Genetic Testing to Guide Risk-Stratified Screens for Breast Cancer

Methodologies underpinning polygenic risk scores estimation: a comprehensive overview

Polygenic risk scores (PRS) have emerged as a promising tool for predicting disease risk and treatment outcomes using genomic data. Thousands of genome-wide association studies (GWAS), primarily involving populations of European ancestry, have supported the development of PRS models. However, these models have not been adequately evaluated in non-European populations, raising concerns about their clinical validity and predictive power across diverse groups. Addressing this issue requires developing novel risk prediction frameworks that leverage genetic characteristics across diverse populations, considering host-microbiome interactions and a broad range of health measures. One of the key aspects in evaluating PRS is understanding the strengths and limitations of various methods for constructing them. In this review, we analyze strengths and limitations of different methods for constructing PRS, including traditional weighted approaches and new methods such as Bayesian and Frequentist penalized regression approaches. Finally, we summarize recent advances in PRS calculation methods development, and highlight key areas for future research, including development of models robust across diverse populations by underlining the complex interplay between genetic variants across diverse ancestral backgrounds in disease risk as well as treatment response prediction. PRS hold great promise for improving disease risk prediction and personalized medicine; therefore, their implementation must be guided by careful consideration of their limitations, biases, and ethical implications to ensure that they are used in a fair, equitable, and responsible manner.

Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations

Polygenic risk scores (PRSs) have improved in predictive performance, but several challenges remain to be addressed before PRSs can be implemented in the clinic, including reduced predictive performance of PRSs in diverse populations, and the interpretation and communication of genetic results to both providers and patients. To address these challenges, the National Human Genome Research Institute-funded Electronic Medical Records and Genomics (eMERGE) Network has developed a framework and pipeline for return of a PRS-based genome-informed risk assessment to 25,000 diverse adults and children as part of a clinical study. From an initial list of 23 conditions, ten were selected for implementation based on PRS performance, medical actionability and potential clinical utility, including cardiometabolic diseases and cancer. Standardized metrics were considered in the selection process, with additional consideration given to strength of evidence in African and Hispanic populations. We then developed a pipeline for clinical PRS implementation (score transfer to a clinical laboratory, validation and verification of score performance), and used genetic ancestry to calibrate PRS mean and variance, utilizing genetically diverse data from 13,475 participants of the All of Us Research Program cohort to train and test model parameters. Finally, we created a framework for regulatory compliance and developed a PRS clinical report for return to providers and for inclusion in an additional genome-informed risk assessment. The initial experience from eMERGE can inform the approach needed to implement PRS-based testing in diverse clinical settings.

Evaluation of European-based polygenic risk score for breast cancer in Ashkenazi Jewish women in Israel

BACKGROUND

Polygenic risk score (PRS), calculated based on genome-wide association studies (GWASs), can improve breast cancer (BC) risk assessment. To date, most BC GWASs have been performed in individuals of European (EUR) ancestry, and the generalisation of EUR-based PRS to other populations is a major challenge. In this study, we examined the performance of EUR-based BC PRS models in Ashkenazi Jewish (AJ) women.

METHODS

We generated PRSs based on data on EUR women from the Breast Cancer Association Consortium (BCAC). We tested the performance of the PRSs in a cohort of 2161 AJ women from Israel (1437 cases and 724 controls) from BCAC (BCAC cohort from Israel (BCAC-IL)). In addition, we tested the performance of these EUR-based BC PRSs, as well as the established 313-SNP EUR BC PRS, in an independent cohort of 181 AJ women from Hadassah Medical Center (HMC) in Israel.

RESULTS

In the BCAC-IL cohort, the highest OR per 1 SD was 1.56 (±0.09). The OR for AJ women at the top 10% of the PRS distribution compared with the middle quintile was 2.10 (±0.24). In the HMC cohort, the OR per 1 SD of the EUR-based PRS that performed best in the BCAC-IL cohort was 1.58±0.27. The OR per 1 SD of the commonly used 313-SNP BC PRS was 1.64 (±0.28).

