Polygenic susceptibility to prostate and breast cancer: implications for personalised screening

Real and artificial controversies in breast cancer screening

SUMMARY We review the apparent disparities between different reviews of the effects of mammographic screening on mortality from breast cancer and overdiagnosis. When results of each review are expressed with respect to a common population and a common baseline, all find a substantial mortality benefit and variation among estimates is minor. There are genuine disagreements about overdiagnosis, but methods that take account of lead time and underlying incidence trends yield estimates of overdiagnosis that are modest and are outweighed by the mortality benefit. There is potential for individualized screening regimens, particularly with respect to breast density.

Strategies for developing prediction models from genome-wide association studies

Genome-wide association studies (GWASs) have identified hundreds of single nucleotide polymorphisms (SNPs) associated with complex human diseases. However, risk prediction models based on them have limited discriminatory accuracy. It has been suggested that including many such SNPs can improve predictive performance. Here, we studied various aspects of model building to improve discriminatory accuracy, as measured by the area under the receiver operating characteristic curve (AUC), including: (1) How well does a one-phase procedure that selects SNPs and estimates odds ratios on the same data perform? (2) How should training data be allocated between SNP selection (Phase 1) and estimation (Phase 2) in a two-phase procedure? (3) Should SNP selection be based on P-value thresholding or ranking P-values? (4) How many SNPs should be selected? and (5) Is multivariate estimation preferred to univariate estimation in the presence of linkage disequilibrium (LD)? We used realistic estimates of the distributions of genetic effect sizes, allele frequencies, and LD patterns based on GWAS data for Crohn's disease and prostate cancer. Theory and simulations were used to estimate AUC. Empirical risk models based on 10,000 cases and controls had considerably lower AUC than theoretically achievable. The most critical aspect of prediction model building was initial SNP selection. The single-phase procedure achieved higher AUC than the two-phase procedure. Multivariate estimation did not perform as well as univariate (marginal) estimation. For complex diseases and samples of 10,000 or fewer cases and controls, one should limit the number of SNPs to tens or hundreds.

Life insurance: genomic stratification and risk classification

With the development and increasing accessibility of new genomic tools such as next-generation sequencing, genome-wide association studies, and genomic stratification models, the debate on genetic discrimination in the context of life insurance became even more complex, requiring a review of current practices and the exploration of new scenarios. In this perspective, a multidisciplinary group of international experts representing different interests revisited the genetics and life insurance debate during a 2-day symposium ‘Life insurance: breast cancer research and genetic risk prediction seminar' held in Quebec City, Canada on 24 and 25 September 2012. Having reviewed the current legal, social, and ethical issues on the use of genomic information in the context of life insurance, the Expert Group identified four main questions: (1) Have recent developments in genomics and related sciences changed the contours of the genetics and life insurance debate? (2) Are genomic results obtained in a research context relevant for life insurance underwriting? (3) Should predictive risk assessment and risk stratification models based on genomic data also be used for life insurance underwriting? (4) What positive actions could stakeholders in the debate take to alleviate concerns over the use of genomic information by life insurance underwriters? This paper presents a summary of the discussions and the specific action items recommended by the Expert Group.

Inherited genetic susceptibility to breast cancer: the beginning of the end or the end of the beginning?

Genome-wide association studies have identified 72 loci associated with breast cancer susceptibility. Seventeen of these are known to predispose to other cancers. High-penetrance susceptibility loci for breast cancer usually result from coding alterations, principally in genes involved in DNA repair, whereas almost all of the associations identified through genome-wide association studies are found in noncoding regions of the genome and are likely to involve regulation of genes in multiple pathways. However, the genes underlying most associations are not yet known. In this review, we summarize the findings from genome-wide association studies in breast cancer and describe the genes and mechanisms that are likely to be involved in the tumorigenesis process. We also discuss approaches to fine-scale mapping of susceptibility regions used to identify the likely causal variant(s) underlying the associations, a major challenge in genetic epidemiology. Finally, we discuss the potential impact of such findings on personalized medicine and future avenues for screening, prediction, and prevention programs.

