Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes

Breast Density: Where Are We Now?

Breast density refers to the amount of fibroglandular tissue relative to fat on mammography and is determined either qualitatively through visual assessment or quantitatively. It is a heritable and dynamic trait associated with age, race/ethnicity, body mass index, and hormonal factors. Increased breast density has important clinical implications including the potential to mask malignancy and as an independent risk factor for the development of breast cancer. Breast density has been incorporated into breast cancer risk models. Given the impact of dense breasts on the interpretation of mammography, supplemental screening may be indicated.

Cancer risks among first-degree relatives of women with a genetic predisposition to breast cancer

BACKGROUND

Associations between germline alterations in women and cancer risks among their relatives are largely unknown.

METHODS

We identified women from 2 Swedish cohorts Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) and prevalent KARMA (pKARMA), including 28 362 women with genotyping data and 13 226 with sequencing data. Using Swedish Multi-Generation Register, we linked these women to 133 389 first-degree relatives. Associations between protein-truncating variants in 8 risk genes and breast cancer polygenic risk score in index women and cancer risks among their relatives were modeled via Cox regression.

RESULTS

Female relatives of index women who were protein-truncating variant carriers in any of the 8 risk genes had an increased breast cancer risk compared with those of noncarriers (hazard ratio [HR] = 1.85, 95% confidence interval [CI] = 1.52 to 2.27), with the strongest association found for protein-truncating variants in BRCA1 and 2. These relatives had a statistically higher risk of early onset than late-onset breast cancer (P = .001). Elevated breast cancer risk was also observed in female relatives of index women with higher polygenic risk score (HR per SD = 1.28, 95% CI = 1.23 to 1.32). The estimated lifetime risk was 22.3% for female relatives of protein-truncating variant carriers and 14.4% for those related to women in the top polygenic risk score quartile. Moreover, relatives of index women with protein-truncating variant presence (HR = 1.30, 95% CI = 1.06 to 1.59) or higher polygenic risk score (HR per SD = 1.04, 95% CI = 1.01 to 1.07) were also at higher risk of nonbreast hereditary breast and ovary cancer syndrome-related cancers.

CONCLUSIONS

Protein-truncating variants of risk genes and higher polygenic risk score in index women are associated with an increased risk of breast and other hereditary breast and ovary syndrome-related cancers among relatives.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Multi-organ imaging-derived polygenic indexes for brain and body health

The UK Biobank (UKB) imaging project is a crucial resource for biomedical research, but is limited to 100,000 participants due to cost and accessibility barriers. Here we used genetic data to predict heritable imaging-derived phenotypes (IDPs) for a larger cohort. We developed and evaluated 4,375 IDP genetic scores (IGS) derived from UKB brain and body images. When applied to UKB participants who were not imaged, IGS revealed links to numerous phenotypes and stratified participants at increased risk for both brain and somatic diseases. For example, IGS identified individuals at higher risk for Alzheimer's disease and multiple sclerosis, offering additional insights beyond traditional polygenic risk scores of these diseases. When applied to independent external cohorts, IGS also stratified those at high disease risk in the All of Us Research Program and the Alzheimer's Disease Neuroimaging Initiative study. Our results demonstrate that, while the UKB imaging cohort is largely healthy and may not be the most enriched for disease risk management, it holds immense potential for stratifying the risk of various brain and body diseases in broader external genetic cohorts.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

IMPACT

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

Body mass index stratification optimizes polygenic prediction of type 2 diabetes in cross-biobank analyses

Type 2 diabetes (T2D) shows heterogeneous body mass index (BMI) sensitivity. Here, we performed stratification based on BMI to optimize predictions for BMI-related diseases. We obtained BMI-stratified datasets using data from more than 195,000 individuals (nT2D = 55,284) from BioBank Japan (BBJ) and UK Biobank. T2D heritability in the low-BMI group was greater than that in the high-BMI group. Polygenic predictions of T2D toward low-BMI targets had pseudo-R2 values that were more than 22% higher than BMI-unstratified targets. Polygenic risk scores (PRSs) from low-BMI discovery outperformed PRSs from high BMI, while PRSs from BMI-unstratified discovery performed best. Pathway-specific PRSs demonstrated the biological contributions of pathogenic pathways. Low-BMI T2D cases showed higher rates of neuropathy and retinopathy. Combining BMI stratification and a method integrating cross-population effects, T2D predictions showed greater than 37% improvements over unstratified-matched-population prediction. We replicated findings in the Tohoku Medical Megabank (n = 26,000) and the second BBJ cohort (n = 33,096). Our findings suggest that target stratification based on existing traits can improve the polygenic prediction of heterogeneous diseases.

Cancer-specific risk prediction with a serum microRNA signature

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

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

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

Polygenic risk score for acute rejection based on donor-recipient non-HLA genotype mismatch

BACKGROUND

Acute rejection (AR) after kidney transplantation is an important allograft complication. To reduce the risk of post-transplant AR, determination of kidney transplant donor-recipient mismatching focuses on blood type and human leukocyte antigens (HLA), while it remains unclear whether non-HLA genetic mismatching is related to post-transplant complications.

METHODS

We carried out a genome-wide scan (HLA and non-HLA regions) on AR with a large kidney transplant cohort of 784 living donor-recipient pairs of European ancestry. An AR polygenic risk score (PRS) was constructed with the non-HLA single nucleotide polymorphisms (SNPs) filtered by independence (r2 < 0.2) and P-value (< 1×10-3) criteria. The PRS was validated in an independent cohort of 352 living donor-recipient pairs.

RESULTS

By the genome-wide scan, we identified one significant SNP rs6749137 with HR = 2.49 and P-value = 2.15×10-8. 1,307 non-HLA PRS SNPs passed the clumping plus thresholding and the PRS exhibited significant association with the AR in the validation cohort (HR = 1.54, 95% CI = (1.07, 2.22), p = 0.019). Further pathway analysis attributed the PRS genes into 13 categories, and the over-representation test identified 42 significant biological processes, the most significant of which is the cell morphogenesis (GO:0000902), with 4.08 fold of the percentage from homo species reference and FDR-adjusted P-value = 8.6×10-4.

