Distribution of breast cancer risk from SNPs and classical risk factors in women of routine screening age in the UK

An advanced machine learning method for simultaneous breast cancer risk prediction and risk ranking in Chinese population: A prospective cohort and modeling study

BACKGROUND

Breast cancer (BC) risk-stratification tools for Asian women that are highly accurate and can provide improved interpretation ability are lacking. We aimed to develop risk-stratification models to predict long- and short-term BC risk among Chinese women and to simultaneously rank potential non-experimental risk factors.

METHODS

The Breast Cancer Cohort Study in Chinese Women, a large ongoing prospective dynamic cohort study, includes 122,058 women aged 25-70 years from the eastern part of China. We developed multiple machine-learning risk prediction models using parametric models (penalized logistic regression, bootstrap, and ensemble learning), which were the short-term ensemble penalized logistic regression (EPLR) risk prediction model and the ensemble penalized long-term (EPLT) risk prediction model to estimate BC risk. The models were assessed based on calibration and discrimination, and following this assessment, they were externally validated in new study participants from 2017 to 2020.

RESULTS

The AUC values of the short-term EPLR risk prediction model were 0.800 for the internal validation and 0.751 for the external validation set. For the long-term EPLT risk prediction model, the area under the receiver operating characteristic curve was 0.692 and 0.760 in internal and external validations, respectively. The net reclassification improvement index of the EPLT relative to the Gail and the Han Chinese Breast Cancer Prediction Model (HCBCP) models for external validation was 0.193 and 0.233, respectively, indicating that the EPLT model has higher classification accuracy.

CONCLUSIONS

We developed the EPLR and EPLT models to screen populations with a high risk of developing BC. These can serve as useful tools to aid in risk-stratified screening and BC prevention.

Breast cancer risk prediction using Tyrer-Cuzick algorithm with an 18-SNPs polygenic risk score in a European population with below-average breast cancer incidence

GOALS

To determine whether an 18 single nucleotide polymorphisms (SNPs) polygenic risk score (PRS18) improves breast cancer (BC) risk prediction for women at above-average risk of BC, aged 40-49, in a Central European population with BC incidence below EU average.

METHODS

502 women aged 40-49 years at the time of BC diagnosis completed a questionnaire on BC risk factors (as per Tyrer-Cuzick algorithm) with data known at age 40 and before BC diagnosis. Blood samples were collected for DNA isolation. 250 DNA samples from healthy women aged 50 served as a control cohort. 18 BC-associated SNPs were genotyped in both groups and PRS18 was calculated. The predictive power of PRS18 to detect BC was evaluated using a ROC curve. 10-year BC risk was calculated using the Tyrer-Cuzick algorithm adapted to the Slovenian incidence rate (S-IBIS): first based on questionnaire-based risk factors and, second, including PRS18.

RESULTS

The AUC for PRS18 was 0.613 (95 % CI 0.570-0.657). 83.3 % of women were classified at above-average risk for BC with S-IBIS without PRS18 and 80.7 % when PRS18 was included.

CONCLUSION

BC risk prediction models and SNPs panels should not be automatically used in clinical practice in different populations without prior population-based validation. In our population the addition of an 18SNPs PRS to questionnaire-based risk factors in the Tyrer-Cuzick algorithm in general did not improve BC risk stratification, however, some improvements were observed at higher BC risk scores and could be valuable in distinguishing women at intermediate and high risk of BC.

From BRCA1 to Polygenic Risk Scores: Mutation-Associated Risks in Breast Cancer-Related Genes

Background

There has been huge progress over the last 30 years in identifying the familial component of breast cancer.

Summary

Currently around 20% is explained by the high-risk genes BRCA1 and BRCA2, a further 2% by other high-penetrance genes, and around 5% by the moderate risk genes ATM and CHEK2. In contrast, the more than 300 low-penetrance single-nucleotide polymorphisms (SNP) now account for around 28% and they are predicted to account for most of the remaining 45% yet to be found. Even for high-risk genes which confer a 40-90% risk of breast cancer, these SNP can substantially affect the level of breast cancer risk. Indeed, the strength of family history and hormonal and reproductive factors is very important in assessing risk even for a BRCA carrier. The risks of contralateral breast cancer are also affected by SNP as well as by the presence of high or moderate risk genes. Genetic testing using gene panels is now commonplace.

Key-Messages

There is a need for a more parsimonious approach to panels only testing those genes with a definite 2-fold increased risk and only testing those genes with challenging management implications, such as CDH1 and TP53, when there is strong clinical indication to do so. Testing of SNP alongside genes is likely to provide a more accurate risk assessment.

