Addition of a 161-SNP polygenic risk score to family history-based risk prediction: impact on clinical management in non-BRCA1/2 breast cancer families

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.

Validation of a clinical breast cancer risk assessment tool combining a polygenic score for all ancestries with traditional risk factors

PURPOSE

We previously described a combined risk score (CRS) that integrates a multiple-ancestry polygenic risk score (MA-PRS) with the Tyrer-Cuzick (TC) model to assess breast cancer (BC) risk. Here, we present a longitudinal validation of CRS in a real-world cohort.

METHODS

This study included 130,058 patients referred for hereditary cancer genetic testing and negative for germline pathogenic variants in BC-associated genes. Data were obtained by linking genetic test results to medical claims (median follow-up 12.1 months). CRS calibration was evaluated by the ratio of observed to expected BCs.

RESULTS

Three hundred forty BCs were observed over 148,349 patient-years. CRS was well-calibrated and demonstrated superior calibration compared with TC in high-risk deciles. MA-PRS alone had greater discriminatory accuracy than TC, and CRS had approximately 2-fold greater discriminatory accuracy than MA-PRS or TC. Among those classified as high risk by TC, 32.6% were low risk by CRS, and of those classified as low risk by TC, 4.3% were high risk by CRS. In cases where CRS and TC classifications disagreed, CRS was more accurate in predicting incident BC.

CONCLUSION

CRS was well-calibrated and significantly improved BC risk stratification. Short-term follow-up suggests that clinical implementation of CRS should improve outcomes for patients of all ancestries through personalized risk-based screening and prevention.

Clinical Application of Polygenic Risk Score in IgA Nephropathy

Genome-wide association studies (GWASs) have identified 30 independent genetic variants associated with IgA nephropathy (IgAN). A genetic risk score (GRS) represents the number of risk alleles carried and thus captures an individual's genetic risk. However, whether and which polygenic risk score crucial for the evaluation of any potential personal or clinical utility on risk and prognosis are still obscure. We constructed different GRS models based on different sets of variants, which were top single nucleotide polymorphisms (SNPs) reported in the previous GWASs. The case-control GRS analysis included 3365 IgAN patients and 8842 healthy individuals. The association between GRS and clinical variability, including age at diagnosis, clinical parameters, Oxford pathology classification, and kidney prognosis was further evaluated in a prospective cohort of 1747 patients. Three GRS models (15 SNPs, 21 SNPs, and 55 SNPs) were constructed after quality control. The patients with the top 20% GRS had 2.42-(15 SNPs, p = 8.12 × 10-40), 3.89-(21 SNPs, p = 3.40 × 10-80) and 3.73-(55 SNPs, p = 6.86 × 10-81) fold of risk to develop IgAN compared to the patients with the bottom 20% GRS, with area under the receiver operating characteristic curve (AUC) of 0.59, 0.63, and 0.63 in group discriminations, respectively. A positive correlation between GRS and microhematuria, mesangial hypercellularity, segmental glomerulosclerosis and a negative correlation on the age at diagnosis, body mass index (BMI), mean arterial pressure (MAP), serum C3, triglycerides can be observed. Patients with the top 20% GRS also showed a higher risk of worse prognosis for all three models (1.36, 1.42, and 1.36 fold of risk) compared to the remaining 80%, whereas 21 SNPs model seemed to show a slightly better fit in prediction. Collectively, a higher burden of risk variants is associated with earlier disease onset and a higher risk of a worse prognosis. This may be informational in translating knowledge on IgAN genetics into disease risk prediction and patient stratification.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-023-00138-6.

The benefit of adding polygenic risk scores, lifestyle factors, and breast density to family history and genetic status for breast cancer risk and surveillance classification of unaffected women from germline CHEK2 c.1100delC families

To determine the changes in surveillance category by adding a polygenic risk score based on 311 breast cancer (BC)-associated variants (PRS311), questionnaire-based risk factors and breast density on personalized BC risk in unaffected women from Dutch CHEK2 c.1100delC families. In total, 117 unaffected women (58 heterozygotes and 59 non-carriers) from CHEK2 families were included. Blood-derived DNA samples were genotyped with the GSAMDv3-array to determine PRS311. Lifetime BC risk was calculated in CanRisk, which uses data from the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA). Women, were categorized into three surveillance groups. The surveillance advice was reclassified in 37.9 % of heterozygotes and 32.2 % of non-carriers after adding PRS311. Including questionnaire-based risk factors resulted in an additional change in 20.0 % of heterozygotes and 13.2 % of non-carriers; and a subanalysis showed that adding breast density on top shifted another 17.9 % of heterozygotes and 33.3 % of non-carriers. Overall, the majority of heterozygotes were reclassified to a less intensive surveillance, while non-carriers would require intensified surveillance. The addition of PRS311, questionnaire-based risk factors and breast density to family history resulted in a more personalized BC surveillance advice in CHEK2-families, which may lead to more efficient use of surveillance.