CONCLUSIONS

Our results show the importance of donor-recipient mismatching in non-HLA regions. Additional work will be needed to understand the role of SNPs included in the PRS and to further improve donor-recipient genetic matching algorithms. Trial registry: Deterioration of Kidney Allograft Function Genomics (NCT00270712) and Genomics of Kidney Transplantation (NCT01714440) are registered on ClinicalTrials.gov.

Characterizing the Relationship between Expression Quantitative Trait Loci (eQTLs), DNA Methylation Quantitative Trait Loci (mQTLs), and Breast Cancer Risk Variants

PURPOSE

To assess the association of a polygenic risk score (PRS) for functional genetic variants with the risk of developing breast cancer.

METHODS

Summary data-based Mendelian randomization (SMR) and heterogeneity in dependent instruments (HEIDI) were used to identify breast cancer risk variants associated with gene expression and DNA methylation levels. A new SMR-based PRS was computed from the identified variants (functional PRS) and compared to an established 313-variant breast cancer PRS (GWAS PRS). The two scores were evaluated in 3560 breast cancer cases and 3383 non-cancer controls and also in a prospective study (n = 10,213) comprising 418 cases.

RESULTS

We identified 149 variants showing pleiotropic association with breast cancer risk (eQTLHEIDI > 0.05 = 9, mQTLHEIDI > 0.05 = 165). The discriminatory ability of the functional PRS (AUCcontinuous [95% CI]: 0.540 [0.526 to 0.553]) was found to be lower than that of the GWAS PRS (AUCcontinuous [95% CI]: 0.609 [0.596 to 0.622]). Even when utilizing 457 distinct variants from both the functional and GWAS PRS, the combined discriminatory performance remained below that of the GWAS PRS (AUCcontinuous, combined [95% CI]: 0.561 [0.548 to 0.575]). A binary high/low-risk classification based on the 80th centile PRS in controls revealed a 6% increase in cases using the GWAS PRS compared to the functional PRS. The functional PRS identified an additional 12% of high-risk cases but also led to a 13% increase in high-risk classification among controls. Similar findings were observed in the SCHS prospective cohort, where the GWAS PRS outperformed the functional PRS, and the highest-performing PRS, a combined model, did not significantly improve over the GWAS PRS.

CONCLUSIONS

While this study identified potentially functional variants associated with breast cancer risk, their inclusion did not substantially enhance the predictive accuracy of the GWAS PRS.

The therapeutic effect and targets of herba Sarcandrae on breast cancer and the construction of a prognostic signature consisting of inflammation-related genes

Background

The prevalence of breast cancer (BRCA), which is common among women, is on the rise. This study applied network pharmacology to explore the potential mechanism of action of herba sarcandrae in BRCA and construct a prognostic signature composed of inflammation-related genes.

Methods

The active ingredients of herba sarcandrae were screened using the SymMap, TCMID, and TCMSP platforms, and the molecular targets were determined in the UniProt database. The "drug-active compound-potential target" network was established with Cytoscape 3.7.2. The molecular targets were subjected to disease ontology, gene ontology (GO), and Kyoto Encyclopedia of Genes (KEGG) analyses. AutoDock software was used for molecular docking. Differentially expressed genes (DEGs) related to inflammation were obtained from the BRCA Cancer Genome Atlas (TCGA) database. In the training cohort, the univariate Cox regression model was applied to preliminarily screen prognostic genes. A multigene signature was built by the least absolute shrinkage and selection operator (LASSO) regression model, followed by validation through Kaplan‒Meier, Cox, and receiver operating characteristic (ROC) analyses.

Results

Forty-one active compounds were identified, and 265 therapeutic targets for herba sarcandrae were predicted. GO enrichment results revealed significant enrichment of biological processes, such as response to xenobiotic stimuli, response to nutrient levels, and response to lipopolysaccharide. KEGG analysis revealed significant enrichment of pathways such as AGE-RAGE and chemical carcinogenesis receptor activation signaling pathways. In addition, the herbs Marc-Andre and rutin were shown to mediate BRCA cell proliferation and apoptosis via the interferon regulatory factor 1 (IRF1)/signal transducer and activator of transcription 3 (STAT3)/programmed death-ligand 1 (PD-L1) pathway. Sixteen inflammatory signatures, including BST2, GPR132, IL12B, IL18, IL1R1, IL2RB, IRF1, and others, were constructed, and the risk score was found to be a strong independent prognostic factor for overall survival in BRCA patients. The 16-inflammation signature was associated with several clinical features (age, clinical stage, T, and N classifications) and could reflect immune cell infiltration in tumor microenvironments with different immune cells.

Conclusions

Herba sarcandrae and rutin were shown to mediate BRCA cell proliferation and apoptosis via the IRF1/STAT3/PD-L1 pathway, and the 16-member inflammatory signature might be a novel biomarker for predicting BRCA patient prognosis, providing more accurate guidance for clinical treatment prognosis evaluation and having important reference value for individualized treatment selection.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Polygenic architecture of brain structure and function, behaviors, and psychopathologies in children

The human brain undergoes structural and functional changes during childhood, a critical period in cognitive and behavioral development. Understanding the genetic architecture of the brain development in children can offer valuable insights into the development of the brain, cognition, and behaviors. Here, we integrated brain imaging-genetic-phenotype data from over 8,600 preadolescent children of diverse ethnic backgrounds using multivariate statistical techniques. We found a low-to-moderate level of SNP-based heritability in most IDPs, which is lower compared to the adult brain. Using sparse generalized canonical correlation analysis (SGCCA), we identified several covariation patterns among genome-wide polygenic scores (GPSs) of 29 traits, 7 different modalities of brain imaging-derived phenotypes (IDPs), and 266 cognitive and psychological phenotype data. In structural MRI, significant positive associations were observed between total grey matter volume, left ventral diencephalon volume, surface area of right accumbens and the GPSs of cognition-related traits. Conversely, negative associations were found with the GPSs of ADHD, depression and neuroticism. Additionally, we identified a significant positive association between educational attainment GPS and regional brain activation during the N-back task. The BMI GPS showed a positive association with fractional anisotropy (FA) of connectivity between the cerebellum cortex and amygdala in diffusion MRI, while the GPSs for educational attainment and cannabis use were negatively associated with the same IDPs. Our GPS-based prediction models revealed substantial genetic contributions to cognitive variability, while the genetic basis for many mental and behavioral phenotypes remained elusive. This study delivers a comprehensive map of the relationships between genetic profiles, neuroanatomical diversity, and the spectrum of cognitive and behavioral traits in preadolescence.