Risk for breast cancer and management of unaffected individuals with non-BRCA hereditary breast cancer

About 5%-10% of breast cancer is hereditary with BRCA1 and BRCA2 being the most common genes associated with hereditary breast cancer (HBC). Several additional genes have recently been associated with HBC. These genes can be classified as highly or moderately penetrant genes with lifetime risk >30% or 17%-30%, respectively. Highly penetrant genes associated with HBC include TP53, PTEN, CDH1, STK11, and PALB2. While, moderately penetrant genes include CHEK2, ATM, BARD1, BRIP1, NBN, NF1, RAD51D, and MSH6. Breast cancer risk and recommendations for screening and risk-reduction vary by gene. In general, screening breast MRI is recommended for women at >20% lifetime risk, which includes women with mutations in highly penetrant genes and the majority (but not all) moderately penetrant genes. Consideration of chemoprevention is recommended for women with mutations in high and moderately penetrant genes. Risk-reducing mastectomy does reduce the risk of breast cancer to the greatest extent and can be considered for women with highly penetrant genes. However, this procedure is associated with significant morbidities that should be considered, especially given the benefit of using screening breast MRI for high-risk women. BSO is only recommended for women with mutations in genes associate with increased risk for ovarian cancer and not as a breast cancer risk-reducing strategy. As more women undergo testing, additional genes may be identified and risk estimates for current genes and management recommendations may be modified.

Breast cancer risk based on adapted IBIS prediction model in Slovenian women aged 40-49 years - could it be better?

Background The aim of the study was to assess the proportion of women that would be classified as at above-average risk of breast cancer based on the 10 year-risk prediction of the Slovenian breast cancer incidence rate (S-IBIS) program in two presumably above-average breast cancer risk populations in age group 40-49 years: (i) women referred for any reason to diagnostic breast centres and (ii) women who were diagnosed with breast cancer aged 40-49 years. Breast cancer is the commonest female cancer in Slovenia, with an incidence rate below European average. The Tyrer-Cuzick breast cancer risk assessment algorithm was recently adapted to S-IBIS. In Slovenia a tailored mammographic screening for women at above average risk in age group 40-49 years is considered in the future. S-IBIS is a possible tool to select population at above-average risk of breast cancer for tailored screening. Patients and methods In 357 healthy women aged 40-49 years referred for any reason to diagnostic breast centres and in 367 female breast cancer patients aged 40-49 years at time of diagnosis 10-years breast cancer risk was calculated using the S-IBIS software. The proportion of women classified as above-average risk of breast cancer was calculated for each subgroup of the study population. Results 48.7% of women in the Breast centre group and 39.2% of patients in the breast cancer group had above-average 10-year breast cancer risk. Positive family history of breast cancer was more prevalent in the Breast centre group (p < 0.05). Conclusions Inclusion of additional risk factors into the S-IBIS is warranted in the populations with breast cancer incidence below European average to reliably stratify women into breast cancer risk groups.

A case-control evaluation of 143 single nucleotide polymorphisms for breast cancer risk stratification with classical factors and mammographic density

Panels of single nucleotide polymorphisms (SNPs) stratify risk for breast cancer in women from the general population, but studies are needed assess their use in a fully comprehensive model including classical risk factors, mammographic density and more than 100 SNPs associated with breast cancer. A case-control study was designed (1,668 controls, 405 cases) in women aged 47-73 years attending routine screening in Manchester UK, and enrolled in a wider study to assess methods for risk assessment. Risk from classical questionnaire risk factors was assessed using the Tyrer-Cuzick model; mean percentage visual mammographic density was scored by two independent readers. DNA extracted from saliva was genotyped at selected SNPs using the OncoArray. A predefined polygenic risk score based on 143 SNPs was calculated (SNP143). The odds ratio (OR, and 95% confidence interval, CI) per interquartile range (IQ-OR) of SNP143 was estimated unadjusted and adjusted for Tyrer-Cuzick and breast density. Secondary analysis assessed risk by oestrogen receptor (ER) status. The primary polygenic risk score was well calibrated (O/E OR 1.10, 95% CI 0.86-1.34) and accuracy was retained after adjustment for Tyrer-Cuzick risk and mammographic density (IQ-OR unadjusted 2.12, 95% CI% 1.75-2.42; adjusted 2.06, 95% CI 1.75-2.42). SNP143 was a risk factor for ER+ and ER- breast cancer (adjusted IQ-OR, ER+ 2.11, 95% CI 1.78-2.51; ER- 1.81, 95% CI 1.16-2.84). In conclusion, polygenic risk scores based on a large number of SNPs improve risk stratification in combination with classical risk factors and mammographic density, and SNP143 was similarly predictive for ER-positive and ER-negative disease.

