BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors

Hormonal Contraception and Breast Cancer Risk for Carriers of Germline Mutations in BRCA1 and BRCA2

PURPOSE

It is uncertain whether, and to what extent, hormonal contraceptives increase breast cancer (BC) risk for germline BRCA1 or BRCA2 mutation carriers.

METHODS

Using pooled observational data from four prospective cohort studies, associations between hormonal contraceptive use and BC risk for unaffected female BRCA1 and BRCA2 mutation carriers were assessed using Cox regression.

RESULTS

Of 3,882 BRCA1 and 1,509 BRCA2 mutation carriers, 53% and 71%, respectively, had ever used hormonal contraceptives for at least 1 year (median cumulative duration of use, 4.8 and 5.7 years, respectively). Overall, 488 BRCA1 and 191 BRCA2 mutation carriers developed BC during median follow-up of 5.9 and 5.6 years, respectively. Although for BRCA1 mutation carriers, neither current nor past use of hormonal contraceptives for at least 1 year was statistically significantly associated with BC risk (hazard ratio [HR], 1.40 [95% CI, 0.94 to 2.08], P = .10 for current use; 1.16 [0.80 to 1.69], P = .4, 1.40 [0.99 to 1.97], P = .05, and 1.27 [0.98 to 1.63], P = .07 for past use 1-5, 6-10, and >10 years before, respectively), ever use was associated with increased risk (HR, 1.29 [95% CI, 1.04 to 1.60], P = .02). Furthermore, BC risk increased with longer cumulative duration of use, with an estimated proportional increase in risk of 3% (1%-5%, P = .002) for each additional year of use. For BRCA2 mutation carriers, there was no evidence that current or ever use was associated with increased BC risk (HR, 0.70 [95% CI, 0.33 to 1.47], P = .3 and 1.07 [0.73 to 1.57], P = .7, respectively).

CONCLUSION

Hormonal contraceptives were associated with increased BC risk for BRCA1 mutation carriers, especially if used for longer durations. Decisions about their use in women with BRCA1 mutations should carefully weigh the risks and benefits for each individual.

Effect of monthly reminders by telephone message on women's beliefs and practice behaviours regarding breast self-examination: A randomized controlled study

AIMS

This study was conducted to examine the effect of monthly telephone message reminders after training on women's beliefs and practice behaviours regarding breast self-examination.

METHODS

This randomized controlled study was conducted with 83 women aged 20-69 years living in Turkey between September 2021 and July 2022. Women were randomly assigned (1:1) to the intervention (n = 41) or control group (n = 42), both groups received online breast self-examination training, and the intervention group received monthly reminders on their mobile phones for 3 months. Participants completed the Champion's Health Belief Model Scale and breast self-examination practice evaluation form at baseline and 3 months after intervention.

RESULTS

After the intervention, the mean scores of the benefits and self-efficacy subscales of Champion's Health Belief Model Scales were significantly higher in the intervention group compared to the control group, and the mean score of barriers was lower. The rate of performing breast self-exam regularly and at the appropriate time was higher in the intervention group. The rate of forgetting to perform breast self-examination was higher in control group.

CONCLUSION

A monthly reminder message may be recommended to increase women's belief in breast self-examination and increase regular practice.

Screening embryos for polygenic disease risk: a review of epidemiological, clinical, and ethical considerations

BACKGROUND

The genetic composition of embryos generated by in vitro fertilization (IVF) can be examined with preimplantation genetic testing (PGT). Until recently, PGT was limited to detecting single-gene, high-risk pathogenic variants, large structural variants, and aneuploidy. Recent advances have made genome-wide genotyping of IVF embryos feasible and affordable, raising the possibility of screening embryos for their risk of polygenic diseases such as breast cancer, hypertension, diabetes, or schizophrenia. Despite a heated debate around this new technology, called polygenic embryo screening (PES; also PGT-P), it is already available to IVF patients in some countries. Several articles have studied epidemiological, clinical, and ethical perspectives on PES; however, a comprehensive, principled review of this emerging field is missing.

OBJECTIVE AND RATIONALE

This review has four main goals. First, given the interdisciplinary nature of PES studies, we aim to provide a self-contained educational background about PES to reproductive specialists interested in the subject. Second, we provide a comprehensive and critical review of arguments for and against the introduction of PES, crystallizing and prioritizing the key issues. We also cover the attitudes of IVF patients, clinicians, and the public towards PES. Third, we distinguish between possible future groups of PES patients, highlighting the benefits and harms pertaining to each group. Finally, our review, which is supported by ESHRE, is intended to aid healthcare professionals and policymakers in decision-making regarding whether to introduce PES in the clinic, and if so, how, and to whom.

SEARCH METHODS

We searched for PubMed-indexed articles published between 1/1/2003 and 1/3/2024 using the terms 'polygenic embryo screening', 'polygenic preimplantation', and 'PGT-P'. We limited the review to primary research papers in English whose main focus was PES for medical conditions. We also included papers that did not appear in the search but were deemed relevant.

OUTCOMES

The main theoretical benefit of PES is a reduction in lifetime polygenic disease risk for children born after screening. The magnitude of the risk reduction has been predicted based on statistical modelling, simulations, and sibling pair analyses. Results based on all methods suggest that under the best-case scenario, large relative risk reductions are possible for one or more diseases. However, as these models abstract several practical limitations, the realized benefits may be smaller, particularly due to a limited number of embryos and unclear future accuracy of the risk estimates. PES may negatively impact patients and their future children, as well as society. The main personal harms are an unindicated IVF treatment, a possible reduction in IVF success rates, and patient confusion, incomplete counselling, and choice overload. The main possible societal harms include discarded embryos, an increasing demand for 'designer babies', overemphasis of the genetic determinants of disease, unequal access, and lower utility in people of non-European ancestries. Benefits and harms will vary across the main potential patient groups, comprising patients already requiring IVF, fertile people with a history of a severe polygenic disease, and fertile healthy people. In the United States, the attitudes of IVF patients and the public towards PES seem positive, while healthcare professionals are cautious, sceptical about clinical utility, and concerned about patient counselling.

WIDER IMPLICATIONS

The theoretical potential of PES to reduce risk across multiple polygenic diseases requires further research into its benefits and harms. Given the large number of practical limitations and possible harms, particularly unnecessary IVF treatments and discarded viable embryos, PES should be offered only within a research context before further clarity is achieved regarding its balance of benefits and harms. The gap in attitudes between healthcare professionals and the public needs to be narrowed by expanding public and patient education and providing resources for informative and unbiased genetic counselling.

ESR Essentials: screening for breast cancer - general recommendations by EUSOBI

Breast cancer is the most frequently diagnosed cancer in women accounting for about 30% of all new cancer cases and the incidence is constantly increasing. Implementation of mammographic screening has contributed to a reduction in breast cancer mortality of at least 20% over the last 30 years. Screening programs usually include all women irrespective of their risk of developing breast cancer and with age being the only determining factor. This approach has some recognized limitations, including underdiagnosis, false positive cases, and overdiagnosis. Indeed, breast cancer remains a major cause of cancer-related deaths in women undergoing cancer screening. Supplemental imaging modalities, including digital breast tomosynthesis, ultrasound, breast MRI, and, more recently, contrast-enhanced mammography, are available and have already shown potential to further increase the diagnostic performances. Use of breast MRI is recommended in high-risk women and women with extremely dense breasts. Artificial intelligence has also shown promising results to support risk categorization and interval cancer reduction. The implementation of a risk-stratified approach instead of a "one-size-fits-all" approach may help to improve the benefit-to-harm ratio as well as the cost-effectiveness of breast cancer screening. KEY POINTS: Regular mammography should still be considered the mainstay of the breast cancer screening. High-risk women and women with extremely dense breast tissue should use MRI for supplemental screening or US if MRI is not available. Women need to participate actively in the decision to undergo personalized screening. KEY RECOMMENDATIONS: Mammography is an effective imaging tool to diagnose breast cancer in an early stage and to reduce breast cancer mortality (evidence level I). Until more evidence is available to move to a personalized approach, regular mammography should be considered the mainstay of the breast cancer screening. High-risk women should start screening earlier; first with yearly breast MRI which can be supplemented by yearly or biennial mammography starting at 35-40 years old (evidence level I). Breast MRI screening should be also offered to women with extremely dense breasts (evidence level I). If MRI is not available, ultrasound can be performed as an alternative, although the added value of supplemental ultrasound regarding cancer detection remains limited. Individual screening recommendations should be made through a shared decision-making process between women and physicians.