Clinical utility of polygenic risk scores: a critical 2023 appraisal

Since their first appearance in the context of schizophrenia and bipolar disorder in 2009, polygenic risk scores (PRSs) have been described for a large number of common complex diseases. However, the clinical utility of PRSs in disease risk assessment or therapeutic decision making is likely limited because PRSs usually only account for the heritable component of a trait and ignore the etiological role of environment and lifestyle. We surveyed the current state of PRSs for various diseases, including breast cancer, diabetes, prostate cancer, coronary artery disease, and Parkinson disease, with an extra focus upon the potential improvement of clinical scores by their combination with PRSs. We observed that the diagnostic and prognostic performance of PRSs alone is consistently low, as expected. Moreover, combining a PRS with a clinical score at best led to moderate improvement of the power of either risk marker. Despite the large number of PRSs reported in the scientific literature, prospective studies of their clinical utility, particularly of the PRS-associated improvement of standard screening or therapeutic procedures, are still rare. In conclusion, the benefit to individual patients or the health care system in general of PRS-based extensions of existing diagnostic or treatment regimens is still difficult to judge.

Learning from cancer to address COVID-19

Patients with cancer have been disproportionately affected by the novel coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Knowledge collected during the last three decades of cancer research has helped the medical research community worldwide to respond to many of the challenges raised by COVID-19, during the pandemic. The review, briefly summarizes the underlying biology and risk factors of COVID-19 and cancer, and aims to present recent evidence on cellular and molecular relationship between the two diseases, with a focus on those that are related to the hallmarks of cancer and uncovered in the first less than three years of the pandemic (2020-2022). This may not only help answer the question "Why cancer patients are considered to be at a particularly high risk of developing severe COVID-19 illness?", but also helped treatments of patients during the COVID-19 pandemic. The last session highlights the pioneering mRNA studies and the breakthrough discovery on nucleoside-modifications of mRNA by Katalin Karikó, which led to the innovation and development of the mRNA-based SARSCoV-2 vaccines saving lives of millions and also opened the door for a new era of vaccines and a new class of therapeutics.

Clinical applicability of the Polygenic Risk Score for breast cancer risk prediction in familial cases

BACKGROUND

Common low-risk variants are presently not used to guide clinical management of familial breast cancer (BC). We explored the additive impact of a 313-variant-based Polygenic Risk Score (PRS₃₁₃) relative to standard gene testing in non-BRCA1/2 Dutch BC families.

METHODS

We included 3918 BC cases from 3492 Dutch non-BRCA1/2 BC families and 3474 Dutch population controls. The association of the standardised PRS₃₁₃ with BC was estimated using a logistic regression model, adjusted for pedigree-based family history. Family history of the controls was imputed for this analysis. SEs were corrected to account for relatedness of individuals. Using the BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) V.5 model, lifetime risks were retrospectively calculated with and without individual PRS₃₁₃. For 2586 cases and 2584 controls, the carrier status of pathogenic variants (PVs) in ATM, CHEK2 and PALB2 was known.

RESULTS

The family history-adjusted PRS₃₁₃ was significantly associated with BC (per SD OR=1.97, 95% CI 1.84 to 2.11). Including the PRS₃₁₃ in BOADICEA family-based risk prediction would have changed screening recommendations in up to 27%, 36% and 34% of cases according to BC screening guidelines from the USA, UK and the Netherlands (National Comprehensive Cancer Network, National Institute for Health and Care Excellence, and Netherlands Comprehensive Cancer Organisation), respectively. For the population controls, without information on family history, this was up to 39%, 44% and 58%, respectively. Among carriers of PVs in known moderate BC susceptibility genes, the PRS₃₁₃ had the largest impact for CHEK2 and ATM.