CONCLUSIONS

Extant EUR GWAS data can be used for generating PRSs that identify AJ women with markedly elevated risk of BC and therefore hold promise for improving BC risk assessment in AJ women.

Investigation of germline variants in Bahraini women with breast cancer using next-generation sequencing based-multigene panel

Germline variants in BRCA1 and BRCA2 (BRCA1/2) genes are the most common cause of hereditary breast cancer. However, a significant number of cases are not linked to these two genes and additional high-, moderate- and low-penetrance genes have been identified in breast cancer. The advent of next-generation sequencing (NGS) allowed simultaneous sequencing of multiple cancer-susceptibility genes and prompted research in this field. So far, cancer-predisposition genes other than BRCA1/2 have not been studied in the population of Bahrain. We performed a targeted NGS using a multi-panel covering 180 genes associated with cancer predisposition to investigate the spectrum and frequency of germline variants in 54 women with a positive personal and/or family history of breast cancer. Sequencing analysis revealed germline variants in 29 (53.7%) patients. Five pathogenic/likely pathogenic variants in four DNA repair pathway-related genes were identified in five unrelated patients (9.3%). Two BRCA1 variants, namely the missense variant c.287A>G (p.Asp96Gly) and the truncating variant c.1066C>T (p.Gln356Ter), were detected in two patients (3.7%). Three variants in non-BRCA1/2 genes were detected in three patients (1.85% each) with a strong family history of breast cancer. These included a monoallelic missense variant c.1187G>A (p.Gly396Asp) in MUTYH gene, and two truncating variants namely c.3343C>T (p.Arg1115Ter) in MLH3 gene and c.1826G>A (p.Trp609Ter) in PMS1 gene. Other variants of uncertain significance (VUS) were also detected, and some of them were found together with the deleterious variants. In this first application of NGS-based multigene testing in Bahraini women with breast cancer, we show that multigene testing can yield additional genomic information on low-penetrance genes, although the clinical significance of these genes has not been fully appreciated yet. Our findings also provide valuable epidemiological information for future studies and highlight the importance of genetic testing, and an NGS-based multigene analysis may be applied supplementary to traditional genetic counseling.

Evaluation of a genetic risk score computed using human chromosomal-scale length variation to predict breast cancer

INTRODUCTION

The ability to accurately predict whether a woman will develop breast cancer later in her life, should reduce the number of breast cancer deaths. Different predictive models exist for breast cancer based on family history, BRCA status, and SNP analysis. The best of these models has an accuracy (area under the receiver operating characteristic curve, AUC) of about 0.65. We have developed computational methods to characterize a genome by a small set of numbers that represent the length of segments of the chromosomes, called chromosomal-scale length variation (CSLV).

METHODS

We built machine learning models to differentiate between women who had breast cancer and women who did not based on their CSLV characterization. We applied this procedure to two different datasets: the UK Biobank (1534 women with breast cancer and 4391 women who did not) and the Cancer Genome Atlas (TCGA) 874 with breast cancer and 3381 without.

RESULTS

We found a machine learning model that could predict breast cancer with an AUC of 0.836 95% CI (0.830.0.843) in the UK Biobank data. Using a similar approach with the TCGA data, we obtained a model with an AUC of 0.704 95% CI (0.702, 0.706). Variable importance analysis indicated that no single chromosomal region was responsible for significant fraction of the model results.

CONCLUSION

In this retrospective study, chromosomal-scale length variation could effectively predict whether or not a woman enrolled in the UK Biobank study developed breast cancer.