Next Generation Sequencing in Newborn Screening in the United Kingdom National Health Service

Next generation DNA sequencing (NGS) has the potential to improve the diagnostic and prognostic utility of newborn screening programmes. This study assesses the feasibility of automating NGS on dried blood spot (DBS) DNA in a United Kingdom National Health Service (UK NHS) laboratory. An NGS panel targeting the entire coding sequence of five genes relevant to disorders currently screened for in newborns in the UK was validated on DBS DNA. An automated process for DNA extraction, NGS and bioinformatics analysis was developed. The process was tested on DBS to determine feasibility, turnaround time and cost. The analytical sensitivity of the assay was 100% and analytical specificity was 99.96%, with a mean 99.5% concordance of variant calls between DBS and venous blood samples in regions with ≥30× coverage (96.8% across all regions; all variant calls were single nucleotide variants (SNVs), with indel performance not assessed). The pipeline enabled processing of up to 1000 samples a week with a turnaround time of four days from receipt of sample to reporting. This study concluded that it is feasible to automate targeted NGS on routine DBS samples in a UK NHS laboratory setting, but it may not currently be cost effective as a first line test.

Use of Single-Nucleotide Polymorphisms and Mammographic Density Plus Classic Risk Factors for Breast Cancer Risk Prediction

IMPORTANCE

Single-nucleotide polymorphisms (SNPs) have demonstrated an association with breast cancer susceptibility, but there is limited evidence on how to incorporate them into current breast cancer risk prediction models.

OBJECTIVE

To determine whether a panel of 18 SNPs (SNP18) may be used to predict breast cancer in combination with classic risk factors and mammographic density.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study enrolled a subcohort of 9363 women, aged 46 to 73 years, without a previous breast cancer diagnosis from the larger prospective cohort of the PROCAS study (Predicting Risk of Cancer at Screening) specifically to evaluate breast cancer risk-assessment methods. Enrollment took place from October 2009 through June 2015 from multiple population-based screening centers in Greater Manchester, England. Follow-up continued through January 5, 2017.

EXPOSURES

Genotyping of 18 SNPs, visual-assessment percentage mammographic density, and classic risk assessed by the Tyrer-Cuzick risk model from a self-completed questionnaire at cohort entry.

MAIN OUTCOMES AND MEASURES

The predictive ability of SNP18 for breast cancer diagnosis (invasive and ductal carcinoma in situ) was assessed using logistic regression odds ratios per interquartile range of the predicted risk.

RESULTS

A total of 9363 women were enrolled in this study (mean [range] age, 59 [46-73] years). Of these, 466 were found to have breast cancer (271 prevalent; 195 incident). SNP18 was similarly predictive when unadjusted or adjusted for mammographic density and classic factors (odds ratios per interquartile range, respectively, 1.56; 95% CI, 1.38-1.77 and 1.53; 95% CI, 1.35-1.74), with observed risks being very close to expected (adjusted observed-to-expected odds ratio, 0.98; 95% CI, 0.69-1.28). A combined risk assessment indicated 18% of the subcohort to be at 5% or greater 10-year risk, compared with 30% of all cancers, 35% of interval-detected cancers, and 42% of stage 2+ cancers. In contrast, 33% of the subcohort were at less than 2% risk but accounted for only 18%, 17%, and 15% of the total, interval, and stage 2+ breast cancers, respectively.

CONCLUSIONS AND RELEVANCE

SNP18 added substantial information to risk assessment based on the Tyrer-Cuzick model and mammographic density. A combined risk is likely to aid risk-stratified screening and prevention strategies.

Personalized prevention in high risk individuals: Managing hormones and beyond

Increasing numbers of women are being identified at 'high-risk' of breast cancer, defined by The National Institute of Health and Care Excellence (NICE) as a 10-year risk of ≥8%. Classically women have been so identified through family history based risk algorithms or genetic testing of high-risk genes. Recent research has shown that assessment of mammographic density and single nucleotide polymorphisms (SNPs), when combined with established risk factors, trebles the number of women reaching the high risk threshold. The options for risk reduction in such women include endocrine chemoprevention with the selective estrogen receptor modulators tamoxifen and raloxifene or the aromatase inhibitors anastrozole or exemestane. NICE recommends offering anastrozole to postmenopausal women at high-risk of breast cancer as cost effectiveness analysis showed this to be cost saving to the National Health Service. Overall uptake to chemoprevention has been disappointingly low but this may improve with the improved efficacy of aromatase inhibitors, particularly the lack of toxicity to the endometrium and thrombogenic risks. Novel approaches to chemoprevention under investigation include lower dose and topical tamoxifen, denosumab, anti-progestins and metformin. Although oophorectomy is usually only recommended to women at increased risk of ovarian cancer it has been shown in numerous studies to reduce breast cancer risks in the general population and in those with mutations in BRCA1/2. However, recent evidence from studies that have confined analysis to true prospective follow up have cast doubt on the efficacy of oophorectomy to reduce breast cancer risk in BRCA1 mutation carriers, at least in the short-term.