CONCLUSIONS

Our results support the application of the PRS₃₁₃ in risk prediction for genetically uninformative BC families and families with a PV in moderate BC risk genes.

Integration of a Cross-Ancestry Polygenic Model With Clinical Risk Factors Improves Breast Cancer Risk Stratification

PURPOSE

To develop and validate a cross-ancestry integrated risk score (caIRS) that combines a cross-ancestry polygenic risk score (caPRS) with a clinical estimator for breast cancer (BC) risk. We hypothesized that the caIRS is a better predictor of BC risk than clinical risk factors across diverse ancestry groups.

METHODS

We used diverse retrospective cohort data with longitudinal follow-up to develop a caPRS and integrate it with the Tyrer-Cuzick (T-C) clinical model. We tested the association between the caIRS and BC risk in two validation cohorts including > 130,000 women. We compared model discrimination for 5-year and remaining lifetime BC risk between the caIRS and T-C and assessed how the caIRS would affect screening in the clinic.

RESULTS

The caIRS outperformed T-C alone for all populations tested in both validation cohorts and contributed significantly to risk prediction beyond T-C. The area under the receiver operating characteristic curve improved from 0.57 to 0.65, and the odds ratio per standard deviation increased from 1.35 (95% CI, 1.27 to 1.43) to 1.79 (95% CI, 1.70 to 1.88) in validation cohort 1 with similar improvements observed in validation cohort 2. We observed the largest gain in positive predictive value using the caIRS in Black/African American women across both validation cohorts, with an approximately two-fold increase and an equivalent negative predictive value as the T-C. In a multivariate, age-adjusted logistic regression model including both caIRS and T-C, caIRS remained significant, indicating that caIRS provides information over T-C alone.

CONCLUSION

Adding a caPRS to the T-C model improves BC risk stratification for women of multiple ancestries, which could have implications for screening recommendations and prevention.

Polygenic risk scores indicate extreme ages at onset of breast cancer in female BRCA1/2 pathogenic variant carriers

Background

Clinical management of women carrying a germline pathogenic variant (PV) in the BRCA1/2 genes demands for accurate age-dependent estimators of breast cancer (BC) risks, which were found to be affected by a variety of intrinsic and extrinsic factors. Here we assess the contribution of polygenic risk scores (PRSs) to the occurrence of extreme phenotypes with respect to age at onset, namely, primary BC diagnosis before the age of 35 years (early diagnosis, ED) and cancer-free survival until the age of 60 years (late/no diagnosis, LD) in female BRCA1/2 PV carriers.

Methods

Overall, estrogen receptor (ER)-positive, and ER-negative BC PRSs as developed by Kuchenbaecker et al. for BC risk discrimination in female BRCA1/2 PV carriers were employed for PRS computation in a curated sample of 295 women of European descent carrying PVs in the BRCA1 (n=183) or the BRCA2 gene (n=112), and did either fulfill the ED criteria (n=162, mean age at diagnosis: 28.3 years, range: 20 to 34 years) or the LD criteria (n=133). Binomial logistic regression was applied to assess the association of standardized PRSs with either ED or LD under adjustment for patient recruitment criteria for germline testing and localization of BRCA1/2 PVs in the corresponding BC or ovarian cancer (OC) cluster regions.

Results

For BRCA1 PV carriers, the standardized overall BC PRS displayed the strongest association with ED (odds ratio (OR) = 1.62; 95% confidence interval (CI): 1.16–2.31, p<0.01). Additionally, statistically significant associations of selection for the patient recruitment criteria for germline testing and localization of pathogenic PVs outside the BRCA1 OC cluster region with ED were observed. For BRCA2 PV carriers, the standardized PRS for ER-negative BC displayed the strongest association (OR = 2.27, 95% CI: 1.45–3.78, p<0.001).

Conclusions

PRSs contribute to the development of extreme phenotypes of female BRCA1/2 PV carriers with respect to age at primary BC diagnosis. Construction of optimized PRS SNP sets for BC risk stratification in BRCA1/2 PV carriers should be the task of future studies with larger, well-defined study samples. Furthermore, our results provide further evidence, that localization of PVs in BC/OC cluster regions might be considered in BC risk calculations for unaffected BRCA1/2 PV carriers.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12885-022-09780-1).