Breast cancer incidence and mortality by metabolic syndrome and obesity: The Women's Health Initiative

BACKGROUND

In the Women's Health Initiative (WHI) randomized trial, dietary intervention significantly reduced breast cancer mortality, especially in women with more metabolic syndrome (MetS) components. Therefore, this study investigated the associations of MetS and obesity with postmenopausal breast cancer after long-term follow-up in the WHI clinical trials.

METHODS

A total of 68,132 postmenopausal women, without prior breast cancer and with normal mammogram, were entered into WHI randomized clinical trials; 63,330 women with an entry MetS score comprised the study population. At entry, body mass index (BMI) was determined; MetS score (0, 1-2, and 3-4) included the following: (1) high waist circumference (≥88 cm), (2) high blood pressure (systolic ≥130 mm Hg and/or diastolic ≥85 mm Hg, or hypertension history), (3) high-cholesterol history, and (4) diabetes history. Study outcomes included breast cancer incidence, breast cancer mortality, deaths after breast cancer, and results by hormone receptor status.

RESULTS

After a >20-year mortality follow-up, a higher MetS score (3-4), adjusted for BMI, was significantly associated with more poor prognosis, estrogen receptor (ER)-positive, progesterone receptor (PR)-negative cancers (p = .03), 53% more deaths after breast cancer (p < .001), and 44% higher breast cancer mortality (p = .03). Obesity status, adjusted for MetS score, was significantly associated with more good prognosis, ER-positive, PR-positive cancers (p < .001), more total breast cancers (p < .001), and more deaths after breast cancer (p < .001), with higher breast cancer mortality only in women with severe obesity (BMI, ≥35 kg/m2; p < .001).

CONCLUSIONS

MetS and obesity status have independent, but differential, adverse associations with breast cancer receptor subtypes and breast cancer mortality risk. Both represent separate targets for breast cancer prediction and prevention strategies.

Imputation Server PGS: an automated approach to calculate polygenic risk scores on imputation servers

Polygenic scores (PGS) enable the prediction of genetic predisposition for a wide range of traits and diseases by calculating the weighted sum of allele dosages for genetic variants associated with the trait or disease in question. Present approaches for calculating PGS from genotypes are often inefficient and labor-intensive, limiting transferability into clinical applications. Here, we present 'Imputation Server PGS', an extension of the Michigan Imputation Server designed to automate a standardized calculation of polygenic scores based on imputed genotypes. This extends the widely used Michigan Imputation Server with new functionality, bringing the simplicity and efficiency of modern imputation to the PGS field. The service currently supports over 4489 published polygenic scores from publicly available repositories and provides extensive quality control, including ancestry estimation to report population stratification. An interactive report empowers users to screen and compare thousands of scores in a fast and intuitive way. Imputation Server PGS provides a user-friendly web service, facilitating the application of polygenic scores to a wide range of genetic studies and is freely available at https://imputationserver.sph.umich.edu.

Generalizability of PGS313 for breast cancer risk in a Los Angeles biobank

Polygenic scores (PGSs) summarize the combined effect of common risk variants and are associated with breast cancer risk in patients without identifiable monogenic risk factors. One of the most well-validated PGSs in breast cancer to date is PGS313, which was developed from a Northern European biobank but has shown attenuated performance in non-European ancestries. We further investigate the generalizability of the PGS313 for American women of European (EA), African (AFR), Asian (EAA), and Latinx (HL) ancestry within one institution with a singular electronic health record (EHR) system, genotyping platform, and quality control process. We found that the PGS313 achieved overlapping areas under the receiver operator characteristic (ROC) curve (AUCs) in females of HL (AUC = 0.68, 95% confidence interval [CI] = 0.65-0.71) and EA ancestry (AUC = 0.70, 95% CI = 0.69-0.71) but lower AUCs for the AFR and EAA populations (AFR: AUC = 0.61, 95% CI = 0.56-0.65; EAA: AUC = 0.64, 95% CI = 0.60-0.680). While PGS313 is associated with hormone-receptor-positive (HR+) disease in EA Americans (odds ratio [OR] = 1.42, 95% CI = 1.16-1.64), this association is lost in African, Latinx, and Asian Americans. In summary, we found that PGS313 was significantly associated with breast cancer but with attenuated accuracy in women of AFR and EAA descent within a singular health system in Los Angeles. Our work further highlights the need for additional validation in diverse cohorts prior to the clinical implementation of PGSs.

Genetic Screening-Emerging Issues

In many countries, some form of genetic screening is offered to all or part of the population, either in the form of well-organized screening programs or in a less formalized way. Screening can be offered at different phases of life, such as preconception, prenatal, neonatal and later in life. Screening should only be offered if the advantages outweigh the disadvantages. Technical innovations in testing and treatment are driving changes in the field of prenatal and neonatal screening, where many jurisdictions have organized population-based screening programs. As a result, a greater number and wider range of conditions are being added to the programs, which can benefit couples' reproductive autonomy (preconception and prenatal screening) and improve early diagnosis to prevent irreversible health damage in children (neonatal screening) and in adults (cancer and cascade screening). While many developments in screening are technology-driven, citizens may also express a demand for innovation in screening, as was the case with non-invasive prenatal testing. Relatively new emerging issues for genetic screening, especially if testing is performed using DNA sequencing, relate to organization, data storage and interpretation, benefit-harm ratio and distributive justice, information provision and follow-up, all connected to acceptability in current healthcare systems.