Preventive therapy for cancer

Therapeutic cancer prevention is a developing area that can gain a lot from the successes in the prevention of cardiovascular diseases. Although weight control and physical activity are important in the prevention of both diseases, several other preventive measures exist. Low-dose aspirin for cancer prevention is often cited as the most important approach in terms of population benefit, and should be offered to those older than 50 years of age without hypertension or risk factors for gastrointestinal bleeding. Universal vaccination against the human papillomavirus, ideally with the nine-valent vaccine, also offers substantial benefits for the whole population if given before infection occurs (ie, typically at age 12-14 years). Other therapies, such as anti-oestrogen drugs for breast cancer prevention, should be targeted to high-risk groups to maintain a favourable benefit-risk ratio. Better algorithms for identification and improved platforms to reach these groups, such as during a screening visit, remain a key priority to allow existing knowledge to inform public health. Many other promising compounds have been identified, often as components of food, but results suggesting increased disease incidence with β carotene and vitamin E administration indicate that such treatments need rigorous evaluation before acceptance.

Impact of a Panel of 88 Single Nucleotide Polymorphisms on the Risk of Breast Cancer in High-Risk Women: Results From Two Randomized Tamoxifen Prevention Trials

Purpose At least 94 common single nucleotide polymorphisms (SNPs) are associated with breast cancer. The extent to which an SNP panel can refine risk in women who receive preventive therapy has not been directly assessed previously. Materials and Methods A risk score on the basis of 88 SNPs (SNP88) was investigated in a nested case-control study of women enrolled in the International Breast Intervention Study (IBIS-I) or the Royal Marsden study. A total of 359 women who developed cancer were matched to 636 controls by age, trial, follow-up time, and treatment arm. Genotyping was done using the OncoArray. Conditional logistic regression and matched concordance indices (mC) were used to measure the performance of SNP88 alone and with other breast cancer risk factors assessed using the Tyrer-Cuzick (TC) model. Results SNP88 was predictive of breast cancer risk overall (interquartile range odds ratio [IQ-OR], 1.37; 95% CI, 1.14 to 1.66; mC, 0.55), but mainly for estrogen receptor-positive disease (IQ-OR, 1.44; 95% CI, 1.16 to 1.79; P for heterogeneity = .10) versus estrogen receptor-negative disease. However, the observed risk of SNP88 was only 46% (95% CI, 19% to 74%) of expected. No significant interaction was observed with treatment arm (placebo IQ-OR, 1.46; 95% CI, 1.13 to 1.87; tamoxifen IQ-OR, 1.25; 95% CI, 0.96 to 1.64; P for heterogeneity = .5). The predictive power was similar to the TC model (IQ-OR, 1.45; 95% CI, 1.21 to 1.73; mC, 0.55), but SNP88 was independent of TC (Spearman rank-order correlation, 0.012; P = .7), and when combined multiplicatively, a substantial improvement was seen (IQ-OR, 1.64; 95% CI, 1.36 to 1.97; mC, 0.60). Conclusion A polygenic risk score may be used to refine risk from the TC or similar models in women who are at an elevated risk of breast cancer and considering preventive therapy. Recalibration may be necessary for accurate risk assessment.

The impact of a panel of 18 SNPs on breast cancer risk in women attending a UK familial screening clinic: a case-control study

BACKGROUND

Breast cancer familial risk clinics offer screening and preventive strategies. While BRCA1/BRCA2 genetic testing provides important risk information for some women, panels of more common breast cancer risk genetic variants may have relevance to greater numbers of women with familial risk.

METHODS

Three polygenic risk scores (PRS) based on 18 SNPs were investigated in a case-control study of women attending a familial risk clinic. PRS were derived from published general European population allele ORs and frequencies (18-SNPs (SNP18)). In women with BRCA1/BRCA2 mutations, 3 SNPs/13 SNPs, respectively, generated the PRS estimates. In total, 364 incident breast cancer cases (112 with BRCA1/2 mutations) were matched with 1605 controls (691 BRCA1/2) by age last mammogram and BRCA1/2 genetic test result. 87 women with cancer before attendance were also considered. Logistic regression was used to measure PRS performance through ORs per IQR and calibration of the observed to expected (O/E) logarithm relative risk when unadjusted and adjusted for phenotypic risk factors assessed by the Tyrer-Cuzick (TC) model.

RESULTS

SNP18 was predictive for non-carriers of BRCA1/2 mutations (IQR OR 1.55, 95% CI 1.29 to 1.87, O/E 96%). Findings were unaffected by adjustment from TC (IQR OR 1.56, 95% CI 1.29 to 1.89) or when prior cancers were included (IQR OR 1.55, 95% CI 1.30 to 1.87). There was some evidence to support polygenic scores with weights for individuals with BRCA1/2 mutations (BRCA1 IQR OR 1.44, 95% CI 1.17 to 1.76; BRCA2 IQ OR 1.44, 95% CI 0.90 to 2.31).