Improving the communication of multifactorial cancer risk assessment results for different audiences: a co-design process

BACKGROUND

Multifactorial cancer risk prediction tools, such as CanRisk, are increasingly being incorporated into routine healthcare. Understanding risk information and communicating risk is challenging and healthcare professionals rely substantially on the outputs of risk prediction tools to communicate results. This work aimed to produce a new CanRisk report so users can directly access key information and communicate risk estimates effectively.

METHODS

Over a 13-month period, we led an 8-step co-design process with patients, the public, and healthcare professionals. Steps comprised 1) think aloud testing of the original CanRisk report; 2) structured feedback on the original report; 3) literature review; 4) development of a new report prototype; 5) first round of structured feedback; 6) updating the new report prototype; 7) second round of structured feedback; and 8) finalising and publishing the new CanRisk report.

RESULTS

We received 56 sets of feedback from 34 stakeholders. Overall, the original CanRisk report was not suitable for patients and the public. Building on the feedback, the new report has an overview of the information presented: section one summarises key information for individuals; sections two and three present information for healthcare professionals in different settings. New features also include explanatory text, definitions, graphs, keys and tables to support the interpretation of the information.

DISCUSSION

This co-design experience shows the value of collaboration for the successful communication of complex health information. As a result, the new CanRisk report has the potential to better support shared decision-making processes about cancer risk management across clinical settings.

Family history and genetic risk score combined to guide cancer risk stratification: A prospective cohort study

Family history (FH) of cancer and polygenic risk scores (PRS) are pivotal for cancer risk assessment, yet their combined impact remains unclear. Participants in the UK Biobank (UKB) were recruited between 2006 and 2010, with complete follow-up data updated until February 2020 for Scotland and January 2021 for England and Wales. Using UKB data (N = 442,399), we constructed PRS and incidence-weighted overall cancer PRS (CPRS). FH was assessed through self-reported standardized questions. Among 202,801 men (34.6% with FH) and 239,598 women (42.0% with FH), Cox regression was used to examine the associations between FH, PRS, and cancer risk. We found a significant dose-response relationship between FH of cancer and corresponding cancer risk (Ptrend < .05), with over 10 significant pairs of cross-cancer effects of FH. FH and PRS are positively correlated and independent. Joint effects of FH of cancer (multiple cancers) and PRS (CPRS) on corresponding cancer risk were observed: for instance, compared with participants with no FH of cancer and low PRS, men with FH of cancer and high PRS had the highest risk of colorectal cancer (hazard ratio [HR]: 3.69, 95% confidence interval [CI]: 3.01-4.52). Additive interactions were observed in prostate and overall cancer risk for men and breast cancer for women, with the most significant result being a relative excess risk of interaction (RERI) of 2.98, accounting for ~34% of the prostate cancer risk. In conclusion, FH and PRS collectively contribute to cancer risk, supporting their combined application in personalized risk assessment and early intervention strategies.

Development of a breast cancer risk assessment and primary prevention pathway for women aged 30-39 years: Views of UK primary care providers on the role of primary care

BACKGROUND

Identifying women aged 30-39 years at increased risk of developing breast cancer would allow them to receive screening and prevention offers. For this to be feasible, the practicalities of organising risk assessment and primary prevention must be acceptable to the healthcare professionals who would be responsible for delivery. It has been proposed that primary care providers are best placed to deliver a breast cancer risk assessment and primary prevention pathway. The present study aimed to investigate a range of primary care provider's views on the development and implementation of a breast cancer risk assessment and primary prevention pathway within primary care for women aged 30-39 years.

METHODS

Twenty-five primary care providers working at general practices in either Greater Manchester or Cambridgeshire and Peterborough participated in five focus groups (n = 18) and seven individual interviews. Data were analysed thematically and organised using a framework approach.

RESULTS

Three themes were developed. Challenges with delivering a breast cancer risk assessment and primary prevention pathway within primary care highlights that primary care are willing to facilitate but not lead delivery of such a pathway given the challenges with existing workload pressures and concerns about ensuring effective clinical governance. Primary care's preferred level of involvement describes the aspects of the pathway participants thought primary care could be involved in, namely co-ordinating data collection for risk assessment and calculating and communicating risk. Requirements for primary care involvement captures the need to provide a training and education package to address deficits in knowledge prior to involvement. Additionally, the reservations primary care have about being involved in the management of women identified as being at increased risk are discussed and suggestions are provided for facilitating primary care to take on this role.

CONCLUSIONS

Despite optimism that primary care might lead a breast cancer risk assessment and primary prevention pathway, participants had a range of concerns that should be considered when developing such a pathway.

Polygenic scores and their applications in kidney disease

Genome-wide association studies (GWAS) have uncovered thousands of risk variants that individually have small effects on the risk of human diseases, including chronic kidney disease, type 2 diabetes, heart diseases and inflammatory disorders, but cumulatively explain a substantial fraction of disease risk, underscoring the complexity and pervasive polygenicity of common disorders. This complexity poses unique challenges to the clinical translation of GWAS findings. Polygenic scores combine small effects of individual GWAS risk variants across the genome to improve personalized risk prediction. Several polygenic scores have now been developed that exhibit sufficiently large effects to be considered clinically actionable. However, their clinical use is limited by their partial transferability across ancestries and a lack of validated models that combine polygenic, monogenic, family history and clinical risk factors. Moreover, prospective studies are still needed to demonstrate the clinical utility and cost-effectiveness of polygenic scores in clinical practice. Here, we discuss evolving methods for developing polygenic scores, best practices for validating and reporting their performance, and the study designs that will empower their clinical implementation. We specifically focus on the polygenic scores relevant to nephrology and other chronic, complex diseases and review their key limitations, necessary refinements and potential clinical applications.

The risk of endocrine interventions in carriers of a genetic predisposition for breast and gynecologic cancers: recommendations of the German Consortium for Hereditary Breast and Ovarian Cancer

PURPOSE

To support doctors in counselling women with genetic predisposition for breast or gynecologic cancers on endocrine interventions.

METHODS

Evidence on the safety of endocrine interventions for fertility treatment, contraception, hormone replacement therapy after risk-reducing salpingo-oophorectomy (RRSO) or treatment of symptoms during peri- and postmenopause was analysed for carriers of probably pathogenic and pathogenic variants in BRCA1 or BRCA2 (BRCA1/2-pV), in other breast and ovarian cancer genes and the Lynch Syndrome. Cancer risks were compared with data on risks for the general population.

RESULTS

Data on risk modulation of endocrine interventions in women with genetic predisposition is limited. Ovarian hyperstimulation for fertility treatment may be performed. Oral contraceptives should not be used to reduce ovarian cancer risk in BRCA1/2-pV carriers. Premenopausal BRCA1/2-pV carriers and carriers of pV in Lynch Syndrome genes should be offered hormone replacement therapy (HRT) after RRSO, to prevent diseases caused by estrogen deficiency.

CONCLUSION

Effect direction and strength of risk modulation by endocrine interventions is similar to the general population. Participation of individuals at risk in prospective registries is recommended.

Breast cancer risk assessment for prescription of menopausal hormone therapy in women with a family history of breast cancer: an epidemiological modelling study

BACKGROUND

Menopausal hormone therapy (MHT) can alleviate menopausal symptoms but has been associated with an increased risk of breast cancer. MHT prescription should be preceded by individualised risk/benefit evaluation; however, data outlining the impact of family history alongside different MHT therapeutic approaches are lacking.

AIM

To quantify the risks associated with MHT use in women with varying breast cancer family histories of developing and dying from breast cancer.

DESIGN AND SETTING

An epidemiological modelling study for women in England using the BOADICEA breast cancer prediction model and data relating to MHT use and breast cancer risk taken from research by the Collaborative Group on Hormonal Factors in Breast Cancer.

METHOD

The risk of developing and dying from breast cancer between the ages of 50 and 80 years was modelled in women with four different breast cancer family history profiles: 'average', 'modest', 'intermediate', and 'strong' by using 1) background risks of breast cancer by age and family history, 2) relative risks for breast cancer associated with MHT use, and 3) 10-year breast cancer-specific net mortality rates. This study modelled use of combined oestrogen-progestogen MHT (cyclical or continuous) and oestrogen-only MHT.

