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

An app promoting weight gain prevention via healthy behaviours amongst young women with a family history of breast cancer: Acceptability and usability assessment

BACKGROUND

Breast cancer is the most frequent female malignancy in the UK. Around 20% of cases are linked to weight gain, excess weight and health behaviours. We designed a weight gain prevention, health behaviour intervention for young women at increased risk.

METHODS

The study comprised a single arm observational study over 2 months testing acceptability and usability of the intervention: online group welcome event, app and private Facebook group. Females aged 18-35 years at moderate or high risk of breast cancer (>17% lifetime risk) were recruited via invite letters and social media posts. The app included behaviour change techniques and education content. Online questionnaires were completed at baseline, as well as at 1 and 2 months. We also assessed feasibility of study procedures.

RESULTS

Both recruitment methods were successful. Thirty-five women were recruited, 26% via social media posts. Median age was 33 (interquartile range = 28.2-34.5) years, the majority (94.1%) were of White ethnicity. Thirty-four participants were included in the analyses, of which 94% downloaded the app. Median self-monitoring logs per participant during the study period was 10.0 (interquartile range = 4.8-28.8). App quality mean (SD) score was 3.7 (0.6) at 1 and 2 months (scale: 1-5). Eighty-nine per cent rated the app at average or above at 1 month and 75.0% at 2 months. Nineteen women (55.9%) joined the Facebook group and there were 61 comments and 83 reactions and votes from participants during the study period.

CONCLUSIONS

This first iteration of the app and intervention was well received and is suitable to progress to the next stage of refining and further testing.

Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank

BACKGROUND

Previous studies have demonstrated that incorporating a polygenic risk score (PRS) to existing risk prediction models for breast cancer improves model fit, but to determine its clinical utility the impact on risk categorization needs to be established. We add a PRS to two well-established models and quantify the difference in classification using the net reclassification improvement (NRI).

METHODS

We analyzed data from 126,490 post-menopausal women of "White British" ancestry, aged 40 to 69 years at baseline from the UK Biobank prospective cohort. The breast cancer outcome was derived from linked registry data and hospital records. We combined a PRS for breast cancer with 10-year risk scores from the Tyrer-Cuzick and Gail models, and compared these to the risk scores from the models using phenotypic variables alone. We report metrics of discrimination and classification, and consider the importance of the risk threshold selected.

RESULTS

The Harrell's C statistic of the 10-year risk from the Tyrer-Cuzick and Gail models was 0.57 and 0.54, respectively, increasing to 0.67 when the PRS was included. Inclusion of the PRS gave a positive NRI for cases in both models [0.080 (95% confidence interval (CI), 0.053-0.104) and 0.051 (95% CI, 0.030-0.073), respectively], with negligible impact on controls.

CONCLUSIONS

The addition of a PRS for breast cancer to the well-established Tyrer-Cuzick and Gail models provides a substantial improvement in the prediction accuracy and risk stratification.

IMPACT

These findings could have important implications for the ongoing discussion about the value of PRS in risk prediction models and screening.

Breast Cancer Polygenic-Risk Score Influence on Risk-Reducing Endocrine Therapy Use: Genetic Risk Estimate (GENRE) Trial 1-Year and 2-Year Follow-Up

Refinement of breast cancer risk estimates with a polygenic-risk score (PRS) may improve uptake of risk-reducing endocrine therapy (ET). A previous clinical trial assessed the influence of adding a PRS to traditional risk estimates on ET use. We stratified participants according to PRS-refined breast cancer risk and evaluated ET use and ET-related quality of life (QOL) at 1-year (previously reported) and 2-year follow-ups. Of 151 participants, 58 (38.4%) initiated ET, and 22 (14.6%) discontinued ET by 2 years; 42 (27.8%) and 36 (23.8%) participants were using ET at 1- and 2-year follow-ups, respectively. At the 2-year follow-up, 39% of participants with a lifetime breast cancer risk of 40.1% to 100.0%, 18% with a 20.1% to 40.0% risk, and 16% with a 0.0% to 20.0% risk were taking ET (overall P = 0.01). Moreover, 40% of participants whose breast cancer risk increased by 10% or greater with addition of the PRS to a traditional breast cancer-risk model were taking ET versus 0% whose risk decreased by 10% or greater (P = 0.004). QOL was similar for participants taking or not taking ET at 1- and 2-year follow-ups, although most who discontinued ET did so because of adverse effects. However, these QOL results may have been skewed by the long interval between QOL surveys and lack of baseline QOL data. PRS-informed breast cancer prevention counseling has a lasting, but waning, effect over time. Additional follow-up studies are needed to address the effect of PRS on ET adherence, ET-related QOL, supplemental breast cancer screening, and other risk-reducing behaviors.

PREVENTION RELEVANCE

Risk-reducing medications for breast cancer are considerably underused. Informing women at risk with precise and individualized risk assessment tools may substantially affect the incidence of breast cancer. In our study, a risk assessment tool (IBIS-polygenic-risk score) yielded promising results, with 39% of women at highest risk starting preventive medication.

A Systematic Review and Critical Assessment of Breast Cancer Risk Prediction Tools Incorporating a Polygenic Risk Score for the General Population

Single nucleotide polymorphisms (SNPs) in the form of a polygenic risk score (PRS) have emerged as a promising factor that could improve the predictive performance of breast cancer (BC) risk prediction tools. This study aims to appraise and critically assess the current evidence on these tools. Studies were identified using Medline, EMBASE and the Cochrane Library up to November 2022 and were included if they described the development and/ or validation of a BC risk prediction model using a PRS for women of the general population and if they reported a measure of predictive performance. We identified 37 articles, of which 29 combined genetic and non-genetic risk factors using seven different risk prediction tools. Most models (55.0%) were developed on populations from European ancestry and performed better than those developed on populations from other ancestry groups. Regardless of the number of SNPs in each PRS, models combining a PRS with genetic and non-genetic risk factors generally had better discriminatory accuracy (AUC from 0.52 to 0.77) than those using a PRS alone (AUC from 0.48 to 0.68). The overall risk of bias was considered low in most studies. BC risk prediction tools combining a PRS with genetic and non-genetic risk factors provided better discriminative accuracy than either used alone. Further studies are needed to cross-compare their clinical utility and readiness for implementation in public health practices.

Breast cancer polygenic risk scores derived in White European populations are not calibrated for women of Ashkenazi Jewish descent

PURPOSE

Polygenic risk scores (PRSs) are a major component of accurate breast cancer (BC) risk prediction but require ethnicity-specific calibration. Ashkenazi Jewish (AJ) population is assumed to be of White European (WE) origin in some commercially available PRSs despite differing effect allele frequencies (EAFs). We conducted a case-control study of WE and AJ women from the Predicting Risk of Cancer at Screening Study. The Breast Cancer in Northern Israel Study provided a separate AJ population-based case-control validation series.

METHODS

All women underwent Illumina OncoArray single-nucleotide variation (SNV; formerly single-nucleotide polymorphism [SNP]) analysis. Two PRSs were assessed, SNV142 and SNV78. A total of 221 of 2243 WE women (discovery: cases = 111; controls = 110; validation: cases = 651; controls = 1772) and 221 AJ women (cases = 121; controls = 110) were included from the UK study; the Israeli series consisted of 2045 AJ women (cases = 1331; controls = 714). EAFs were obtained from the Genome Aggregation Database.

