To estimate the likely number and predictive strength of cancer-associated single nucleotide polymorphisms (SNPs) that are yet to be discovered for seven common cancers.
From the statistical power of published genome-wide association studies, we estimated the number of undetected susceptibility loci and the distribution of effect sizes for all cancers. Assuming a log-normal model for risks and multiplicative relative risks for SNPs, family history (FH), and known risk factors, we estimated the area under the receiver operating characteristic curve (AUC) and the proportion of patients with risks above risk thresholds for screening. From additional prevalence data, we estimated the positive predictive value and the ratio of non–patient cases to patient cases (false-positive ratio) for various risk thresholds.
Age-specific discriminatory accuracy (AUC) for models including FH and foreseeable SNPs ranged from 0.575 for ovarian cancer to 0.694 for prostate cancer. The proportions of patients in the highest decile of population risk ranged from 16.2% for ovarian cancer to 29.4% for prostate cancer. The corresponding false-positive ratios were 241 for colorectal cancer, 610 for ovarian cancer, and 138 or 280 for breast cancer in women age 50 to 54 or 40 to 44 years, respectively.
Foreseeable common SNP discoveries may not permit identification of small subsets of patients that contain most cancers. Usefulness of screening could be diminished by many false positives. Additional strong risk factors are needed to improve risk discrimination.
One purpose for seeking common alleles that are associated with disease is to use them to improve models for projecting individualized disease risk. Two genome-wide association studies and a study of candidate genes recently identified seven common single-nucleotide polymorphisms (SNPs) that were associated with breast cancer risk in independent samples. These seven SNPs were located in FGFR2, TNRC9, MAP3K1, LSP1, CASP8, chromosomal region 8q, and chromosomal region 2q35. I used estimates of relative risks and allele frequencies from these studies to estimate how much these SNPs could improve discriminatory accuracy measured as the area under the receiver operating characteristic curve (AUC). A model with these seven SNPs (AUC = 0.574) and a hypothetical model with 14 such SNPs (AUC = 0.604) have less discriminatory accuracy than a model, the National Cancer Institute's Breast Cancer Risk Assessment Tool (BCRAT), which is based on ages at menarche and at first live birth, family history of breast cancer, and history of breast biopsy examinations (AUC = 0.607). Adding the seven SNPs to BCRAT improved discriminatory accuracy to an AUC of 0.632, which was, however, less than the improvement from adding mammographic density. Thus, these seven common alleles provide less discriminatory accuracy than BCRAT but have the potential to improve the discriminatory accuracy of BCRAT modestly. Experience to date and quantitative arguments indicate that a huge increase in the numbers of case patients with breast cancer and control subjects would be required in genome-wide association studies to find enough SNPs to achieve high discriminatory accuracy.
One purpose for seeking common alleles that are associated with disease is to use them to improve models for projecting individualized disease risk. Two genome-wide association studies and a study of candidate genes recently identified seven common single-nucleotide polymorphisms (SNPs) that were associated with breast cancer risk in independent samples. These seven SNPs were located in FGFR2, TNRC9 (now known as TOX3), MAP3K1, LSP1, CASP8, chromosomal region 8q, and chromosomal region 2q35. I used estimates of relative risks and allele frequencies from these studies to estimate how much these SNPs could improve discriminatory accuracy measured as the area under the receiver operating characteristic curve (AUC). A model with these seven SNPs (AUC = 0.574) and a hypothetical model with 14 such SNPs (AUC = 0.604) have less discriminatory accuracy than a model, the National Cancer Institute’s Breast Cancer Risk Assessment Tool (BCRAT), that is based on ages at menarche and at first live birth, family history of breast cancer, and history of breast biopsy examinations (AUC = 0.607). Adding the seven SNPs to BCRAT improved discriminatory accuracy to an AUC of 0.632, which was, however, less than the improvement from adding mammographic density. Thus, these seven common alleles provide less discriminatory accuracy than BCRAT but have the potential to improve the discriminatory accuracy of BCRAT modestly. Experience to date and quantitative arguments indicate that a huge increase in the numbers of case patients with breast cancer and control subjects would be required in genome-wide association studies to find enough SNPs to achieve high discriminatory accuracy.
