, Karen A Jameson, Shona M Kerr, Ivana Kolcic, Florian Kronenberg, Jason Z Liu, Jonathan Marchini, Tricia McKeever, Andrew D Morris, Anna-Carin Olin, David J Porteous, Dirkje S Postma, Stephen S Rich, Susan M Ring, Fernando Rivadeneira, Thierry Rochat, Avan Aihie Sayer, Ian Sayers, Peter D Sly, George Davey Smith, Akshay Sood, John M Starr, André G Uitterlinden, Judith M Vonk, S Goya Wannamethee, Peter H Whincup, Cisca Wijmenga, O Dale Williams, Andrew Wong, Massimo Mangino, Kristin D Marciante, Wendy L McArdle, Bernd Meibohm, Alanna C Morrison, Kari E North, Ernst Omenaas, Lyle J Palmer, Kirsi H Pietiläinen, Isabelle Pin, Ozren Pola[sbreve]ek, Anneli Pouta, Bruce M Psaty, Anna-Liisa Hartikainen, Taina Rantanen, Samuli Ripatti, Jerome I Rotter, Igor Rudan, Alicja R Rudnicka, Holger Schulz, So-Youn Shin, Tim D Spector, Ida Surakka, Veronique Vitart, Henry Völzke, Nicholas J Wareham, Nicole M Warrington, H-Erich Wichmann, Sarah H Wild, Jemma B Wilk, Matthias Wjst, Alan F Wright, Lina Zgaga, Tatijana Zemunik, Craig E Pennell, Fredrik Nyberg, Diana Kuh, John W Holloway, H Marike Boezen, Debbie A Lawlor, Richard W Morris, Nicole Probst-Hensch, Jaakko Kaprio, James F Wilson, Caroline Hayward, Mika Kähönen, Joachim Heinrich, Arthur W Musk, Deborah L Jarvis, Sven Gläser, Marjo-Riitta Järvelin, Bruno H Ch Stricker, Paul Elliott, George T O'Connor, David P Strachan, Stephanie J London, Ian P Hall, Vilmundur Gudnason, Martin D Tobin. (2011) Genome-wide association and large-scale follow up identifies 16 new loci influencing lung function. Nature Genetics 43:11, 1082-1090
CrossRef16
Petra Seibold, Rebecca Hein, Peter Schmezer, Per Hall, Jianjun Liu, Norbert Dahmen, Dieter Flesch-Janys, Odilia Popanda, Jenny Chang-Claude. (2011) Polymorphisms in oxidative stress-related genes and postmenopausal breast cancer risk. International Journal of Cancer 129:6, 1467-1476
CrossRef17
Dimosthenis E Ziogas, Dimitrios H Roukos. (2011) Genome diagnostics: next-generation sequencing, new genome-wide association studies and clinical challenges. Expert Review of Molecular Diagnostics 11:7, 663-666
CrossRef18
I. Bozovic-Spasojevic, E. Azambuja, Worta McCaskill-Stevens, P. Dinh, F. Cardoso. (2011) Chemoprevention for breast cancer. Cancer Treatment Reviews
CrossRef19
C. P. Kratz, M. H. Greene, G. Bratslavsky, J. Shi. (2011) A stratified genetic risk assessment for testicular cancer. International Journal of Andrology 34:4pt2, e98-e102
CrossRef20
Charles C. Chung, Stephen J. Chanock. (2011) Current status of genome-wide association studies in cancer. Human Genetics 130:1, 59-78
CrossRef21
S. W. Fletcher. (2011) Breast Cancer Screening: A 35-Year Perspective. Epidemiologic Reviews 33:1, 165-175
CrossRef22
Nora Pashayan, Paul Pharoah. (2011) Translating genomics into improved population screening: hype or hope?. Human Genetics 130:1, 19-21
CrossRef23
Celine M. Vachon. (2011) Genome-Wide Association Studies Go Green: Novel and Cost-Effective Opportunities for Identifying Genetic Associations. Mayo Clinic Proceedings 86:7, 597-599
CrossRef24
Odysseas Zoras, Christos S Katsios, Dimitrios H Roukos. (2011) Personalizing prophylactic surgery in cancer: current practice and future perspectives from new genome-wide association studies. Expert Review of Anticancer Therapy 11:6, 813-816
