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

Development and testing of a polygenic risk score for breast cancer aggressiveness

Aggressive breast cancers portend a poor prognosis, but current polygenic risk scores (PRSs) for breast cancer do not reliably predict aggressive cancers. Aggressiveness can be effectively recapitulated using tumor gene expression profiling. Thus, we sought to develop a PRS for the risk of recurrence score weighted on proliferation (ROR-P), an established prognostic signature. Using 2363 breast cancers with tumor gene expression data and single nucleotide polymorphism (SNP) genotypes, we examined the associations between ROR-P and known breast cancer susceptibility SNPs using linear regression models. We constructed PRSs based on varying p-value thresholds and selected the optimal PRS based on model r2 in 5-fold cross-validation. We then used Cox proportional hazards regression to test the ROR-P PRS's association with breast cancer-specific survival in two independent cohorts totaling 10,196 breast cancers and 785 events. In meta-analysis of these cohorts, higher ROR-P PRS was associated with worse survival, HR per SD = 1.13 (95% CI 1.06-1.21, p = 4.0 × 10-4). The ROR-P PRS had a similar magnitude of effect on survival as a comparator PRS for estrogen receptor (ER)-negative versus positive cancer risk (PRSER-/ER+). Furthermore, its effect was minimally attenuated when adjusted for PRSER-/ER+, suggesting that the ROR-P PRS provides additional prognostic information beyond ER status. In summary, we used integrated analysis of germline SNP and tumor gene expression data to construct a PRS associated with aggressive tumor biology and worse survival. These findings could potentially enhance risk stratification for breast cancer screening and prevention.

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.

Development and validation of genome-wide polygenic risk scores for predicting breast cancer incidence in Japanese females: a population-based case-cohort study

PURPOSE

This study aimed to develop an ancestry-specific polygenic risk scores (PRSs) for the prediction of breast cancer events in Japanese females and validate it in a longitudinal cohort study.

METHODS

Using publicly available summary statistics of female breast cancer genome-wide association study (GWAS) of Japanese and European ancestries, we, respectively, developed 31 candidate genome-wide PRSs using pruning and thresholding (P + T) and LDpred methods with varying parameters. Among the candidate PRS models, the best model was selected using a case-cohort dataset (63 breast cancer cases and 2213 sub-cohorts of Japanese females during a median follow-up of 11.9 years) according to the maximal predictive ability by Harrell's C-statistics. The best-performing PRS for each derivation GWAS was evaluated in another independent case-cohort dataset (260 breast cancer cases and 7845 sub-cohorts of Japanese females during a median follow-up of 16.9 years).

RESULTS

For the best PRS model involving 46,861 single nucleotide polymorphisms (SNPs; P + T method with P_T = 0.05 and R² = 0.2) derived from Japanese-ancestry GWAS, the Harrell's C-statistic was 0.598 ± 0.018 in the evaluation dataset. The age-adjusted hazard ratio for breast cancer in females with the highest PRS quintile compared with those in the lowest PRS quintile was 2.47 (95% confidence intervals, 1.64-3.70). The PRS constructed using Japanese-ancestry GWAS demonstrated better predictive performance for breast cancer in Japanese females than that using European-ancestry GWAS (Harrell's C-statistics 0.598 versus 0.586).

CONCLUSION

This study developed a breast cancer PRS for Japanese females and demonstrated the usefulness of the PRS for breast cancer risk stratification.

METTL1 gene polymorphisms synergistically confer hepatoblastoma susceptibility

INTRODUCTION

Hepatoblastoma is a rare but devastating pediatric liver malignancy. Overexpressed methyltransferase-like 1 (METTL1) is a methyltransferase that catalyzes essential N7-methylguanosine (m7G) modification of eukaryotic mRNA. Accumulating evidence has revealed the oncogenic potential of METTL1. However, whether METTL1 gene polymorphisms confer susceptibility to hepatoblastoma has not been reported. This study aimed to identify causal relationships between genetic variants of this gene and susceptibility to hepatoblastoma.

MATERIALS AND METHODS

Using the TaqMan assay, we genotyped three METTL1 polymorphisms (rs2291617 G > T, rs10877013 T > C, rs10877012 T > G) in germline DNA samples from 1759 Chinese children of Han ethnicity (313 cases vs. 1446 controls).

RESULTS

None of these polymorphisms were associated with hepatoblastoma risk. However, combination analysis showed that children with 1 to 3 risk genotypes were associated with increased hepatoblastoma risk (adjusted odds ratio = 1.47, 95% confidence interval  1.07-2.02; P = 0.018). Stratified analyses revealed significant effects of combined polymorphisms mainly among young children (< 17 months of age), boys, and those with advanced hepatoblastoma.

CONCLUSION

We identified some potential functional METTL1 gene polymorphisms that work together to increase the risk of hepatoblastoma among Chinese Han children; single polymorphism showed only weak effects. These METTL1 polymorphisms may be promising biomarkers for screening high-risk individuals for hepatoblastoma. These findings are inspiring and deserve to be validated among individuals of different ethnicities.

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

Background

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

Methods

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

Results

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

Conclusions

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

Supplementary Information

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

Association Between SNPs in the One-Carbon Metabolism Pathway and the Risk of Female Breast Cancer in a Chinese Population

Objective

The aim of this study is to assess the relationship between the single-nucleotide polymorphism (SNP) in the one-carbon metabolism pathway (MTR rs1805087; MTHFR rs1801133; ALDH1L1 rs2002287, rs2276731; DNMT1 rs16999593, rs2228611; DNMT3B rs2424908) and the risk of female breast cancer (BC) in a Chinese population.