Several single nucleotide polymorphisms (SNPs) are associated with an increased risk of breast cancer. The clinical utility of genotyping individuals at these loci is not known. Subjects were 519 unaffected women without BRCA mutations. Gail, Claus, and IBIS models were used to estimate absolute breast cancer risks. Subjects were then genotyped at 15 independent risk loci. Published per-allele and genotype-specific odds ratios were used to calculate the composite cumulative genomic risk (CGR) for each subject. Affected age- and ethnicity-matched BRCA mutation-negative women were also genotyped as a comparison group for the calculation of discriminatory accuracy. The CGR was used to adjust absolute breast cancer risks calculated by Gail, Claus and IBIS models to determine the proportion of subjects whose recommendations for chemoprevention or MRI screening might be altered (reclassified) by such adjustment. Mean lifetime breast cancer risks calculated using the Gail, Claus, and IBIS models were 19.4, 13.0, and 17.7%, respectively. CGR did not correlate with breast cancer risk as calculated using any model. CGR was significantly higher in affected women (mean 3.35 vs. 3.12, P = 0.009). The discriminatory accuracy of the CGR alone was 0.55 (SE 0.019; P = 0.006). CGR adjustment of model-derived absolute risk estimates would have altered clinical recommendations for chemoprevention in 11–19% of subjects and for MRI screening in 8–32%. CGR has limited discriminatory accuracy. However, the use of a genomic risk term to adjust model-derived estimates has the potential to alter individual recommendations. These observations warrant investigation to evaluate the calibration of adjusted risk estimates.
Breast cancer risk; Single nucleotide polymorphism; Risk prediction model
Introduction: Accurate individualised breast cancer risk assessment is essential to provide risk–benefit analysis prior to initiating interventions designed to lower breast cancer risk. Several mathematical models for the estimation of individual breast cancer risk have been proposed. However, no single model integrates family history, hormonal factors, and benign breast disease in a comprehensive fashion. A new model by Tyrer and Cuzick has addressed these deficiencies. Therefore, this study has assessed the goodness of fit and discriminatory value of the Tyrer–Cuzick model against established models namely Gail, Claus, and Ford.
Methods: The goodness of fit and discriminatory accuracy of the models was assessed using data from 1933 women attending the Family History Evaluation and Screening Programme, of whom 52 developed cancer. All models were applied to these women over a mean follow up of 5.27 years to estimate risk of breast cancer.
Results: The ratios (95% confidence intervals) of expected to observed numbers of breast cancers were 0.48 (0.37 to 0.64) for Gail, 0.56 (0.43 to 0.75) for Claus, 0.49 (0.37 to 0.65) for Ford, and 0.81 (0.62 to 1.08) for Tyrer–Cuzick. The accuracy of the models for individual cases was evaluated using ROC curves. These showed that the area under the curve was 0.735 for Gail, 0.716 for Claus, 0.737 for Ford, and 0.762 for Tyrer–Cuzick.
Conclusion: The Tyrer–Cuzick model is the most consistently accurate model for prediction of breast cancer. The Gail, Claus, and Ford models all significantly underestimate risk, although the accuracy of the Claus model may be improved by adjustments for other risk factors.
In this review, we examine the evidence for mammographic density as an independent risk factor for breast cancer, describe the risk prediction models that have incorporated density, and discuss the current and future implications of using mammographic density in clinical practice. Mammographic density is a consistent and strong risk factor for breast cancer in several populations and across age at mammogram. Recently, this risk factor has been added to existing breast cancer risk prediction models, increasing the discriminatory accuracy with its inclusion, albeit slightly. With validation, these models may replace the existing Gail model for clinical risk assessment. However, absolute risk estimates resulting from these improved models are still limited in their ability to characterize an individual's probability of developing cancer. Promising new measures of mammographic density, including volumetric density, which can be standardized using full-field digital mammography, will likely result in a stronger risk factor and improve accuracy of risk prediction models.
It is uncertain whether evidence supports routinely estimating a postmenopausal woman's risk of breast cancer and intervening to reduce risk.
We systematically reviewed prospective studies about models and sex hormone levels to assess breast cancer risk and used meta-analysis with random effects models to summarize the predictive accuracy of breast density. We also reviewed prospective studies of the effects of exercise, weight management, healthy diet, moderate alcohol consumption, and fruit and vegetable intake on breast cancer risk, and used random effects models for a meta-analyses of tamoxifen and raloxifene for primary prevention of breast cancer. All studies reviewed were published before June 2008, and all statistical tests were two-sided.
Risk models that are based on demographic characteristics and medical history had modest discriminatory accuracy for estimating breast cancer risk (c-statistics range = 0.58–0.63). Breast density was strongly associated with breast cancer (relative risk [RR] = 4.03, 95% confidence interval [CI] = 3.10 to 5.26, for Breast Imaging Reporting and Data System category IV vs category I; RR = 4.20, 95% CI = 3.61 to 4.89, for >75% vs <5% of dense area), and adding breast density to models improved discriminatory accuracy (c-statistics range = 0.63–0.66). Estradiol was also associated with breast cancer (RR range = 2.0–2.9, comparing the highest vs lowest quintile of estradiol, P < .01). Most studies found that exercise, weight reduction, low-fat diet, and reduced alcohol intake were associated with a decreased risk of breast cancer. Tamoxifen and raloxifene reduced the risk of estrogen receptor–positive invasive breast cancer and invasive breast cancer overall.