CrossRef25
E. Comen, L. Balistreri, M. Gönen, A. Dutra-Clarke, M. Fazio, J. Vijai, Z. Stadler, N. Kauff, T. Kirchhoff, C. Hudis, K. Offit, M. Robson. (2011) Discriminatory accuracy and potential clinical utility of genomic profiling for breast cancer risk in BRCA-negative women. Breast Cancer Research and Treatment 127:2, 479-487
CrossRef26
N Pashayan, S W Duffy, S Chowdhury, T Dent, H Burton, D E Neal, D F Easton, R Eeles, P Pharoah. (2011) Polygenic susceptibility to prostate and breast cancer: implications for personalised screening. British Journal of Cancer 104:10, 1656-1663
CrossRef27
Mitchell H. Gail. (2011) Personalized estimates of breast cancer risk in clinical practice and public health. Statistics in Medicine 30:10, 1090-1104
CrossRef28
Edward A. Ruiz-Narváez. (2011) What is a functional locus? Understanding the genetic basis of complex phenotypic traits. Medical Hypotheses 76:5, 638-642
CrossRef29
Dimitrios H Roukos. (2011) Networks medicine: from reductionism to evidence of complex dynamic biomolecular interactions. Pharmacogenomics 12:5, 695-698
CrossRef30
W Chua, K A Charles, V E Baracos, S J Clarke. (2011) Neutrophil/lymphocyte ratio predicts chemotherapy outcomes in patients with advanced colorectal cancer. British Journal of Cancer 104:8, 1288-1295
CrossRef31
Andrew Y. Shuen, William D. Foulkes. (2011) Inherited Mutations in Breast Cancer Genes—Risk and Response. Journal of Mammary Gland Biology and Neoplasia 16:1, 3-15
CrossRef32
D H Roukos. (2011) Trastuzumab and beyond: sequencing cancer genomes and predicting molecular networks. The Pharmacogenomics Journal 11:2, 81-92
CrossRef33
Christos Katsios, Dimitrios H Roukos. (2011) Missing heritability, next-generation genome-wide association studies and primary cancer prevention: an Atlantean illusion?. Future Oncology 7:4, 477-480
CrossRef34
Charalabos Batsis. (2011) Two-Stage Breast Cancer Screening in the Developing World. World Journal of Surgery 35:3, 698-699
CrossRef35
Dimosthenis E Ziogas, Christos S Katsios, Dimitrios H Roukos. (2011) Preventing familial breast and ovarian cancer: major research advances with little implication. Women's Health 7:2, 135-138
CrossRef36
J. P. Evans, E. M. Meslin, T. M. Marteau, T. Caulfield. (2011) Deflating the Genomic Bubble. Science 331:6019, 861-862
CrossRef37
Fang Chen, Daniel O Stram, Loïc Le Marchand, Kristine R Monroe, Laurence N Kolonel, Brian E Henderson, Christopher A Haiman. (2011) Caution in generalizing known genetic risk markers for breast cancer across all ethnic/racial populations. European Journal of Human Genetics 19:2, 243-245
CrossRef38
Peter T. Simpson, Ana-Cristina Vargas, Fares Al-Ejeh, Kum Kum Khanna, Georgia Chenevix-Trench, Sunil R. Lakhani. (2011) Application of molecular findings to the diagnosis and management of breast disease: recent advances and challenges. Human Pathology 42:2, 153-165
CrossRef39
George C Zografos, Dimitrios H Roukos. (2011) Innovative biomarker development for personalized medicine in breast cancer care. Biomarkers in Medicine 5:1, 73-78
CrossRef40