RESULTS

For a woman of 'average' family history taking no MHT, the cumulative breast cancer risk (age 50-80 years) is 9.8%, and the risk of dying from the breast cancer is 1.7%. In this model, 5 years' exposure to combined-cyclical MHT (age 50-55 years) was calculated to increase these risks to 11.0% and 1.8%, respectively. For a woman with a 'strong' family history taking no MHT, the cumulative breast cancer risk is 19.6% (age 50-80 years), and the risk of dying from the breast cancer is 3.2%. With 5 years' exposure to MHT (age 50-55 years), this model showed that these risks increase to 22.4% and 3.5%, respectively.

CONCLUSION

In this model, both family history and MHT are associated with increased risk of breast cancer. Estimates of the risks of breast cancer associated with MHT for women with different family histories can be used to support decision making around MHT prescription for women experiencing menopausal symptoms.

Protein-truncating and rare missense variants in ATM and CHEK2 and associations with cancer in UK Biobank whole-exome sequence data

BACKGROUND

Deleterious germline variants in ATM and CHEK2 have been associated with a moderately increased risk of breast cancer. Risks for other cancers remain unclear.

METHODS

Cancer associations for coding variants in ATM and CHEK2 were evaluated using whole-exome sequence data from UK Biobank linked to cancer registration data (348 488 participants), and analysed both as a retrospective case-control and a prospective cohort study. Odds ratios, hazard ratios, and combined relative risks (RRs) were estimated by cancer type and gene. Separate analyses were performed for protein-truncating variants (PTVs) and rare missense variants (rMSVs; allele frequency <0.1%).

RESULTS

PTVs in ATM were associated with increased risks of nine cancers at p<0.001 (pancreas, oesophagus, lung, melanoma, breast, ovary, prostate, bladder, lymphoid leukaemia (LL)), and three at p<0.05 (colon, diffuse non-Hodgkin's lymphoma (DNHL), rectosigmoid junction). Carriers of rMSVs had increased risks of four cancers (p<0.05: stomach, pancreas, prostate, Hodgkin's disease (HD)). RRs were highest for breast, prostate, and any cancer where rMSVs lay in the FAT or PIK domains, and had a Combined Annotation Dependent Depletion score in the highest quintile.PTVs in CHEK2 were associated with three cancers at p<0.001 (breast, prostate, HD) and six at p<0.05 (oesophagus, melanoma, ovary, kidney, DNHL, myeloid leukaemia). Carriers of rMSVs had increased risks of five cancers (p<0.001: breast, prostate, LL; p<0.05: melanoma, multiple myeloma).

CONCLUSION

PTVs in ATM and CHEK2 are associated with a wide range of cancers, with the highest RR for pancreatic cancer in ATM PTV carriers. These findings can inform genetic counselling of carriers.

Rare Germline Variants in DNA Repair Genes Detected in BRCA-Negative Finnish Patients with Early-Onset Breast Cancer

BACKGROUND

Breast cancer is the most common malignancy, with a mean age of onset of approximately 60 years. Only a minority of breast cancer patients present with an early onset at or before 40 years of age. An exceptionally young age at diagnosis hints at a possible genetic etiology. Currently, known pathogenic genetic variants only partially explain the disease burden of younger patients. Thus, new knowledge is warranted regarding additional risk variants. In this study, we analyzed DNA repair genes to identify additional variants to shed light on the etiology of early-onset breast cancer.

METHODS

Germline whole-exome sequencing was conducted in a cohort of 63 patients diagnosed with breast cancer at or before 40 years of age (median 33, mean 33.02, range 23-40 years) with no known pathogenic variants in BRCA genes. After filtering, all detected rare variants were sorted by pathogenicity prediction scores (CADD score and REVEL) to identify the most damaging genetic changes. The remaining variants were then validated by comparison to a validation cohort of 121 breast cancer patients with no preselected age at cancer diagnosis (mean 51.4 years, range 28-80 years). Analysis of novel exonic variants was based on protein structure modeling.

RESULTS

Five novel, deleterious variants in the genes WRN, RNF8, TOP3A, ERCC2, and TREX2 were found in addition to a splice acceptor variant in RNF4 and two frameshift variants in EXO1 and POLE genes, respectively. There were also multiple previously reported putative risk variants in other DNA repair genes.

CONCLUSIONS

Taken together, whole-exome sequencing yielded 72 deleterious variants, including 8 novel variants that may play a pivotal role in the development of early-onset breast cancer. Although more studies are warranted, we demonstrate that young breast cancer patients tend to carry multiple deleterious variants in one or more DNA repair genes.

Using polygenic risk modification to improve breast cancer prevention: study protocol for the PRiMo multicentre randomised controlled trial

INTRODUCTION

Established personal and familial risk factors contribute collectively to a woman's risk of breast or ovarian cancer. Existing clinical services offer genetic testing for pathogenic variants in high-risk genes to investigate these risks but recent information on the role of common genomic variants, in the form of a Polygenic Risk Score (PRS), has provided the potential to further personalise breast and ovarian cancer risk assessment. Data from cohort studies support the potential of an integrated risk assessment to improve targeted risk management but experience of this approach in clinical practice is limited.

METHODS AND ANALYSIS

The polygenic risk modification trial is an Australian multicentre prospective randomised controlled trial of integrated risk assessment including personal and family risk factors with inclusion of breast and ovarian PRS vs standard care. The study will enrol women, unaffected by cancer, undergoing predictive testing at a familial cancer clinic for a pathogenic variant in a known breast cancer (BC) or ovarian cancer (OC) predisposition gene (BRCA1, BRCA2, PALB2, CHEK2, ATM, RAD51C, RAD51D). Array-based genotyping will be used to generate breast cancer (313 SNP) and ovarian cancer (36 SNP) PRS. A suite of materials has been developed for the trial including an online portal for patient consent and questionnaires, and a clinician education programme to train healthcare providers in the use of integrated risk assessment. Long-term follow-up will evaluate differences in the assessed risk and management advice, patient risk management intentions and adherence, patient-reported experience and outcomes, and the health service implications of personalised risk assessment.

ETHICS AND DISSEMINATION

This study has been approved by the Human Research Ethics Committee of Peter MacCallum Cancer Centre and at all participating centres. Study findings will be disseminated via peer-reviewed publications and conference presentations, and directly to participants.

TRIAL REGISTRATION NUMBER

ACTRN12621000009819.

Limitations in next-generation sequencing-based genotyping of breast cancer polygenic risk score loci

Considering polygenic risk scores (PRSs) in individual risk prediction is increasingly implemented in genetic testing for hereditary breast cancer (BC) based on next-generation sequencing (NGS). To calculate individual BC risks, the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) with the inclusion of the BCAC 313 or the BRIDGES 306 BC PRS is commonly used. The PRS calculation depends on accurately reproducing the variant allele frequencies (AFs) and, consequently, the distribution of PRS values anticipated by the algorithm. Here, the 324 loci of the BCAC 313 and the BRIDGES 306 BC PRS were examined in population-specific database gnomAD and in real-world data sets of five centers of the German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC), to determine whether these expected AFs can be reproduced by NGS-based genotyping. Four PRS loci were non-existent in gnomAD v3.1.2 non-Finnish Europeans, further 24 loci showed noticeably deviating AFs. In real-world data, between 11 and 23 loci were reported with noticeably deviating AFs, and were shown to have effects on final risk prediction. Deviations depended on the sequencing approach, variant caller and calling mode (forced versus unforced) employed. Therefore, this study demonstrates the necessity to apply quality assurance not only in terms of sequencing coverage but also observed AFs in a sufficiently large cohort, when implementing PRSs in a routine diagnostic setting. Furthermore, future PRS design should be guided by the technical reproducibility of expected AFs across commonly used genotyping methods, especially NGS, in addition to the observed effect sizes.