Evidence from this study supports screening for breast cancer risk in all postmenopausal women by use of risk factors and breast density and considering chemoprevention for those found to be at high risk. Several lifestyle changes with the potential to prevent breast cancer should be recommended regardless of risk.
There is increasing interest in adding common genetic variants
identified through genome wide association studies (GWAS) to breast cancer
risk prediction models. First results from such models showed modest
benefits in terms of risk discrimination. Heterogeneity of breast cancer as
defined by hormone-receptor status has not been considered in this context.
In this study we investigated the predictive capacity of 32 GWAS-detected
common variants for breast cancer risk, alone and in combination with
classical risk factors, and for tumors with different hormone receptor
Material and Methods
Within the Breast and Prostate Cancer Cohort Consortium (BPC3), we
analyzed 6009 invasive breast cancer cases and 7827 matched controls of
European ancestry, with data on classical breast cancer risk factors and 32
common gene variants identified through GWAS. Discriminatory ability with
respect to breast cancer of specific hormone receptor-status was assessed
with the age- and cohort-adjusted concordance statistic
(AUROCa). Absolute risk scores were
calculated with external reference data. Integrated discrimination
improvement (IDI) was used to measure improvements in risk prediction.
We found a small but steady increase in discriminatory ability with
increasing numbers of genetic variants included in the model (difference in
AUROCa going from 2.7 to 4%). Discriminatory ability
for all models varied strongly by hormone receptor status
Discussion and Conclusion
Adding information on common polymorphisms provides small but
statistically significant improvements in the quality of breast cancer risk
prediction models. We consistently observed better performance for receptor
positive cases, but the gain in discriminatory quality is not sufficient for
breast cancer; risk prediction; genetic factors; hormone receptor status
Benign breast diseases (BBD) encompass a number of histologic subtypes, with varying risks of subsequent breast cancer. Information on previous benign breast disease biopsies has been incorporated into breast cancer risk prediction models; however, the type of histologic lesion has not been taken into account. Given the substantial heterogeneity in breast cancer risk dependent on type of benign lesion, we evaluated whether incorporating this level of detail improves the discriminatory power of risk classification models.
Using data from the Nurses’ Health Study breast cancer nested case-control study (240 cases; 1036 controls), we determined predictors of categories of BBD lesions and developed imputation models. The type of BBD, imputed for each cohort member
reporting a diagnosis, was added to a modified version of the Rosner-Colditz breast cancer risk prediction model.
Compared to the model with only previous BBD (yes/no), the model with categories of benign breast disease was significantly improved (p<0.0001). Overall, including type of BBD increased the concordance statistic from 0.628 to 0.635. Using risk reclassification, inclusion of the type of BBD resulted in a
17% increase in incidence per increase of 1 risk decile, holding the model without BBD type risk decile constant.
Although these data suggest that inclusion of category of BBD may improve breast cancer risk classification, the clinical utility of such a model will depend on the consistency of histologic classification of benign breast disease lesions.
benign breast disease; breast cancer; risk prediction; Rosner-Colditz
To determine whether information from genetic risk variants for diabetes is associated with cardiovascular events incidence.
From the about 30 known genes associated with diabetes, we genotyped single-nucleotide polymorphisms at the 10 loci most associated with type-2 diabetes in 425 subjects from the MASS-II Study, a randomized study in patients with multi-vessel coronary artery disease. The combined genetic information was evaluated by number of risk alleles for diabetes. Performance of genetic models relative to major cardiovascular events incidence was analyzed through Kaplan-Meier curve comparison and Cox Hazard Models and the discriminatory ability of models was assessed for cardiovascular events by calculating the area under the ROC curve.
Genetic information was able to predict 5-year incidence of major cardiovascular events and overall-mortality in non-diabetic individuals, even after adjustment for potential confounders including fasting glycemia. Non-diabetic individuals with high genetic risk had a similar incidence of events then diabetic individuals (cumulative hazard of 33.0 versus 35.1% of diabetic subjects). The addition of combined genetic information to clinical predictors significantly improved the AUC for cardiovascular events incidence (AUC = 0.641 versus 0.610).
Combined information of genetic variants for diabetes risk is associated to major cardiovascular events incidence, including overall mortality, in non-diabetic individuals with coronary artery disease.