RESULTS

In the UK study, the mean SNV142 PRS demonstrated good calibration and discrimination in WE population, with mean PRS of 1.33 (95% CI 1.18-1.48) in cases and 1.01 (95% CI 0.89-1.13) in controls. In AJ women from Manchester, the mean PRS of 1.54 (1.38-1.70) in cases and 1.20 (1.08-1.32) in controls demonstrated good discrimination but overestimation of BC relative risk. After adjusting for EAFs for the AJ population, mean risk was corrected (mean SNV142 PRS cases = 1.30 [95% CI 1.16-1.44] and controls = 1.02 [95% CI 0.92-1.12]). This was recapitulated in the larger Israeli data set with good discrimination (area under the curve = 0.632 [95% CI 0.607-0.657] for SNV142).

CONCLUSION

AJ women should not be given BC relative risk predictions based on PRSs calibrated to EAFs from the WE population. PRSs need to be recalibrated using AJ-derived EAFs. A simple recalibration using the mean PRS adjustment ratio likely performs well.

A Breast Cancer Polygenic Risk Score Is Feasible for Risk Stratification in the Norwegian Population

BACKGROUND

Statistical associations of numerous single nucleotide polymorphisms with breast cancer (BC) have been identified in genome-wide association studies (GWAS). Recent evidence suggests that a Polygenic Risk Score (PRS) can be a useful risk stratification instrument for a BC screening strategy, and a PRS test has been developed for clinical use. The performance of the PRS is yet unknown in the Norwegian population.

AIM

To evaluate the performance of PRS models for BC in a Norwegian dataset.

METHODS

We investigated a sample of 1053 BC cases and 7094 controls from different regions of Norway. PRS values were calculated using four PRS models, and their performance was evaluated by the area under the curve (AUC) and the odds ratio (OR). The effect of the PRS on the age of onset of BC was determined by a Cox regression model, and the lifetime absolute risk of developing BC was calculated using the iCare tool.

RESULTS

The best performing PRS model included 3820 SNPs, which yielded an AUC = 0.625 and an OR = 1.567 per one standard deviation increase. The PRS values of the samples correlate with an increased risk of BC, with a hazard ratio of 1.494 per one standard deviation increase (95% confidence interval of 1.406-1.588). The individuals in the highest decile of the PRS have at least twice the risk of developing BC compared to the individuals with a median PRS. The results in this study with Norwegian samples are coherent with the findings in the study conducted using Estonian and UK Biobank samples.

CONCLUSION

The previously validated PRS models have a similar observed accuracy in the Norwegian data as in the UK and Estonian populations. A PRS provides a meaningful association with the age of onset of BC and lifetime risk. Therefore, as suggested in Estonia, a PRS may also be integrated into the screening strategy for BC in Norway.

Combining Molecular and Radiomic Features for Risk Assessment in Breast Cancer

Breast cancer risk is highly variable within the population and current research is leading the shift toward personalized medicine. By accurately assessing an individual woman's risk, we can reduce the risk of over/undertreatment by preventing unnecessary procedures or by elevating screening procedures. Breast density measured from conventional mammography has been established as one of the most dominant risk factors for breast cancer; however, it is currently limited by its ability to characterize more complex breast parenchymal patterns that have been shown to provide additional information to strengthen cancer risk models. Molecular factors ranging from high penetrance, or high likelihood that a mutation will show signs and symptoms of the disease, to combinations of gene mutations with low penetrance have shown promise for augmenting risk assessment. Although imaging biomarkers and molecular biomarkers have both individually demonstrated improved performance in risk assessment, few studies have evaluated them together. This review aims to highlight the current state of the art in breast cancer risk assessment using imaging and genetic biomarkers.

Evaluation of an AI Model to Assess Future Breast Cancer Risk

Background Accurate breast cancer risk assessment after a negative screening result could enable better strategies for early detection. Purpose To evaluate a deep learning algorithm for risk assessment based on digital mammograms. Materials and Methods A retrospective observational matched case-control study was designed using the OPTIMAM Mammography Image Database from the National Health Service Breast Screening Programme in the United Kingdom from February 2010 to September 2019. Patients with breast cancer (cases) were diagnosed following a mammographic screening or between two triannual screening rounds. Controls were matched based on mammography device, screening site, and age. The artificial intelligence (AI) model only used mammograms at screening before diagnosis. The primary objective was to assess model performance, with a secondary objective to assess heterogeneity and calibration slope. The area under the receiver operating characteristic curve (AUC) was estimated for 3-year risk. Heterogeneity according to cancer subtype was assessed using a likelihood ratio interaction test. Statistical significance was set at P < .05. Results Analysis included patients with screen-detected (median age, 60 years [IQR, 55-65 years]; 2044 female, including 1528 with invasive cancer and 503 with ductal carcinoma in situ [DCIS]) or interval (median age, 59 years [IQR, 53-65 years]; 696 female, including 636 with invasive cancer and 54 with DCIS) breast cancer and 1:1 matched controls, each with a complete set of mammograms at the screening preceding diagnosis. The AI model had an overall AUC of 0.68 (95% CI: 0.66, 0.70), with no evidence of a significant difference between interval and screen-detected (AUC, 0.69 vs 0.67; P = .085) cancer. The calibration slope was 1.13 (95% CI: 1.01, 1.26). There was similar performance for the detection of invasive cancer versus DCIS (AUC, 0.68 vs 0.66; P = .057). The model had higher performance for advanced cancer risk (AUC, 0.72 ≥stage II vs 0.66 Collapse

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MESH Headings

• Humans
• Female
• Middle Aged
• Breast Neoplasms/diagnostic imaging
• Carcinoma, Intraductal, Noninfiltrating
• Artificial Intelligence
• Case-Control Studies
• Retrospective Studies
• State Medicine

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Grants

• 28689 Cancer Research UK
• C49757/A28689 Cancer Research UK

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Affiliation(s)