Methods

A population-based case-control study was conducted, involving a total of 439 BC patients and 439 age-matched healthy controls. We adopted Sequence MASSarray to identify genotyping, and used immunohistochemistry (IHC) to test the expression of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor-2 (HER-2) in tumor tissue.

Results

We found that rs16999593 (TC/CC vs TT: adjusted OR=1.38, 95% CI: 1.03-1.84, p=0.030) was associated with an increased risk of BC, while rs2228611 was related to a decreased BC risk (GA/AA vs GG: adjusted OR=0.74, 95% CI: 0.56-0.97, p=0.030). In addition, stratified analysis revealed that DNMT1 rs16999593, rs2228611 and ALDH1L1 rs2002287 contributed to the risk of BC, with associations with ER, PR and HER-2 expression.

Conclusion

In summary, this study revealed that DNMT1 rs16999593 and rs2228611 were associated with BC risk.

Mammography screening and mortality by risk status in the California teachers study

Background

The debate continues among medical professionals regarding the frequency, starting age, and stopping age for mammography screening. Some experts suggest tailoring recommendations based on individuals’ personal breast cancer risk. Previous studies have not compared the impact of annual versus biennial mammography stratified by age group and risk category. The purpose of this study was to examine the relationship between mammography frequency and mortality by age group and risk category in the California Teachers Study.

Methods

Using data from study questionnaires from 93,438 women between the ages of 40 and 85 and linkages to the California Cancer Registry and other indices, overall and breast cancer-specific mortality by mammography frequency were estimated using multivariable Cox proportional hazards models, stratified by age group and risk category at baseline as determined by the Gail breast cancer risk model.

Results

During the follow-up period of 20 years, overall mortality risk was lower in women who had annual or biennial mammography compared to less frequent or no mammography in all age groups. Annual mammography was associated with lower overall mortality risk compared to biennial mammography among women age 50–85. This difference was especially apparent in women age 60–74, regardless of estimated Gail risk category at baseline. Breast cancer-specific mortality was lower among women who had annual mammography compared to biennial or less frequent mammography among women age 60–74, regardless of their baseline risk.

Conclusions

Our findings suggest that at least biennial mammography is beneficial to most women age 40–85 and that annual mammography is more beneficial than biennial mammography to most women age 50–85 in terms of overall mortality.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-09071-1.

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

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

Serum semaphorin 4C as a diagnostic biomarker in breast cancer: A multicenter retrospective study

Background

To date, there is no approved blood‐based biomarker for breast cancer detection. Herein, we aimed to assess semaphorin 4C (SEMA4C), a pivotal protein involved in breast cancer progression, as a serum diagnostic biomarker.

Methods

We included 6,213 consecutive inpatients from Tongji Hospital, Qilu Hospital, and Hubei Cancer Hospital. Training cohort and two validation cohorts were introduced for diagnostic exploration and validation. A pan‐cancer cohort was used to independently explore the diagnostic potential of SEMA4C among solid tumors. Breast cancer patients who underwent mass excision prior to modified radical mastectomy were also analyzed. We hypothesized that increased pre‐treatment serum SEMA4C levels, measured using optimized in‐house enzyme‐linked immunosorbent assay kits, could detect breast cancer. The endpoints were diagnostic performance, including area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Post‐surgery pathological diagnosis was the reference standard and breast cancer staging followed the TNM classification. There was no restriction on disease stage for eligibilities.

Results

We included 2667 inpatients with breast lesions, 2378 patients with other solid tumors, and 1168 healthy participants. Specifically, 118 patients with breast cancer were diagnosed with stage 0 (5.71%), 620 with stage I (30.00%), 966 with stage II (46.73%), 217 with stage III (10.50%), and 8 with stage IV (0.39%). Patients with breast cancer had significantly higher serum SEMA4C levels than benign breast tumor patients and normal controls (P < 0.001). Elevated serum SEMA4C levels had AUC of 0.920 (95% confidence interval [CI]: 0.900–0.941) and 0.932 (95%CI: 0.911–0.953) for breast cancer detection in the two validation cohorts. The AUCs for detecting early‐stage breast cancer (n = 366) and ductal carcinoma in situ (n = 85) were 0.931 (95%CI: 0.916–0.946) and 0.879 (95%CI: 0.832–0.925), respectively. Serum SEMA4C levels significantly decreased after surgery, and the reduction was more striking after modified radical mastectomy, compared with mass excision (P < 0.001). The positive rate of enhanced serum SEMA4C levels was 84.77% for breast cancer and below 20.75% for the other 14 solid tumors.

Conclusions

Serum SEMA4C demonstrated promising potential as a candidate biomarker for breast cancer diagnosis. However, validation in prospective settings and by other study groups is warranted.

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.

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

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

Background

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

Summary

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

Key-Messages

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

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

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

Cases in Precision Medicine: The Role of Polygenic Risk Scores in Breast Cancer Risk Assessment

Polygenic risk scores (PRSs) have been consistently associated with elevated breast cancer risk in cohort studies and are associated with risk in both women with and those without a family history of breast cancer. However, before clinical implementation, several issues must be addressed, including understanding the potential clinical utility and optimal method to communicate personalized screening recommendations that incorporate the PRS. Several trials are under way to answer some of these questions and facilitate clinical implementation. Because these PRSs have been developed in women of European ancestry, it is important to understand the limitations of their predictive ability in other ancestral groups. Finally, the value of the PRS will lie in considering it along with other clinical, familial, and rare genetic factors that are currently used in personalized risk assessment of breast cancer.