Clinical Trial Registration Information
Medicine, Angioplasty, or Surgery Study (MASS II). Unique identifier: ISRCTN66068876 URL.
Three lung cancer (LC) models have recently been constructed to predict an individual's absolute risk of LC within a defined period. Given their potential application in prevention strategies, a comparison of their accuracy in an independent population is important.
We used data for 3197 patients with LC and 1703 cancer-free controls recruited to an ongoing case–control study at the Harvard School of Public Health and Massachusetts General Hospital. We estimated the 5-year LC risk for each risk model and compared the discriminatory power, accuracy, and clinical utility of these models.
Overall, the Liverpool Lung Project (LLP) and Spitz models had comparable discriminatory power (0.69), whereas the Bach model had significantly lower power (0.66; P=0.02). Positive predictive values were highest with the Spitz models, whereas negative predictive values were highest with the LLP model. The Spitz and Bach models had lower sensitivity but better specificity than did the LLP model.
We observed modest differences in discriminatory power among the three LC risk models, but discriminatory powers were moderate at best, highlighting the difficulty in developing effective risk models.
lung cancer; risk model; 5-year lung cancer risk; relative risks; discriminatory power
Most of the genetic variants identified from genome-wide association studies of breast cancer have not been validated in Asian women. No risk assessment model that incorporates both genetic and clinical predictors is currently available to predict breast cancer risk in this population.
We analyzed 12 single-nucleotide polymorphisms (SNPs) identified in recent genome-wide association studies mostly of women of European ancestry as being associated with the risk of breast cancer in 3039 case patients and 3082 control subjects who participated in the Shanghai Breast Cancer Study. All participants were interviewed in person to obtain information regarding known and suspected risk factors for breast cancer. The c statistic, a measure of discrimination ability with a value ranging from 0.5 (random classification) to 1.0 (perfect classification), was estimated to evaluate the contribution of genetic and established clinical predictors of breast cancer to a newly established risk assessment model for Chinese women. Clinical predictors included in the model were age at menarche, age at first live birth, waist-to-hip ratio, family history of breast cancer, and a previous diagnosis of benign breast disease. The utility of the models in risk stratification was evaluated by estimating the proportion of breast cancer patients in the general population that could be accounted for above a given risk threshold as predicted by the models. All statistical tests were two-sided.
Eight SNPs (rs2046210, rs1219648, rs3817198, rs8051542, rs3803662, rs889312, rs10941679, and rs13281615), each of which reflected a genetically independent locus, were found to be associated with the risk of breast cancer. A dose–response association was observed between the risk of breast cancer and the genetic risk score, which is an aggregate measure of the effect of these eight SNPs (odds ratio for women in the highest quintile of genetic risk score vs those in the lowest = 1.85, 95% confidence interval = 1.58 to 2.18, Ptrend = 2.5 × 10−15). The genetic risk score, the waist-to-hip ratio, and a previous diagnosis of benign breast disease were the top three predictors of the risk of breast cancer, each contributing statistically significantly (P < .001) to the full risk assessment model. The model, with a c statistic of 0.6295 after adjustment for overfitting, showed promise for stratifying women into different risk groups; women in the top 30% risk group accounted for nearly 50% of the breast cancers diagnosed in the general population.
A risk assessment model that includes both genetic markers and clinical predictors may be useful to classify Asian women into relevant risk groups for cost-efficient screening and other prevention programs.
Over the last decade several breast cancer risk alleles have been identified which has led to an increased interest in individualised risk prediction for clinical purposes.
We investigate the performance of an up-to-date 18 breast cancer risk single-nucleotide polymorphisms (SNPs), together with mammographic percentage density (PD), body mass index (BMI) and clinical risk factors in predicting absolute risk of breast cancer, empirically, in a well characterised Swedish case-control study of postmenopausal women. We examined the efficiency of various prediction models at a population level for individualised screening by extending a recently proposed analytical approach for estimating number of cases captured.
The performance of a risk prediction model based on an initial set of seven breast cancer risk SNPs is improved by additionally including eleven more recently established breast cancer risk SNPs (P = 4.69 × 10-4). Adding mammographic PD, BMI and all 18 SNPs to a Swedish Gail model improved the discriminatory accuracy (the AUC statistic) from 55% to 62%. The net reclassification improvement was used to assess improvement in classification of women into low, intermediate, and high categories of 5-year risk (P = 8.93 × 10-9). For scenarios we considered, we estimated that an individualised screening strategy based on risk models incorporating clinical risk factors, mammographic density and SNPs, captures 10% more cases than a screening strategy using the same resources, based on age alone. Estimates of numbers of cases captured by screening stratified by age provide insight into how individualised screening programs might appear in practice.