The best linear unbiased prediction (BLUP) method as a tool to estimate the lifetime risk of pancreatic ductal adenocarcinoma in high-risk individuals with no known pathogenic germline variants

Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer-related death in the Western world. The number of diagnosed cases and the mortality rate are almost equal as the majority of patients present with advanced disease at diagnosis. Between 4 and 10% of pancreatic cancer cases have an apparent hereditary background, known as hereditary pancreatic cancer (HPC) and familial pancreatic cancer (FPC), when the genetic basis is unknown. Surveillance of high-risk individuals (HRI) from these families by imaging aims to detect PDAC at an early stage to improve prognosis. However, the genetic basis is unknown in the majority of HRIs, with only around 10-13% of families carrying known pathogenic germline mutations. The aim of this study was to assess an individual's genetic cancer risk based on sex and personal and family history of cancer. The Best Linear Unbiased Prediction (BLUP) methodology was used to estimate an individual's predicted risk of developing cancer during their lifetime. The model uses different demographic factors in order to estimate heritability. A reliable estimation of heritability for pancreatic cancer of 0.27 on the liability scale, and 0.07 at the observed data scale as obtained, which is different from zero, indicating a polygenic inheritance pattern of PDAC. BLUP was able to correctly discriminate PDAC cases from healthy individuals and those with other cancer types. Thus, providing an additional tool to assess PDAC risk HRI with an assumed genetic predisposition in the absence of known pathogenic germline mutations.

Breast Cancer Screening in the Intermediate-Risk Population: Falling Through the Cracks?

Breast cancer screening guidelines vary for women at intermediate risk (15%-20% lifetime risk) for developing breast cancer across jurisdictions. Currently available risk assessment models have differing strengths and weaknesses, creating difficulty and ambiguity in selecting the most appropriate model to utilize. Clarifying which model to utilize in individual circumstances may help determine the best screening guidelines to use for each individual.

Mammographic Texture versus Conventional Cumulus Measure of Density in Breast Cancer Risk Prediction: A Literature Review

Mammographic textures show promise as breast cancer risk predictors, distinct from mammographic density. Yet, there is a lack of comprehensive evidence to determine the relative strengths as risk predictor of textures and density and the reliability of texture-based measures. We searched the PubMed database for research published up to November 2023, which assessed breast cancer risk associations [odds ratios (OR)] with texture-based measures and percent mammographic density (PMD), and their discrimination [area under the receiver operating characteristics curve (AUC)], using same datasets. Of 11 publications, for textures, six found stronger associations (P < 0.05) with 11% to 508% increases on the log scale by study, and four found weaker associations (P < 0.05) with 14% to 100% decreases, compared with PMD. Risk associations remained significant when fitting textures and PMD together. Eleven of 17 publications found greater AUCs for textures than PMD (P < 0.05); increases were 0.04 to 0.25 by study. Discrimination from PMD and these textures jointly was significantly higher than from PMD alone (P < 0.05). Therefore, different textures could capture distinct breast cancer risk information, partially independent of mammographic density, suggesting their joint role in breast cancer risk prediction. Some textures could outperform mammographic density for predicting breast cancer risk. However, obtaining reliable texture-based measures necessitates addressing various issues. Collaboration of researchers from diverse fields could be beneficial for advancing this complex field.

BOADICEA model: updates to the BRCA2 breast cancer risks for ages 60 years and older

Breast cancer risks in older BRCA2 pathogenic variant carriers are understudied. Recent studies show a marked decline in the relative risk at older ages. We used data from two large studies to update the breast cancer risks in the BOADICEA model for BRCA2 carriers 60 years and older.

Validation of the BOADICEA model in a prospective cohort of BRCA1/2 pathogenic variant carriers

BACKGROUND

No validation has been conducted for the BOADICEA multifactorial breast cancer risk prediction model specifically in BRCA1/2 pathogenic variant (PV) carriers to date. Here, we evaluated the performance of BOADICEA in predicting 5-year breast cancer risks in a prospective cohort of BRCA1/2 PV carriers ascertained through clinical genetic centres.

METHODS

We evaluated the model calibration and discriminatory ability in the prospective TRANsIBCCS cohort study comprising 1614 BRCA1 and 1365 BRCA2 PV carriers (209 incident cases). Study participants had lifestyle, reproductive, hormonal, anthropometric risk factor information, a polygenic risk score based on 313 SNPs and family history information.

RESULTS

The full multifactorial model considering family history together with all other risk factors was well calibrated overall (E/O=1.07, 95% CI: 0.92 to 1.24) and in quintiles of predicted risk. Discrimination was maximised when all risk factors were considered (Harrell's C-index=0.70, 95% CI: 0.67 to 0.74; area under the curve=0.79, 95% CI: 0.76 to 0.82). The model performance was similar when evaluated separately in BRCA1 or BRCA2 PV carriers. The full model identified 5.8%, 12.9% and 24.0% of BRCA1/2 PV carriers with 5-year breast cancer risks of <1.65%, <3% and <5%, respectively, risk thresholds commonly used for different management and risk-reduction options.

CONCLUSION

BOADICEA may be used to aid personalised cancer risk management and decision-making for BRCA1 and BRCA2 PV carriers. It is implemented in the free-access CanRisk tool (https://www.canrisk.org/).

Breast density effect on the sensitivity of digital screening mammography in a UK cohort

OBJECTIVES

To assess the performance of breast cancer screening by category of breast density and age in a UK screening cohort.

METHODS

Raw full-field digital mammography data from a single site in the UK, forming a consecutive 3-year cohort of women aged 50 to 70 years from 2016 to 2018, were obtained retrospectively. Breast density was assessed using Volpara software. Examinations were grouped by density category and age group (50-60 and 61-70 years) to analyse screening performance. Statistical analysis was performed to determine the association between density categories and age groups. Volumetric breast density was assessed as a binary classifier of interval cancers (ICs) to find an optimal density threshold.

RESULTS

Forty-nine thousand nine-hundred forty-eight screening examinations (409 screen-detected cancers (SDCs) and 205 ICs) were included in the analysis. Mammographic sensitivity, SDC/(SDC + IC), decreased with increasing breast density from 75.0% for density a (p = 0.839, comparisons made to category b), to 73.5%, 59.8% (p = 0.001), and 51.3% (p < 0.001) in categories b, c, and d, respectively. IC rates were highest in the densest categories with rates of 1.8 (p = 0.039), 3.2, 5.7 (p < 0.001), and 7.9 (p < 0.001) per thousand for categories a, b, c, and d, respectively. The recall rate increased with breast density, leading to more false positive recalls, especially in the younger age group. There was no significant difference between the optimal density threshold found, 6.85, and that Volpara defined as the b/c boundary, 7.5.

CONCLUSIONS

The performance of screening is significantly reduced with increasing density with IC rates in the densest category four times higher than in women with fatty breasts. False positives are a particular issue for the younger subgroup without prior examinations.

CLINICAL RELEVANCE STATEMENT

In women attending screening there is significant underdiagnosis of breast cancer in those with dense breasts, most marked in the highest density category but still three times higher than in women with fatty breasts in the second highest category.

KEY POINTS

Breast density can mask cancers leading to underdiagnosis on mammography. Interval cancer rate increased with breast density categories 'a' to 'd'; 1.8 to 7.9 per thousand. Recall rates increased with increasing breast density, leading to more false positive recalls.

Breast Cancer is Increased in Women with Primary Ovarian Insufficiency

CONTEXT

DNA damage/repair gene variants are associated with both primary ovarian insufficiency (POI) and cancer risk.

OBJECTIVE

We hypothesized that a subset of women with POI and family members would have increased risk for cancer.

DESIGN

Case-control population-based study using records from 1995-2022.

SETTING

Two major Utah academic healthcare systems serving 85% of the state.

SUBJECTS

Women with POI (n=613) were identified using ICD codes and reviewed for accuracy. Relatives were linked using the Utah Population Database.

INTERVENTION

Cancer diagnoses were identified using the Utah Cancer Registry.

MAIN OUTCOME MEASURES

The relative risk of cancer in women with POI and relatives was estimated by comparison to population rates. Whole genome sequencing was performed on a subset of women.

RESULTS

Breast cancer was increased in women with POI (OR [95%CI] 2.20 [1.30, 3.47]; p=0.0023) and there was a nominally significant increase in ovarian cancer. Probands with POI were 36.5±4.3 years and 59.5±12.7 years when diagnosed with POI and cancer, respectively. Causal and candidate gene variants for cancer and POI were identified.Among second-degree relatives of these women, there was an increased risk of breast (1.28 [1.08, 1.52]; p=0.0078) and colon cancer (1.50 [1.14, 1.94]; p=0.0036). Prostate cancer was increased in first- (1.64 [1.18, 2.23]; p=0.0026), second- (1.54 [1.32, 1.79]; p<0.001), and third-degree relatives (1.33 [1.20, 1.48]; p<0.001).