Molecular genetics of breast and ovarian cancer: recent advances and clinical implications

Over the last few years, evidence has been accumulated that several susceptibility genes exist that differentially impact on the lifetime risk for breast or ovarian cancer. High-to-moderate penetrance alleles have been identified in genes involved in DNA double-strand break signaling and repair, and many low-penetrance susceptibility loci have been identified through genome-wide association studies. In this review, we briefly summarize present knowledge about breast and ovarian cancer susceptibility genes and discuss their implications for risk prediction and therapy.

Hereditary breast cancer: ever more pieces to the polygenic puzzle

Several susceptibility genes differentially impact on the lifetime risk for breast cancer. Technological advances over the past years have enabled the detection of genetic risk factors through high-throughput screening of large breast cancer case-control series. High- to intermediate penetrance alleles have now been identified in more than 20 genes involved in DNA damage signalling and repair, and more than 70 low-penetrance loci have been discovered through recent genome-wide association studies. In addition to classical germ-line mutation and single-nucleotide polymorphism, copy number variation and somatic mosaicism have been proposed as potential predisposing mechanisms. Many of the identified loci also appear to influence breast tumour characteristics such as estrogen receptor status. In this review, we briefly summarize present knowledge about breast cancer susceptibility genes and discuss their implications for risk prediction and clinical practice.

Risk assessment to guide prostate cancer screening decisions: a cost-effectiveness analysis

OBJECTIVES

To apply the most recent evidence from randomised trials of prostate-specific antigen (PSA) screening and explore the potential value of risk assessments to guide the use of PSA screening in practice.

DESIGN

A decision model that incorporated a Markov process was developed in 2012 to estimate the net benefit and cost of PSA screening versus no screening as a function of baseline risk.

MAIN OUTCOME MEASURES

Quality-adjusted life-2013s (QALYs) and costs.

RESULTS

The harms of screening outweighed the benefits under a number of plausible scenarios. Conclusions were sensitive to the estimated quality-of-life impacts of prostate cancer treatment as well as the incidence of cancers not detected by screening tests (poorer prognosis) and those that were detected by screening tests (better prognosis). The base-case incremental cost-effectiveness ratio of PSA screening was $168,611 per QALY for men with average risk, $73,452 per QALY for men with two times the average risk, and $22,938 [corrected] per QALY for men with five times the average risk.

CONCLUSIONS

PSA screening was not found to be cost-effective for men at an average-to-high risk of prostate cancer, but may be cost-effective for men at very high risk. Inexpensive approaches for identifying men at very high risk are needed, as is further research on the size of clinical benefit of early detection in this population. The potential for the costs of risk assessment to be offset by reduced costs of PSA screening also warrants investigation.

Personalized screening for cancers: should we consider polygenic profiling?

Polygenic profiling and risk stratification for population-based screening for cancer improve the efficiency of the screening programs. Translation of genomics into personalized screening programs requires evidence from empirical research on the balance of benefits and harms of personalized screening, and engagement with the public, professionals and policy makers.

Will SNPs be useful predictors of normal tissue radiosensitivity in the future?

The ability to predict individual risk of radiation-induced normal tissue complications is a long sought goal in radiobiology. The last decade saw increasing interest in identifying associations between single nucleotide polymorphisms (SNPs) and normal tissue complication risk. Nevertheless, it remains controversial whether SNPs will be useful predictors of normal tissue radiosensitivity. This paper provides a summary of a scientific debate held at the 31st ESTRO conference in which four scientists argued in favor or against the motion that SNPs will be useful predictors of normal tissue radiosensitivity in the future.

Incorporating genomics into breast and prostate cancer screening: assessing the implications

Individual risk prediction and stratification based on polygenic profiling may be useful in disease prevention. Risk-stratified population screening based on multiple factors including a polygenic risk profile has the potential to be more efficient than age-stratified screening. In this article, we summarize the implications of personalized screening for breast and prostate cancers. We report the opinions of multidisciplinary international experts who have explored the scientific, ethical, and logistical aspects of stratified screening. We have identified (i) the need to recognize the benefits and harms of personalized screening as compared with existing screening methods, (ii) that the use of genetic data highlights complex ethical issues including discrimination against high-risk individuals by insurers and employers and patient autonomy in relation to genetic testing of minors, (iii) the need for transparency and clear communication about risk scores, about harms and benefits, and about reasons for inclusion and exclusion from the risk-based screening process, and (iv) the need to develop new professional competences and to assess cost-effectiveness and acceptability of stratified screening programs before implementation. We conclude that health professionals and stakeholders need to consider the implications of incorporating genetic information in intervention strategies for health-care planning in the future.