Towards implementation of comprehensive breast cancer risk prediction tools in health care for personalised prevention

Advances in knowledge about breast cancer risk factors have led to the development of more comprehensive risk models. These integrate information on a variety of risk factors such as lifestyle, genetics, family history, and breast density. These risk models have the potential to deliver more personalised breast cancer prevention. This is through improving accuracy of risk estimates, enabling more effective targeting of preventive options and creating novel prevention pathways through enabling risk estimation in a wider variety of populations than currently possible. The systematic use of risk tools as part of population screening programmes is one such example. A clear understanding of how such tools can contribute to the goal of personalised prevention can aid in understanding and addressing barriers to implementation. In this paper we describe how emerging models, and their associated tools can contribute to the goal of personalised healthcare for breast cancer through health promotion, early disease detection (screening) and improved management of women at higher risk of disease. We outline how addressing specific challenges on the level of communication, evidence, evaluation, regulation, and acceptance, can facilitate implementation and uptake.

Polygenic Risk Scores for Prediction of Gastric Cancer Based on Bioinformatics Screening and Validation of Functional lncRNA SNPs

INTRODUCTION

Single-nucleotide polymorphisms (SNPs) are used to stratify the risk of gastric cancer. However, no study included gastric cancer-related long noncoding RNA (lncRNA) SNPs into the risk model for evaluation. This study aimed to replicate the associations of 21 lncRNA SNPs and to construct an individual risk prediction model for gastric cancer.

METHODS

The bioinformatics method was used to screen gastric cancer-related lncRNA functional SNPs and verified in population. Gastric cancer risk prediction models were constructed using verified SNPs based on polygenic risk scores (PRSs).

RESULTS

Twenty-one SNPs were screened, and the multivariate unconditional logistic regression analysis showed that 14 lncRNA SNPs were significantly associated with gastric cancer. In the distribution of genetic risk score in cases and controls, the mean value of PRS in cases was higher than that in controls. Approximately 20.1% of the cases was caused by genetic variation (P = 1.9 × 10-34) in optimal PRS model. The individual risk of gastric cancer in the lowest 10% of PRS was 82.1% (95% confidence interval [CI]: 0.102, 0.314) lower than that of the general population. The risk of gastric cancer in the highest 10% of PRS was 5.75-fold that of the general population (95% CI: 3.09, 10.70). The introduction of family history of tumor (area under the curve, 95% CI: 0.752, 0.69-0.814) and Helicobacter pylori infection (area under the curve, 95% CI: 0.773, 0.702-0.843) on the basis of PRS could significantly improve the recognition ability of the model.

DISCUSSION

PRSs based on lncRNA SNPs could identify individuals with high risk of gastric cancer and combined with risk factors could improve the stratification.

Association between a 46-SNP Polygenic Risk Score and melanoma risk in Dutch patients with familial melanoma

BACKGROUND

Familial clustering of melanoma suggests a shared genetic predisposition among family members, but only 10%-40% of familial cases carry a pathogenic variant in a known high-risk melanoma susceptibility gene. We investigated whether a melanoma-specific Polygenic Risk Score (PRS) is associated with melanoma risk in patients with genetically unexplained familial melanoma.

METHODS

Dutch familial melanoma cases (n=418) were genotyped for 46 SNPs previously identified as independently associated with melanoma risk. The 46-SNP PRS was calculated and standardised to 3423 healthy controls (sPRS) and the association between PRS and melanoma risk was modelled using logistic regression. Within the case series, possible differences were further explored by investigating the PRS in relation to (1) the number of primary melanomas in a patient and (2) the extent of familial clustering of melanoma.

RESULTS

The PRS was significantly associated with melanoma risk, with a per-SD OR of 2.12 (95% CI 1.90 to 2.35, p<0.001), corresponding to a 5.70-fold increased risk (95% CI 3.93 to 8.28) when comparing the top 90th to the middle 40-60th PRS percentiles. The mean PRS was significantly higher in cases with multiple primary melanomas than in cases with a single melanoma (sPRS 1.17 vs 0.71, p=0.001). Conversely, cases from high-density melanoma families had a lower (but non-significant) mean PRS than cases from low-density families (sPRS 0.60 vs 0.94, p=0.204).

CONCLUSION

Our work underlines the significance of a PRS in determining melanoma susceptibility and encourages further exploration of the diagnostic value of a PRS in genetically unexplained melanoma families.