Taken together, genetic risk factors and mammographic density offer moderate improvements to clinical risk factor models for predicting breast cancer.
A heminested inverse PCR (HIP) for the amplification of sequences flanking the Mycobacterium tuberculosis insertion sequence IS6110 has been developed. The method depends upon primers that anneal to IS6110 at sites between its 5' end and the closest BsrFI site. The accuracy of HIP was demonstrated by the amplification of sequences within plasmid constructs carrying one or two copies of the insertion sequence IS986 in different orientations. The identities of the amplicons produced from strains carrying a single copy of IS6110 were verified by nucleotide sequencing. Analyses of 204 M. tuberculosis strains including those involved in outbreaks showed that IS6110 HIP is highly discriminatory and reproducible. HIP fingerprinting of these 204 strains generated 136 distinct types, and its discriminatory power was equivalent to that of standard restriction fragment length polymorphism analysis. The method is therefore of value for the rapid fingerprinting of M. tuberculosis strains for epidemiological purposes.
To improve efficacy of breast cancer screening and prevention programs, it requires a risk assessment model with high discriminatory power. This study aimed to assess classification performance of using computed bilateral mammographic density asymmetry to predict risk of individual women developing breast cancer in near-term. The database includes 451 cases with multiple screening mammography examinations. The first (baseline) examinations of all case were interpreted negative. In the next sequential examinations, 187 cases developed cancer or surgically excised high-risk lesions, 155 remained negative (not-recalled), and 109 were recalled benign cases. From each of two bilateral cranio-caudal view images acquired from the baseline examination, we computed two features of average pixel value and local pixel value fluctuation. We then computed mean and difference of each feature computed from two images. When applying the computed features and other two risk factors (woman’s age and subjectively rated mammographic density) to predict risk of cancer development, areas under receiver operating characteristic curves (AUC) were computed to evaluate the discriminatory/classification performance. The AUCs are 0.633±0.030, 0.535±0.036, 0.567±0.031, and 0.719±0.027 when using woman’s age, subjectively rated, computed mean and asymmetry of mammographic density, to classify between two groups of cancer-verified and negative cases, respectively. When using an equal-weighted fusion method to combine woman’s age and computed density asymmetry, AUC increased to 0.761±0.025 (p<0.05). The study demonstrated that bilateral mammographic density asymmetry could be a significantly stronger risk factor associated to the risk of women developing breast cancer in near-term than woman’s age and assessed mean mammographic density.
Breast cancer risk assessment; Full-field digital mammography; Mammographic density; Risk model
Recent studies have identified single nucleotide polymorphisms (SNPs) that significantly modify breast cancer risk in BRCA1 and BRCA2 mutation carriers. Since these risk modifiers were originally identified as genetic risk factors for breast cancer in genome-wide association studies (GWASs), additional risk modifiers for BRCA1 and BRCA2 may be identified from promising signals discovered in breast cancer GWAS. A total of 350 SNPs identified as candidate breast cancer risk factors (P < 1 × 10−3) in two breast cancer GWAS studies were genotyped in 3451 BRCA1 and 2006 BRCA2 mutation carriers from nine centers. Associations with breast cancer risk were assessed using Cox models weighted for penetrance. Eight SNPs in BRCA1 carriers and 12 SNPs in BRCA2 carriers, representing an enrichment over the number expected, were significantly associated with breast cancer risk (Ptrend < 0.01). The minor alleles of rs6138178 in SNRPB and rs6602595 in CAMK1D displayed the strongest associations in BRCA1 carriers (HR = 0.78, 95% CI: 0.69–0.90, Ptrend = 3.6 × 10−4 and HR = 1.25, 95% CI: 1.10–1.41, Ptrend = 4.2 × 10−4), whereas rs9393597 in LOC134997 and rs12652447 in FBXL7 showed the strongest associations in BRCA2 carriers (HR = 1.55, 95% CI: 1.25–1.92, Ptrend = 6 × 10−5 and HR = 1.37, 95% CI: 1.16–1.62, Ptrend = 1.7 × 10−4). The magnitude and direction of the associations were consistent with the original GWAS. In subsequent risk assessment studies, the loci appeared to interact multiplicatively for breast cancer risk in BRCA1 and BRCA2 carriers. Promising candidate SNPs from GWAS were identified as modifiers of breast cancer risk in BRCA1 and BRCA2 carriers. Upon further validation, these SNPs together with other genetic and environmental factors may improve breast cancer risk assessment in these populations.