Celeste Damiani From the Center for Human Technologies, Istituto Italiano di Tecnologia, Via Melen 83, Genoa 16152, Italy (C.D.); Wolfson Institute of Population Health, Queen Mary University of London, London, UK (C.D., E.F.L., J.C., A.R.B.); Institute of Computer Science (ICS), Foundation of Research and Technology Hellas, Heraklion, Crete, Greece (G.K.); Joint for Director Breast Screening, University Hospitals Coventry and Warwickshire NHS Trust Coventry, Coventry, UK (M.S.); Department of Oncoplastic Breast Surgery, University Hospitals of Leicester NHS Trust, Leicester, UK (M.A.A.); Consumer member at National Cancer Research Institute, Breast Group, London, UK (J.R., C.P.); and University of Warwick, WMG, Coventry, UK (G.M.)
Grigorios Kalliatakis From the Center for Human Technologies, Istituto Italiano di Tecnologia, Via Melen 83, Genoa 16152, Italy (C.D.); Wolfson Institute of Population Health, Queen Mary University of London, London, UK (C.D., E.F.L., J.C., A.R.B.); Institute of Computer Science (ICS), Foundation of Research and Technology Hellas, Heraklion, Crete, Greece (G.K.); Joint for Director Breast Screening, University Hospitals Coventry and Warwickshire NHS Trust Coventry, Coventry, UK (M.S.); Department of Oncoplastic Breast Surgery, University Hospitals of Leicester NHS Trust, Leicester, UK (M.A.A.); Consumer member at National Cancer Research Institute, Breast Group, London, UK (J.R., C.P.); and University of Warwick, WMG, Coventry, UK (G.M.)
Muthyala Sreenivas From the Center for Human Technologies, Istituto Italiano di Tecnologia, Via Melen 83, Genoa 16152, Italy (C.D.); Wolfson Institute of Population Health, Queen Mary University of London, London, UK (C.D., E.F.L., J.C., A.R.B.); Institute of Computer Science (ICS), Foundation of Research and Technology Hellas, Heraklion, Crete, Greece (G.K.); Joint for Director Breast Screening, University Hospitals Coventry and Warwickshire NHS Trust Coventry, Coventry, UK (M.S.); Department of Oncoplastic Breast Surgery, University Hospitals of Leicester NHS Trust, Leicester, UK (M.A.A.); Consumer member at National Cancer Research Institute, Breast Group, London, UK (J.R., C.P.); and University of Warwick, WMG, Coventry, UK (G.M.)
Miaad Al-Attar From the Center for Human Technologies, Istituto Italiano di Tecnologia, Via Melen 83, Genoa 16152, Italy (C.D.); Wolfson Institute of Population Health, Queen Mary University of London, London, UK (C.D., E.F.L., J.C., A.R.B.); Institute of Computer Science (ICS), Foundation of Research and Technology Hellas, Heraklion, Crete, Greece (G.K.); Joint for Director Breast Screening, University Hospitals Coventry and Warwickshire NHS Trust Coventry, Coventry, UK (M.S.); Department of Oncoplastic Breast Surgery, University Hospitals of Leicester NHS Trust, Leicester, UK (M.A.A.); Consumer member at National Cancer Research Institute, Breast Group, London, UK (J.R., C.P.); and University of Warwick, WMG, Coventry, UK (G.M.)
Janice Rose From the Center for Human Technologies, Istituto Italiano di Tecnologia, Via Melen 83, Genoa 16152, Italy (C.D.); Wolfson Institute of Population Health, Queen Mary University of London, London, UK (C.D., E.F.L., J.C., A.R.B.); Institute of Computer Science (ICS), Foundation of Research and Technology Hellas, Heraklion, Crete, Greece (G.K.); Joint for Director Breast Screening, University Hospitals Coventry and Warwickshire NHS Trust Coventry, Coventry, UK (M.S.); Department of Oncoplastic Breast Surgery, University Hospitals of Leicester NHS Trust, Leicester, UK (M.A.A.); Consumer member at National Cancer Research Institute, Breast Group, London, UK (J.R., C.P.); and University of Warwick, WMG, Coventry, UK (G.M.)
Clare Pudney From the Center for Human Technologies, Istituto Italiano di Tecnologia, Via Melen 83, Genoa 16152, Italy (C.D.); Wolfson Institute of Population Health, Queen Mary University of London, London, UK (C.D., E.F.L., J.C., A.R.B.); Institute of Computer Science (ICS), Foundation of Research and Technology Hellas, Heraklion, Crete, Greece (G.K.); Joint for Director Breast Screening, University Hospitals Coventry and Warwickshire NHS Trust Coventry, Coventry, UK (M.S.); Department of Oncoplastic Breast Surgery, University Hospitals of Leicester NHS Trust, Leicester, UK (M.A.A.); Consumer member at National Cancer Research Institute, Breast Group, London, UK (J.R., C.P.); and University of Warwick, WMG, Coventry, UK (G.M.)
Emily F Lane From the Center for Human Technologies, Istituto Italiano di Tecnologia, Via Melen 83, Genoa 16152, Italy (C.D.); Wolfson Institute of Population Health, Queen Mary University of London, London, UK (C.D., E.F.L., J.C., A.R.B.); Institute of Computer Science (ICS), Foundation of Research and Technology Hellas, Heraklion, Crete, Greece (G.K.); Joint for Director Breast Screening, University Hospitals Coventry and Warwickshire NHS Trust Coventry, Coventry, UK (M.S.); Department of Oncoplastic Breast Surgery, University Hospitals of Leicester NHS Trust, Leicester, UK (M.A.A.); Consumer member at National Cancer Research Institute, Breast Group, London, UK (J.R., C.P.); and University of Warwick, WMG, Coventry, UK (G.M.)
Jack Cuzick From the Center for Human Technologies, Istituto Italiano di Tecnologia, Via Melen 83, Genoa 16152, Italy (C.D.); Wolfson Institute of Population Health, Queen Mary University of London, London, UK (C.D., E.F.L., J.C., A.R.B.); Institute of Computer Science (ICS), Foundation of Research and Technology Hellas, Heraklion, Crete, Greece (G.K.); Joint for Director Breast Screening, University Hospitals Coventry and Warwickshire NHS Trust Coventry, Coventry, UK (M.S.); Department of Oncoplastic Breast Surgery, University Hospitals of Leicester NHS Trust, Leicester, UK (M.A.A.); Consumer member at National Cancer Research Institute, Breast Group, London, UK (J.R., C.P.); and University of Warwick, WMG, Coventry, UK (G.M.)
Giovanni Montana From the Center for Human Technologies, Istituto Italiano di Tecnologia, Via Melen 83, Genoa 16152, Italy (C.D.); Wolfson Institute of Population Health, Queen Mary University of London, London, UK (C.D., E.F.L., J.C., A.R.B.); Institute of Computer Science (ICS), Foundation of Research and Technology Hellas, Heraklion, Crete, Greece (G.K.); Joint for Director Breast Screening, University Hospitals Coventry and Warwickshire NHS Trust Coventry, Coventry, UK (M.S.); Department of Oncoplastic Breast Surgery, University Hospitals of Leicester NHS Trust, Leicester, UK (M.A.A.); Consumer member at National Cancer Research Institute, Breast Group, London, UK (J.R., C.P.); and University of Warwick, WMG, Coventry, UK (G.M.)
Adam R Brentnall From the Center for Human Technologies, Istituto Italiano di Tecnologia, Via Melen 83, Genoa 16152, Italy (C.D.); Wolfson Institute of Population Health, Queen Mary University of London, London, UK (C.D., E.F.L., J.C., A.R.B.); Institute of Computer Science (ICS), Foundation of Research and Technology Hellas, Heraklion, Crete, Greece (G.K.); Joint for Director Breast Screening, University Hospitals Coventry and Warwickshire NHS Trust Coventry, Coventry, UK (M.S.); Department of Oncoplastic Breast Surgery, University Hospitals of Leicester NHS Trust, Leicester, UK (M.A.A.); Consumer member at National Cancer Research Institute, Breast Group, London, UK (J.R., C.P.); and University of Warwick, WMG, Coventry, UK (G.M.)

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Randomised controlled trial of breast cancer and multiple disease prevention weight loss programmes vs written advice amongst women attending a breast cancer family history clinic

BACKGROUND

Overweight and obesity are common amongst women attending breast cancer Family History, Risk and Prevention Clinics (FHRPCs). Overweight increases risk of breast cancer (BC) and conditions including¹ cardiovascular disease (CVD) and type-2 diabetes (T2D). Clinics provide written health behaviour advice with is likely to have minimal effects. We assessed efficacy of two remotely delivered weight loss programmes vs. written advice.

METHOD

210 women with overweight or obesity attending three UK FHRPCs were randomised to either a BC prevention programme (BCPP) framed to reduce risk of BC (n = 86), a multiple disease prevention programme (MDPP) framed to reduce risk of BC, CVD and T2D (n = 87), or written advice (n = 37). Change in weight and health behaviours were assessed at 12-months.

RESULTS

Weight loss at 12 months was -6.3% (-8.2, -4.5) in BCPP, -6.0% (-7.9, -4.2) in MDPP and -3.3% (-6.2, -0.5) in the written group (p = 0.451 across groups). The percentage losing ≥10% weight in these groups were respectively 34%, 23% and 14% (p = 0.038 across groups).