Communicating polygenic risk scores in the familial breast cancer clinic

OBJECTIVE

To describe the communication of polygenic risk scores (PRS) in the familial breast cancer setting.

METHODS

Consultations between genetic healthcare providers (GHP) and female patients who received their PRS for breast cancer risk were recorded (n = 65). GHPs included genetic counselors (n = 8) and medical practitioners (n = 5) (i.e. clinical geneticists and oncologists). A content analysis was conducted and logistic regression was used to assess differences in communication behaviors between genetic counselors (n = 8) and medical practitioners (n = 5).

RESULTS

Of the 65 patients, 31 (47.7 %) had a personal history of breast cancer, 18 of whom received an increased PRS (relative risk >1.2). 25/34 unaffected patients received an increased PRS. Consultations were primarily clinician-driven and focused on biomedical information. There was little difference between the biomedical information provided by genetic counselors and medical practitioners. However, genetic counselors were significantly more likely to utilize strategies to build patient rapport and counseling techniques.

CONCLUSIONS

Our findings provide one of the earliest reports on how breast cancer PRSs are communicated to women.

PRACTICE IMPLICATIONS

Key messages for communicating PRSs were identified, namely: discussing differences between polygenic and monogenic testing, the multifactorial nature of breast cancer risk, polygenic inheritance and current limitation of PRSs.

Evaluating Polygenic Risk Scores for Breast Cancer in Women of African Ancestry

BACKGROUND

Polygenic risk scores (PRSs) have been demonstrated to identify women of European, Asian, and Latino ancestry at elevated risk of developing breast cancer (BC). We evaluated the performance of existing PRSs trained in European ancestry populations among women of African ancestry.

METHODS

We assembled genotype data for women of African ancestry, including 9241 case subjects and 10 193 control subjects. We evaluated associations of 179- and 313-variant PRSs with overall and subtype-specific BC risk. PRS discriminatory accuracy was assessed using area under the receiver operating characteristic curve. We also evaluated a recalibrated PRS, replacing the index variant with variants in each region that better captured risk in women of African ancestry and estimated lifetime absolute risk of BC in African Americans by PRS category.

RESULTS

For overall BC, the odds ratio per SD of the 313-variant PRS (PRS313) was 1.27 (95% confidence interval [CI] = 1.23 to 1.31), with an area under the receiver operating characteristic curve of 0.571 (95% CI = 0.562 to 0.579). Compared with women with average risk (40th-60th PRS percentile), women in the top decile of PRS313 had a 1.54-fold increased risk (95% CI = 1.38-fold to 1.72-fold). By age 85 years, the absolute risk of overall BC was 19.6% for African American women in the top 1% of PRS313 and 6.7% for those in the lowest 1%. The recalibrated PRS did not improve BC risk prediction.

CONCLUSION

The PRSs stratify BC risk in women of African ancestry, with attenuated performance compared with that reported in European, Asian, and Latina populations. Future work is needed to improve BC risk stratification for women of African ancestry.

Improving reporting standards for polygenic scores in risk prediction studies

Polygenic risk scores (PRSs), which often aggregate results from genome-wide association studies, can bridge the gap between initial discovery efforts and clinical applications for the estimation of disease risk using genetics. However, there is notable heterogeneity in the application and reporting of these risk scores, which hinders the translation of PRSs into clinical care. Here, in a collaboration between the Clinical Genome Resource (ClinGen) Complex Disease Working Group and the Polygenic Score (PGS) Catalog, we present the Polygenic Risk Score Reporting Standards (PRS-RS), in which we update the Genetic Risk Prediction Studies (GRIPS) Statement to reflect the present state of the field. Drawing on the input of experts in epidemiology, statistics, disease-specific applications, implementation and policy, this comprehensive reporting framework defines the minimal information that is needed to interpret and evaluate PRSs, especially with respect to downstream clinical applications. Items span detailed descriptions of study populations, statistical methods for the development and validation of PRSs and considerations for the potential limitations of these scores. In addition, we emphasize the need for data availability and transparency, and we encourage researchers to deposit and share PRSs through the PGS Catalog to facilitate reproducibility and comparative benchmarking. By providing these criteria in a structured format that builds on existing standards and ontologies, the use of this framework in publishing PRSs will facilitate translation into clinical care and progress towards defining best practice.