Selection, optimization, and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse populations

Polygenic risk scores (PRS) have improved in predictive performance supporting their use in clinical practice. Reduced predictive performance of PRS in diverse populations can exacerbate existing health disparities. The NHGRI-funded eMERGE Network is returning a PRS-based genome-informed risk assessment to 25,000 diverse adults and children. We assessed PRS performance, medical actionability, and potential clinical utility for 23 conditions. Standardized metrics were considered in the selection process with additional consideration given to strength of evidence in African and Hispanic populations. Ten conditions were selected with a range of high-risk thresholds: atrial fibrillation, breast cancer, chronic kidney disease, coronary heart disease, hypercholesterolemia, prostate cancer, asthma, type 1 diabetes, obesity, and type 2 diabetes. We developed a pipeline for clinical PRS implementation, used genetic ancestry to calibrate PRS mean and variance, created a framework for regulatory compliance, and developed a PRS clinical report. eMERGE's experience informs the infrastructure needed to implement PRS-based implementation in diverse clinical settings.

Surgical Management of Hereditary Breast Cancer

The identification that breast cancer is hereditary was first described in the nineteenth century. With the identification of the BRCA1 and BRCA 2 breast/ovarian cancer susceptibility genes in the mid-1990s and the introduction of genetic testing, significant advancements have been made in tailoring surveillance, guiding decisions on medical or surgical risk reduction and cancer treatments for genetic variant carriers. This review discusses various medical and surgical management options for hereditary breast cancers.

Mutational profile of hereditary breast and ovarian cancer - Establishing genetic testing guidelines in a developing country

PURPOSE

Because many countries lack the capacity to follow the international guidelines for genetic testing, we suggest the specific approach for establishing local genetic testing guidelines that could be applied in developing countries. We focus on hereditary breast (BC) and ovarian cancer (OC) in Serbia.

METHODS

From the cohort of 550 persons who were referred for genetic counseling at the Institute for Oncology and Radiology of Serbia, 392 were selected. Personal and family histories were collected and germline DNA was sequenced with NGS in a panel of 20 genes.

RESULTS

Pathogenic (PV) and likely-pathogenic variants (LPV) were detected in 8 genes with the frequency of 23.7%. The most frequent were in BRCA1(7.6%), BRCA2(4.8%), PALB2(4.1%) and CHEK2(3.8%). They were also detected in ATM(1.8%), NBN(0.8%), TP53(0.5%) and RAD51C(0.3%). Whereas high carrier probability (CP), bilateral BC, BC and OC in the same patient and family history (FH) of BC/OC, were the strongest predictors for BRCA1/2 PV/LPV, lower CP values and early age of BC onset without FH were associated with higher frequency of PALB2 and CHEK2 PV/LPV.

CONCLUSIONS

Population specific studies to identify specific mutational patterns and predictors of PV/LPV should be conducted in order to make scientifically sound and cost-effective guidelines for genetic testing in developing countries.

Understanding BRCA2 Function as a Tumor Suppressor Based on Domain-Specific Activities in DNA Damage Responses

BRCA2 is an essential genome stability gene that has various functions in cells, including roles in homologous recombination, G2 checkpoint control, protection of stalled replication forks, and promotion of cellular resistance to numerous types of DNA damage. Heterozygous mutation of BRCA2 is associated with an increased risk of developing cancers of the breast, ovaries, pancreas, and other sites, thus BRCA2 acts as a classic tumor suppressor gene. However, understanding BRCA2 function as a tumor suppressor is severely limited by the fact that ~70% of the encoded protein has not been tested or assigned a function in the cellular DNA damage response. Remarkably, even the specific role(s) of many known domains in BRCA2 are not well characterized, predominantly because stable expression of the very large BRCA2 protein in cells, for experimental purposes, is challenging. Here, we review what is known about these domains and the assay systems that are available to study the cellular roles of BRCA2 domains in DNA damage responses. We also list criteria for better testing systems because, ultimately, functional assays for assessing the impact of germline and acquired mutations identified in genetic screens are important for guiding cancer prevention measures and for tailored cancer treatments.

LDpred2: better, faster, stronger

Motivation

Polygenic scores have become a central tool in human genetics research. LDpred is a popular method for deriving polygenic scores based on summary statistics and a matrix of correlation between genetic variants. However, LDpred has limitations that may reduce its predictive performance.