Associations of one-carbon metabolism-related gene polymorphisms with breast cancer risk are modulated by diet, being higher when adherence to the Mediterranean dietary pattern is low

PURPOSE

Breast cancer is more likely attributed to a combination of genetic variations and lifestyle factors. Both one-carbon metabolism and diet-related factors could interfere with the carcinogenesis of breast cancer (BC), but whether diet consumed underlie a specific metabolism pathway could influence the impact of genetic variants on breast cancer risk remains equivocal.

METHODS

A case-control study of the Chinese female population (818 cases, 935 controls). 13 SNPs in eight one-carbon metabolism-related genes (MTHFD1, TYMS, MTRR, MAT2B, CDO1, FOLR1, UNG2, ADA) were performed. Diet was assessed by a validated food-frequency questionnaire. We examined the associations of the adherence to the Mediterranean dietary pattern (MDP) and single-nucleotide polymorphisms (SNPs) of one-carbon metabolism with breast cancer risk. We constructed an aggregate polygenic risk score (PRS) to test the additive effects of genetic variants and analyzed the gene-diet interactions.

RESULTS

High adherence (highest quartile) to the MDP decreased the risk of breast cancer among post- but not premenopausal women, respectively (OR = 0.54, 95% CI = 0.38 to 0.78 and 0.90, 0.53 to 1.53). Neither of the polymorphisms or haplotypes was associated with breast cancer risk, irrespective of menopause. However, a high PRS (highest quartile) was associated with more than a doubling risk in both post- and premenopausal women, respectively (OR = 1.95, 95% CI = 1.32 to 2.87 and 2.09, 1.54 to 2.85). We found a gene-diet interaction with adherence to the MDP for aggregate PRS (P-interaction = 0.000) among postmenopausal women. When adherence to the MDP was low (< median), carries with high PRS (highest quartile) had higher BC risk (OR = 2.80, 95% CI = 1.55 to 5.07) than low PRS (lowest quartile), while adherence to the MDP was high (≥ median), the association disappeared (OR = 1.57, 95% CI = 0.92 to 2.66).

CONCLUSION

High adherence to the MDP may counteract the genetic predisposition associated with one-carbon metabolism on breast cancer risk in postmenopausal women.

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.

A Polygenic Risk Score for Breast Cancer in US Latinas and Latin American Women

Background

More than 180 single nucleotide polymorphisms (SNPs) associated with breast cancer susceptibility have been identified; these SNPs can be combined into polygenic risk scores (PRS) to predict breast cancer risk. Because most SNPs were identified in predominantly European populations, little is known about the performance of PRS in non-Europeans. We tested the performance of a 180-SNP PRS in Latinas, a large ethnic group with variable levels of Indigenous American, European, and African ancestry.

Methods

We conducted a pooled case-control analysis of US Latinas and Latin American women (4658 cases and 7622 controls). We constructed a 180-SNP PRS consisting of SNPs associated with breast cancer risk (P < 5 × 10^–8). We evaluated the association between the PRS and breast cancer risk using multivariable logistic regression, and assessed discrimination using an area under the receiver operating characteristic curve. We also assessed PRS performance across quartiles of Indigenous American genetic ancestry. All statistical tests were two-sided.

Results

Of 180 SNPs tested, 142 showed directionally consistent associations compared with European populations, and 39 were nominally statistically significant (P < .05). The PRS was associated with breast cancer risk, with an odds ratio per SD increment of 1.58 (95% confidence interval [CI = 1.52 to 1.64) and an area under the receiver operating characteristic curve of 0.63 (95% CI = 0.62 to 0.64). The discrimination of the PRS was similar between the top and bottom quartiles of Indigenous American ancestry.

Conclusions

The 180-SNP PRS predicts breast cancer risk in Latinas, with similar performance as reported for Europeans. The performance of the PRS did not vary substantially according to Indigenous American ancestry.

Is It the Era for Personalized Screening?

Breast cancer screening is a recognized tool for early detection of the disease in asymptomatic women, improving treatment efficacy and reducing the mortality rate. There is raised awareness that a "one-size-fits-all" approach cannot be applied for breast cancer screening. Currently, despite specific guidelines for a minority of women who are at very high risk of breast cancer, all other women are still treated alike. This article reviews the current recommendations for breast cancer risk assessment and breast cancer screening in average-risk and higher-than-average-risk women. Also discussed are new developments and future perspectives for personalized breast cancer screening.