Following the High-Risk Patient: Breast Cancer Risk-Based Screening

Breast cancer (BC) is the most common cancer occurring in women in the USA today, and accounts for more than 40,000 deaths annually (Giaquinto in CA Cancer J Clin 72: 524-541, 2022). While breast cancer survival has improved over the past decades, incidence has increased, and diagnoses are being made at younger ages. This emphasizes the importance of risk evaluation, accurate prediction, and effective mitigation and risk reduction strategies. Enhanced screening can help detect cancers at an earlier stage, thus improving morbidity and mortality. This review addresses the recognition of women at high-risk for BC and monitoring strategies for those at high risk.

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

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

Estimation of age of onset and progression of breast cancer by absolute risk dependent on polygenic risk score and other risk factors

BACKGROUND

Genetic, lifestyle, reproductive, and anthropometric factors are associated with the risk of developing breast cancer. However, it is not yet known whether polygenic risk score (PRS) and absolute risk based on a combination of risk factors are associated with the risk of progression of breast cancer. This study aims to estimate the distribution of sojourn time (pre-clinical screen-detectable period) and mammographic sensitivity by absolute breast cancer risk derived from polygenic profile and the other risk factors.

METHODS

The authors used data from a population-based case-control study. Six categories of 10-year absolute risk based on different combinations of risk factors were derived using the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm. Women were classified into low, medium, and high-risk groups. The authors constructed a continuous-time multistate model. To calculate the sojourn time, they simulated the trajectories of subjects through the disease states.

RESULTS

There was little difference in sojourn time with a large overlap in the 95% confidence interval (CI) between the risk groups across the six risk categories and PRS studied. However, the age of entry into the screen-detectable state varied by risk category, with the mean age of entry of 53.4 years (95% CI, 52.2-54.1) and 57.0 years (95% CI, 55.1-57.7) in the high-risk and low-risk women, respectively.

CONCLUSION

In risk-stratified breast screening, the age at the start of screening, but not necessarily the frequency of screening, should be tailored to a woman's risk level. The optimal risk-stratified screening strategy that would improve the benefit-to-harm balance and the cost-effectiveness of the screening programs needs to be studied.

Genome-wide association analyses of breast cancer in women of African ancestry identify new susceptibility loci and improve risk prediction

We performed genome-wide association studies of breast cancer including 18,034 cases and 22,104 controls of African ancestry. Genetic variants at 12 loci were associated with breast cancer risk (P < 5 × 10-8), including associations of a low-frequency missense variant rs61751053 in ARHGEF38 with overall breast cancer (odds ratio (OR) = 1.48) and a common variant rs76664032 at chromosome 2q14.2 with triple-negative breast cancer (TNBC) (OR = 1.30). Approximately 15.4% of cases with TNBC carried six risk alleles in three genome-wide association study-identified TNBC risk variants, with an OR of 4.21 (95% confidence interval = 2.66-7.03) compared with those carrying fewer than two risk alleles. A polygenic risk score (PRS) showed an area under the receiver operating characteristic curve of 0.60 for the prediction of breast cancer risk, which outperformed PRS derived using data from females of European ancestry. Our study markedly increases the population diversity in genetic studies for breast cancer and demonstrates the utility of PRS for risk prediction in females of African ancestry.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Cutting-edge approaches for targeted drug delivery in breast cancer: beyond conventional therapies

Breast cancer is a global health challenge with staggering statistics underscoring its pervasive impact. The burden of this disease is measured in terms of its prevalence and the challenges it poses to healthcare systems, necessitating a closer look at its epidemiology and impact. Current breast cancer treatments, including surgery, chemotherapy, radiation therapy, and targeted therapies, have made significant strides in improving patient outcomes. However, they are not without limitations, often leading to adverse effects and the development of drug resistance. This comprehensive review delves into the complex landscape of breast cancer, including its incidence, current treatment modalities, and the inherent limitations of existing therapeutic approaches. It also sheds light on the promising role of nanotechnology, encompassing both inorganic and organic nanoparticles equipped with the ability to selectively deliver therapeutic agents to tumor sites, in the battle against breast cancer. The review also addresses the emerging therapies, their associated challenges, and the future prospects of targeted drug delivery in breast cancer management.

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

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

Breast Cancer Polygenic Risk Score Validation and Effects of Variable Imputation

Breast cancer (BC) is a complex disease affecting one in eight women in the USA. Advances in population genomics have led to the development of polygenic risk scores (PRSs) with the potential to augment current risk models, but replication is often limited. We evaluated 2 robust PRSs with 313 and 3820 SNPs and the effects of multiple genotype imputation replications in BC cases and control populations. Biological samples from BC cases and cancer-free controls were drawn from three European ancestry cohorts. Genotyping on the Illumina Global Screening Array was followed by stringent quality control measures and 20 genotype imputation replications. A total of 468 unrelated cases and 4337 controls were scored, revealing significant differences in mean PRS percentiles between cases and controls (p < 0.001) for both SNP sets (313-SNP PRS: 52.81 and 48.07; 3820-SNP PRS: 55.45 and 49.81), with receiver operating characteristic curve analysis showing area under the curve values of 0.596 and 0.603 for the 313-SNP and 3820-SNP PRS, respectively. PRS fluctuations (from ~2-3% up to 9%) emerged across imputation iterations. Our study robustly reaffirms the predictive capacity of PRSs for BC by replicating their performance in an independent BC population and showcases the need to average imputed scores for reliable outcomes.