CONCLUSIONS

PRS may be used to refine risk assessment for women at increased familial risk who test negative/have low likelihood of BRCA1/2 mutations. They may alter the recommended prevention strategy for many women attending family history clinics.

Using Breast Cancer Risk Associated Polymorphisms to Identify Women for Breast Cancer Chemoprevention

BACKGROUND

Breast cancer can be prevented with selective estrogen receptor modifiers (SERMs) and aromatase inhibitors (AIs). The US Preventive Services Task Force recommends that women with a 5-year breast cancer risk ≥3% consider chemoprevention for breast cancer. More than 70 single nucleotide polymorphisms (SNPs) have been associated with breast cancer. We sought to determine how to best integrate risk information from SNPs with other risk factors to risk stratify women for chemoprevention.

METHODS

We used the risk distribution among women ages 35-69 estimated by the Breast Cancer Surveillance Consortium (BCSC) risk model. We modeled the effect of adding 70 SNPs to the BCSC model and examined how this would affect how many women are reclassified above and below the threshold for chemoprevention.

RESULTS

We found that most of the benefit of SNP testing a population is achieved by testing a modest fraction of the population. For example, if women with a 5-year BCSC risk of >2.0% are tested (~21% of all women), ~75% of the benefit of testing all women (shifting women above or below 3% 5-year risk) would be derived. If women with a 5-year risk of >1.5% are tested (~36% of all women), ~90% of the benefit of testing all women would be derived.

CONCLUSION

SNP testing is effective for reclassification of women for chemoprevention, but is unlikely to reclassify women with <1.5% 5-year risk. These results can be used to implement an efficient two-step testing approach to identify high risk women who may benefit from chemoprevention.

Improvement in risk prediction, early detection and prevention of breast cancer in the NHS Breast Screening Programme and family history clinics: a dual cohort study

Background

In the UK, women are invited for 3-yearly mammography screening, through the NHS Breast Screening Programme (NHSBSP), from the ages of 47–50 years to the ages of 69–73 years. Women with family histories of breast cancer can, from the age of 40 years, obtain enhanced surveillance and, in exceptionally high-risk cases, magnetic resonance imaging. However, no NHSBSP risk assessment is undertaken. Risk prediction models are able to categorise women by risk using known risk factors, although accurate individual risk prediction remains elusive. The identification of mammographic breast density (MD) and common genetic risk variants [single nucleotide polymorphisms (SNPs)] has presaged the improved precision of risk models.

Objectives

To (1) identify the best performing model to assess breast cancer risk in family history clinic (FHC) and population settings; (2) use information from MD/SNPs to improve risk prediction; (3) assess the acceptability and feasibility of offering risk assessment in the NHSBSP; and (4) identify the incremental costs and benefits of risk stratified screening in a preliminary cost-effectiveness analysis.

Design

Two cohort studies assessing breast cancer incidence.

Setting

High-risk FHC and the NHSBSP Greater Manchester, UK.

Participants

A total of 10,000 women aged 20–79 years [Family History Risk Study (FH-Risk); UK Clinical Research Network identification number (UKCRN-ID) 8611] and 53,000 women from the NHSBSP [aged 46–73 years; Predicting the Risk of Cancer At Screening (PROCAS) study; UKCRN-ID 8080].

Interventions

Questionnaires collected standard risk information, and mammograms were assessed for breast density by a number of techniques. All FH-Risk and 10,000 PROCAS participants participated in deoxyribonucleic acid (DNA) studies. The risk prediction models Manual method, Tyrer–Cuzick (TC), BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) and Gail were used to assess risk, with modelling based on MD and SNPs. A preliminary model-based cost-effectiveness analysis of risk stratified screening was conducted.

Main outcome measures

Breast cancer incidence.

Data sources

The NHSBSP; cancer registration.

Results

A total of 446 women developed incident breast cancers in FH-Risk in 97,958 years of follow-up. All risk models accurately stratified women into risk categories. TC had better risk precision than Gail, and BOADICEA accurately predicted risk in the 6268 single probands. The Manual model was also accurate in the whole cohort. In PROCAS, TC had better risk precision than Gail [area under the curve (AUC) 0.58 vs. 0.54], identifying 547 prospective breast cancers. The addition of SNPs in the FH-Risk case–control study improved risk precision but was not useful inBRCA1(breast cancer 1 gene) families. Risk modelling of SNPs in PROCAS showed an incremental improvement from using SNP18 used in PROCAS to SNP67. MD measured by visual assessment score provided better risk stratification than automatic measures, despite wide intra- and inter-reader variability. Using a MD-adjusted TC model in PROCAS improved risk stratification (AUC = 0.6) and identified significantly higher rates (4.7 per 10,000 vs. 1.3 per 10,000;p < 0.001) of high-stage cancers in women with above-average breast cancer risks. It is not possible to provide estimates of the incremental costs and benefits of risk stratified screening because of lack of data inputs for key parameters in the model-based cost-effectiveness analysis.