Impact of Personalized Genetic Breast Cancer Risk Estimation With Polygenic Risk Scores on Preventive Endocrine Therapy Intention and Uptake

Endocrine therapy is underutilized to reduce breast cancer incidence among women at increased risk. Polygenic risk scores (PRSs) assessing 77 breast cancer genetic susceptibility loci personalizes risk estimates. We examined effect of personalized PRS breast cancer risk prediction on intention to take and endocrine therapy uptake among women at increased risk. Eligible participants had a 10-year breast cancer risk ≥5% by Tyrer-Cuzick model [International Breast Cancer Intervention Study (IBIS)] or ≥3.0 % 5-year Gail Model risk with no breast cancer history or hereditary breast cancer syndrome. Breast cancer risk was estimated, endocrine therapy options were discussed, and endocrine therapy intent was assessed at baseline. After genotyping, PRS-updated breast cancer risk estimates, endocrine therapy options, and intent to take endocrine therapy were reassessed; endocrine therapy uptake was assessed during follow-up. From March 2016 to October 2017, 151 patients were enrolled [median (range) age, 56.1 (36.0-76.4 years)]. Median 10-year and lifetime IBIS risks were 7.9% and 25.3%. Inclusion of PRS increased lifetime IBIS breast cancer risk estimates for 81 patients (53.6%) and reduced risk for 70 (46.4%). Of participants with increased breast cancer risk by PRS, 39 (41.9%) had greater intent to take endocrine therapy; of those with decreased breast cancer risk by PRS, 28 (46.7%) had less intent to take endocrine therapy (P < 0.001). On multivariable regression, increased breast cancer risk by PRS was associated with greater intent to take endocrine therapy (P < 0.001). Endocrine therapy uptake was greater among participants with increased breast cancer risk by PRS (53.4%) than with decreased risk (20.9%; P < 0.001). PRS testing influenced intent to take and endocrine therapy uptake. Assessing PRS effect on endocrine therapy adherence is needed.Prevention Relevance: Counseling women at increased breast cancer risk using polygenic risk score (PRS) risk estimates can significantly impact preventive endocrine therapy uptake. Further development of PRS testing to personalize breast cancer risk assessments and endocrine therapy counselling may serve to potentially reduce the incidence of breast cancer in the future.

Comparative Validation of Breast Cancer Risk Prediction Models and Projections for Future Risk Stratification

BACKGROUND

External validation of risk models is critical for risk-stratified breast cancer prevention. We used the Individualized Coherent Absolute Risk Estimation (iCARE) as a flexible tool for risk model development and comparative model validation and to make projections for population risk stratification.

METHODS

Performance of two recently developed models, one based on the Breast and Prostate Cancer Cohort Consortium analysis (iCARE-BPC3) and another based on a literature review (iCARE-Lit), were compared with two established models (Breast Cancer Risk Assessment Tool and International Breast Cancer Intervention Study Model) based on classical risk factors in a UK-based cohort of 64 874 white non-Hispanic women (863 patients) age 35-74 years. Risk projections in a target population of US white non-Hispanic women age 50-70 years assessed potential improvements in risk stratification by adding mammographic breast density (MD) and polygenic risk score (PRS).

RESULTS

The best calibrated models were iCARE-Lit (expected to observed number of cases [E/O] = 0.98, 95% confidence interval [CI] = 0.87 to 1.11) for women younger than 50 years, and iCARE-BPC3 (E/O = 1.00, 95% CI = 0.93 to 1.09) for women 50 years or older. Risk projections using iCARE-BPC3 indicated classical risk factors can identify approximately 500 000 women at moderate to high risk (>3% 5-year risk) in the target population. Addition of MD and a 313-variant PRS is expected to increase this number to approximately 3.5 million women, and among them, approximately 153 000 are expected to develop invasive breast cancer within 5 years.

CONCLUSIONS

iCARE models based on classical risk factors perform similarly to or better than BCRAT or IBIS in white non-Hispanic women. Addition of MD and PRS can lead to substantial improvements in risk stratification. However, these integrated models require independent prospective validation before broad clinical applications.

Genetic Factors, Adherence to Healthy Lifestyle Behavior, and Risk of Invasive Breast Cancer Among Women in the UK Biobank

BACKGROUND

Breast cancer is considered to result from a combination of genetic and lifestyle-related factors, but the degree to which an overall healthy lifestyle may attenuate the impact of multiple genetic variants on invasive breast cancer risk remains equivocal.

METHODS

Using Cox proportional hazards regression models, we examined the association of a modified healthy lifestyle index (HLI) with risk of invasive breast cancer by genetic risk group among 146 326 women from the UK Biobank. We generated an HLI score based on a combination of diet, physical activity, smoking, alcohol consumption and anthropometry, and a polygenic risk score (PRS) using 304 breast cancer-associated genetic loci.

RESULTS

Among premenopausal and postmenopausal women, a favorable lifestyle (highest tertile) was associated with 22% and 31% reductions in invasive breast cancer risk, respectively (hazard ratio [HR]high vs low = 0.78, 95% confidence interval [CI] = 0.64 to 0.94; HRhigh vs low = 0.69, 95% CI = 0.63 to 0.77, respectively), whereas a high PRS (highest tertile) was associated with more than a doubling in the risk in both groups. For premenopausal women, the greatest risk reduction in association with the HLI was seen among those with a high PRS (HRhigh vs low = 0.73, 95% CI = 0.75 to 0.95). In postmenopausal women, those with a favorable lifestyle had 30%, 29%, and 32% reductions in risk of invasive breast cancer in the low, intermediate, and high PRS groups, respectively (HRhigh vs low = 0.70, 95% CI = 0.56 to 0.88; HRhigh vs low = 0.71, 95% CI = 0.59 to 0.84; and HRhigh vs low = 0.68, 95% CI = 0.59 to 0.78, respectively). There was an additive but not multiplicative interaction between the HLI score and PRS for postmenopausal and, to a lesser extent, premenopausal women.

CONCLUSION

Our findings support the view that an overall healthy lifestyle may attenuate the impact of genetic factors on invasive breast cancer risk among women of European ancestry.