Enhancing prediction accuracy of coronary artery disease through machine learning-driven genomic variant selection

Machine learning (ML) methods are increasingly becoming crucial in genome-wide association studies for identifying key genetic variants or SNPs that statistical methods might overlook. Statistical methods predominantly identify SNPs with notable effect sizes by conducting association tests on individual genetic variants, one at a time, to determine their relationship with the target phenotype. These genetic variants are then used to create polygenic risk scores (PRSs), estimating an individual's genetic risk for complex diseases like cancer or cardiovascular disorders. Unlike traditional methods, ML algorithms can identify groups of low-risk genetic variants that improve prediction accuracy when combined in a mathematical model. However, the application of ML strategies requires addressing the feature selection challenge to prevent overfitting. Moreover, ensuring the ML model depends on a concise set of genomic variants enhances its clinical applicability, where testing is feasible for only a limited number of SNPs. In this study, we introduce a robust pipeline that applies ML algorithms in combination with feature selection (ML-FS algorithms), aimed at identifying the most significant genomic variants associated with the coronary artery disease (CAD) phenotype. The proposed computational approach was tested on individuals from the UK Biobank, differentiating between CAD and non-CAD individuals within this extensive cohort, and benchmarked against standard PRS-based methodologies like LDpred2 and Lassosum. Our strategy incorporates cross-validation to ensure a more robust evaluation of genomic variant-based prediction models. This method is commonly applied in machine learning strategies but has often been neglected in previous studies assessing the predictive performance of polygenic risk scores. Our results demonstrate that the ML-FS algorithm can identify panels with as few as 50 genetic markers that can achieve approximately 80% accuracy when used in combination with known risk factors. The modest increase in accuracy over PRS performances is noteworthy, especially considering that PRS models incorporate a substantially larger number of genetic variants. This extensive variant selection can pose practical challenges in clinical settings. Additionally, the proposed approach revealed novel CAD-genetic variant associations.

An ensemble penalized regression method for multi-ancestry polygenic risk prediction

Great efforts are being made to develop advanced polygenic risk scores (PRS) to improve the prediction of complex traits and diseases. However, most existing PRS are primarily trained on European ancestry populations, limiting their transferability to non-European populations. In this article, we propose a novel method for generating multi-ancestry Polygenic Risk scOres based on enSemble of PEnalized Regression models (PROSPER). PROSPER integrates genome-wide association studies (GWAS) summary statistics from diverse populations to develop ancestry-specific PRS with improved predictive power for minority populations. The method uses a combination ofL 1 (lasso) andL 2 (ridge) penalty functions, a parsimonious specification of the penalty parameters across populations, and an ensemble step to combine PRS generated across different penalty parameters. We evaluate the performance of PROSPER and other existing methods on large-scale simulated and real datasets, including those from 23andMe Inc., the Global Lipids Genetics Consortium, and All of Us. Results show that PROSPER can substantially improve multi-ancestry polygenic prediction compared to alternative methods across a wide variety of genetic architectures. In real data analyses, for example, PROSPER increased out-of-sample prediction R2 for continuous traits by an average of 70% compared to a state-of-the-art Bayesian method (PRS-CSx) in the African ancestry population. Further, PROSPER is computationally highly scalable for the analysis of large SNP contents and many diverse populations.

An Ensemble Penalized Regression Method for Multi-ancestry Polygenic Risk Prediction

Great efforts are being made to develop advanced polygenic risk scores (PRS) to improve the prediction of complex traits and diseases. However, most existing PRS are primarily trained on European ancestry populations, limiting their transferability to non-European populations. In this article, we propose a novel method for generating multi-ancestry Polygenic Risk scOres based on enSemble of PEnalized Regression models (PROSPER). PROSPER integrates genome-wide association studies (GWAS) summary statistics from diverse populations to develop ancestry-specific PRS with improved predictive power for minority populations. The method uses a combination of ℒ 1 (lasso) and ℒ 2 (ridge) penalty functions, a parsimonious specification of the penalty parameters across populations, and an ensemble step to combine PRS generated across different penalty parameters. We evaluate the performance of PROSPER and other existing methods on large-scale simulated and real datasets, including those from 23andMe Inc., the Global Lipids Genetics Consortium, and All of Us. Results show that PROSPER can substantially improve multi-ancestry polygenic prediction compared to alternative methods across a wide variety of genetic architectures. In real data analyses, for example, PROSPER increased out-of-sample prediction R2 for continuous traits by an average of 70% compared to a state-of-the-art Bayesian method (PRS-CSx) in the African ancestry population. Further, PROSPER is computationally highly scalable for the analysis of large SNP contents and many diverse populations.

Genetic Risk, Health-Associated Lifestyle, and Risk of Early-onset Total Cancer and Breast Cancer

Importance

Early-onset cancer (diagnosed under 50 years of age) is associated with aggressive disease characteristics and its rising incidence is a global concern. The association between healthy lifestyle and early-onset cancer and whether it varies by common genetic variants is unknown.

Objective

To examine the associations between genetic risk, lifestyle, and risk of early-onset cancers.

Design Setting and Participants

We analyzed a prospective cohort of 66,308 white British participants who were under age 50 and free of cancer at baseline in the UK Biobank.

Exposures

Sex-specific composite total cancer polygenic risk scores (PRSs), a breast cancer-specific PRS, and sex-specific health-associated lifestyle scores (HLSs, which summarize smoking status, body mass index [males only], physical activity, alcohol consumption, and diet).

Main Outcomes and Measures

Hazard ratios (HRs) and 95% confidence intervals (CIs) for early-onset total and breast cancer.

Results

A total of 1,247 incident invasive early-onset cancer cases (female: 820, male: 427, breast: 386) were documented. In multivariable-adjusted analyses with 2-year latency, higher genetic risk (highest vs. lowest tertile of PRS) was associated with significantly increased risks of early-onset total cancer in females (HR, 95% CI: 1.85, 1.50-2.29) and males (1.94, 1.45-2.59) as well as early-onset breast cancer in females (3.06, 2.20-4.25). An unfavorable lifestyle (highest vs. lowest category of HLS) was associated with higher risk of total cancer and breast cancer in females across genetic risk categories; the association with total cancer was stronger in the highest genetic risk category than the lowest: HRs in females and men were 1.85 (1.02, 3.36), 3.27 (0.78, 13.72) in the highest genetic risk category and 1.15 (0.44, 2.98), 1.16 (0.39, 3.40) in the lowest.