There is extensive evidence that increases in blood and tissue concentrations of steroid hormones and of insulin-like growth factor I (IGF-I) are associated with breast cancer risk. However, studies of common variation in genes involved in steroid hormone and IGF-I metabolism have yet to provide convincing evidence that such variants predict breast cancer risk. The Breast and Prostate Cancer Cohort Consortium (BPC3) is a collaboration of large US and European cohorts. We genotyped 1416 tagging single nucleotide polymorphisms (SNPs) in 37 steroid hormone metabolism genes and 24 IGF-I pathway genes in 6292 cases of breast cancer and 8135 controls, mostly Caucasian, postmenopausal women from the BPC3. We also imputed 3921 additional SNPs in the regions of interest. None of the SNPs tested was significantly associated with breast cancer risk, after correction for multiple comparisons. The results remained null when cases and controls were stratified by age at diagnosis/recruitment, advanced or nonadvanced disease, body mass index, with or without in situ cases; or restricted to Caucasians. Among 770 estrogen receptor-negative cases, an SNP located 3′ of growth hormone receptor (GHR) was marginally associated with increased risk after correction for multiple testing (Ptrend = 1.5 × 10−4). We found no significant overall associations between breast cancer and common germline variation in 61 genes involved in steroid hormone and IGF-I metabolism in this large, comprehensive study. Although previous studies have shown that variations in these genes can influence endogenous hormone levels, the magnitude of the effect of single SNPs does not appear to be sufficient to alter breast cancer risk.
Previous studies in mouse models and pilot epidemiology studies have demonstrated that inherited polymorphisms are associated with inherited risk of tumor progression and poor outcome in human breast cancer. To extend these studies and gain better understanding of the function of inherited polymorphism in breast cancer progression, a validation prognosis study was performed in a large independent breast cancer patient population.
The study population consisted of 1863 Dutch patients with operable primary breast cancer from Rotterdam, The Netherlands. Genomic DNA was genotyped for the missense Pro436Leu RRP1B single nucleotide polymorphism (SNP) rs9306160 and the intronic SIPA1 SNP rs2448490 by SNP-specific PCR.
A significant association of variants in RRP1B with metastasis-free survival was observed (P = 0.012), validating the role of RRP1B with inherited metastatic susceptibility. Stratification of patients revealed that association with patients' survival was found to be specifically restricted to estrogen receptor positive, lymph node-negative (ER+/LN-) patients (P = 0.011). The specific association with metastasis-free survival only in ER+/LN- patients was replicated for SIPA1, a second metastasis susceptibility gene known to physically interact with RRP1B (P = 0.006). Combining the genotypes of these two genes resulted in the significant ability to discriminate patients with poor metastasis-free survival (HR: 0.40, 95% CI: 0.24 to 0.68, P = 0.001).
These results validate SIPA1 and RRP1B as metastasis susceptibility genes and suggest that genotyping assays may be a useful supplement to other clinical and molecular indicators of prognosis. The results also suggest that lymphatic and hematogeneous metastases are genetically distinct that may involve different mechanisms. If true, these results suggest that metastatic disease, like primary breast cancer, may be multiple diseases and that stratification of late stage patients may therefore be required to fully understand breast cancer progression and metastasis.
Various common genetic susceptibility loci have been identified for breast cancer; however, it is unclear how they combine with lifestyle/environmental risk factors to influence risk. We undertook an international collaborative study to assess gene-environment interaction for risk of breast cancer. Data from 24 studies of the Breast Cancer Association Consortium were pooled. Using up to 34,793 invasive breast cancers and 41,099 controls, we examined whether the relative risks associated with 23 single nucleotide polymorphisms were modified by 10 established environmental risk factors (age at menarche, parity, breastfeeding, body mass index, height, oral contraceptive use, menopausal hormone therapy use, alcohol consumption, cigarette smoking, physical activity) in women of European ancestry. We used logistic regression models stratified by study and adjusted for age and performed likelihood ratio tests to assess gene–environment interactions. All statistical tests were two-sided. We replicated previously reported potential interactions between LSP1-rs3817198 and parity (Pinteraction = 2.4×10−6) and between CASP8-rs17468277 and alcohol consumption (Pinteraction = 3.1×10−4). Overall, the per-allele odds ratio (95% confidence interval) for LSP1-rs3817198 was 1.08 (1.01–1.16) in nulliparous women and ranged from 1.03 (0.96–1.10) in parous women with one birth to 1.26 (1.16–1.37) in women with at least four births. For CASP8-rs17468277, the per-allele OR was 0.91 (0.85–0.98) in those with an alcohol intake of <20 g/day and 1.45 (1.14–1.85) in those who drank ≥20 g/day. Additionally, interaction was found between 1p11.2-rs11249433 and ever being parous (Pinteraction = 5.3×10−5), with a per-allele OR of 1.14 (1.11–1.17) in parous women and 0.98 (0.92–1.05) in nulliparous women. These data provide first strong evidence that the risk of breast cancer associated with some common genetic variants may vary with environmental risk factors.