DISCUSSION

BCPP and MDPP programmes resulted in more women achieving ≥10% weight loss, but no evidence of additional benefits of MDPP. A multicentre RCT to test the BCPP across UK FHRPCs is warranted. Clinical Trial Registration ISRCTN16431108.

Polygenic risk scores and breast cancer risk prediction

Polygenic Risk Scores (PRS) are a major component of accurate breast cancer risk prediction and have the potential to improve screening and prevention strategies. PRS combine the risk from Single nucleotide polymorphisms (SNPs) associated with breast cancer in Genome Wide Association Studies (GWAS) and explain over 30% of breast cancer heritability. When incorporated into risk models, the more personalised risk assessment derived from PRS, help identify women at higher risk of breast cancer development and enables the implementation of stratified screening and prevention approaches. This review describes the role of PRS in breast cancer risk prediction including the development of PRS and their clinical application. We have also examined the role of PRS within more well-established risk prediction models which incorporate known classic risk factors and discuss the interaction of PRS with these factors and their capacity to predict breast cancer subtypes. Before PRS can be implemented on a population-wide scale, there are several challenges that must be addressed. Perhaps the most pressing of these is the use of PRS in women of non-White European origin, where PRS have been shown to have attenuated risk prediction both in discrimination and calibration. We discuss progress in developing and applying PRS in non-white European populations. PRS represent a significant advance in breast cancer risk prediction and their further development will undoubtedly enhance personalisation.

Detection of IL-1β, VEGF and IL-4 with their novel genetic variations in breast cancer patients

Interleukin-1β (IL-1β), vascular endothelial growth factor (VEGF), and IL-4 serum levels and new genetic mutations in breast cancer (BC) patients were assessed in the current study. The serum levels of the examined cytokines in 40 BC patients and 40 control subjects were assessed using the ELISA technique. In order to identify genotype variants of the IL-1β, IL-4, and VEGF genes in 40 Formalin Fixed Paraffin Embedded (FFPE) samples with BC and 10 FFPE samples from healthy women's breast tissue, Sanger sequencing was used. According to this study, BC patients had significantly lower serum concentrations of IL-4 and significantly higher quantities of the tumor markers, CA15-3, IL-1β, and VEGF. In terms of genotype alterations, a total of 21 mutations in three trialed genes (eight in IL-1β, 10 in IL-4, and three in VEGF) were found in BC patients. The results of the current investigation suggested that angiogenesis and the development of BC may be significantly influenced by the genetic differences and higher levels of the examined cytokines.

Genetic architecture of mammographic density as a risk factor for breast cancer: a systematic review

BACKGROUND

Mammography Density (MD) is a potential risk marker that is influenced by genetic polymorphisms and can subsequently modulate the risk of breast cancer. This qualitative systematic review summarizes the genes and biological pathways involved in breast density and discusses the potential clinical implications in view of the genetic risk profile for breast density.

METHODS

The terms related to "Common genetic variations" and "Breast density" were searched in Scopus, PubMed, and Web of Science databases. Gene pathways analysis and assessment of protein interactions were also performed.

RESULTS

Eighty-six studies including 111 genes, reported a significant association between mammographic density in different populations. ESR1, IGF1, IGFBP3, and ZNF365 were the most prevalent genes. Moreover, estrogen metabolism, signal transduction, and prolactin signaling pathways were significantly related to the associated genes. Mammography density was an associated phenotype, and eight out of 111 genes, including COMT, CYP19A1, CYP1B1, ESR1, IGF1, IGFBP1, IGFBP3, and LSP1, were modifiers of this trait.

CONCLUSION

Genes involved in developmental processes and the evolution of secondary sexual traits play an important role in determining mammographic density. Due to the effect of breast tissue density on the risk of breast cancer, these genes may also be associated with breast cancer risk.

Association and performance of polygenic risk scores for breast cancer among French women presenting or not a familial predisposition to the disease

BACKGROUND

Three partially overlapping breast cancer polygenic risk scores (PRS) comprising 77, 179 and 313 SNPs have been proposed for European-ancestry women by the Breast Cancer Association Consortium (BCAC) for improving risk prediction in the general population. However, the effect of these SNPs may vary from one country to another and within a country because of other factors.

OBJECTIVE

To assess their associated risk and predictive performance in French women from (1) the CECILE population-based case-control study, (2) BRCA1 or BRCA2 (BRCA1/2) pathogenic variant (PV) carriers from the GEMO study, and (3) familial breast cancer cases with no BRCA1/2 PV and unrelated controls from the GENESIS study.

RESULTS

All three PRS were associated with breast cancer in all studies, with odds ratios per standard deviation varying from 1.7 to 2.0 in CECILE and GENESIS, and hazard ratios varying from 1.1 to 1.4 in GEMO. The predictive performance of PRS₃₁₃ in CECILE was similar to that reported in BCAC but lower than that in GENESIS (area under the receiver operating characteristic curve (AUC) = 0.67 and 0.75, respectively). PRS were less performant in BRCA2 and BRCA1 PV carriers (AUC = 0.58 and 0.54 respectively).

CONCLUSION

Our results are in line with previous validation studies in the general population and in BRCA1/2 PV carriers. Additionally, we showed that PRS may be of clinical utility for women with a strong family history of breast cancer and no BRCA1/2 PV, and for those carrying a predicted PV in a moderate-risk gene like ATM, CHEK2 or PALB2.

Estimating the Cost of 3 Risk Prediction Strategies for Potential Use in the United Kingdom National Breast Screening Program

Background

Economic evaluations have suggested that risk-stratified breast cancer screening may be cost-effective but have used assumptions to estimate the cost of risk prediction. The aim of this study was to identify and quantify the resource use and associated costs required to introduce a breast cancer risk-stratification approach into the English national breast screening program.

Methods

A micro-costing study, conducted alongside a cohort-based prospective trial (BC-PREDICT), identified the resource use and cost per individual (£; 2021 price year) of providing a risk-stratification strategy at a woman's first mammography. Costs were calculated for 3 risk-stratification approaches: Tyrer-Cuzick survey, Tyrer-Cuzick with Volpara breast-density measurement, and Tyrer-Cuzick with Volpara breast-density measurement and testing for 142 single nucleotide polymorphisms (SNP). Costs were determined for the intervention as implemented in the trial and in the health service.

Results

The cost of providing the risk-stratification strategy was calculated to be £16.45 for the Tyrer-Cuzick survey approach, £21.82 for the Tyrer-Cuzick with Volpara breast-density measurement, and £102.22 for the Tyrer-Cuzick with Volpara breast-density measurement and SNP testing.

Limitations

This study did not use formal expert elicitation methods to synthesize estimates.

Conclusion

The costs of risk prediction using a survey and breast density measurement were low, but adding SNP testing substantially increases costs. Implementation issues present in the trial may also significantly increase the cost of risk prediction.

Implications

This is the first study to robustly estimate the cost of risk-stratification for breast cancer screening. The cost of risk prediction using questionnaires and automated breast density measurement was low, but full economic evaluations including accurate costs are required to provide evidence of the cost-effectiveness of risk-stratified breast cancer screening.

Highlights

Economic evaluations have suggested that risk-stratified breast cancer screening may be a cost-effective use of resources in the United Kingdom.Current estimates of the cost of risk stratification are based on pragmatic assumptions.This study provides estimates of the cost of risk stratification using 3 strategies and when these strategies are implemented perfectly and imperfectly in the health system.The cost of risk stratification is relatively low unless single nucleotide polymorphisms are included in the strategy.