CONCLUSIONS

Data suggest common genetic risk for POI and reproductive cancers. Tools are needed to predict cancer risk in women with POI and potentially to counsel about risks of hormone replacement therapy.

Evaluation of polygenic scoring methods in five biobanks shows larger variation between biobanks than methods and finds benefits of ensemble learning

Methods of estimating polygenic scores (PGSs) from genome-wide association studies are increasingly utilized. However, independent method evaluation is lacking, and method comparisons are often limited. Here, we evaluate polygenic scores derived via seven methods in five biobank studies (totaling about 1.2 million participants) across 16 diseases and quantitative traits, building on a reference-standardized framework. We conducted meta-analyses to quantify the effects of method choice, hyperparameter tuning, method ensembling, and the target biobank on PGS performance. We found that no single method consistently outperformed all others. PGS effect sizes were more variable between biobanks than between methods within biobanks when methods were well tuned. Differences between methods were largest for the two investigated autoimmune diseases, seropositive rheumatoid arthritis and type 1 diabetes. For most methods, cross-validation was more reliable for tuning hyperparameters than automatic tuning (without the use of target data). For a given target phenotype, elastic net models combining PGS across methods (ensemble PGS) tuned in the UK Biobank provided consistent, high, and cross-biobank transferable performance, increasing PGS effect sizes (β coefficients) by a median of 5.0% relative to LDpred2 and MegaPRS (the two best-performing single methods when tuned with cross-validation). Our interactively browsable online-results and open-source workflow prspipe provide a rich resource and reference for the analysis of polygenic scoring methods across biobanks.

The relationship between demographic factors and known risk factors with breast cancer in women aged 30-69

Background

Breast cancer is one of the most important causes of cancer deaths in women. The present study was conducted to determine the relationship between demographic factors and known risk factors with breast cancer in women aged 30-69.

Method

This case-control study was conducted with two matched and unmatched control groups. Three hundred fifty women aged 30-69 with breast cancer, 350 age-matched women without cancer, and 350 not age-matched women were included in the study. Controls were selected from the records of women whose breast cancer screening results were normal. Study subjects were evaluated regarding the risk factors for breast cancer. The data collection tool was a checklist including the risk factors investigated in the integrated health system. The collected data were analyzed utilizing SPSS22 software at a significance level of less than 0.05.

Results

The average age in the case group was 46.63±11.77 years and 49.61±8.39 in the unmatched control group. The average age of marriage in the case group was 21.54±4.31, and the average age of women at first pregnancy in the case group was 24.06±3.39 years. In the case group, 163 people (46.57%) lived in the city, 221 people (63.14%) were over 40 years old, and 337 people (96.28%) were married. In multivariate analysis, the variable 'age of marriage' 0.821 (0.691-0.976) and 'age of first pregnancy' 1.213 (1.020-1.443) showed a significant relationship with breast cancer which were observed as predictors of breast cancer in comparison to the unmatched control group (P-value <0.05).

Conclusion

The age of the first pregnancy and the type of delivery were observed as predictors of breast cancer. Therefore, by performing breast cancer screening in women who are exposed to these risk factors, early diagnosis of the disease and increasing the speed of their treatment can be significantly helped.

Deep Learning for Breast Cancer Risk Prediction: Application to a Large Representative UK Screening Cohort

Purpose To develop an artificial intelligence (AI) deep learning tool capable of predicting future breast cancer risk from a current negative screening mammographic examination and to evaluate the model on data from the UK National Health Service Breast Screening Program. Materials and Methods The OPTIMAM Mammography Imaging Database contains screening data, including mammograms and information on interval cancers, for more than 300 000 female patients who attended screening at three different sites in the United Kingdom from 2012 onward. Cancer-free screening examinations from women aged 50-70 years were performed and classified as risk-positive or risk-negative based on the occurrence of cancer within 3 years of the original examination. Examinations with confirmed cancer and images containing implants were excluded. From the resulting 5264 risk-positive and 191 488 risk-negative examinations, training (n = 89 285), validation (n = 2106), and test (n = 39 351) datasets were produced for model development and evaluation. The AI model was trained to predict future cancer occurrence based on screening mammograms and patient age. Performance was evaluated on the test dataset using the area under the receiver operating characteristic curve (AUC) and compared across subpopulations to assess potential biases. Interpretability of the model was explored, including with saliency maps. Results On the hold-out test set, the AI model achieved an overall AUC of 0.70 (95% CI: 0.69, 0.72). There was no evidence of a difference in performance across the three sites, between patient ethnicities, or across age groups. Visualization of saliency maps and sample images provided insights into the mammographic features associated with AI-predicted cancer risk. Conclusion The developed AI tool showed good performance on a multisite, United Kingdom-specific dataset. Keywords: Deep Learning, Artificial Intelligence, Breast Cancer, Screening, Risk Prediction Supplemental material is available for this article. ©RSNA, 2024.

A structured process for the validation of a decision-analytic model: application to a cost-effectiveness model for risk-stratified national breast screening

BACKGROUND

Decision-makers require knowledge of the strengths and weaknesses of decision-analytic models used to evaluate healthcare interventions to be able to confidently use the results of such models to inform policy. A number of aspects of model validity have previously been described, but no systematic approach to assessing the validity of a model has been proposed. This study aimed to consolidate the different aspects of model validity into a step-by-step approach to assessing the strengths and weaknesses of a decision-analytic model.

METHODS

A pre-defined set of steps were used to conduct the validation process of an exemplar early decision-analytic-model-based cost-effectiveness analysis of a risk-stratified national breast cancer screening programme [UK healthcare perspective; lifetime horizon; costs (£; 2021)]. Internal validation was assessed in terms of descriptive validity, technical validity and face validity. External validation was assessed in terms of operational validation, convergent validity (or corroboration) and predictive validity.

RESULTS

The results outline the findings of each step of internal and external validation of the early decision-analytic-model and present the validated model (called 'MANC-RISK-SCREEN'). The positive aspects in terms of meeting internal validation requirements are shown together with the remaining limitations of MANC-RISK-SCREEN.

CONCLUSION

Following a transparent and structured validation process, MANC-RISK-SCREEN has been shown to have satisfactory internal and external validity for use in informing resource allocation decision-making. We suggest that MANC-RISK-SCREEN can be used to assess the cost-effectiveness of exemplars of risk-stratified national breast cancer screening programmes (NBSP) from the UK perspective.

IMPLICATIONS

A step-by-step process for conducting the validation of a decision-analytic model was developed for future use by health economists. Using this approach may help researchers to fully demonstrate the strengths and limitations of their model to decision-makers.

Artificial Intelligence for Breast Cancer Risk Assessment

Breast cancer risk prediction models based on common clinical risk factors are used to identify women eligible for high-risk screening and prevention. Unfortunately, these models have only modest discriminatory accuracy with disparities in performance in underrepresented race and ethnicity groups. The field of artificial intelligence (AI) and deep learning are rapidly advancing the field of breast cancer risk prediction with the development of mammography-based AI breast cancer risk models. Early studies suggest mammography-based AI risk models may perform better than traditional risk factor-based models with more equitable performance.

BRCA awareness and testing experience in the UK Jewish population: a qualitative study

BACKGROUND

1 in 40 UK Jewish individuals carry a pathogenic variant in BRCA1/BRCA2. Traditional testing criteria miss half of carriers, and so population genetic testing is being piloted for Jewish people in England. There has been no qualitative research into the factors influencing BRCA awareness and testing experience in this group. This study aimed to explore these and inform improvements for the implementation of population genetic testing.

METHODS

Qualitative study of UK Jewish adults who have undergone BRCA testing. We conducted one-to-one semistructured interviews via telephone or video call using a predefined topic guide, until sufficient information power was reached. Interviews were audio-recorded, transcribed verbatim and interpreted using applied thematic analysis.

RESULTS

32 individuals were interviewed (28 carriers, 4 non-carriers). We interpreted five themes intersecting across six time points of the testing pathway: (1) individual differences regarding personal/family history of cancer, demographics and personal attitudes/approach; (2) healthcare professionals' support; (3) pathway access and integration; (4) nature of family/partner relationships; and (5) Jewish community factors. Testing was largely triggered by connecting information to a personal/family history of cancer. No participants reported decision regret, although there was huge variation in satisfaction. Suggestions were given around increasing UK Jewish community awareness, making information and support services personally relevant and proactive case management of carriers.