Improving reporting standards for polygenic scores in risk prediction studies

Polygenic risk scores (PRSs), which often aggregate results from genome-wide association studies, can bridge the gap between initial discovery efforts and clinical applications for the estimation of disease risk using genetics. However, there is notable heterogeneity in the application and reporting of these risk scores, which hinders the translation of PRSs into clinical care. Here, in a collaboration between the Clinical Genome Resource (ClinGen) Complex Disease Working Group and the Polygenic Score (PGS) Catalog, we present the Polygenic Risk Score Reporting Standards (PRS-RS), in which we update the Genetic Risk Prediction Studies (GRIPS) Statement to reflect the present state of the field. Drawing on the input of experts in epidemiology, statistics, disease-specific applications, implementation and policy, this comprehensive reporting framework defines the minimal information that is needed to interpret and evaluate PRSs, especially with respect to downstream clinical applications. Items span detailed descriptions of study populations, statistical methods for the development and validation of PRSs and considerations for the potential limitations of these scores. In addition, we emphasize the need for data availability and transparency, and we encourage researchers to deposit and share PRSs through the PGS Catalog to facilitate reproducibility and comparative benchmarking. By providing these criteria in a structured format that builds on existing standards and ontologies, the use of this framework in publishing PRSs will facilitate translation into clinical care and progress towards defining best practice.

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.

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.

Understanding polygenic models, their development and the potential application of polygenic scores in healthcare

The use of genomic information to better understand and prevent common complex diseases has been an ongoing goal of genetic research. Over the past few years, research in this area has proliferated with several proposed methods of generating polygenic scores. This has been driven by the availability of larger data sets, primarily from genome-wide association studies and concomitant developments in statistical methodologies. Here we provide an overview of the methodological aspects of polygenic model construction. In addition, we consider the state of the field and implications for potential applications of polygenic scores for risk estimation within healthcare.

Breast cancer polygenic risk scores in the clinical cancer genetic counseling setting: Current practices and impact on patient management

Multivariate risk models are commonly used in clinical practice to estimate a woman's lifetime risk for breast cancer and assist in implementation of appropriate screening and risk reduction strategies. More recently, breast cancer polygenic risk scores (PRS) have been derived and integrated into these models to further improve risk estimation. While breast cancer PRS have been offered by two clinical diagnostic laboratories since 2017, little is known about the extent to which genetic counselors are ordering breast cancer PRS or incorporating the results into patient management. This study surveyed U.S. cancer genetic counselors from October 2019 to January 2020 to identify and understand their current practices with breast cancer PRS, to determine the impact of breast cancer PRS on patient management, and to anticipate future genetic counselor practices with breast cancer PRS. Fewer than half of respondents (43%, 51/120) had ordered breast cancer PRS and approximately one-third (35%, 16/46) reported that the PRS had changed their medical management recommendations. The majority of cancer genetic counselors had not ordered PRS, most commonly due to (a) lack of clinical guidelines (90%, 60/67), (b) insufficient evidence of clinical utility (88%, 59/67), and (c) lack of availability for patients of non-European ancestry (70%, 47/67). Of genetic counselors who had not ordered breast cancer PRS, only 10% (7/68) did not believe they would order PRS in the future. This is the first study to characterize genetic counselors' experiences with breast cancer PRS. Results from this study indicate that although breast cancer PRS have been clinically available for patients for several years, most cancer genetic counselors are not yet convinced they are ready to be incorporated into patient care.

The emerging field of polygenic risk scores and perspective for use in clinical care

Genetic testing is used widely for diagnostic, carrier and predictive testing in monogenic diseases. Until recently, there were no genetic testing options available for multifactorial complex diseases like heart disease, diabetes and cancer. Genome-wide association studies (GWAS) have been invaluable in identifying single-nucleotide polymorphisms (SNPs) associated with increased or decreased risk for hundreds of complex disorders. For a given disease, SNPs can be combined to generate a cumulative estimation of risk known as a polygenic risk score (PRS). After years of research, PRSs are increasingly used in clinical settings. In this article, we will review the literature on how both genome-wide and restricted PRSs are developed and the relative merit of each. The validation and evaluation of PRSs will also be discussed, including the recognition that PRS validity is intrinsically linked to the methodological and analytical approach of the foundation GWAS together with the ethnic characteristics of that cohort. Specifically, population differences may affect imputation accuracy, risk magnitude and direction. Even as PRSs are being introduced into clinical practice, there is a push to combine them with clinical and demographic risk factors to develop a holistic disease risk. The existing evidence regarding the clinical utility of PRSs is considered across four different domains: informing population screening programs, guiding therapeutic interventions, refining risk for families at high risk, and facilitating diagnosis and predicting prognostic outcomes. The evidence for clinical utility in relation to five well-studied disorders is summarized. The potential ethical, legal and social implications are also highlighted.