Key steps for effective breast cancer prevention

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

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

Importance

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

Objective

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

Design, Setting, and Participants

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

Main Outcomes and Measures

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

Results

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

Conclusions and Relevance

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

Development and Validation of a Clinical Polygenic Risk Score to Predict Breast Cancer Risk

PURPOSE

Women with a family history of breast cancer are frequently referred for hereditary cancer genetic testing, yet < 10% are found to have pathogenic variants in known breast cancer susceptibility genes. Large-scale genotyping studies have identified common variants (primarily single-nucleotide polymorphisms [SNPs]) with individually modest breast cancer risk that, in aggregate, account for considerable breast cancer susceptibility. Here, we describe the development and empirical validation of an SNP-based polygenic breast cancer risk score.

METHODS

A panel of 94 SNPs was examined for association with breast cancer in women of European ancestry undergoing hereditary cancer genetic testing and negative for pathogenic variants in breast cancer susceptibility genes. Candidate polygenic risk scores (PRSs) as predictors of personal breast cancer history were developed through multivariable logistic regression models adjusted for age, cancer history, and ancestry. An optimized PRS was validated in 2 independent cohorts (n = 13,174; n = 141,160).

RESULTS

Within the training cohort (n = 24,259), 4,291 women (18%) had a personal history of breast cancer and 8,725 women (36%) reported breast cancer in a first-degree relative. The optimized PRS included 86 variants and was highly predictive of breast cancer status in both validation cohorts (P = 6.4 × 10-66; P < 10-325). The odds ratio (OR) per unit standard deviation was consistent between validations (OR, 1.45 [95% CI, 1.39 to 1.52]; OR 1.47 [95% CI, 1.45 to 1.49]). In a direct comparison, the 86-SNP PRS outperformed a previously described PRS of 77 SNPs.

CONCLUSION

The validation and implementation of a PRS for women without pathogenic variants in known breast cancer susceptibility genes offers potential for risk stratification to guide surveillance recommendations.

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

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

Risk Models for Breast Cancer and Their Validation

Strategies to prevent cancer and diagnose it early when it is most treatable are needed to reduce the public health burden from rising disease incidence. Risk assessment is playing an increasingly important role in targeting individuals in need of such interventions. For breast cancer many individual risk factors have been well understood for a long time, but the development of a fully comprehensive risk model has not been straightforward, in part because there have been limited data where joint effects of an extensive set of risk factors may be estimated with precision. In this article we first review the approach taken to develop the IBIS (Tyrer-Cuzick) model, and describe recent updates. We then review and develop methods to assess calibration of models such as this one, where the risk of disease allowing for competing mortality over a long follow-up time or lifetime is estimated. The breast cancer risk model model and calibration assessment methods are demonstrated using a cohort of 132,139 women attending mammography screening in the State of Washington, USA.

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.

Concept and benchmarks for assessing narrow-sense validity of genetic risk score values

BACKGROUND

While higher genetic risk score (GRS) has been statistically associated with increased disease risk (broad-sense validity), the concept and tools for assessing the validity of reported GRS values from tests (narrow-sense validity) are underdeveloped.

METHODS

We propose two benchmarks for assessing the narrow-sense validity of GRS. The baseline benchmark requires that the mean GRS value in a general population approximates 1.0. The calibration benchmark assesses the agreement between observed risks and estimated risks (GRS values). We assessed benchmark performance for three prostate cancer (PCa) GRS tests, derived from three SNP panels with increasing stringency of selection criteria, in a PCa chemoprevention trial where 714 of 3225 men were diagnosed with PCa during the 4-year follow-up.

RESULTS

GRS from Panels 1, 2, and 3 were all statistically associated with PCa risk; P = 5.58 × 10^-3 , P = 1 × 10^-3 , and P = 1.5 × 10^-13 , respectively (broad-sense validity). For narrow-sense validity, the mean GRS value among men without PCa was 1.33, 1.09, and 0.98 for Panels 1, 2, and 3, respectively (baseline benchmark). For assessing the calibration benchmark, observed risks were calculated for seven groups of men with GRS values <0.3, 0.3-0.79, 0.8-1.19, 1.2-1.49, 1.5-1.99, 2-2.99, and ≥3. The calibration slope (higher is better) was 0.15, 0.12, and 0.60, and the bias score (lower is better) between the observed risks and GRS values was 0.08, 0.08, and 0.02 for Panels 1, 2, and 3, respectively.

CONCLUSION

Performance differed considerably among GRS tests. We recommend that all GRS tests be evaluated using the two benchmarks before clinical implementation for individual risk assessment.

Why the Gold Standard Approach by Mammography Demands Extension by Multiomics? Application of Liquid Biopsy miRNA Profiles to Breast Cancer Disease Management

In the global context, the epidemic of breast cancer (BC) is evident for the early 21st century. Evidence shows that national mammography screening programs have sufficiently reduced BC related mortality. Therefore, the great utility of the mammography-based screening is not an issue. However, both false positive and false negative BC diagnosis, excessive biopsies, and irradiation linked to mammography application, as well as sub-optimal mammography-based screening, such as in the case of high-dense breast tissue in young females, altogether increase awareness among the experts regarding the limitations of mammography-based screening. Severe concerns regarding the mammography as the “golden standard” approach demanding complementary tools to cover the evident deficits led the authors to present innovative strategies, which would sufficiently improve the quality of the BC management and services to the patient. Contextually, this article provides insights into mammography deficits and current clinical data demonstrating the great potential of non-invasive diagnostic tools utilizing circulating miRNA profiles as an adjunct to conventional mammography for the population screening and personalization of BC management.