CONCLUSIONS

There is a need to improve UK Jewish community BRCA awareness and to highlight personal relevance of testing for individuals without a personal/family history of cancer. Traditional testing criteria caused multiple issues regarding test access and experience. Carriers want information and support services tailored to their individual circumstances.

Tumor initiation and early tumorigenesis: molecular mechanisms and interventional targets

Tumorigenesis is a multistep process, with oncogenic mutations in a normal cell conferring clonal advantage as the initial event. However, despite pervasive somatic mutations and clonal expansion in normal tissues, their transformation into cancer remains a rare event, indicating the presence of additional driver events for progression to an irreversible, highly heterogeneous, and invasive lesion. Recently, researchers are emphasizing the mechanisms of environmental tumor risk factors and epigenetic alterations that are profoundly influencing early clonal expansion and malignant evolution, independently of inducing mutations. Additionally, clonal evolution in tumorigenesis reflects a multifaceted interplay between cell-intrinsic identities and various cell-extrinsic factors that exert selective pressures to either restrain uncontrolled proliferation or allow specific clones to progress into tumors. However, the mechanisms by which driver events induce both intrinsic cellular competency and remodel environmental stress to facilitate malignant transformation are not fully understood. In this review, we summarize the genetic, epigenetic, and external driver events, and their effects on the co-evolution of the transformed cells and their ecosystem during tumor initiation and early malignant evolution. A deeper understanding of the earliest molecular events holds promise for translational applications, predicting individuals at high-risk of tumor and developing strategies to intercept malignant transformation.

Nanoenzymes: A Radiant Hope for the Early Diagnosis and Effective Treatment of Breast and Ovarian Cancers

Breast and ovarian cancers, despite having chemotherapy and surgical treatment, still have the lowest survival rate. Experimental stages using nanoenzymes/nanozymes for ovarian cancer diagnosis and treatment are being carried out, and correspondingly the current treatment approaches to treat breast cancer have a lot of adverse side effects, which is the reason why researchers and scientists are looking for new strategies with less side effects. Nanoenzymes have intrinsic enzyme-like activities and can reduce the shortcomings of naturally occurring enzymes due to the ease of storage, high stability, less expensive, and enhanced efficiency. In this review, we have discussed various ways in which nanoenzymes are being used to diagnose and treat breast and ovarian cancer. For breast cancer, nanoenzymes and their multi-enzymatic properties can control the level of reactive oxygen species (ROS) in cells or tissues, for example, oxidase (OXD) and peroxidase (POD) activity can be used to generate ROS, while catalase (CAT) or superoxide dismutase (SOD) activity can scavenge ROS. In the case of ovarian cancer, most commonly nanoceria is being investigated, and also when folic acid is combined with nanoceria there are additional advantages like inhibition of beta galactosidase. Nanocarriers are also used to deliver small interfering RNA that are effective in cancer treatment. Studies have shown that iron oxide nanoparticles are actively being used for drug delivery, similarly ferritin carriers are used for the delivery of nanozymes. Hypoxia is a major factor in ovarian cancer, therefore MnO2-based nanozymes are being used as a therapy. For cancer diagnosis and screening, nanozymes are being used in sonodynamic cancer therapy for cancer diagnosis and screening, whereas biomedical imaging and folic acid gold particles are also being used for image guided treatments. Nanozyme biosensors have been developed to detect ovarian cancer. This review article summarizes a detailed insight into breast and ovarian cancers in light of nanozymes-based diagnostic and therapeutic approaches.

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.

Cancer-specific risk prediction with a serum microRNA signature

We recently derived and validated a serum-based microRNA risk score (miR-score) that predicted colorectal cancer (CRC) occurrence with very high accuracy within 14 years of follow-up in a population-based cohort study from Germany (ESTHER cohort). Here, we aimed to evaluate associations of the CRC-specific miR-score with the risk of developing other common cancers, including female breast cancer (BC), lung cancer (LC), and prostate cancer (PC), in the ESTHER cohort. MicroRNAs (miRNAs) were profiled by quantitative real-time PCR in serum samples collected at baseline from randomly selected incident cases of BC (n = 90), LC (n = 88), and PC (n = 93) and participants without diagnosis of CRC, LC, BC, or PC (controls, n = 181) until the end of the 17-year follow-up. Multivariate logistic regression models were used to evaluate the associations of the miR-score with BC, LC, and PC incidence. The miR-score showed strong inverse associations with BC and LC incidence [odds ratio per 1 standard deviation increase: 0.60 (95% confidence interval [CI] 0.43-0.82), p = 0.0017, and 0.64 (95% CI 0.48-0.84),p = 0.0015, respectively]. Associations with PC were not statistically significant but pointed in the positive direction. Our study highlights the potential of serum-based miRNA biomarkers for cancer-specific risk prediction. Further large cohort studies aiming to investigate, validate, and optimize the use of circulating miRNA signatures for cancer risk assessment are warranted.

Joint ABS-UKCGG-CanGene-CanVar consensus regarding the use of CanRisk in clinical practice

BACKGROUND

The CanRisk tool, which operationalises the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) is used by Clinical Geneticists, Genetic Counsellors, Breast Oncologists, Surgeons and Family History Nurses for breast cancer risk assessments both nationally and internationally. There are currently no guidelines with respect to the day-to-day clinical application of CanRisk and differing inputs to the model can result in different recommendations for practice.

METHODS

To address this gap, the UK Cancer Genetics Group in collaboration with the Association of Breast Surgery and the CanGene-CanVar programme held a workshop on 16th of May 2023, with the aim of establishing best practice guidelines.

RESULTS

Using a pre-workshop survey followed by structured discussion and in-meeting polling, we achieved consensus for UK best practice in use of CanRisk in making recommendations for breast cancer surveillance, eligibility for genetic testing and the input of available information to undertake an individualised risk assessment.

CONCLUSIONS

Whilst consensus recommendations were achieved, the meeting highlighted some of the barriers limiting the use of CanRisk in clinical practice and identified areas that require further work and collaboration with relevant national bodies and policy makers to incorporate wider use of CanRisk into routine breast cancer risk assessments.

Implementing Multifactorial Risk Assessment with Polygenic Risk Scores for Personalized Breast Cancer Screening in the Population Setting: Challenges and Opportunities

Risk-stratified breast screening has been proposed as a strategy to overcome the limitations of age-based screening. A prospective cohort study was undertaken within the PERSPECTIVE I&I project, which will generate the first Canadian evidence on multifactorial breast cancer risk assessment in the population setting to inform the implementation of risk-stratified screening. Recruited females aged 40-69 unaffected by breast cancer, with a previous mammogram, underwent multifactorial breast cancer risk assessment. The adoption of multifactorial risk assessment, the effectiveness of methods for collecting risk factor information and the costs of risk assessment were examined. Associations between participant characteristics and study sites, as well as data collection methods, were assessed using logistic regression; all p-values are two-sided. Of the 4246 participants recruited, 88.4% completed a risk assessment, with 79.8%, 15.7% and 4.4% estimated at average, higher than average and high risk, respectively. The total per-participant cost for risk assessment was CAD 315. Participants who chose to provide risk factor information on paper/telephone (27.2%) vs. online were more likely to be older (p = 0.021), not born in Canada (p = 0.043), visible minorities (p = 0.01) and have a lower attained education (p < 0.0001) and perceived fair/poor health (p < 0.001). The 34.4% of participants requiring risk factor verification for missing/unusual values were more likely to be visible minorities (p = 0.009) and have a lower attained education (p ≤ 0.006). This study demonstrates the feasibility of risk assessment for risk-stratified screening at the population level. Implementation should incorporate an equity lens to ensure cancer-screening disparities are not widened.

Beyond the Genome: Deciphering the Role of MALAT1 in Breast Cancer Progression

The MALAT1, a huge non-coding RNA, recently came to light as a multifaceted regulator in the intricate landscape of breast cancer (BC) progression. This review explores the multifaceted functions and molecular interactions of MALAT1, shedding light on its profound implications for understanding BC pathogenesis and advancing therapeutic strategies. The article commences by acknowledging the global impact of BC and the pressing need for insights into its molecular underpinnings. It is stated that the core lncRNA MALAT1 has a range of roles in both healthy and diseased cell functions. The core of this review unravels MALAT1's multifaceted role in BC progression, elucidating its participation in critical processes like resistance, invasion, relocation, and proliferating cells to therapy. It explores the intricate mechanisms through which MALAT1 modulates gene expression, interacts with other molecules, and influences signalling pathways. Furthermore, the paper emphasizes MALAT1's clinical significance as a possible prognostic and diagnostic biomarker. Concluding on a forward-looking note, the review highlights the broader implications of MALAT1 in BC biology, such as its connections to therapy resistance and metastasis. It underscores the significance of deeper investigations into these intricate molecular interactions to pave the way for precision medicine approaches. This review highlights the pivotal role of MALAT1 in BC progression by deciphering its multifaceted functions beyond the genome, offering profound insights into its implications for disease understanding and the potential for targeted therapeutic interventions.