Public health implications from COGS and potential for risk stratification and screening

The PHG Foundation led a multidisciplinary program, which used results from COGS research identifying genetic variants associated with breast, ovarian and prostate cancers to model risk-stratified prevention for breast and prostate cancers. Implementing such strategies would require attention to the use and storage of genetic information, the development of risk assessment tools, new protocols for consent and programs of professional education and public engagement.

Prediction of breast cancer risk by genetic risk factors, overall and by hormone receptor status

OBJECTIVE

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 tumours with different hormone receptor status.

MATERIAL AND METHODS

Within the Breast and Prostate Cancer Cohort Consortium, we analysed 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 adjusted and cohort-adjusted concordance statistic (AUROC(a)). Absolute risk scores were calculated with external reference data. Integrated discrimination improvement was used to measure improvements in risk prediction.

RESULTS

We found a small but steady increase in discriminatory ability with increasing numbers of genetic variants included in the model (difference in AUROC(a) going from 2.7% to 4%). Discriminatory ability for all models varied strongly by hormone receptor status.

DISCUSSION AND CONCLUSIONS

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 clinical application.

Characterization of SNPs associated with prostate cancer in men of Ashkenazic descent from the set of GWAS identified SNPs: impact of cancer family history and cumulative SNP risk prediction

BACKGROUND

Genome-wide association studies (GWAS) have identified multiple SNPs associated with prostate cancer (PrCa). Population isolates may have different sets of risk alleles for PrCa constituting unique population and individual risk profiles.

METHODS

To test this hypothesis, associations between 31 GWAS SNPs of PrCa were examined among 979 PrCa cases and 1,251 controls of Ashkenazic descent using logistic regression. We also investigated risks by age at diagnosis, pathological features of PrCa, and family history of cancer. Moreover, we examined associations between cumulative number of risk alleles and PrCa and assessed the utility of risk alleles in PrCa risk prediction by comparing the area under the curve (AUC) for different logistic models.

RESULTS

Of the 31 genotyped SNPs, 8 were associated with PrCa at p ≤ 0.002 (corrected p-value threshold) with odds ratios (ORs) ranging from 1.22 to 1.42 per risk allele. Four SNPs were associated with aggressive PrCa, while three other SNPs showed potential interactions for PrCa by family history of PrCa (rs8102476; 19q13), lung cancer (rs17021918; 4q22), and breast cancer (rs10896449; 11q13). Men in the highest vs. lowest quartile of cumulative number of risk alleles had ORs of 3.70 (95% CI 2.76-4.97); 3.76 (95% CI 2.57-5.50), and 5.20 (95% CI 2.94-9.19) for overall PrCa, aggressive cancer and younger age at diagnosis, respectively. The addition of cumulative risk alleles to the model containing age at diagnosis and family history of PrCa yielded a slightly higher AUC (0.69 vs. 0.64).

CONCLUSION

These data define a set of risk alleles associated with PrCa in men of Ashkenazic descent and indicate possible genetic differences for PrCa between populations of European and Ashkenazic ancestry. Use of genetic markers might provide an opportunity to identify men at highest risk for younger age of onset PrCa; however, their clinical utility in identifying men at highest risk for aggressive cancer remains limited.

Cumulative risk of cancer detection in breast cancer screening by protocol strategy

BACKGROUND

There is little information on the individual risk of screen-detected cancer in women over successive participations. This study aimed to estimate the 10-year cumulative breast cancer detection risk (ductal carcinoma in situ and invasive carcinoma) in a population-based breast cancer screening program according to distinct protocol strategies. A further aim was to determine which strategies maximized the cancer detection risk and how this risk was affected by the radiologic protocol variables.