A. Murray, C. E. Bennett, J. R. B. Perry, M. N. Weedon, R. Consortium, P. A. Jacobs, D. H. Morris, N. Orr, M. J. Schoemaker, M. Jones, A. Ashworth, A. J. Swerdlow. (2011) Common genetic variants are significant risk factors for early menopause: results from the Breakthrough Generations Study. Human Molecular Genetics 20:1, 186-192
CrossRef41
Ying Huang, Dennis G Ballinger, James Y Dai, Ulrike Peters, David A Hinds, David R Cox, Erica Beilharz, Rowan T Chlebowski, Jacques E Rossouw, Anne McTiernan, Thomas Rohan, Ross L Prentice. (2011) Genetic variants in the MRPS30 region and postmenopausal breast cancer risk. Genome Medicine 3:6, 42
CrossRef42
Mark M. Pomerantz, Matthew L. Freedman. (2011) The Genetics of Cancer Risk. The Cancer Journal 17:6, 416-422
CrossRef43
Virginia Kaklamani, Nengjun Yi, Maureen Sadim, Kalliopi Siziopikou, Kui Zhang, Yanfei Xu, Sarah Tofilon, Surbhi Agarwal, Boris Pasche, Christos Mantzoros. (2011) The role of the fat mass and obesity associated gene (FTO) in breast cancer risk. BMC Medical Genetics 12:1, 52
CrossRef44
Keyue Ding, Kent R Bailey, Iftikhar J Kullo. (2011) Genotype-informed estimation of risk of coronary heart disease based on genome-wide association data linked to the electronic medical record. BMC Cardiovascular Disorders 11:1, 66
CrossRef45
R. L. Milne, A. C. Antoniou. (2011) Genetic modifiers of cancer risk for BRCA1 and BRCA2 mutation carriers. Annals of Oncology 22:Supplement 1, i11-i17
CrossRef46
Dimitrios H Roukos. (2011) Innovation versus evidence: to trust direct-to-consumer personal genomic tests?. Expert Review of Molecular Diagnostics 11:1, 1-4
CrossRef47
S.B. Sams. (2011) Performance of Common Genetic Variants in Breast-Cancer Risk Models. Yearbook of Pathology and Laboratory Medicine 2011, 34-35
CrossRef48
J. S. Witte. (2010) Personalized Prostate Cancer Screening: Improving PSA Tests with Genomic Information. Science Translational Medicine 2:62, 62ps55-62ps55
CrossRef49
Noralane M. Lindor, Kiley J. Johnson, Hayden Harvey, V. Shane Pankratz, Susan M. Domchek, Katherine Hunt, Marcia Wilson, M. Cathie Smith, Fergus Couch. (2010) Predicting BRCA1 and BRCA2 gene mutation carriers: comparison of PENN II model to previous study. Familial Cancer 9:4, 495-502
CrossRef50
THS Dent. (2010) Predicting the risk of coronary heart disease. II: The role of novel molecular biomarkers and genetics in estimating risk, and the future of risk prediction. Atherosclerosis 213:2, 352-362
CrossRef51
David Houle, Diddahally R. Govindaraju, Stig Omholt. (2010) Phenomics: the next challenge. Nature Reviews Genetics 11:12, 855-866
CrossRef52
N. R. Cook, N. P. Paynter. (2010) Genetics and Breast Cancer Risk Prediction--Are We There Yet?. JNCI Journal of the National Cancer Institute 102:21, 1605-1606
CrossRef53
M. E. Mealiffe, R. P. Stokowski, B. K. Rhees, R. L. Prentice, M. Pettinger, D. A. Hinds. (2010) Assessment of Clinical Validity of a Breast Cancer Risk Model Combining Genetic and Clinical Information. JNCI Journal of the National Cancer Institute 102:21, 1618-1627
CrossRef54
Ian C. Bennett. (2010) Outcomes of Multimodality Breast Screening for Women at Increased Risk of Familial Breast Cancer: Reply to Letter. World Journal of Surgery 34:11, 2788-2789
CrossRef55
Christos Katsios, Dimitrios H. Roukos. (2010) Ten Years after Human Genome Sequencing: Is Personalized Breast Screening and Prevention Reality?. World Journal of Surgery 34:11, 2786-2787