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

Background

The currently known breast cancer-associated single nucleotide polymorphisms (SNPs) are presently not used to guide clinical management. We explored whether a genetic test that incorporates a SNP-based polygenic risk score (PRS) is clinically meaningful in non-BRCA1/2 high-risk breast cancer families.

Methods

101 non-BRCA1/2 high-risk breast cancer families were included; 323 cases and 262 unaffected female relatives were genotyped. The 161-SNP PRS was calculated and standardised to 327 population controls (sPRS). Association analysis was performed using a Cox-type random effect regression model adjusted by family history. Updated individualised breast cancer lifetime risk scores were derived by combining the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm breast cancer lifetime risk with the effect of the sPRS.

Results

The mean sPRS for cases and their unaffected relatives was 0.70 (SD=0.9) and 0.53 (SD=0.9), respectively. A significant association was found between sPRS and breast cancer, HR=1.16, 95% CI 1.03 to 1.28, p=0.026. Addition of the sPRS to risk prediction based on family history alone changed screening recommendations in 11.5%, 14.7% and 19.8 % of the women according to breast screening guidelines from the USA (National Comprehensive Cancer Network), UK (National Institute for Health and Care Excellence and the Netherlands (Netherlands Comprehensive Cancer Organisation), respectively.

Conclusion

Our results support the application of the PRS in risk prediction and clinical management of women from genetically unexplained breast cancer families.

Systematic evaluation of cancer-specific genetic risk score for 11 types of cancer in The Cancer Genome Atlas and Electronic Medical Records and Genomics cohorts

BACKGROUND

Genetic risk score (GRS) is an odds ratio (OR)-weighted and population-standardized method for measuring cumulative effect of multiple risk-associated single nucleotide polymorphisms (SNPs). We hypothesize that GRS is a valid tool for risk assessment of most common cancers.

METHODS

Utilizing genotype and phenotype data from The Cancer Genome Atlas (TCGA) and Electronic Medical Records and Genomics (eMERGE), we tested 11 cancer-specific GRSs (bladder, breast, colorectal, glioma, lung, melanoma, ovarian, pancreatic, prostate, renal, and thyroid cancer) for association with the respective cancer type. Cancer-specific GRSs were calculated, for the first time in these cohorts, based on previously published risk-associated SNPs using the Caucasian subjects in these two cohorts.

RESULTS

Mean cancer-specific GRS in the population controls of eMERGE approximated the expected value of 1.00 (between 0.98 and 1.02) for all 11 types of cancer. Mean cancer-specific GRS was consistently higher in respective cancer patients than controls for all 11 types of cancer (P < 0.05). When subjects were categorized into low-, average-, and high-risk groups based on cancer-specific GRS (<0.5, 0.5-1.5, and >1.5, respectively), significant dose-response associations of higher cancer-specific GRS with higher OR of respective type of cancer were found for nine types of cancer (P_-trend  < 0.05). More than 64% subjects in the population controls of eMERGE can be classified as high risk for at least one type of these cancers.

CONCLUSION

Validity of GRS for predicting cancer risk is demonstrated for most types of cancer. If confirmed in larger studies, cancer-specific GRS may have the potential for developing personalized cancer screening strategy.

Genetic Testing to Guide Risk-Stratified Screens for Breast Cancer

Breast cancer screening modalities and guidelines continue to evolve and are increasingly based on risk factors, including genetic risk and a personal or family history of cancer. Here, we review genetic testing of high-penetrance hereditary breast and ovarian cancer genes, including BRCA1 and BRCA2, for the purpose of identifying high-risk individuals who would benefit from earlier screening and more sensitive methods such as magnetic resonance imaging. We also consider risk-based screening in the general population, including whether every woman should be genetically tested for high-risk genes and the potential use of polygenic risk scores. In addition to enabling early detection, the results of genetic screens of breast cancer susceptibility genes can be utilized to guide decision-making about when to elect prophylactic surgeries that reduce cancer risk and the choice of therapeutic options. Variants of uncertain significance, especially missense variants, are being identified during panel testing for hereditary breast and ovarian cancer. A finding of a variant of uncertain significance does not provide a basis for increased cancer surveillance or prophylactic procedures. Given that variant classification is often challenging, we also consider the role of multifactorial statistical analyses by large consortia and functional tests for this purpose.

Clinicians' use of breast cancer risk assessment tools according to their perceived importance of breast cancer risk factors: an international survey

The BOADICEA breast cancer (BC) risk assessment model and its associated Web Application v3 (BWA) tool are being extended to incorporate additional genetic and non-genetic BC risk factors. From an online survey through the BOADICEA website and UK, Dutch, French and Swedish national genetic societies, we explored the relationships between the usage frequencies of the BWA and six other common BC risk assessment tools and respondents' perceived importance of BC risk factors. Respondents (N = 443) varied in age, country and clinical seniority but comprised mainly genetics health professionals (82%) and BWA users (93%). Oncology professionals perceived reproductive, hormonal (exogenous) and lifestyle BC risk factors as more important in BC risk assessment compared to genetics professionals (p values < 0.05 to 0.0001). BWA was used more frequently by respondents who gave high weight to breast tumour pathology and low weight to personal BC history as BC risk factors. BWA use was positively related to the weight given to hormonal BC risk factors. The importance attributed to lifestyle and BMI BC risk factors was not associated with the use of BWA or any of the other tools. Next version of the BWA encompassing additional BC risk factors will facilitate more comprehensive BC risk assessment in genetics and oncology practice.