Evaluating the Acceptance and Usability of an App Promoting Weight Gain Prevention and Healthy Behaviors Among Young Women With a Family History of Breast Cancer: Protocol for an Observational Study

BACKGROUND

Breast cancer is the most common form of cancer in women, and around 20% of cases are associated with factors such as adult weight gain, overweight and obesity, and potentially modifiable health behaviors including high alcohol intake, smoking, lack of physical activity, and breastfeeding. Significant weight gain occurs between the ages of 18 and 35 years; hence, this age group could benefit from weight gain prevention interventions. Population studies have reported that women at increased risk of breast cancer account for a disproportionate amount of cases. Thus, there is a particular need to target weight gain prevention and other health behavior interventions for women at increased risk. A literature review identified no evidence-based apps that cover all relevant health behaviors. With patient and participant involvement from the target population, we have developed a new app to promote healthy behaviors among young women at increased risk of breast cancer. Alongside the app, a Facebook group provides peer support, and a virtual welcome event provides an overview of the project and the opportunity to meet the research team and other study participants. The aim of the intervention is to prevent weight gain via changes to eating habits and physical activity levels, and improve other health behaviors associated with breast cancer. The app includes goal setting and self-monitoring of health behaviors and provides education about breast cancer.

OBJECTIVE

This study aims to assess the acceptability and usability of the app in young women at increased risk of breast cancer, and the feasibility of the study procedures for a future, larger efficacy study.

METHODS

Young women (n=35, age 18-35 years) at increased risk of breast cancer (>17% lifetime risk) will be recruited via 2 recruitment procedures: mailed invite from the local breast cancer family history, risk and prevention clinic, and advertisements on social media and websites. Participants will have access to the app and the private Facebook group for 2 months. They will complete questionnaires regarding their health behaviors and breast cancer risk belief at the start and end of the study, complete app rating scales in the middle and at the end of the study, and be invited to give feedback on the app during the study period. Approximately 20 participants will have a semistructured interview at the end of the study regarding their views on the app and trial procedures.

RESULTS

The trial is ongoing, and the publication of results is anticipated in 2023.

CONCLUSIONS

The trial will provide evidence regarding the acceptability and usability of the newly developed app for young women at increased risk of breast cancer. Feedback obtained will be used to improve the app. The trial will also assess the feasibility of the study procedures and how these can be refined for a future efficacy study.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05460650; https://clinicaltrials.gov/ct2/show/NCT05460650.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/41246.

Breast cancer risk stratification in women of screening age: Incremental effects of adding mammographic density, polygenic risk, and a gene panel

PURPOSE

There is great promise in breast cancer risk stratification to target screening and prevention. It is unclear whether adding gene panels to other risk tools improves breast cancer risk stratification and adds discriminatory benefit on a population basis.

METHODS

In total, 10,025 of 57,902 women aged 46 to 73 years in the Predicting Risk of Cancer at Screening study provided DNA samples. A case-control study was used to evaluate breast cancer risk assessment using polygenic risk scores (PRSs), cancer gene panel (n = 33), mammographic density (density residual [DR]), and risk factors collected using a self-completed 2-page questionnaire (Tyrer-Cuzick [TC] model version 8). In total, 525 cases and 1410 controls underwent gene panel testing and PRS calculation (18, 143, and/or 313 single-nucleotide polymorphisms [SNPs]).

RESULTS

Actionable pathogenic variants (PGVs) in BRCA1/2 were found in 1.7% of cases and 0.55% of controls, and overall PGVs were found in 6.1% of cases and 1.3% of controls. A combined assessment of TC8-DR-SNP313 and gene panel provided the best risk stratification with 26.1% of controls and 9.7% of cases identified at <1.4% 10-year risk and 9.01% of controls and 23.3% of cases at ≥8% 10-year risk. Because actionable PGVs were uncommon, discrimination was identical with/without gene panel (with/without: area under the curve = 0.67, 95% CI = 0.64-0.70). Only 7 of 17 PGVs in cases resulted in actionable risk category change. Extended case (n = 644)-control (n = 1779) series with TC8-DR-SNP143 identified 18.9% of controls and only 6.4% of stage 2+ cases at <1.4% 10-year risk and 20.7% of controls and 47.9% of stage 2+ cases at ≥5% 10-year risk.

CONCLUSION

Further studies and economic analysis will determine whether adding panels to PRS is a cost-effective strategy for risk stratification.

Development and validation of polygenic risk scores for prediction of breast cancer and breast cancer subtypes in Chinese women

Background

Studies investigating breast cancer polygenic risk score (PRS) in Chinese women are scarce. The objectives of this study were to develop and validate PRSs that could be used to stratify risk for overall and subtype-specific breast cancer in Chinese women, and to evaluate the performance of a newly proposed Artificial Neural Network (ANN) based approach for PRS construction.

Methods

The PRSs were constructed using the dataset from a genome-wide association study (GWAS) and validated in an independent case-control study. Three approaches, including repeated logistic regression (RLR), logistic ridge regression (LRR) and ANN based approach, were used to build the PRSs for overall and subtype-specific breast cancer based on 24 selected single nucleotide polymorphisms (SNPs). Predictive performance and calibration of the PRSs were evaluated unadjusted and adjusted for Gail-2 model 5-year risk or classical breast cancer risk factors.

Results

The primary PRS_ANN and PRS_LRR both showed modest predictive ability for overall breast cancer (odds ratio per interquartile range increase of the PRS in controls [IQ-OR] 1.76 vs 1.58; area under the receiver operator characteristic curve [AUC] 0.601 vs 0.598) and remained to be predictive after adjustment. Although estrogen receptor negative (ER⁻) breast cancer was poorly predicted by the primary PRSs, the ER⁻ PRSs trained solely on ER⁻ breast cancer cases saw a substantial improvement in predictions of ER⁻ breast cancer.

Conclusions

The 24 SNPs based PRSs can provide additional risk information to help breast cancer risk stratification in the general population of China. The newly proposed ANN approach for PRS construction has potential to replace the traditional approaches, but more studies are needed to validate and investigate its performance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09425-3.

The importance of ethnicity: Are breast cancer polygenic risk scores ready for women who are not of White European origin?

Polygenic risk scores (PRS) for disease risk stratification show great promise for application in general populations, but most are based on data from individuals of White European origin. We assessed two well validated PRS (SNP18, SNP143) in the Predicting-Risk-of-Cancer-At-Screening (PROCAS) study in North-West England for breast cancer prediction based on ethnicity. Overall, 9475 women without breast cancer at study entry, including 645 who subsequently developed invasive breast cancer or ductal carcinoma in situ provided DNA. All were genotyped for SNP18 and a subset of 1868 controls were genotyped for SNP143. For White Europeans both PRS discriminated well between individuals with and without cancer. For n = 395 Black (n = 112), Asian (n = 119), mixed (n = 44) or Jewish (n = 120) women without cancer both PRS overestimated breast cancer risk, being most marked for women of Black and Jewish origin (P < .001). SNP143 resulted in a potential mean 40% breast cancer risk overestimation in the combined group of non-White/non-European origin. SNP-PRS that has been normalized based on White European ethnicity for breast cancer should not be used to predict risk in women of other ethnicities. There is an urgent need to develop PRS specific for other ethnicities, in order to widen access of this technology.