METHODS

Data were drawn from a retrospective cohort of women from nine population-based screening programs in Spain from 1990 to 2006. We used logistic regression with discrete intervals to estimate the cumulative detection risk at 10 years of follow-up according to radiologic variables and protocol strategies.

RESULTS

In women starting screening at the age of 45-59 years, the cumulative risk of screen-detected cancer at 10 years ranged from 11.11 to 16.71 per 1,000 participants according to the protocol strategy. The cumulative detection risk for overall cancer and invasive cancer was the highest with strategies using digital mammography, double reading, and two projections (16.71 and 12.07 ‰, respectively). For ductal carcinoma in situ, cumulative detection risk was the highest with strategies using screen-film, double reading, and two projections (2.32 ‰). The risk was the lowest with strategies using screen-film mammography, single reading, and two projections.

CONCLUSIONS

This study found that at least eleven cancers are detected per 1,000 women screened in the first 10 years of follow-up. Enhanced knowledge of the variability in cumulative risk of screen-detected cancer could improve protocol strategies.

Summary of the evidence of breast cancer service screening outcomes in Europe and first estimate of the benefit and harm balance sheet

OBJECTIVES

To construct a European 'balance sheet' of key outcomes of population-based mammographic breast cancer screening, to inform policy-makers, stakeholders and invited women.

METHODS

From the studies reviewed, the primary benefit of screening, breast cancer mortality reduction, was compared with the main harms, over-diagnosis and false-positive screening results (FPRs).

RESULTS

Pooled estimates of breast cancer mortality reduction among invited women were 25% in incidence-based mortality studies and 31% in case-control studies (38% and 48% among women actually screened). Estimates of over-diagnosis ranged from 1% to 10% of the expected incidence in the absence of screening. The combined estimate of over-diagnosis for screened women, from European studies correctly adjusted for lead time and underlying trend, was 6.5%. For women undergoing 10 biennial screening tests, the estimated cumulative risk of a FPR followed by non-invasive assessment was 17%, and 3% having an invasive assessment. For every 1000 women screened biennially from age 50-51 until age 68-69 and followed up to age 79, an estimated seven to nine lives are saved, four cases are over-diagnosed, 170 women have at least one recall followed by non-invasive assessment with a negative result and 30 women have at least one recall followed by invasive procedures yielding a negative result.

CONCLUSIONS

The chance of saving a woman's life by population-based mammographic screening of appropriate quality is greater than that of over-diagnosis. Service screening in Europe achieves a mortality benefit at least as great as the randomized controlled trials. These outcomes should be communicated to women offered service screening in Europe.

Stratified cancer screening: the practicalities of implementation

BACKGROUND

Improving understanding of the genetic basis of disease susceptibility enables us to estimate individuals' risk of developing cancer and offer them disease prevention, including screening, stratified to reflect that risk. Little attention has so far been given to the implementation of stratified screening. This article reviews the issues that would arise in delivering such tailored approaches to prevention in practice.

RESULTS

Issues analysed include the organisational context within which implementation of stratified prevention would occur, how the offer of screening would be made, making sure consent is adequately informed, how individuals' risk would be assessed, the age at which risk estimation should occur, and the potential use of genetic data for other purposes. The review also considers how management might differ depending on individuals' risk, how their results would be communicated and their follow-up arranged, and the different issues raised by modification of an existing screening programme, such as that for breast cancer, and the establishment of a new one, for example for prostate cancer.

CONCLUSION

Stratified screening based on genetic testing is a radically new approach to prevention. Various organisational issues would need to be considered before it could be introduced, and a number of questions require further research.

Breast cancer risk prediction and mammography biopsy decisions: a model-based study

BACKGROUND

Controversy continues about screening mammography, in part because of the risk of false-negative and false-positive mammograms. Pre-test breast cancer risk factors may improve the positive and negative predictive value of screening.

PURPOSE

To create a model that estimates the potential impact of pre-test risk prediction using clinical and genomic information on the reclassification of women with abnormal mammograms (BI-RADS3 and BI-RADS4 [Breast Imaging-Reporting and Data System]) above and below the threshold for breast biopsy.