Genetics, coronary artery disease, and myocardial revascularization: will novel genetic risk scores bring new answers?

Both percutaneous coronary intervention (PCI) and coronary artery bypass graft surgery (CABG) are options for revascularization in multi-vessel coronary artery disease (CAD). However, the best form of revascularization remains controversial. Results from clinical trials (FREEDOM, SYNTAX, NOBLE, EXCEL) have identified factors related to CAD severity such as diabetes and SYNTAX score as indicators that patients may have better outcomes with CABG compared to PCI. Nevertheless, the discovery of other predictors of optimal revascularization therapy is necessary to improve decision-making and personalize the treatment of multi-vessel CAD. Genome-wide association studies have identified numerous previously unknown DNA variants that increase predisposition for CAD. Recently, a composite polygenic risk score has been developed to better assess the relative contribution of multiple SNPs and quantify overall genetic risk for CAD. High polygenic risk score is associated with increased coronary events and greater benefit from statin therapy in large observational studies. This effect is independent from traditional cardiovascular risk factors. At the same time, randomized clinical trials have shown that CAD severity is a determinant of optimal revascularization treatment. It remains unknown whether polygenic risk score is robustly associated with increased CAD severity at presentation, and whether this score can be used to identify patients who will show greater benefit from revascularization with CABG or with PCI.

Bridging the Data Gap in Breast Cancer Risk Assessment to Enable Widespread Clinical Implementation across the Multiethnic Landscape of the US

Breast cancer remains the second leading cause of cancer death among women and is the most commonly diagnosed cancer in women. Breast cancer risk assessment has been clinically available for nearly 30 years yet is under-utilized in practice for multiple reasons. Incorporation of polygenic risk as well as breast density measurements, promise to increase the accuracy of risk assessment. With that comes the hope that both prevention and screening become more personalized and thus more effective. Incidence rates have been static over the past 15 years and have even increased slightly in African American and Asian/Pacific Islander populations despite the robust data on breast cancer risk reduction measures that exist. Current challenges in reducing breast cancer incidence begin with robust data curation that allows for appropriate risk stratification across our multiethnic population and conclude with the implementation of prevention strategies within our fractured healthcare system.

Risk, Prediction and Prevention of Hereditary Breast Cancer - Large-Scale Genomic Studies in Times of Big and Smart Data

Over the last two decades genetic testing for mutations in BRCA1 and BRCA2 has become standard of care for women and men who are at familial risk for breast or ovarian cancer. Currently, genetic testing more often also includes so-called panel genes, which are assumed to be moderate-risk genes for breast cancer. Recently, new large-scale studies provided more information about the risk estimation of those genes. The utilization of information on panel genes with regard to their association with the individual breast cancer risk might become part of future clinical practice. Furthermore, large efforts have been made to understand the influence of common genetic variants with a low impact on breast cancer risk. For this purpose, almost 450 000 individuals have been genotyped for almost 500 000 genetic variants in the OncoArray project. Based on first results it can be assumed that – together with previously identified common variants – more than 170 breast cancer risk single nucleotide polymorphisms can explain up to 18% of familial breast cancer risk. The knowledge about genetic and non-genetic risk factors and its implementation in clinical practice could especially be of use for individualized prevention. This includes an individualized risk prediction as well as the individualized selection of screening methods regarding imaging and possible lifestyle interventions. The aim of this review is to summarize the most recent developments in this area and to provide an overview on breast cancer risk genes, risk prediction models and their utilization for the individual patient.

Progress in preventive therapy for cancer: a reminiscence and personal viewpoint

Prophylactic drug treatment with aspirin, statins and anti-hypertensive agents has had a major impact on the incidence of cardiovascular disease and is now well established. Progress in therapeutic cancer prevention has been much slower; only recently have effective agents been clearly established. Breast cancer has led the way and endocrine agents used to treat it-notably tamoxifen and the aromatase inhibitors-have now been shown to have a substantial preventive effect as well. However, these agents carry some toxicity and thus identifying high-risk women who are likely to benefit most is a key priority. In contrast, the ability of low-dose aspirin to prevent about one-third of colorectal, gastric, and oesophageal cancers, combined with its much lower toxicity profile, make it attractive for a much larger proportion of the general population. Vaccination against the human papilloma virus is also a preventive intervention with large benefits for the whole population. Here I recall my involvement in these initiatives and offer a personal viewpoint on what has been achieved and what remains to be done.

Can chimerism explain breast/ovarian cancers in BRCA non-carriers from BRCA-positive families?