Conclusions and Relevance

Both genetic and lifestyle factors were independently associated with early-onset total and breast cancer risk. Compared to those with low genetic risk, individuals with a high genetic risk may benefit more from adopting a healthy lifestyle in preventing early-onset cancer.

Integration of polygenic and gut metagenomic risk prediction for common diseases

Multiomics has shown promise in noninvasive risk profiling and early detection of various common diseases. In the present study, in a prospective population-based cohort with ~18 years of e-health record follow-up, we investigated the incremental and combined value of genomic and gut metagenomic risk assessment compared with conventional risk factors for predicting incident coronary artery disease (CAD), type 2 diabetes (T2D), Alzheimer disease and prostate cancer. We found that polygenic risk scores (PRSs) improved prediction over conventional risk factors for all diseases. Gut microbiome scores improved predictive capacity over baseline age for CAD, T2D and prostate cancer. Integrated risk models of PRSs, gut microbiome scores and conventional risk factors achieved the highest predictive performance for all diseases studied compared with models based on conventional risk factors alone. The present study demonstrates that integrated PRSs and gut metagenomic risk models improve the predictive value over conventional risk factors for common chronic diseases.

Clinical genetics in breast cancer

As genetic testing becomes increasingly more accessible and more applicable with a broader range of clinical implications, it may also become more challenging for breast cancer providers to remain up-to-date. This review outlines some of the current clinical guidelines and recent literature surrounding germline genetic testing, as well as genomic testing, in the screening, prevention, diagnosis, and treatment of breast cancer, while identifying potential areas of further research.

Clinical Application of Polygenic Risk Score in IgA Nephropathy

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

Supplementary Information

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

Immunological profile of lactylation-related genes in Crohn's disease: a comprehensive analysis based on bulk and single-cell RNA sequencing data

BACKGROUND

Crohn's disease (CD) is a disease characterized by intestinal immune dysfunction, often accompanied by metabolic abnormalities. Disturbances in lactate metabolism have been found in the intestine of patients with CD, but studies on the role of lactate and related Lactylation in the pathogenesis of CD are still unknown.

METHODS

We identified the core genes associated with Lactylation by downloading and merging three CD-related datasets (GSE16879, GSE75214, and GSE112366) from the GEO database, and analyzed the functions associated with the hub genes and the correlation between their expression levels and immune infiltration through comprehensive analysis. We explored the Lactylation levels of different immune cells using single-cell data and further analyzed the differences in Lactylation levels between inflammatory and non-inflammatory sites.

RESULTS

We identified six Lactylation-related hub genes that are highly associated with CD. Further analysis revealed that these six hub genes were highly correlated with the level of immune cell infiltration. To further clarify the effect of Lactylation on immune cells, we analyzed single-cell sequencing data of immune cells from inflammatory and non-inflammatory sites in CD patients and found that there were significant differences in the levels of Lactylation between different types of immune cells, and that the levels of Lactylation were significantly higher in immune cells from inflammatory sites.

CONCLUSIONS

These results suggest that Lactylation-related genes and their functions are closely associated with changes in inflammatory cells in CD patients.

Ensemble learning for integrative prediction of genetic values with genomic variants

BACKGROUND

Whole genome variants offer sufficient information for genetic prediction of human disease risk, and prediction of animal and plant breeding values. Many sophisticated statistical methods have been developed for enhancing the predictive ability. However, each method has its own advantages and disadvantages, so far, no one method can beat others.

RESULTS

We herein propose an Ensemble Learning method for Prediction of Genetic Values (ELPGV), which assembles predictions from several basic methods such as GBLUP, BayesA, BayesB and BayesCπ, to produce more accurate predictions. We validated ELPGV with a variety of well-known datasets and a serious of simulated datasets. All revealed that ELPGV was able to significantly enhance the predictive ability than any basic methods, for instance, the comparison p-value of ELPGV over basic methods were varied from 4.853E-118 to 9.640E-20 for WTCCC dataset.

CONCLUSIONS

ELPGV is able to integrate the merit of each method together to produce significantly higher predictive ability than any basic methods and it is simple to implement, fast to run, without using genotype data. is promising for wide application in genetic predictions.

The Impact of microRNA SNPS on Breast Cancer: Potential Biomarkers for Disease Detection

Breast cancer is considered a significant health concern worldwide, with genetic predisposition playing a critical role in its etiology. Single nucleotide polymorphisms (SNPs), particularly those within the 3' untranslated regions (3'UTRs) of target genes, are emerging as key factors in breast cancer susceptibility. Specifically, miRNAs have been recognized as possible novel approach for biomarkers discovery for both prognosis and diagnosis due to their direct association with cancer progression. Regional disparities in breast cancer incidence underscore the need for precise interventions, considering socio-cultural and economic factors. This review explores into the differential effects of SNP-miRNA interactions on breast cancer risk, emphasizing both risk-enhancing and protective associations across diverse populations. Furthermore, it explores the clinical implications of these findings, highlighting the potential of personalized approaches in breast cancer management. Additionally, it reviews the evolving therapeutic prospect of microRNAs (miRNAs), extending beyond cancer therapeutics to encompass various diseases, indicative of their versatility as therapeutic agents.

Breast and bowel cancers diagnosed in people 'too young to have cancer': A blueprint for research using family and twin studies

Young breast and bowel cancers (e.g., those diagnosed before age 40 or 50 years) have far greater morbidity and mortality in terms of years of life lost, and are increasing in incidence, but have been less studied. For breast and bowel cancers, the familial relative risks, and therefore the familial variances in age-specific log(incidence), are much greater at younger ages, but little of these familial variances has been explained. Studies of families and twins can address questions not easily answered by studies of unrelated individuals alone. We describe existing and emerging family and twin data that can provide special opportunities for discovery. We present designs and statistical analyses, including novel ideas such as the VALID (Variance in Age-specific Log Incidence Decomposition) model for causes of variation in risk, the DEPTH (DEPendency of association on the number of Top Hits) and other approaches to analyse genome-wide association study data, and the within-pair, ICE FALCON (Inference about Causation from Examining FAmiliaL CONfounding) and ICE CRISTAL (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLysis) approaches to causation and familial confounding. Example applications to breast and colorectal cancer are presented. Motivated by the availability of the resources of the Breast and Colon Cancer Family Registries, we also present some ideas for future studies that could be applied to, and compared with, cancers diagnosed at older ages and address the challenges posed by young breast and bowel cancers.