METHODS

The current study modeled 1-year breast cancer risk in women with abnormal screening mammograms using existing data on breast cancer risk factors, 12 validated breast cancer single-nucleotide polymorphisms (SNPs), and probability of cancer given the BI-RADS category. Examination was made of reclassification of women above and below biopsy thresholds of 1%, 2%, and 3% risk. The Breast Cancer Surveillance Consortium data were collected from 1996 to 2002. Data analysis was conducted in 2010 and 2011.

RESULTS

Using a biopsy risk threshold of 2% and the standard risk factor model, 5% of women with a BI-RADS3 mammogram had a risk above the threshold, and 3% of women with BI-RADS4A mammograms had a risk below the threshold. The addition of 12 SNPs in the model resulted in 8% of women with a BI-RADS3 mammogram above the threshold for biopsy and 7% of women with BI-RADS4A mammograms below the threshold.

CONCLUSIONS

The incorporation of pre-test breast cancer risk factors could change biopsy decisions for a small proportion of women with abnormal mammograms. The greatest impact comes from standard breast cancer risk factors.

Beyond prostate-specific antigen - future biomarkers for the early detection and management of prostate cancer

Prostate-specific antigen is currently commonly used as a screening biomarker for prostate cancer, but it has limitations in both sensitivity and specificity. The development of novel biomarkers for early cancer detection has the potential to improve survival, reduce unnecessary investigations and benefit the health economy. Here we review the use and limitations of prostate-specific antigen and its subtypes, urinary biomarkers including PCA3, alpha-methylacyl-CoA racemase, the TMPRSS2-ERG fusion gene and microseminoprotein-beta, and other novel markers in both serum and urine. Many of these biomarkers are at early stages of development and require evaluation in prospective trials to determine their potential usefulness in clinical practice. Genetic profiling may allow for the targeting of high-risk populations for screening and may offer the opportunity to combine biomarker results with genotype to aid risk assessment.

Genomic medicine: challenges and opportunities for physicians

Medicine has always striven to personalise or stratify approaches towards individual patients, but recently these terms have been applied particularly to denote improved disease sub-classification achieved through new genetic and genomic technologies. Techniques to analyse a person's genetic code have improved in sensitivity exponentially over recent years and at the same time the cost of such analyses has become affordable to routine NHS care. This article highlights the significant opportunities that genomics brings to healthcare, as well as some of the practical and ethical challenges.

Emerging putative biomarkers: the role of alpha 2 and 6 integrins in susceptibility, treatment, and prognosis

The genetic architecture underpinning prostate cancer is complex, polygenic and despite recent significant advances many questions remain. Advances in genetic technologies have greatly improved our ability to identify genetic variants associated with complex disease including prostate cancer. Genome-wide association studies (GWASs) and microarray gene expression studies have identified genetic associations with prostate cancer susceptibility and tumour development. The integrins feature prominently in both studies examining the underlying genetic susceptibility and mechanisms driving prostate tumour development. Integrins are cell adhesion molecules involved in extracellular and intracellular signalling and are imperative for tumour development, migration, and angiogenesis. Although several integrins have been implicated in tumour development, the roles of integrin α(2) and integrin α(6) are the focus of this paper as evidence is now emerging that these integrins are implicit in prostate cancer susceptibility, cancer stem cell biology, angiogenesis, cell migration, and metastases to bone and represent potential biomarkers and therapeutic targets. There currently exists an urgent need to develop tools that differentiate indolent from aggressive prostate cancers and predict how patients will respond to treatment. This paper outlines the evidence supporting the use of α(2) and α(6) integrins in clinical applications for tailored patient treatment.

Population-based screening in the era of genomics

To date, risk profiles based on the known common susceptibility variants have limited value in predicting risk of disease but they could be used for risk stratification in prevention programmes at population level. We illustrate the potential utility of polygenic risk stratification using the case of population-based screening for prostate and breast cancer. We compared the number of individuals eligible for screening and the number of cases potentially detectable by screening in a population undergoing screening based on age alone with a population undergoing stratified screening based on age and polygenic risk profile. Stratified screening strategy based on age and genetic risk would potentially improve the efficiency of screening programmes and reduce their adverse consequences. Organisational, ethical, legal and social issues need to be addressed before stratified screening programmes could be implemented.