Weighted metrics are required when evaluating the performance of prediction models in nested case-control studies

BACKGROUND

Nested case-control (NCC) designs are efficient for developing and validating prediction models that use expensive or difficult-to-obtain predictors, especially when the outcome is rare. Previous research has focused on how to develop prediction models in this sampling design, but little attention has been given to model validation in this context. We therefore aimed to systematically characterize the key elements for the correct evaluation of the performance of prediction models in NCC data.

METHODS

We proposed how to correctly evaluate prediction models in NCC data, by adjusting performance metrics with sampling weights to account for the NCC sampling. We included in this study the C-index, threshold-based metrics, Observed-to-expected events ratio (O/E ratio), calibration slope, and decision curve analysis. We illustrated the proposed metrics with a validation of the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA version 5) in data from the population-based Rotterdam study. We compared the metrics obtained in the full cohort with those obtained in NCC datasets sampled from the Rotterdam study, with and without a matched design.

RESULTS

Performance metrics without weight adjustment were biased: the unweighted C-index in NCC datasets was 0.61 (0.58-0.63) for the unmatched design, while the C-index in the full cohort and the weighted C-index in the NCC datasets were similar: 0.65 (0.62-0.69) and 0.65 (0.61-0.69), respectively. The unweighted O/E ratio was 18.38 (17.67-19.06) in the NCC datasets, while it was 1.69 (1.42-1.93) in the full cohort and its weighted version in the NCC datasets was 1.68 (1.53-1.84). Similarly, weighted adjustments of threshold-based metrics and net benefit for decision curves were unbiased estimates of the corresponding metrics in the full cohort, while the corresponding unweighted metrics were biased. In the matched design, the bias of the unweighted metrics was larger, but it could also be compensated by the weight adjustment.

CONCLUSIONS

Nested case-control studies are an efficient solution for evaluating the performance of prediction models that use expensive or difficult-to-obtain biomarkers, especially when the outcome is rare, but the performance metrics need to be adjusted to the sampling procedure.

Polygenic risk-stratified screening for nasopharyngeal carcinoma in high-risk endemic areas of China: a cost-effectiveness study

Background

Nasopharyngeal carcinoma (NPC) has an extremely high incidence rate in Southern China, resulting in a severe disease burden for the local population. Current EBV serologic screening is limited by false positives, and there is opportunity to integrate polygenic risk scores for personalized screening which may enhance cost-effectiveness and resource utilization.

Methods

A Markov model was developed based on epidemiological and genetic data specific to endemic areas of China, and further compared polygenic risk-stratified screening [subjects with a 10-year absolute risk (AR) greater than a threshold risk underwent EBV serological screening] to age-based screening (EBV serological screening for all subjects). For each initial screening age (30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, and 65-69 years), a modeled cohort of 100,000 participants was screened until age 69, and then followed until age 79.

Results

Among subjects aged 30 to 54 years, polygenic risk-stratified screening strategies were more cost-effective than age-based screening strategies, and almost comprised the cost-effectiveness efficiency frontier. For men, screening strategies with a 1-year frequency and a 10-year absolute risk (AR) threshold of 0.7% or higher were cost-effective, with an incremental cost-effectiveness ratio (ICER) below the willingness to pay (¥203,810, twice the local per capita GDP). Specifically, the strategies with a 10-year AR threshold of 0.7% or 0.8% are the most cost-effective strategies, with an ICER ranging from ¥159,752 to ¥201,738 compared to lower-cost non-dominated strategies on the cost-effectiveness frontiers. The optimal strategies have a higher probability (29.4-35.8%) of being cost-effective compared to other strategies on the frontier. Additionally, they reduce the need for nasopharyngoscopies by 5.1-27.7% compared to optimal age-based strategies. Likewise, for women aged 30-54 years, the optimal strategy with a 0.3% threshold showed similar results. Among subjects aged 55 to 69 years, age-based screening strategies were more cost-effective for men, while no screening may be preferred for women.

Conclusion

Our economic evaluation found that the polygenic risk-stratified screening could improve the cost-effectiveness among individuals aged 30-54, providing valuable guidance for NPC prevention and control policies in endemic areas of China.

Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries

We develop a method, SBayesRC, that integrates genome-wide association study (GWAS) summary statistics with functional genomic annotations to improve polygenic prediction of complex traits. Our method is scalable to whole-genome variant analysis and refines signals from functional annotations by allowing them to affect both causal variant probability and causal effect distribution. We analyze 50 complex traits and diseases using ∼7 million common single-nucleotide polymorphisms (SNPs) and 96 annotations. SBayesRC improves prediction accuracy by 14% in European ancestry and up to 34% in cross-ancestry prediction compared to the baseline method SBayesR, which does not use annotations, and outperforms other methods, including LDpred2, LDpred-funct, MegaPRS, PolyPred-S and PRS-CSx. Investigation of factors affecting prediction accuracy identifies a significant interaction between SNP density and annotation information, suggesting whole-genome sequence variants with annotations may further improve prediction. Functional partitioning analysis highlights a major contribution of evolutionary constrained regions to prediction accuracy and the largest per-SNP contribution from nonsynonymous SNPs.

The heterogeneous cancer phenotype of individuals with biallelic germline pathogenic variants in CHEK2

PURPOSE

Females with biallelic CHEK2 germline pathogenic variants (gPVs) more often develop multiple breast cancers than individuals with monoallelic CHEK2 gPVs. This study is aimed at expanding the knowledge on the occurrence of other malignancies.

METHODS

Exome sequencing of individuals who developed multiple primary malignancies identified 3 individuals with the CHEK2 (NM_007194.4) c.1100del p.(Thr367MetfsTer15) loss-of-function gPV in a biallelic state. We collected the phenotypes of an additional cohort of individuals with CHEK2 biallelic gPVs (n = 291).

RESULTS

In total, 157 individuals (53.4%; 157/294 individuals) developed ≥1 (pre)malignancy. The most common (pre)malignancies next to breast cancer were colorectal- (n = 19), thyroid- (n = 19), and prostate (pre)malignancies (n = 12). Females with biallelic CHEK2 loss-of-function gPVs more frequently developed ≥2 (pre)malignancies and at an earlier age compared with females biallelic for the CHEK2 c.470T>C p.(Ile157Thr) missense variant. Furthermore, 26 males (31%; 26/84 males) with CHEK2 biallelic gPVs developed ≥1 (pre)malignancies of 15 origins.

CONCLUSION

Our study suggests that CHEK2 biallelic gPVs likely increase the susceptibility to develop multiple malignancies in various tissues, both in females and males. However, it is possible that a substantial proportion of individuals with CHEK2 biallelic gPVs is missed as diagnostic testing for CHEK2 often is limited to individuals who developed breast cancer.

The risk-based breast screening (RIBBS) study protocol: a personalized screening model for young women

The optimal mammography screening strategy for women aged 45-49 years is a matter of debate. We present the RIBBS study protocol, a quasi-experimental, prospective, population-based study comparing a risk- and breast density-stratified screening model (interventional cohort) with annual digital mammography (DM) screening (observational control cohort) in a real-world setting. The interventional cohort consists of 10,269 women aged 45 years enrolled between 2020 and 2021 from two provinces of the Veneto Region (northen Italy). At baseline, participants underwent two-view digital breast tomosynthesis (DBT) and completed the Tyrer-Cuzick risk prediction model. Volumetric breast density (VBD) was calculated from DBT and the lifetime risk (LTR) was estimated by including VBD among the risk factors. Based on VBD and LTR, women were classified into five subgroups with specific screening protocols for subsequent screening rounds: (1) LTR ≤ 17% and nondense breast: biennial DBT; (2) LTR ≤ 17% and dense breast: biennial DBT and ultrasound; (3) LTR 17-30% or LTR > 30% without family history of BC, and nondense breast: annual DBT; (4) LTR 17-30% or > 30% without family history of BC, and dense breast: annual DBT and ultrasound; and (5) LTR > 30% and family history of BC: annual DBT and breast MRI. The interventional cohort is still ongoing. An observational, nonequivalent control cohort of 43,000 women aged 45 years participating in an annual DM screening programme was recruited in three provinces of the neighbouring Emilia-Romagna Region. Cumulative incidence rates of advanced BC at three, five, and ten years between the two cohorts will be compared, adjusting for the incidence difference at baseline.Trial registration This study is registered on Clinicaltrials.gov (NCT05675085).