Conclusions

Risk precision can be improved by using DNA and MD, and can potentially be used to stratify NHSBSP screening. It may also identify those at greater risk of high-stage cancers for enhanced screening. The cost-effectiveness of risk stratified screening is currently associated with extensive uncertainty. Additional research is needed to identify data needed for key inputs into model-based cost-effectiveness analyses to identify the impact on health-care resource use and patient benefits.

Future work

A pilot of real-time NHSBSP risk prediction to identify women for chemoprevention and enhanced screening is required.

Funding

The National Institute for Health Research Programme Grants for Applied Research programme. The DNA saliva collection for SNP analysis for PROCAS was funded by the Genesis Breast Cancer Prevention Appeal.

Breast Cancer Risk Prediction Using Clinical Models and 77 Independent Risk-Associated SNPs for Women Aged Under 50 Years: Australian Breast Cancer Family Registry

BACKGROUND

The extent to which clinical breast cancer risk prediction models can be improved by including information on known susceptibility SNPs is not known.

METHODS

Using 750 cases and 405 controls from the population-based Australian Breast Cancer Family Registry who were younger than 50 years at diagnosis and recruitment, respectively, Caucasian and not BRCA1 or BRCA2 mutation carriers, we derived absolute 5-year risks of breast cancer using the BOADICEA, BRCAPRO, BCRAT, and IBIS risk prediction models and combined these with a risk score based on 77 independent risk-associated SNPs. We used logistic regression to estimate the OR per adjusted SD for log-transformed age-adjusted 5-year risks. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC). Calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. We also constructed reclassification tables and calculated the net reclassification improvement.

RESULTS

The ORs for BOADICEA, BRCAPRO, BCRAT, and IBIS were 1.80, 1.75, 1.67, and 1.30, respectively. When combined with the SNP-based score, the corresponding ORs were 1.96, 1.89, 1.80, and 1.52. The corresponding AUCs were 0.66, 0.65, 0.64, and 0.57 for the risk prediction models, and 0.70, 0.69, 0.66, and 0.63 when combined with the SNP-based score.

CONCLUSIONS

By combining a 77 SNP-based score with clinical models, the AUC for predicting breast cancer before age 50 years improved by >20%.

IMPACT

Our estimates of the increased performance of clinical risk prediction models from including genetic information could be used to inform targeted screening and prevention.

Cancer Prevention: Obstacles, Challenges and the Road Ahead

Approaches to reduce the global burden of cancer include two major strategies: screening and early detection and active preventive intervention. The latter is the topic of this Commentary and spans a broad range of activities. The genetic heterogeneity and complexity of advanced cancers strongly support the rationale for early interruption of the carcinogenic process and an enhanced focus on prevention as a priority strategy to reduce the burden of cancer; however, the focus of cancer prevention management should be on individuals at high risk and on primary localized disease in which screening and detection should also play a vital role. The timing and dose of (chemo-)preventive intervention also affects response. The intervention may be ineffective if the target population is very high risk or already presenting with preneoplastic lesions with cellular changes that cannot be reversed. The field needs to move beyond general concepts of carcinogenesis to targeted organ site prevention approaches in patients at high risk, as is currently being done for breast and colorectal cancers. Establishing the benefit of new cancer preventive interventions will take years and possibly decades, depending on the outcome being evaluated. We also propose that comparative effectiveness research designs and the value of information obtained from large-scale prevention studies are necessary in order for preventive interventions to become a routine part of cancer management.

Refining Breast Cancer Risk Stratification: Additional Genes, Additional Information

Recent advances in genomic technology have enabled far more rapid, less expensive sequencing of multiple genes than was possible only a few years ago. Advances in bioinformatics also facilitate the interpretation of large amounts of genomic data. New strategies for cancer genetic risk assessment include multiplex sequencing panels of 5 to more than 100 genes (in which rare mutations are often associated with at least two times the average risk of developing breast cancer) and panels of common single-nucleotide polymorphisms (SNPs), combinations of which are generally associated with more modest cancer risks (more than twofold). Although these new multiple-gene panel tests are used in oncology practice, questions remain about the clinical validity and the clinical utility of their results. To translate this increasingly complex genetic information for clinical use, cancer risk prediction tools are under development that consider the joint effects of all susceptibility genes, together with other established breast cancer risk factors. Risk-adapted screening and prevention protocols are underway, with ongoing refinement as genetic knowledge grows. Priority areas for future research include the clinical validity and clinical utility of emerging genetic tests; the accuracy of developing cancer risk prediction models; and the long-term outcomes of risk-adapted screening and prevention protocols, in terms of patients' experiences and survival.