Results

Here, we present LDpred2, a new version of LDpred that addresses these issues. We also provide two new options in LDpred2: a ‘sparse’ option that can learn effects that are exactly 0, and an ‘auto’ option that directly learns the two LDpred parameters from data. We benchmark predictive performance of LDpred2 against the previous version on simulated and real data, demonstrating substantial improvements in robustness and predictive accuracy compared to LDpred1. We then show that LDpred2 also outperforms other polygenic score methods recently developed, with a mean AUC over the 8 real traits analyzed here of 65.1%, compared to 63.8% for lassosum, 62.9% for PRS-CS and 61.5% for SBayesR. Note that LDpred2 provides more accurate polygenic scores when run genome-wide, instead of per chromosome.

Availability and implementation

LDpred2 is implemented in R package bigsnpr.

Supplementary information

Supplementary data are available at Bioinformatics online.

Democratizing genomics: Leveraging software to make genetics an integral part of routine care

Genetic testing can provide definitive molecular diagnoses and guide clinical management decisions from preconception through adulthood. Innovative solutions for scaling clinical genomics services are necessary if they are to transition from a niche specialty to a routine part of patient care. The expertise of specialists, like genetic counselors and medical geneticists, has traditionally been relied upon to facilitate testing and follow-up, and while ideal, this approach is limited in its ability to integrate genetics into primary care. As individuals, payors, and providers increasingly realize the value of genetics in mainstream medicine, several implementation challenges need to be overcome. These include electronic health record integration, patient and provider education, tools to stay abreast of guidelines, and simplification of the test ordering process. Currently, no single platform offers a holistic view of genetic testing that streamlines the entire process across specialties that begins with identifying at-risk patients in mainstream care settings, providing pretest education, facilitating consent and test ordering, and following up as a "genetic companion" for ongoing management. We describe our vision for using software that includes clinical-grade chatbots and decision support tools, with direct access to genetic counselors and pharmacists within a modular, integrated, end-to-end testing journey.

Referencing BRCA in hereditary cancer risk discussions: In search of an anchor in a sea of uncertainty

As panel testing and exome sequencing are increasingly incorporated into clinical care, clinicians must grapple with how to communicate the risks and treatment decisions surrounding breast cancer genes beyond BRCA1 and BRCA2. In this paper, we examine clinicians' practice of employing BRCA1 and BRCA2 to help contextualize less certain genetic information regarding cancer risk and the possible implications of this practice for patients within the context of an exome sequencing study, NCGENES. We audio-recorded return of results appointments for 14 women who participated in NCGENES, previously had breast cancer, and were suspected of having a hereditary cancer predisposition. These patients were also interviewed four weeks later regarding their understanding of their results. We found that BRCA1 and BRCA2 were held as the gold standard, where clinicians compared what is known about BRCA to the limited understanding of other breast cancer-related genes. BRCA1 and BRCA2 were used as anchors to shape patients' understandings of genetic knowledge, risk, and management, illustrating how the information clinicians provide to patients may work as an external anchor. Yet, presenting BRCA1 and BRCA2 as a means of scientific reassurance can run the risk of patients conflating knowledge about certainty of risk with degree of risk after receiving a result for a moderate penetrance gene. This can be further complicated by misperceptions of the precision of cancer predictability attributed to these or other described 'cancer genes' in public media.

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.

Disparities in Genetic Testing and Care among Black women with Hereditary Breast Cancer

PURPOSE OF REVIEW

Despite a steady improvement in breast cancer survival rates over the past several decades, mortality disparities remain among Black women, who have a 42% higher death rate compared to non-Hispanic white (NHW) women. Hereditary breast cancer (HBC) accounts for 5-10% of all breast cancer cases, the majority of which are due to the BRCA1 and BRCA2 (BRCA) genes. Despite the availability of BRCA testing for over 25 years, there remain disproportionately lower rates of genetic testing among Blacks compared to NHW due to a multitude of factors. The intent of this review is to discuss racial disparities focused on HBC across diverse populations and review the existing gaps to be addressed when delivering gene-based care.