Hereditary breast and ovarian cancer syndrome (HBOC) is most frequently caused by mutations in BRCA1 or BRCA2 (in short, BRCA) genes. The incidence of hereditary breast and ovarian cancer in relatives of BRCA mutation carriers who test negative for the familial mutation (non-carriers) may be increased. However, the data is controversial, and at this time, these individuals are recommended the same cancer surveillance as general population. One possible explanation for BRCA phenocopies (close relatives of BRCA carriers who have developed cancer consistent with HBOC but tested negative for a familial mutation) is natural chimerism where lack of detectable mutation in blood may not rule out the presence of the mutation in the other tissues. To test this hypothesis, archival tumor tissue from eleven BRCA phenocopies was investigated. DNA from the tumor tissue was analyzed using sequence-specific PCR, capillary electrophoresis, and pyrosequencing. The familial mutations were originally detected in the patients’ first-degree relatives by commercial testing. The same testing detected no mutations in the blood of the patients under study. The test methods targeted only the known familial mutation in the tumor tissue. Tumor diagnoses included breast, ovarian, endometrial and primary peritoneal carcinoma. None of the familial mutations were found in the tumor samples tested. These results do not support, but do not completely exclude, the possibility of chimerism in these patients. Further studies with comprehensive sequence analysis in a larger patient group are warranted as a chimeric state would further refine the predictive value of genetic testing to include BRCA phenocopies.

Personalized prevention in high risk individuals: Managing hormones and beyond

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

Evaluation of three polygenic risk score models for the prediction of breast cancer risk in Singapore Chinese

Genome-wide association studies (GWAS) have proven highly successful in identifying single nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk. The majority of these studies are on European populations, with limited SNP association data in other populations. We genotyped 51 GWAS-identified SNPs in two independent cohorts of Singaporean Chinese. Cohort 1 comprised 1294 BC cases and 885 controls and was used to determine odds ratios (ORs); Cohort 2 had 301 BC cases and 243 controls for deriving polygenic risk scores (PRS). After age-adjustment, 11 SNPs were found to be significantly associated with BC risk. Five SNPs were present in <1% of Cohort 1 and were excluded from further PRS analysis. To assess the cumulative effect of the remaining 46 SNPs on BC risk, we generated three PRS models: Model-1 included 46 SNPs; Model-2 included 11 statistically significant SNPs; and Model-3 included the SNPs in Model-2 but excluded SNPs that were in strong linkage disequilibrium with the others. Across Models-1, -2 and -3, women in the highest PRS quartile had the greatest ORs of 1.894 (95% CI = 1.157–3.100), 2.013 (95% CI = 1.227–3.302) and 1.751 (95% CI = 1.073–2.856) respectively, suggesting a direct correlation between PRS and BC risk. Given the potential of PRS in BC risk stratification, our findings suggest the need to tailor the selection of SNPs to be included in an ethnic-specific PRS model.

E-Science technologies in a workflow for personalized medicine using cancer screening as a case study

Objective

We provide an e-Science perspective on the workflow from risk factor discovery and classification of disease to evaluation of personalized intervention programs. As case studies, we use personalized prostate and breast cancer screenings.

Materials and Methods

We describe an e-Science initiative in Sweden, e-Science for Cancer Prevention and Control (eCPC), which supports biomarker discovery and offers decision support for personalized intervention strategies. The generic eCPC contribution is a workflow with 4 nodes applied iteratively, and the concept of e-Science signifies systematic use of tools from the mathematical, statistical, data, and computer sciences.

Results

The eCPC workflow is illustrated through 2 case studies. For prostate cancer, an in-house personalized screening tool, the Stockholm-3 model (S3M), is presented as an alternative to prostate-specific antigen testing alone. S3M is evaluated in a trial setting and plans for rollout in the population are discussed. For breast cancer, new biomarkers based on breast density and molecular profiles are developed and the US multicenter Women Informed to Screen Depending on Measures (WISDOM) trial is referred to for evaluation. While current eCPC data management uses a traditional data warehouse model, we discuss eCPC-developed features of a coherent data integration platform.

Discussion and Conclusion

E-Science tools are a key part of an evidence-based process for personalized medicine. This paper provides a structured workflow from data and models to evaluation of new personalized intervention strategies. The importance of multidisciplinary collaboration is emphasized. Importantly, the generic concepts of the suggested eCPC workflow are transferrable to other disease domains, although each disease will require tailored solutions.

ZNF423: A New Player in Estrogen Receptor-Positive Breast Cancer

Preventive therapy can target hormone-responsive breast cancer (BC) by treatment with selective estrogen receptor modulators (SERMs) and reduce the incidence of BC. Genome-wide association studies have identified single nucleotide polymorphisms (SNPs) with relevant predictive values, SNPs in the ZNF423 gene were associated with decreased risk of BC during SERM therapy, and SNPs in the Cathepsin O gene with an increased risk. ZNF423, which was not previously associated with BC is a multifunctional transcription factor known to have a role in development, neurogenesis, and adipogenesis and is implicated in other types of cancer. ZNF423 is transcriptionally controlled by the homolog ZNF521, early B cell factor transcription factor, epigenetic silencing of the promoter by CpG island hyper-methylation, and also by ZNF423 itself in an auto-regulatory loop. In BC cells, ZNF423 expression is found to be induced by estrogen, dependent on the binding of the estrogen receptor and calmodulin-like 3 to SNPs in ZNP423 intronic sites in proximity to consensus estrogen response elements. ZNF423 has also been shown to play a mechanistic role by trans-activating the tumor suppressor BRCA1 and thus modulating the DNA damage response. Even though recent extensive trial studies did not classify these SNPs with the highest predictive values, for inclusion in polygenic SNP analysis, the mechanism unveiled in these studies has introduced ZNF423 as a factor important in the control of the estrogen response. Here, we aim at providing an overview of ZNF423 expression and functional role in human malignancies, with a specific focus on its implication in hormone-responsive BC.