Barriers to preventive therapy for breast and other major cancers and strategies to improve uptake

The global cancer burden continues to rise and the war on cancer can only be won if improvements in treatment go hand in hand with therapeutic cancer prevention. Despite the availability of several efficacious agents, utilisation of preventive therapy has been poor due to various barriers, such as the lack of physician and patient awareness, fear of side effects, and licensing and indemnity issues. In this review, we discuss these barriers in detail and propose strategies to overcome them. These strategies include improving physician awareness and countering prejudices by highlighting the important differences between preventive therapy and cancer treatment. The importance of the agent-biomarker-cohort (ABC) paradigm to improve effectiveness of preventive therapy cannot be overemphasised. Future research to improve therapeutic cancer prevention needs to include improvements in the prediction of benefits and harms, and improvements in the safety profile of existing agents by experimentation with dose. We also highlight the role of drug repurposing for providing new agents as well as to address the current imbalance between therapeutic and preventive research. In order to move the field of therapeutic cancer prevention forwards, engagement with policymakers to correct research imbalance as well as to remove practical obstacles to implementation is also urgently needed.

SNPs and breast cancer risk prediction for African American and Hispanic women

For African American or Hispanic women, the extent to which clinical breast cancer risk prediction models are improved by including information on susceptibility single nucleotide polymorphisms (SNPs) is unknown, even though these women comprise increasing proportions of the US population and represent a large proportion of the world’s population. We studied 7539 African American and 3363 Hispanic women from the Women’s Health Initiative. The age-adjusted 5-year risks from the BCRAT and IBIS risk prediction models were measured and combined with a risk score based on >70 independent susceptibility SNPs. Logistic regression, adjusting for age group, was used to estimate risk associations with log-transformed age-adjusted 5-year risks. Discrimination was measured by the odds ratio (OR) per standard deviation (SD) and the area under the receiver operator curve (AUC). When considered alone, the ORs for African American women were 1.28 for BCRAT, and 1.04 for IBIS. When combined with the SNP risk score (OR 1.23), the corresponding ORs were 1.39 and 1.22. For Hispanic women the corresponding ORs were 1.25 for BCRAT, and 1.15 for IBIS. When combined with the SNP risk score (OR 1.39), the corresponding ORs were 1.48 and 1.42. There was no evidence that any of the combined models were not well calibrated. Including information on known breast cancer susceptibility loci provides approximately 10 and 19 % improvement in risk prediction using BCRAT for African Americans and Hispanics, respectively. The corresponding figures for IBIS are approximately 18 and 26 %, respectively.

Do Health Professionals Need Additional Competencies for Stratified Cancer Prevention Based on Genetic Risk Profiling?

There is growing evidence that inclusion of genetic information about known common susceptibility variants may enable population risk-stratification and personalized prevention for common diseases including cancer. This would require the inclusion of genetic testing as an integral part of individual risk assessment of an asymptomatic individual. Front line health professionals would be expected to interact with and assist asymptomatic individuals through the risk stratification process. In that case, additional knowledge and skills may be needed. Current guidelines and frameworks for genetic competencies of non-specialist health professionals place an emphasis on rare inherited genetic diseases. For common diseases, health professionals do use risk assessment tools but such tools currently do not assess genetic susceptibility of individuals. In this article, we compare the skills and knowledge needed by non-genetic health professionals, if risk-stratified prevention is implemented, with existing competence recommendations from the UK, USA and Europe, in order to assess the gaps in current competences. We found that health professionals would benefit from understanding the contribution of common genetic variations in disease risk, the rationale for a risk-stratified prevention pathway, and the implications of using genomic information in risk-assessment and risk management of asymptomatic individuals for common disease prevention.

A segregation index combining phenotypic (clinical characteristics) and genotypic (gene expression) biomarkers from a urine sample to triage out patients presenting with hematuria who have a low probability of urothelial carcinoma

Background

Hematuria can be symptomatic of urothelial carcinoma (UC) and ruling out patients with benign causes during primary evaluation is challenging. Patients with hematuria undergoing urological work-ups place significant clinical and financial burdens on healthcare systems. Current clinical evaluation involves processes that individually lack the sensitivity for accurate determination of UC. Algorithms and nomograms combining genotypic and phenotypic variables have largely focused on cancer detection and failed to improve performance. This study aimed to develop and validate a model incorporating both genotypic and phenotypic variables with high sensitivity and a high negative predictive value (NPV) combined to triage out patients with hematuria who have a low probability of having UC and may not require urological work-up.