RECENT FINDINGS

The factors contributing to the racial survival disparity are undoubtedly complex and likely an interplay between tumor biology, genomics, patterns of care and socioeconomic factors. Advances in genomic technologies that now allow for full characterization of germline DNA sequencing are integral in defining the complex and multifactorial cause of breast cancer and may help to explain the existing racial survival disparities.

SUMMARY

Identification of inherited cancer risk may lead to cancer prevention, early cancer detection, treatment guidance, and ultimately has great potential to improve outcomes. Consequently, advances in HBC diagnosis and treatment without widespread implementation have the potential to further widen the existing breast cancer mortality gap between Black and NHW women.

Update on multi-gene panel testing and communication of genetic test results

With technological advances, multi-gene panel testing has become increasingly used to identify patients at risk for hereditary breast cancer (HBC). There are currently evidence-based interventions and breast cancer screening strategies that exist for cancer prevention and early detection among patients with HBC. Moreover, in addition to the personal impact of identifying HBC, this information may be shared with at-risk family members to amplify the benefits of testing and subsequent care among those at high risk. Opportunities and challenges with the utilization of updated multi-gene panel testing for HBC, including: (a) tumor sequencing with germline consequences; (b) genetic counseling implications; and (c) strategies to improve the communication of genetic test results to family members will be reviewed. With the advances and expansion of genetic testing, all health care providers need to be updated on both the importance and complexities of HBC counseling and testing, in order to optimize patient care.

The promise of single-cell mechanophenotyping for clinical applications

Cancer is the second leading cause of death worldwide. Despite the immense research focused in this area, one is still not able to predict disease trajectory. To overcome shortcomings in cancer disease study and monitoring, we describe an exciting research direction: cellular mechanophenotyping. Cancer cells must overcome many challenges involving external forces from neighboring cells, the extracellular matrix, and the vasculature to survive and thrive. Identifying and understanding their mechanical behavior in response to these forces would advance our understanding of cancer. Moreover, used alongside traditional methods of immunostaining and genetic analysis, mechanophenotyping could provide a comprehensive view of a heterogeneous tumor. In this perspective, we focus on new technologies that enable single-cell mechanophenotyping. Single-cell analysis is vitally important, as mechanical stimuli from the environment may obscure the inherent mechanical properties of a cell that can change over time. Moreover, bulk studies mask the heterogeneity in mechanical properties of single cells, especially those rare subpopulations that aggressively lead to cancer progression or therapeutic resistance. The technologies on which we focus include atomic force microscopy, suspended microchannel resonators, hydrodynamic and optical stretching, and mechano-node pore sensing. These technologies are poised to contribute to our understanding of disease progression as well as present clinical opportunities.

Healthcare Utilization and Costs after Receiving a Positive BRCA1/2 Result from a Genomic Screening Program

Population genomic screening has been demonstrated to detect at-risk individuals who would not be clinically identified otherwise. However, there are concerns about the increased utilization of unnecessary services and the associated increase in costs. The objectives of this study are twofold: (1) determine whether there is a difference in healthcare utilization and costs following disclosure of a pathogenic/likely pathogenic (P/LP) BRCA1/2 variant via a genomic screening program, and (2) measure the post-disclosure uptake of National Comprehensive Cancer Network (NCCN) guideline-recommended risk management. We retrospectively reviewed electronic health record (EHR) and billing data from a female population of BRCA1/2 P/LP variant carriers without a personal history of breast or ovarian cancer enrolled in Geisinger’s MyCode genomic screening program with at least a one-year post-disclosure observation period. We identified 59 women for the study cohort out of 50,726 MyCode participants. We found no statistically significant differences in inpatient and outpatient utilization and average total costs between one-year pre- and one-year post-disclosure periods ($18,821 vs. $19,359, p = 0.76). During the first year post-disclosure, 49.2% of women had a genetic counseling visit, 45.8% had a mammography and 32.2% had an MRI. The uptake of mastectomy and oophorectomy was 3.5% and 11.8%, respectively, and 5% of patients received chemoprevention.

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.