Polygenic risk score-based prediction of breast cancer risk in Taiwanese women with dense breast using a retrospective cohort study

Mammographic screening has contributed to a significant reduction in breast cancer mortality. Several studies have highlighted the correlation between breast density, as detected through mammography, and a higher likelihood of developing breast cancer. A polygenic risk score (PRS) is a numerical score that is calculated based on an individual's genetic information. This study aims to explore the potential roles of PRS as candidate markers for breast cancer development and investigate the genetic profiles associated with clinical characteristics in Asian females with dense breasts. This is a retrospective cohort study integrated breast cancer screening, population genotyping, and cancer registry database. The PRSs of the study cohort were estimated using genotyping data of 77 single nucleotide polymorphisms based on the PGS000001 Catalog. A subgroup analysis was conducted for females without breast symptoms. Breast cancer patients constituted a higher proportion of individuals in PRS Q4 (37.8% vs. 24.8% in controls). Among dense breast patients with no symptoms, the high PRS group (Q4) consistently showed a significantly elevated breast cancer risk compared to the low PRS group (Q1-Q3) in both univariate (OR = 2.25, 95% CI 1.43-3.50, P < 0.001) and multivariate analyses (OR: 2.23; 95% CI 1.41-3.48, P < 0.001). The study was extended to predict breast cancer risk using common low-penetrance risk variants in a PRS model, which could be integrated into personalized screening strategies for Taiwanese females with dense breasts without prominent symptoms.

ProteoMixture: A cell type deconvolution tool for bulk tissue proteomic data

Numerous multi-omic investigations of cancer tissue have documented varying and poor pairwise transcript:protein quantitative correlations, and most deconvolution tools aiming to predict cell type proportions (cell admixture) have been developed and credentialed using transcript-level data alone. To estimate cell admixture using protein abundance data, we analyzed proteome and transcriptome data generated from contrived admixtures of tumor, stroma, and immune cell models or those selectively harvested from the tissue microenvironment by laser microdissection from high grade serous ovarian cancer (HGSOC) tumors. Co-quantified transcripts and proteins performed similarly to estimate stroma and immune cell admixture (r ≥ 0.63) in two commonly used deconvolution algorithms, ESTIMATE or ConsensusTME. We further developed and optimized protein-based signatures estimating cell admixture proportions and benchmarked these using bulk tumor proteomic data from over 150 patients with HGSOC. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomic data are available at https://lmdomics.org/ProteoMixture/.

Genomes in clinical care

In the era of precision medicine, genome sequencing (GS) has become more affordable and the importance of genomics and multi-omics in clinical care is increasingly being recognized. However, how to scale and effectively implement GS on an institutional level remains a challenge for many. Here, we present Genome First and Ge-Med, two clinical implementation studies focused on identifying the key pillars and processes that are required to make routine GS and predictive genomics a reality in the clinical setting. We describe our experience and lessons learned for a variety of topics including test logistics, patient care processes, data reporting, and infrastructure. Our model of providing clinical care and comprehensive genomic analysis from a single source may be used by other centers with a similar structure to facilitate the implementation of omics-based personalized health concepts in medicine.

Causation and familial confounding as explanations for the associations of polygenic risk scores with breast cancer: Evidence from innovative ICE FALCON and ICE CRISTAL analyses

A polygenic risk score (PRS) combines the associations of multiple genetic variants that could be due to direct causal effects, indirect genetic effects, or other sources of familial confounding. We have developed new approaches to assess evidence for and against causation by using family data for pairs of relatives (Inference about Causation from Examination of FAmiliaL CONfounding [ICE FALCON]) or measures of family history (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLyses [ICE CRISTAL]). Inference is made from the changes in regression coefficients of relatives' PRSs or PRS and family history before and after adjusting for each other. We applied these approaches to two breast cancer PRSs and multiple studies and found that (a) for breast cancer diagnosed at a young age, for example, <50 years, there was no evidence that the PRSs were causal, while (b) for breast cancer diagnosed at later ages, there was consistent evidence for causation explaining increasing amounts of the PRS-disease association. The genetic variants in the PRS might be in linkage disequilibrium with truly causal variants and not causal themselves. These PRSs cause minimal heritability of breast cancer at younger ages. There is also evidence for nongenetic factors shared by first-degree relatives that explain breast cancer familial aggregation. Familial associations are not necessarily due to genes, and genetic associations are not necessarily causal.

The clinical relevance of a polygenic risk score for type 2 diabetes mellitus in the Korean population

The clinical utility of a type 2 diabetes mellitus (T2DM) polygenic risk score (PRS) in the East Asian population remains underexplored. We aimed to examine the potential prognostic value of a T2DM PRS and assess its viability as a clinical instrument. We first established a T2DM PRS for 5490 Korean individuals using East Asian Biobank data (269,487 samples). Subsequently, we assessed the predictive capability of this T2DM PRS in a prospective longitudinal study with baseline data and data from seven additional follow-ups. Our analysis showed that the T2DM PRS could predict the transition of glucose tolerance stages from normal glucose tolerance to prediabetes and from prediabetes to T2DM. Moreover, T2DM patients in the top-decile PRS group were more likely to be treated with insulin (hazard ratio = 1.69, p value = 2.31E-02) than were those in the remaining PRS groups. T2DM PRS values were significantly high in the severe diabetes subgroup, characterized by insulin resistance and β -cell dysfunction (p value = 0.0012). The prediction models with the T2DM PRS had significantly greater Harrel's C-indices than did corresponding models without it. By utilizing prospective longitudinal study data and extensive clinical risk factor information, our analysis provides valuable insights into the multifaceted clinical utility of the T2DM PRS.