Polygenic risk scores: the future of cancer risk prediction, screening, and precision prevention

Genome-wide association studies (GWASs) have shown that the genetic architecture of cancers are highly polygenic and enabled researchers to identify genetic risk loci for cancers. The genetic variants associated with a cancer can be combined into a polygenic risk score (PRS), which captures part of an individual's genetic susceptibility to cancer. Recently, PRSs have been widely used in cancer risk prediction and are shown to be capable of identifying groups of individuals who could benefit from the knowledge of their probabilistic susceptibility to cancer, which leads to an increased interest in understanding the potential utility of PRSs that might further refine the assessment and management of cancer risk. In this context, we provide an overview of the major discoveries from cancer GWASs. We then review the methodologies used for PRS construction, and describe steps for the development and evaluation of risk prediction models that include PRS and/or conventional risk factors. Potential utility of PRSs in cancer risk prediction, screening, and precision prevention are illustrated. Challenges and practical considerations relevant to the implementation of PRSs in health care settings are discussed.

Can the epigenome contribute to risk stratification for cancer onset?

The increasing burden of cancer requires identifying and protecting individuals at highest risk. The epigenome provides an indispensable complement to genetic alterations for a risk stratification approach for the following reasons: gene transcription necessary for cancer onset is directed by epigenetic modifications and many risk factors studied so far have been associated with alterations related to the epigenome. The risk level depends on the plasticity of the epigenome during phases of life particularly sensitive to environmental and dietary impacts. Modifications in the activity of DNA regulatory regions and altered chromatin compaction may accumulate, hence leading to the increase of cancer risk. Moreover, tissue architecture directs the unique organization of the epigenome for each tissue and cell type, which allows the epigenome to control cancer risk in specific organs. Investigations of epigenetic signatures of risk should help identify a continuum of alterations leading to a threshold beyond which the epigenome cannot maintain homeostasis. We propose that this threshold may be similar in the population for a given tissue, but the pace to reach this threshold will depend on the combination of germline inheritance and the risk and protective factors encountered, particularly during windows of epigenetic susceptibility, by individuals.

Elevated risk thresholds predict endocrine risk-reducing medication use in the Athena screening registry

Risk-reducing endocrine therapy use, though the benefit is validated, is extremely low. The FDA has approved tamoxifen and raloxifene for a 5-year Breast Cancer Risk Assessment Tool (BCRAT) risk ≥ 1.67%. We examined the threshold at which high-risk women are likely to be using endocrine risk-reducing therapies among Athena Breast Health Network participants from 2011-2018. We identified high-risk women by a 5-year BCRAT risk ≥ 1.67% and those in the top 10% and 2.5% risk thresholds by age. We estimated the odds ratio (OR) of current medication use based on these thresholds using logistic regression. One thousand two hundred and one (1.2%) of 104,223 total participants used medication. Of the 33,082 participants with 5-year BCRAT risk ≥ 1.67%, 772 (2.3%) used medication. Of 2445 in the top 2.5% threshold, 209 (8.6%) used medication. Participants whose 5-year risk exceeded 1.67% were more likely to use medication than those whose risk was below this threshold, OR 3.94 (95% CI = 3.50-4.43). The top 2.5% was most strongly associated with medication usage, OR 9.50 (8.13-11.09) compared to the bottom 97.5%. Women exceeding a 5-year BCRAT ≥ 1.67% had modest medication use. We demonstrate that women in the top 2.5% have higher odds of medication use than those in the bottom 97.5% and compared to a risk of 1.67%. The top 2.5% threshold would more effectively target medication use and is being tested prospectively in a randomized control clinical trial.

Performance of the IBIS/Tyrer-Cuzick model of breast cancer risk by race and ethnicity in the Women's Health Initiative

BACKGROUND

The IBIS/Tyrer-Cuzick model is used clinically to guide breast cancer screening and prevention, but was developed primarily in non-Hispanic White women. Little is known about its long-term performance in a racially/ethnically diverse population.

METHODS

The Women's Health Initiative study enrolled postmenopausal women from 1993-1998. Women were included who were aged <80 years at enrollment with no prior breast cancer or mastectomy and with data required for IBIS/Tyrer-Cuzick calculation (weight; height; ages at menarche, first birth, and menopause; menopausal hormone therapy use; and family history of breast or ovarian cancer). Calibration was assessed by the ratio of observed breast cancer cases to the number expected by the IBIS/Tyrer-Cuzick model (O/E; calculated as the sum of cumulative hazards). Differential discrimination was tested for by self-reported race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, Asian or Pacific Islander, and American Indian or Alaskan Native) using Cox regression. Exploratory analyses, including simulation of a protective single-nucleotide polymorphism (SNP), rs140068132 at 6q25, were performed.

RESULTS

During follow-up (median 18.9 years, maximum 23.4 years), 6783 breast cancer cases occurred among 90,967 women. IBIS/Tyrer-Cuzick was well calibrated overall (O/E ratio = 0.95; 95% CI, 0.93-0.97) and in most racial/ethnic groups, but overestimated risk for Hispanic women (O/E ratio = 0.75; 95% CI, 0.62-0.90). Discrimination did not differ by race/ethnicity. Exploratory simulation of the protective SNP suggested improved IBIS/Tyrer-Cuzick calibration for Hispanic women (O/E ratio = 0.80; 95% CI, 0.66-0.96).

CONCLUSIONS

The IBIS/Tyrer-Cuzick model is well calibrated for several racial/ethnic groups over 2 decades of follow-up. Studies that incorporate genetic and other risk factors, particularly among Hispanic women, are essential to improve breast cancer-risk prediction.

Prospective Evaluation of the Addition of Polygenic Risk Scores to Breast Cancer Risk Models

Background

The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm and the International Breast Cancer Intervention Study breast cancer risk models are used to provide advice on screening intervals and chemoprevention. We evaluated the performance of these models, which now incorporate polygenic risk scores (PRSs), using a prospective cohort study.

Methods

We used a case-cohort design, involving women in the Melbourne Collaborative Cohort Study aged 50-75 years when surveyed in 2003-2007, of whom 408 had a first primary breast cancer diagnosed within 10 years (cases), and 2783 were from the subcohort. Ten-year risks were calculated based on lifestyle factors, family history data, and a 313-variant PRS. Discrimination was assessed using a C-statistic compared with 0.50 and calibration using the ratio of expected to observed number of cases (E/O).

Results

When the PRS was added to models with lifestyle factors and family history, the C-statistic (95% confidence interval [CI]) increased from 0.57 (0.54 to 0.60) to 0.62 (0.60 to 0.65) using IBIS and from 0.56 (0.53 to 0.59) to 0.62 (0.59 to 0.64) using BOADICEA. IBIS underpredicted risk (E/O = 0.62, 95% CI = 0.48 to 0.80) for women in the lowest risk category (<1.7%) and overpredicted risk (E/O = 1.40, 95% CI = 1.18 to 1.67) in the highest risk category (≥5%), using the Hosmer-Lemeshow test for calibration in quantiles of risk and a 2-sided P value less than  .001. BOADICEA underpredicted risk (E/O = 0.82, 95% CI = 0.67 to 0.99) in the second highest risk category (3.4%-5%); the Hosmer-Lemeshow test and a 2-sided P value was equal to .02.

Conclusions

Although the inclusion of a 313 genetic variant PRS doubles discriminatory accuracy (relative to reference 0.50), models with and without this PRS have relatively modest discrimination and might require recalibration before their clinical and wider use are promoted.

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.