Potential usefulness of single nucleotide polymorphisms to identify persons at high cancer risk: an evaluation of seven common cancers

PURPOSE

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Genetic variants associated with predisposition to prostate cancer and potential clinical implications

Prostate cancer is the commonest cancer in the developed world. There is an inherited component to this disease as shown in familial and twin studies. However, the discovery of these variants has been difficult. The emergence of genome-wide association studies has led to the identification of over 46 susceptibility loci. Their clinical utility to predict risk, response to treatment, or treatment toxicity, remains undefined. Large consortia are needed to achieve adequate statistical power to answer these genetic-clinical and genetic-epidemiological questions. International collaborations are currently underway to link genetic with clinical/epidemiological data to develop risk prediction models, which could direct screening and treatment programs.

Common genetic variants in prostate cancer risk prediction--results from the NCI Breast and Prostate Cancer Cohort Consortium (BPC3)

BACKGROUND

One of the goals of personalized medicine is to generate individual risk profiles that could identify individuals in the population that exhibit high risk. The discovery of more than two-dozen independent single-nucleotide polymorphism markers in prostate cancer has raised the possibility for such risk stratification. In this study, we evaluated the discriminative and predictive ability for prostate cancer risk models incorporating 25 common prostate cancer genetic markers, family history of prostate cancer, and age.

METHODS

We fit a series of risk models and estimated their performance in 7,509 prostate cancer cases and 7,652 controls within the National Cancer Institute Breast and Prostate Cancer Cohort Consortium (BPC3). We also calculated absolute risks based on SEER incidence data.

RESULTS

The best risk model (C-statistic = 0.642) included individual genetic markers and family history of prostate cancer. We observed a decreasing trend in discriminative ability with advancing age (P = 0.009), with highest accuracy in men younger than 60 years (C-statistic = 0.679). The absolute ten-year risk for 50-year-old men with a family history ranged from 1.6% (10th percentile of genetic risk) to 6.7% (90th percentile of genetic risk). For men without family history, the risk ranged from 0.8% (10th percentile) to 3.4% (90th percentile).

CONCLUSIONS

Our results indicate that incorporating genetic information and family history in prostate cancer risk models can be particularly useful for identifying younger men that might benefit from prostate-specific antigen screening.

IMPACT

Although adding genetic risk markers improves model performance, the clinical utility of these genetic risk models is limited.

Breast cancer risk prediction and individualised screening based on common genetic variation and breast density measurement

Introduction

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.

Methods

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.

Results

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.

Conclusions

Taken together, genetic risk factors and mammographic density offer moderate improvements to clinical risk factor models for predicting breast cancer.

Comprehensive evaluation of the impact of 14 genetic variants on colorectal cancer phenotype and risk

To comprehensively evaluate the impact of recently identified colorectal cancer (CRC) variants at 1q41, 3q26.2, 8q23.3, 8q24.21, 10p14, 11q23.1, 12q13.13, 14q22.2, 15q13.3, 16q22.1, 18q21.1, 19q13.11, 20p12.3, and 20q13.33 on risk and CRC phenotype, the authors analyzed 8,878 cases and 6,051 controls from the United Kingdom ascertained in 1999-2007. The impact of variants on the familial CRC risk was enumerated from age-, sex-, and calendar-specific CRC rates in the 50,924 first-degree relatives of cases. Each of the 14 susceptibility loci independently influences CRC with the risk increasing with increasing number of risk alleles carried (per allele odds ratio = 1.13; P = 2.99 × 10(-58)) and, for those within the upper quintile, there is a 2.3-fold increased risk. In first-degree relatives of cases with ≤17, 18-21, and ≥22 risk alleles, standardized incidence ratios were 1.76, 2.08, and 2.25, respectively. Although the discriminatory attributes of the 14 CRC susceptibility loci for individual risk prediction are poor (area under the curve = 0.58), they may allow subgroups of the population at different CRC risks to be distinguished.