Breast cancer involves combined effects of numerous genetic, environmental, and behavioral risk factors that are unique to each individual. High risk genes, such as BRCA1 and BRCA2, account for only a small proportion of disease occurrence. Recent genome-wide research has identified more than 20 common genetic variants, which individually alter breast cancer risk very moderately. We undertook an international collaborative study to determine whether the effect of these genetic variants vary with environmental factors, such as parity, body mass index (BMI), height, oral contraceptive use, menopausal hormone therapy use, alcohol consumption, cigarette smoking, and physical activity, which are known to affect risk of developing breast cancer. Using pooled data from 24 studies of the Breast Cancer Association Consortium (BCAC), we provide first convincing evidence that the breast cancer risk associated with a genetic variant in LSP1 differs with the number of births and that the risk associated with a CASP8 variant is altered by high alcohol consumption. The effect of an additional genetic variant might also be modified by reproductive factors. This knowledge will stimulate new research towards a better understanding of breast cancer development.
Two major breast cancer sub-types are defined by the expression of estrogen receptors on tumour cells. Cancers with large numbers of receptors are termed estrogen receptor positive and those with few are estrogen receptor negative. Using genome-wide single nucleotide polymorphism genotype data for a sample of early-onset breast cancer patients we developed a Support Vector Machine (SVM) classifier from 200 germline variants associated with estrogen receptor status (p<0.0005). Using a linear kernel Support Vector Machine, we achieved classification accuracy exceeding 93%. The model indicates that polygenic variation in more than 100 genes is likely to underlie the estrogen receptor phenotype in early-onset breast cancer. Functional classification of the genes involved identifies enrichment of functions linked to the immune system, which is consistent with the current understanding of the biological role of estrogen receptors in breast cancer.
The Gail model is widely used for the assessment of risk of invasive breast cancer based on recognized clinical risk factors. In recent years, a substantial number of single-nucleotide polymorphisms (SNPs) associated with breast cancer risk have been identified. However, it remains unclear how to effectively integrate clinical and genetic risk factors for risk assessment.
Seven SNPs associated with breast cancer risk were selected from the literature and genotyped in white non-Hispanic women in a nested case–control cohort of 1664 case patients and 1636 control subjects within the Women’s Health Initiative Clinical Trial. SNP risk scores were computed based on previously published odds ratios assuming a multiplicative model. Combined risk scores were calculated by multiplying Gail risk estimates by the SNP risk scores. The independence of Gail risk and SNP risk was evaluated by logistic regression. Calibration of relative risks was evaluated using the Hosmer–Lemeshow test. The performance of the combined risk scores was evaluated using receiver operating characteristic curves. The net reclassification improvement (NRI) was used to assess improvement in classification of women into low (<1.5%), intermediate (1.5%–2%), and high (>2%) categories of 5-year risk. All tests of statistical significance were two-sided.
The SNP risk score was nearly independent of Gail risk. There was good agreement between predicted and observed SNP relative risks. In the analysis for receiver operating characteristic curves, the combined risk score was more discriminating, with area under the curve of 0.594 compared with area under the curve of 0.557 for Gail risk alone (P < .001). Classification also improved for 5.6% of case patients and 2.9% of control subjects, showing an NRI value of 0.085 (P = 1.0 × 10−5). Focusing on women with intermediate Gail risk resulted in an improved NRI of 0.195 (P = 8.6 × 10−5).
Combining validated common genetic risk factors with clinical risk factors resulted in modest improvement in classification of breast cancer risks in white non-Hispanic postmenopausal women. Classification performance was further improved by focusing on women at intermediate risk.
The prediction of the genetic disease risk of an individual is a powerful public health tool. While predicting risk has been successful in diseases which follow simple Mendelian inheritance, it has proven challenging in complex diseases for which a large number of loci contribute to the genetic variance. The large numbers of single nucleotide polymorphisms now available provide new opportunities for predicting genetic risk of complex diseases with high accuracy.