EMQN best practice guidelines for genetic testing in hereditary breast and ovarian cancer

Hereditary Breast and Ovarian Cancer (HBOC) is a genetic condition associated with increased risk of cancers. The past decade has brought about significant changes to hereditary breast and ovarian cancer (HBOC) diagnostic testing with new treatments, testing methods and strategies, and evolving information on genetic associations. These best practice guidelines have been produced to assist clinical laboratories in effectively addressing the complexities of HBOC testing, while taking into account advancements since the last guidelines were published in 2007. These guidelines summarise cancer risk data from recent studies for the most commonly tested high and moderate risk HBOC genes for laboratories to refer to as a guide. Furthermore, recommendations are provided for somatic and germline testing services with regards to clinical referral, laboratory analyses, variant interpretation, and reporting. The guidelines present recommendations where 'must' is assigned to advocate that the recommendation is essential; and 'should' is assigned to advocate that the recommendation is highly advised but may not be universally applicable. Recommendations are presented in the form of shaded italicised statements throughout the document, and in the form of a table in supplementary materials (Table S4). Finally, for the purposes of encouraging standardisation and aiding implementation of recommendations, example report wording covering the essential points to be included is provided for the most common HBOC referral and reporting scenarios. These guidelines are aimed primarily at genomic scientists working in diagnostic testing laboratories.

Low-frequency and rare genetic variants associated with rheumatoid arthritis risk

Rheumatoid arthritis (RA) has an estimated heritability of nearly 50%, which is particularly high in seropositive RA. HLA alleles account for a large proportion of this heritability, in addition to many common single-nucleotide polymorphisms with smaller individual effects. Low-frequency and rare variants, such as those captured by next-generation sequencing, can also have a large role in heritability in some individuals. Rare variant discovery has informed the development of drugs such as inhibitors of PCSK9 and Janus kinases. Some 34 low-frequency and rare variants are currently associated with RA risk. One variant (19:10352442G>C in TYK2) was identified in five separate studies, and might therefore represent a promising therapeutic target. Following a set of best practices in future studies, including studying diverse populations, using large sample sizes, validating RA and serostatus, replicating findings, adjusting for other variants and performing functional assessment, could help to ensure the relevance of identified variants. Exciting opportunities are now on the horizon for genetics in RA, including larger datasets and consortia, whole-genome sequencing and direct applications of findings in the management, and especially treatment, of RA.

Comprehensive Inherited Risk Estimation for Risk-Based Breast Cancer Screening in Women

PURPOSE

Family history (FH) and pathogenic variants (PVs) are used for guiding risk surveillance in selected high-risk women but little is known about their impact for breast cancer screening on population level. In addition, polygenic risk scores (PRSs) have been shown to efficiently stratify breast cancer risk through combining information about common genetic factors into one measure.

METHODS

In longitudinal real-life data, we evaluate PRS, FH, and PVs for stratified screening. Using FinnGen (N = 117,252), linked to the Mass Screening Registry for breast cancer (1992-2019; nationwide organized biennial screening for age 50-69 years), we assessed the screening performance of a breast cancer PRS and compared its performance with FH of breast cancer and PVs in moderate- (CHEK2)- to high-risk (PALB2) susceptibility genes.

RESULTS

Effect sizes for FH, PVs, and high PRS (>90th percentile) were comparable in screening-aged women, with similar implications for shifting age at screening onset. A high PRS identified women more likely to be diagnosed with breast cancer after a positive screening finding (positive predictive value [PPV], 39.5% [95% CI, 37.6 to 41.5]). Combinations of risk factors increased the PPVs up to 45% to 50%. A high PRS conferred an elevated risk of interval breast cancer (hazard ratio [HR], 2.78 [95% CI, 2.00 to 3.86] at age 50 years; HR, 2.48 [95% CI, 1.67 to 3.70] at age 60 years), and women with a low PRS (<10th percentile) had a low risk for both interval- and screen-detected breast cancers.

CONCLUSION

Using real-life screening data, this study demonstrates the effectiveness of a breast cancer PRS for risk stratification, alone and combined with FH and PVs. Further research is required to evaluate their impact in a prospective risk-stratified screening program, including cost-effectiveness.

Identifying somatic fingerprints of cancers defined by germline and environmental risk factors

Numerous studies over the past generation have identified germline variants that increase specific cancer risks. Simultaneously, a revolution in sequencing technology has permitted high-throughput annotations of somatic genomes characterizing individual tumors. However, examining the relationship between germline variants and somatic alteration patterns is hugely challenged by the large numbers of variants in a typical tumor, the rarity of most individual variants, and the heterogeneity of tumor somatic fingerprints. In this article, we propose statistical methodology that frames the investigation of germline-somatic relationships in an interpretable manner. The method uses meta-features embodying biological contexts of individual somatic alterations to implicitly group rare mutations. Our team has used this technique previously through a multilevel regression model to diagnose with high accuracy tumor site of origin. Herein, we further leverage topic models from computational linguistics to achieve interpretable lower-dimensional embeddings of the meta-features. We demonstrate how the method can identify distinctive somatic profiles linked to specific germline variants or environmental risk factors. We illustrate the method using The Cancer Genome Atlas whole-exome sequencing data to characterize somatic tumor fingerprints in breast cancer patients with germline BRCA1/2 mutations and in head and neck cancer patients exposed to human papillomavirus.

Personalized survivorship care: Routine breast cancer risk assessment in the gynecologic oncology clinic

INTRODUCTION

Gynecologic and breast cancers share several risk factors. Breast cancer risk assessment tools can identify those at elevated risk and allow for enhanced breast surveillance and chemoprevention, however such tools are underutilized. We aim to evaluate the use of routine breast cancer risk assessment in a gynecologic oncology clinic.

METHODS

A patient-facing web-based tool was used to collect personal and family history and run four validated breast cancer risk assessment models (Tyrer-Cuzick (TC), Gail, BRCAPRO, and Claus) in a gynecologic oncology clinic. We evaluated completion of the tools and identification of patients at elevated risk for breast cancer using the four validated models.

RESULTS

A total of 99 patients were included in this analysis. The BRCAPRO model had the highest completion rate (84.8%), followed by the TC model (74.7%), Gail model (74.7%), and the Claus model (52.1%). The TC model identified 21.6% of patients completing the model as having ≥20% lifetime risk of breast cancer, compared to 6.8% by the Gail model, and 0% for both the BRCAPRO and Claus models. The Gail model identified 52.5% of patients as having ≥1.67% 5-year risk of breast cancer. Among patients identified as high-risk for breast cancer and eligible for screening, 9/9 (100%) were referred to a high-risk breast clinic.

CONCLUSION

Among patients that completed the TC breast cancer risk assessment in a gynecologic oncology clinic, approximately 1 in 5 were identified to be at significantly elevated lifetime risk for breast cancer. The gynecologic oncologist's office might offer a convenient and feasible setting to incorporate this risk assessment into routine patient care, as gynecologic oncologists often have long-term patient relationships and participate in survivorship care.

A perspective on genetic and polygenic risk scores-advances and limitations and overview of associated tools

Polygenetic Risk Scores are used to evaluate an individual's vulnerability to developing specific diseases or conditions based on their genetic composition, by taking into account numerous genetic variations. This article provides an overview of the concept of Polygenic Risk Scores (PRS). We elucidate the historical advancements of PRS, their advantages and shortcomings in comparison with other predictive methods, and discuss their conceptual limitations in light of the complexity of biological systems. Furthermore, we provide a survey of published tools for computing PRS and associated resources. The various tools and software packages are categorized based on their technical utility for users or prospective developers. Understanding the array of available tools and their limitations is crucial for accurately assessing and predicting disease risks, facilitating early interventions, and guiding personalized healthcare decisions. Additionally, we also identify potential new avenues for future bioinformatic analyzes and advancements related to PRS.