Assessing Risk of Breast Cancer: A Review of Risk Prediction Models

Accurate and individualized breast cancer risk assessment can be used to guide personalized screening and prevention recommendations. Existing risk prediction models use genetic and nongenetic risk factors to provide an estimate of a woman's breast cancer risk and/or the likelihood that she has a BRCA1 or BRCA2 mutation. Each model is best suited for specific clinical scenarios and may have limited applicability in certain types of patients. For example, the Breast Cancer Risk Assessment Tool, which identifies women who would benefit from chemoprevention, is readily accessible and user-friendly but cannot be used in women under 35 years of age or those with prior breast cancer or lobular carcinoma in situ. Emerging research on deep learning-based artificial intelligence (AI) models suggests that mammographic images contain risk indicators that could be used to strengthen existing risk prediction models. This article reviews breast cancer risk factors, describes the appropriate use, strengths, and limitations of each risk prediction model, and discusses the emerging role of AI for risk assessment.

Comparative validation of the BOADICEA and Tyrer-Cuzick breast cancer risk models incorporating classical risk factors and polygenic risk in a population-based prospective cohort of women of European ancestry

BACKGROUND

The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) and the Tyrer-Cuzick breast cancer risk prediction models are commonly used in clinical practice and have recently been extended to include polygenic risk scores (PRS). In addition, BOADICEA has also been extended to include reproductive and lifestyle factors, which were already part of Tyrer-Cuzick model. We conducted a comparative prospective validation of these models after incorporating the recently developed 313-variant PRS.

METHODS

Calibration and discrimination of 5-year absolute risk was assessed in a nested case-control sample of 1337 women of European ancestry (619 incident breast cancer cases) aged 23-75 years from the Generations Study.

RESULTS

The extended BOADICEA model with reproductive/lifestyle factors and PRS was well calibrated across risk deciles; expected-to-observed ratio (E/O) at the highest risk decile :0.97 (95 % CI 0.51 - 1.86) for women younger than 50 years and 1.09 (0.66 - 1.80) for women 50 years or older. Adding reproductive/lifestyle factors and PRS to the BOADICEA model improved discrimination modestly in younger women (area under the curve (AUC) 69.7 % vs. 69.1%) and substantially in older women (AUC 64.6 % vs. 56.8%). The Tyrer-Cuzick model with PRS showed evidence of overestimation at the highest risk decile: E/O = 1.54(0.81 - 2.92) for younger and 1.73 (1.03 - 2.90) for older women.

CONCLUSION

The extended BOADICEA model identified women in a European-ancestry population at elevated breast cancer risk more accurately than the Tyrer-Cuzick model with PRS. With the increasing availability of PRS, these analyses can inform choice of risk models incorporating PRS for risk stratified breast cancer prevention among women of European ancestry.

Integrating Clinical and Polygenic Factors to Predict Breast Cancer Risk in Women Undergoing Genetic Testing

PURPOSE

Screening and prevention decisions for women at increased risk of developing breast cancer depend on genetic and clinical factors to estimate risk and select appropriate interventions. Integration of polygenic risk into clinical breast cancer risk estimators can improve discrimination. However, correlated genetic effects must be incorporated carefully to avoid overestimation of risk.

MATERIALS AND METHODS

A novel Fixed-Stratified method was developed that accounts for confounding when adding a new factor to an established risk model. A combined risk score (CRS) of an 86-single-nucleotide polymorphism polygenic risk score and the Tyrer-Cuzick v7.02 clinical risk estimator was generated with attenuation for confounding by family history. Calibration and discriminatory accuracy of the CRS were evaluated in two independent validation cohorts of women of European ancestry (N = 1,615 and N = 518). Discrimination for remaining lifetime risk was examined by age-adjusted logistic regression. Risk stratification with a 20% risk threshold was compared between CRS and Tyrer-Cuzick in an independent clinical cohort (N = 32,576).

RESULTS

Simulation studies confirmed that the Fixed-Stratified method produced accurate risk estimation across patients with different family history. In both validation studies, CRS and Tyrer-Cuzick were significantly associated with breast cancer. In an analysis with both CRS and Tyrer-Cuzick as predictors of breast cancer, CRS added significant discrimination independent of that captured by Tyrer-Cuzick (P < 10-11 in validation 1; P < 10-7 in validation 2). In an independent cohort, 18% of women shifted breast cancer risk categories from their Tyrer-Cuzick-based risk compared with risk estimates by CRS.

CONCLUSION

Integrating clinical and polygenic factors into a risk model offers more effective risk stratification and supports a personalized genomic approach to breast cancer screening and prevention.

Long-Term Evaluation of Women Referred to a Breast Cancer Family History Clinic (Manchester UK 1987-2020)

Simple Summary

This study reports the management of women at high risk for breast cancer over a 33 years period. The aim was to summarize the numbers seen and to report the results of our studies on gene testing, the outcomes of screening and the success of preventive methods including lifestyle change, chemoprevention and risk-reducing mastectomy. We also discuss how the clinical Family History Service may be improved in the future.

Abstract

Clinics for women concerned about their family history of breast cancer are widely established. A Family History Clinic was set-up in Manchester, UK, in 1987 in a Breast Unit serving a population of 1.8 million. In this review, we report the outcome of risk assessment, screening and prevention strategies in the clinic and propose future approaches. Between 1987–2020, 14,311 women were referred, of whom 6.4% were from known gene families, 38.2% were at high risk (≥30% lifetime risk), 37.7% at moderate risk (17–29%), and 17.7% at an average/population risk who were discharged. A total of 4168 (29.1%) women were eligible for genetic testing and 736 carried pathogenic variants, predominantly in BRCA1 and BRCA2 but also other genes (5.1% of direct referrals). All women at high or moderate risk were offered annual mammographic screening between ages 30 and 40 years old: 646 cancers were detected in women at high and moderate risk (5.5%) with a detection rate of 5 per 1000 screens. Incident breast cancers were largely of good prognosis and resulted in a predicted survival advantage. All high/moderate-risk women were offered lifestyle prevention advice and 14–27% entered various lifestyle studies. From 1992–2003, women were offered entry into IBIS-I (tamoxifen) and IBIS-II (anastrozole) trials (12.5% of invitees joined). The NICE guidelines ratified the use of tamoxifen and raloxifene (2013) and subsequently anastrozole (2017) for prevention; 10.8% women took up the offer of such treatment between 2013–2020. Since 1994, 7164 eligible women at ≥25% lifetime risk of breast cancer were offered a discussion of risk-reducing breast surgery and 451 (6.2%) had surgery. New approaches in all aspects of the service are needed to build on these results.

Simplified Breast Risk Tool Integrating Questionnaire Risk Factors, Mammographic Density, and Polygenic Risk Score: Development and Validation

BACKGROUND

Clinical use of breast cancer risk prediction requires simplified models. We evaluate a simplified version of the validated Rosner-Colditz model and add percent mammographic density (MD) and polygenic risk score (PRS), to assess performance from ages 45-74. We validate using the Mayo Mammography Health Study (MMHS).

METHODS

We derived the model in the Nurses' Health Study (NHS) based on: MD, 77 SNP PRS and a questionnaire score (QS; lifestyle and reproductive factors). A total of 2,799 invasive breast cancer cases were diagnosed from 1990-2000. MD (using Cumulus software) and PRS were assessed in a nested case-control study. We assess model performance using this case-control dataset and evaluate 10-year absolute breast cancer risk. The prospective MMHS validation dataset includes 21.8% of women age <50, and 434 incident cases identified over 10 years of follow-up.

RESULTS

In the NHS, MD has the highest odds ratio (OR) for 10-year risk prediction: OR_{per SD} = 1.48 [95% confidence interval (CI): 1.31-1.68], followed by PRS, OR_{per SD} = 1.37 (95% CI: 1.21-1.55) and QS, OR_{per SD} = 1.25 (95% CI: 1.11-1.41). In MMHS, the AUC adjusted for age + MD + QS 0.650; for age + MD + QS + PRS 0.687, and the NRI was 6% in cases and 16% in controls.