We have derived simple deterministic formulae to predict the accuracy of predicted genetic risk from population or case control studies using a genome-wide approach and assuming a dichotomous disease phenotype with an underlying continuous liability. We show that the prediction equations are special cases of the more general problem of predicting the accuracy of estimates of genetic values of a continuous phenotype. Our predictive equations are responsive to all parameters that affect accuracy and they are independent of allele frequency and effect distributions. Deterministic prediction errors when tested by simulation were generally small. The common link among the expressions for accuracy is that they are best summarized as the product of the ratio of number of phenotypic records per number of risk loci and the observed heritability.
This study advances the understanding of the relative power of case control and population studies of disease. The predictions represent an upper bound of accuracy which may be achievable with improved effect estimation methods. The formulae derived will help researchers determine an appropriate sample size to attain a certain accuracy when predicting genetic risk.
The fibroblast growth factor receptor 2 gene (FGFR2) has been associated with the risk of breast cancer in multiple ethnic populations, and its effect has been suggested to be hormone-dependent. A large, 2-stage, population-based case-control study was conducted in urban Shanghai, China, during the periods of 1996–1998 and 2002–2005. Exposure and genotyping information from 2,073 patients with breast cancer and 2,084 age-matched population controls was available for evaluation of the interactions between FGFR2 polymorphisms and exogenous estrogen exposure in the development of breast cancer. A logistic regression model was used to compute adjusted odds ratios and 95% confidence intervals. Of 20 genotyped and 25 imputed single nucleotide polymorphisms (SNPs), 22 were significantly associated with breast cancer. Three genotyped SNPs in close linkage disequilibrium, rs2303568, rs3135730, and rs1078806, and an imputed SNP of rs755793 in complete linkage disequilibrium with other 8 SNPs were observed to interact significantly with oral contraceptive (OC) use. The SNP-cancer association was evident only among OC users, and the OC use was only associated with the risk of breast cancer among carriers of these minor alleles at these loci. These findings suggest that genetic variants in FGFR2 may modify the role of OC use in causing breast cancer in Chinese women.
breast neoplasms; contraceptives, oral; hormone replacement therapy; polymorphism, single nucleotide; receptor, fibroblast growth factor, type 2
Recently, the Breast Cancer Association Consortium (BCAC) conducted a multi-stage genome-wide association study and identified 11 single nucleotide polymorphisms (SNPs) associated with breast cancer risk. Given the high degree of heritability of mammographic density and its strong association with breast cancer, it was hypothesised that breast cancer susceptibility loci may also be associated with breast density and provide insight into the biology of breast density and how it influences breast cancer risk.
We conducted an analysis in the Nurses' Health Study (n = 1121) to assess the relation between 11 breast cancer susceptibility loci and mammographic density. At the time of their mammogram, 217 women were premenopausal and 904 women were postmenopausal. We used generalised linear models adjusted for covariates to determine the mean percentage of breast density according to genotype.
Overall, no association between the 11 breast cancer susceptibility loci and mammographic density was seen. Among the premenopausal women, three SNPs (rs12443621 [TNRc9/LOC643714], rs3817198 [lymphocyte-specific protein-1] and rs4666451) were marginally associated with mammographic density (p < 0.10). All three of these SNPs showed an association that was consistent with the direction in which these alleles influence breast cancer risk. The difference in mean percentage mammographic density comparing homozygous wildtypes to homozygous variants ranged from 6.3 to 8.0%. None of the 11 breast cancer loci were associated with postmenopausal breast density.
Overall, breast cancer susceptibility loci identified through a genome-wide association study do not appear to be associated with breast cancer risk.
Obesity has been shown to increase breast cancer risk. FTO is a novel gene which has been identified through genome wide association studies (GWAS) to be related to obesity. Our objective was to evaluate tissue expression of FTO in breast and the role of FTO SNPs in predicting breast cancer risk.
We performed a case-control study of 354 breast cancer cases and 364 controls. This study was conducted at Northwestern University. We examined the role of single nucleotide polymorphisms (SNPs) of intron 1 of FTO in breast cancer risk. We genotyped cases and controls for four SNPs: rs7206790, rs8047395, rs9939609 and rs1477196. We also evaluated tissue expression of FTO in normal and malignant breast tissue.
We found that all SNPs were significantly associated with breast cancer risk with rs1477196 showing the strongest association. We showed that FTO is expressed both in normal and malignant breast tissue. We found that FTO genotypes provided powerful classifiers to predict breast cancer risk and a model with epistatic interactions further improved the prediction accuracy with a receiver operating characteristic (ROC) curves of 0.68.
In conclusion we have shown a significant expression of FTO in malignant and normal breast tissue and that FTO SNPs in intron 1 are significantly associated with breast cancer risk. Furthermore, these FTO SNPs are powerful classifiers in predicting breast cancer risk.