Methods

Expression of IGFBP5, HOXA13, MDK, CDK1 and CXCR2 genes in a voided urine sample (genotypic) and age, gender, frequency of macrohematuria and smoking history (phenotypic) data were collected from 587 patients with macrohematuria. Logistic regression was used to develop predictive models for UC. A combined genotypic-phenotypic model (G + P INDEX) was compared with genotypic (G INDEX) and phenotypic (P INDEX) models. Area under receiver operating characteristic curves (AUC) defined the performance of each INDEX: high sensitivity, NPV >0.97 and a high test-negative rate was considered optimal for triaging out patients. The robustness of the G + P INDEX was tested in 40 microhematuria patients without UC.

Results

The G + P INDEX offered a bias-corrected AUC of 0.86 compared with 0.61 and 0.83, for the P and G INDEXs respectively. When the test-negative rate was 0.4, the G + P INDEX (sensitivity = 0.95; NPV = 0.98) offered improved performance compared with the G INDEX (sensitivity = 0.86; NPV = 0.96). 80% of patients with microhematuria who did not have UC were correctly triaged out using the G + P INDEX, therefore not requiring a full urological work-up.

Conclusion

The adoption of G + P INDEX enables a significant change in clinical utility. G + P INDEX can be used to segregate hematuria patients with a low probability of UC with a high degree of confidence in the primary evaluation. Triaging out low-probability patients early significantly reduces the need for expensive and invasive work-ups, thereby lowering diagnosis-related adverse events and costs.

Breast cancer prevention across the cancer care continuum

OBJECTIVES

To review the current state of breast cancer prevention from primary prevention through survivorship, highlight cross-cutting issues, and discuss strategies for clinical integration and future research.

DATA SOURCES

Published articles between 1985 and 2015 and original research.

CONCLUSION

Cancer risk persists across the lifespan. Interprofessional strategies to reduce morbidity and mortality from cancer include primary, secondary, and tertiary prevention (survivorship). Prevention strategies across the cancer care continuum are cross-cutting and focus on measures to: prevent the onset of disease, identify and treat asymptomatic persons who have already developed risk factors or preclinical disease, and restore function, minimize the negative effects of disease, and prevent disease-related complications.

IMPLICATIONS FOR NURSING PRACTICE

Oncology nurses and advanced practice nurses are vital in the delivery of breast cancer prevention strategies.

Update on breast cancer risk prediction and prevention

PURPOSE OF REVIEW

Breast cancer is the most common cancer in women worldwide. This review will focus on current prevention strategies for women at high risk.

RECENT FINDINGS

The identification of women who are at high risk of developing breast cancer is key to breast cancer prevention. Recent findings have shown that the inclusion of breast density and a panel of low-penetrance genetic polymorphisms can improve risk estimation compared with previous models. Preventive therapy with aromatase inhibitors has produced large reductions in breast cancer incidence in postmenopausal women. Tamoxifen confers long-term protection and is the only proven preventive treatment for premenopausal women. Several other agents, including metformin, bisphosphonates, aspirin and statins, have been found to be effective in nonrandomized settings.

SUMMARY

There are many options for the prevention of oestrogen-positive breast cancer, in postmenopausal women who can be given a selective oestrogen receptor modulator or an aromatase inhibitor. It still remains unclear how to prevent oestrogen-negative breast cancer, which occurs more often in premenopausal women. Identification of women at high risk of the disease is crucial, and the inclusion of breast density and a panel of genetic polymorphisms, which individually have low penetrance, can improve risk assessment.

Risk determination and prevention of breast cancer

Breast cancer is an increasing public health problem. Substantial advances have been made in the treatment of breast cancer, but the introduction of methods to predict women at elevated risk and prevent the disease has been less successful. Here, we summarize recent data on newer approaches to risk prediction, available approaches to prevention, how new approaches may be made, and the difficult problem of using what we already know to prevent breast cancer in populations. During 2012, the Breast Cancer Campaign facilitated a series of workshops, each covering a specialty area of breast cancer to identify gaps in our knowledge. The risk-and-prevention panel involved in this exercise was asked to expand and update its report and review recent relevant peer-reviewed literature. The enlarged position paper presented here highlights the key gaps in risk-and-prevention research that were identified, together with recommendations for action. The panel estimated from the relevant literature that potentially 50% of breast cancer could be prevented in the subgroup of women at high and moderate risk of breast cancer by using current chemoprevention (tamoxifen, raloxifene, exemestane, and anastrozole) and that, in all women, lifestyle measures, including weight control, exercise, and moderating alcohol intake, could reduce breast cancer risk by about 30%. Risk may be estimated by standard models potentially with the addition of, for example, mammographic density and appropriate single-nucleotide polymorphisms. This review expands on four areas: (a) the prediction of breast cancer risk, (b) the evidence for the effectiveness of preventive therapy and lifestyle approaches to prevention, (c) how understanding the biology of the breast may lead to new targets for prevention, and (d) a summary of published guidelines for preventive approaches and measures required for their implementation. We hope that efforts to fill these and other gaps will lead to considerable advances in our efforts to predict risk and prevent breast cancer over the next 10 years.