Clustering of known low and moderate risk alleles rather than a novel recessive high-risk gene in non-BRCA1/2 sib trios affected with breast cancer

Breast cancer risk is approximately twice as high in first‐degree relatives of female breast cancer cases than in women in the general population. Less than half of this risk can be attributed to the currently known genetic risk factors. Recessive risk alleles represent a relatively underexplored explanation for the remainder of familial risk. To address this, we selected 19 non‐BRCA1/2 breast cancer families in which at least three siblings were affected, while no first‐degree relatives of the previous or following generation had breast cancer. Germline DNA from one of the siblings was subjected to exome sequencing, while all affected siblings were genotyped using SNP arrays to assess haplotype sharing and to calculate a polygenic risk score (PRS) based on 160 low‐risk variants. We found no convincing candidate recessive alleles among exome sequencing variants in genomic regions for which all three siblings shared two haplotypes. However, we found two families in which all affected siblings carried the CHEK2*1100delC. In addition, the average normalized PRS of the “recessive” family probands (0.81) was significantly higher than that in both general population cases (0.35, P = .026) and controls (P = .0004). These findings suggest that the familial aggregation is, at least in part, explained by a polygenic effect of common low‐risk variants and rarer intermediate‐risk variants, while we did not find evidence of a role for novel recessive risk alleles.

What's new?

To find new breast cancer susceptibility alleles, these authors tested families in which at least three affected siblings had non‐BRCA1/2 breast cancer. No new susceptibility alleles emerged, but the analysis did reveal that on average, women from these families who had cancer had significantly higher polygenic risk scores than either sporadic cases or controls. This result highlights the importance of moderate risk alleles acting together in familial breast cancer.

Clinical applications of polygenic breast cancer risk: a critical review and perspectives of an emerging field

Polygenic factors are estimated to account for an additional 18% of the familial relative risk of breast cancer, with those at the highest level of polygenic risk distribution having a least a twofold increased risk of the disease. Polygenic testing promises to revolutionize health services by providing personalized risk assessments to women at high-risk of breast cancer and within population breast screening programs. However, implementation of polygenic testing needs to be considered in light of its current limitations, such as limited risk prediction for women of non-European ancestry. This article aims to provide a comprehensive review of the evidence for polygenic breast cancer risk, including the discovery of variants associated with breast cancer at the genome-wide level of significance and the use of polygenic risk scores to estimate breast cancer risk. We also review the different applications of this technology including testing of women from high-risk breast cancer families with uninformative genetic testing results, as a moderator of monogenic risk, and for population screening programs. Finally, a potential framework for introducing testing for polygenic risk in familial cancer clinics and the potential challenges with implementing this technology in clinical practice are discussed.

Towards clinical utility of polygenic risk scores

Prediction of disease risk is an essential part of preventative medicine, often guiding clinical management. Risk prediction typically includes risk factors such as age, sex, family history of disease and lifestyle (e.g. smoking status); however, in recent years, there has been increasing interest to include genomic information into risk models. Polygenic risk scores (PRS) aggregate the effects of many genetic variants across the human genome into a single score and have recently been shown to have predictive value for multiple common diseases. In this review, we summarize the potential use cases for seven common diseases (breast cancer, prostate cancer, coronary artery disease, obesity, type 1 diabetes, type 2 diabetes and Alzheimer’s disease) where PRS has or could have clinical utility. PRS analysis for these diseases frequently revolved around (i) risk prediction performance of a PRS alone and in combination with other non-genetic risk factors, (ii) estimation of lifetime risk trajectories, (iii) the independent information of PRS and family history of disease or monogenic mutations and (iv) estimation of the value of adding a PRS to specific clinical risk prediction scenarios. We summarize open questions regarding PRS usability, ancestry bias and transferability, emphasizing the need for the next wave of studies to focus on the implementation and health-economic value of PRS testing. In conclusion, it is becoming clear that PRS have value in disease risk prediction and there are multiple areas where this may have clinical utility.