CrossRef56
M. Beekman, C. Nederstigt, H. E. D. Suchiman, D. Kremer, R. van der Breggen, N. Lakenberg, W. G. Alemayehu, A. J. M. de Craen, R. G. J. Westendorp, D. I. Boomsma, E. J. C. de Geus, J. J. Houwing-Duistermaat, B. T. Heijmans, P. E. Slagboom. (2010) Genome-wide association study (GWAS)-identified disease risk alleles do not compromise human longevity. Proceedings of the National Academy of Sciences 107:42, 18046-18049
CrossRef57
Christos Katsios, Odysseas Zoras, Dimitrios H Roukos. (2010) Cancer genome sequencing and potential application in oncology. Future Oncology 6:10, 1527-1531
CrossRef58
Zsofia K. Stadler, Joseph Vijai, Peter Thom, Tomas Kirchhoff, Nichole A.L. Hansen, Noah D. Kauff, Mark Robson, Kenneth Offit. (2010) Genome-wide Association Studies of Cancer Predisposition. Hematology/Oncology Clinics of North America 24:5, 973-996
CrossRef59
Hubert E. Blum. (2010) Individualized medicine 2010. Journal of Cellular and Molecular Medicine 14:9, 2257-2263
CrossRef60
Barbara K. Dunn, Peter Greenwald. (2010) Cancer Prevention II: Introduction. Seminars in Oncology 37:4, 321-326
CrossRef61
John W. Holloway, Syed H. Arshad, Stephen T. Holgate. (2010) Using genetics to predict the natural history of asthma?. Journal of Allergy and Clinical Immunology 126:2, 200-209
CrossRef62
Eva Szabo. (2010) Phase II Cancer Prevention Clinical Trials. Seminars in Oncology 37:4, 359-366
CrossRef63
Kathleen Calzone, Daniel Wattendorf, Barbara K. Dunn. (2010) The Application of Genetics and Genomics to Cancer Prevention. Seminars in Oncology 37:4, 407-418
CrossRef64
Banu Arun, Barbara K. Dunn, Leslie G. Ford, Anne Ryan. (2010) Breast Cancer Prevention Trials: Large and Small Trials. Seminars in Oncology 37:4, 367-383
CrossRef65
Dimitrios H Roukos. (2010) Next-generation, genome sequencing-based biomarkers: concerns and challenges for medical practice. Biomarkers in Medicine 4:4, 583-586
CrossRef66
Christos Katsios, Dimitrios H Roukos. (2010) Individual genomes and personalized medicine: life diversity and complexity. Personalized Medicine 7:4, 347-350
CrossRef67
Ju-Hyun Park, Sholom Wacholder, Mitchell H Gail, Ulrike Peters, Kevin B Jacobs, Stephen J Chanock, Nilanjan Chatterjee. (2010) Estimation of effect size distribution from genome-wide association studies and implications for future discoveries. Nature Genetics 42:7, 570-575
CrossRef68
D. J. Hunter, S. J. Chanock. (2010) Genome-Wide Association Studies and "The Art of the Soluble". JNCI Journal of the National Cancer Institute 102:12, 836-837
CrossRef69
J. P. A. Ioannidis, P. Castaldi, E. Evangelou. (2010) A Compendium of Genome-Wide Associations for Cancer: Critical Synopsis and Reappraisal. JNCI Journal of the National Cancer Institute 102:12, 846-858
CrossRef70
Steven A Narod. (2010) Genes, the environment, and breast cancer. The Lancet 375:9732, 2123-2124
CrossRef71
M. H. Gail, P. L. Mai. (2010) Comparing Breast Cancer Risk Assessment Models. JNCI Journal of the National Cancer Institute 102:10, 665-668
CrossRef72
Devilee, Peter, Rookus, Matti A., . (2010) A Tiny Step Closer to Personalized Risk Prediction for Breast Cancer. New England Journal of Medicine 362:11, 1043-1045
Full Text