CONCLUSION

A simplified assessment of QS, MD, and PRS performs consistently to discriminate those at high 10-year breast cancer risk.

IMPACT

This simplified model provides accurate estimation of 10-year risk of invasive breast cancer that can be used in a clinical setting to identify women who may benefit from chemopreventive intervention.See related commentary by Tehranifar et al., p. 587.

Breast Cancer Polygenic Risk Score and Contralateral Breast Cancer Risk

Previous research has shown that polygenic risk scores (PRSs) can be used to stratify women according to their risk of developing primary invasive breast cancer. This study aimed to evaluate the association between a recently validated PRS of 313 germline variants (PRS313) and contralateral breast cancer (CBC) risk. We included 56,068 women of European ancestry diagnosed with first invasive breast cancer from 1990 onward with follow-up from the Breast Cancer Association Consortium. Metachronous CBC risk (N = 1,027) according to the distribution of PRS313 was quantified using Cox regression analyses. We assessed PRS313 interaction with age at first diagnosis, family history, morphology, ER status, PR status, and HER2 status, and (neo)adjuvant therapy. In studies of Asian women, with limited follow-up, CBC risk associated with PRS313 was assessed using logistic regression for 340 women with CBC compared with 12,133 women with unilateral breast cancer. Higher PRS313 was associated with increased CBC risk: hazard ratio per standard deviation (SD) = 1.25 (95%CI = 1.18-1.33) for Europeans, and an OR per SD = 1.15 (95%CI = 1.02-1.29) for Asians. The absolute lifetime risks of CBC, accounting for death as competing risk, were 12.4% for European women at the 10th percentile and 20.5% at the 90th percentile of PRS313. We found no evidence of confounding by or interaction with individual characteristics, characteristics of the primary tumor, or treatment. The C-index for the PRS313 alone was 0.563 (95%CI = 0.547-0.586). In conclusion, PRS313 is an independent factor associated with CBC risk and can be incorporated into CBC risk prediction models to help improve stratification and optimize surveillance and treatment strategies.

Key steps for effective breast cancer prevention

Despite decades of laboratory, epidemiological and clinical research, breast cancer incidence continues to rise. Breast cancer remains the leading cancer-related cause of disease burden for women, affecting one in 20 globally and as many as one in eight in high-income countries. Reducing breast cancer incidence will likely require both a population-based approach of reducing exposure to modifiable risk factors and a precision-prevention approach of identifying women at increased risk and targeting them for specific interventions, such as risk-reducing medication. We already have the capacity to estimate an individual woman's breast cancer risk using validated risk assessment models, and the accuracy of these models is likely to continue to improve over time, particularly with inclusion of newer risk factors, such as polygenic risk and mammographic density. Evidence-based risk-reducing medications are cheap, widely available and recommended by professional health bodies; however, widespread implementation of these has proven challenging. The barriers to uptake of, and adherence to, current medications will need to be considered as we deepen our understanding of breast cancer initiation and begin developing and testing novel preventives.

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.

Association of a Polygenic Risk Score With Breast Cancer Among Women Carriers of High- and Moderate-Risk Breast Cancer Genes

Importance

To date, few studies have examined the extent to which polygenic single-nucleotide variation (SNV) (formerly single-nucleotide polymorphism) scores modify risk for carriers of pathogenic variants (PVs) in breast cancer susceptibility genes. In previous reports, polygenic risk modification was reduced for BRCA1 and BRCA2 PV carriers compared with noncarriers, but limited information is available for carriers of CHEK2, ATM, or PALB2 PVs.

Objective

To examine an 86-SNV polygenic risk score (PRS) for BRCA1, BRCA2, CHEK2, ATM, and PALB2 PV carriers.

Design, Setting, and Participants

A retrospective case-control study using data on 150 962 women tested with a multigene hereditary cancer panel between July 19, 2016, and January 11, 2019, was conducted in a commercial testing laboratory. Participants included women of European ancestry between the ages of 18 and 84 years.

Main Outcomes and Measures

Multivariable logistic regression was used to examine the association of the 86-SNV score with invasive breast cancer after adjusting for age, ancestry, and personal and/or family cancer history. Effect sizes, expressed as standardized odds ratios (ORs) with 95% CIs, were assessed for carriers of PVs in each gene as well as for noncarriers.

Results

The median age at hereditary cancer testing of the population was 48 years (range, 18-84 years); there were 141 160 noncarriers in addition to carriers of BRCA1 (n = 2249), BRCA2 (n = 2638), CHEK2 (n = 2564), ATM (n = 1445), and PALB2 (n = 906) PVs included in the analysis. The 86-SNV score was associated with breast cancer risk in each of the carrier populations (P < 1 × 10-4). Stratification was more pronounced for noncarriers (OR, 1.47; 95% CI, 1.45-1.49) and CHEK2 PV carriers (OR, 1.49; 95% CI, 1.36-1.64) than for carriers of BRCA1 (OR, 1.20; 95% CI, 1.10-1.32) or BRCA2 (OR, 1.23; 95% CI, 1.12-1.34) PVs. Odds ratios for ATM (OR, 1.37; 95% CI, 1.21-1.55) and PALB2 (OR, 1.34; 95% CI, 1.16-1.55) PV carrier populations were intermediate between those for BRCA1/2 and CHEK2 noncarriers.

Conclusions and Relevance

In this study, the 86-SNV score was associated with modified risk for carriers of BRCA1, BRCA2, CHEK2, ATM, and PALB2 PVs. This finding supports previous reports of reduced PRS stratification for BRCA1 and BRCA2 PV carriers compared with noncarriers. Modification of risk in CHEK2 carriers associated with the 86-SNV score appeared to be similar to that observed in women without a PV. Larger studies are needed to provide more refined estimates of polygenic modification of risk for women with PVs in other moderate-penetrance genes.

The introduction of risk stratified screening into the NHS breast screening Programme: views from British-Pakistani women

BACKGROUND

UK national guidelines suggest women at high-risk of breast cancer should be offered more frequent screening or preventative medications. Currently, only 1 in 6 high-risk women are identified. One route to identify more high-risk women is via multifactorial risk assessment as part of the UK's NHS Breast Screening Programme (NHSBSP). As lower socioeconomic and minority ethnic populations continue to experience barriers to screening, it is important that any new service does not exacerbate issues further. To inform service development, this study explored views of women from underserved backgrounds regarding the introduction of risk stratification into the NHSBSP.

METHODS

Nineteen semi-structured interviews were conducted with British-Pakistani women from low socioeconomic backgrounds from East Lancashire, UK. Fourteen interviews were conducted via an interpreter.

RESULTS

Thematic analysis produced three themes. Attitudes toward risk awareness concerns the positive views women have toward the idea of receiving personalised breast cancer risk information. Anticipated barriers to accessibility emphasises the difficulties associated with women's limited English skills for accessing information, and their I.T proficiency for completing an online risk assessment questionnaire. Acceptability of risk communication strategy highlights the diversity of opinion regarding the suitability of receiving risk results via letter, with the option for support from a healthcare professional deemed essential.

CONCLUSIONS

The idea of risk stratification was favourable amongst this underserved community. To avoid exacerbating inequities, this new service should provide information in multiple languages and modalities and offer women the opportunity to speak to a healthcare professional about risk. This service should also enable completion of personal risk information via paper questionnaires, as well as online.