Development and evaluation of a polygenic risk score for lung cancer in never-smoking women: A large-scale prospective Chinese cohort study

The proportion of lung cancer in never smokers is rising, especially among Asian women, but there is no effective early detection tool. Here, we developed a polygenic risk score (PRS), which may help to identify the population with higher risk of lung cancer in never-smoking women. We first performed a large GWAS meta-analysis (8595 cases and 8275 controls) to systematically identify the susceptibility loci for lung cancer in never-smoking Asian women and then generated a PRS using GWAS datasets. Furthermore, we evaluated the utility and effectiveness of PRS in an independent Chinese prospective cohort comprising 55 266 individuals. The GWAS meta-analysis identified eight known loci and a novel locus (5q11.2) at the genome-wide statistical significance level of P < 5 × 10-8 . Based on the summary statistics of GWAS, we derived a polygenic risk score including 21 variants (PRS-21) for lung cancer in never-smoking women. Furthermore, PRS-21 had a hazard ratio (HR) per SD of 1.29 (95% CI = 1.18-1.41) in the prospective cohort. Compared with participants who had a low genetic risk, those with an intermediate (HR = 1.32, 95% CI: 1.00-1.72) and high (HR = 2.09, 95% CI: 1.56-2.80) genetic risk had a significantly higher risk of incident lung cancer. The addition of PRS-21 to the conventional risk model yielded a modest significant improvement in AUC (0.697 to 0.711) and net reclassification improvement (24.2%). The GWAS-derived PRS-21 significantly improves the risk stratification and prediction accuracy for incident lung cancer in never-smoking Asian women, demonstrating the potential for identification of high-risk individuals and early screening.

The impact of circulating protein levels identified by affinity proteomics on short-term, overall breast cancer risk

OBJECTIVE

Current breast cancer risk prediction scores and algorithms can potentially be further improved by including molecular markers. To this end, we studied the association of circulating plasma proteins using Proximity Extension Assay (PEA) with incident breast cancer risk.

SUBJECTS

In this study, we included 1577 women participating in the prospective KARMA mammographic screening cohort.

RESULTS

In a targeted panel of 164 proteins, we found 8 candidates nominally significantly associated with short-term breast cancer risk (P < 0.05). Similarly, in an exploratory panel consisting of 2204 proteins, 115 were found nominally significantly associated (P < 0.05). However, none of the identified protein levels remained significant after adjustment for multiple testing. This lack of statistically significant findings was not due to limited power, but attributable to the small effect sizes observed even for nominally significant proteins. Similarly, adding plasma protein levels to established risk factors did not improve breast cancer risk prediction accuracy.

CONCLUSIONS

Our results indicate that the levels of the studied plasma proteins captured by the PEA method are unlikely to offer additional benefits for risk prediction of short-term overall breast cancer risk but could provide interesting insights into the biological basis of breast cancer in the future.

Polygenic risk scores, radiation treatment exposures and subsequent cancer risk in childhood cancer survivors

Survivors of childhood cancer are at increased risk for subsequent cancers attributable to the late effects of radiotherapy and other treatment exposures; thus, further understanding of the impact of genetic predisposition on risk is needed. Combining genotype data for 11,220 5-year survivors from the Childhood Cancer Survivor Study and the St Jude Lifetime Cohort, we found that cancer-specific polygenic risk scores (PRSs) derived from general population, genome-wide association study, cancer loci identified survivors of European ancestry at increased risk of subsequent basal cell carcinoma (odds ratio per s.d. of the PRS: OR = 1.37, 95% confidence interval (CI) = 1.29-1.46), female breast cancer (OR = 1.42, 95% CI = 1.27-1.58), thyroid cancer (OR = 1.48, 95% CI = 1.31-1.67), squamous cell carcinoma (OR = 1.20, 95% CI = 1.00-1.44) and melanoma (OR = 1.60, 95% CI = 1.31-1.96); however, the association for colorectal cancer was not significant (OR = 1.19, 95% CI = 0.94-1.52). An investigation of joint associations between PRSs and radiotherapy found more than additive increased risks of basal cell carcinoma, and breast and thyroid cancers. For survivors with radiotherapy exposure, the cumulative incidence of subsequent cancer by age 50 years was increased for those with high versus low PRS. These findings suggest a degree of shared genetic etiology for these malignancy types in the general population and survivors, which remains evident in the context of strong radiotherapy-related risk.

[News in breast oncology genetics for female and male population]

OBJECTIVES

Breast oncology genetics emerged almost 30 years ago with the discovery of the BRCA1 and BRCA2 genes. The evolution of analytical practices has progressively allowed access to tests whose results now have a considerable impact on the management of both female and male breast cancers. The Sénologie commission of the Collège national des gynécologues et obstétriciens français (CNGOF) asked five specialists in breast surgery, oncology and oncological genetics to draw up a summary of the oncogenetic testing criteria used and the clinical implications for the female and male population of the test results, with or without an identified causal variant. In the case of proven genetic risk, surveillance, risk-reduction strategies, and the specificities of surgical and medical management (with PARP inhibitors in particular) were updated.

METHODS

This summary was based on national and international guidelines on the monitoring and therapeutic management of genetic risk, and a recent review of the literature covering the last five years.

RESULTS

Despite successive technical developments, the probability of identifying a causal variant in a situation suggestive of a predisposition to breast and ovarian cancer remains around 10% in France. The risk of breast cancer in women with a causal variant of the BRCA1, BRCA2, PALB2, TP53, CDH1 and PTEN genes is estimated at between 35% and 85% at age 70. The presence of a causal variant in one of these genes is the subject of different recommendations for men and women, concerning both surveillance, the age of onset and imaging modalities of which vary according to the genes involved, and risk-reduction surgery, which is possible for women as soon as their risk level exceeds 30% and remains exceptionally indicated for men. In the case of breast cancer, PARP inhibitors are a promising new class of treatment for BRCA germline mutations.

CONCLUSION

A discipline resolutely focused on understanding molecular mechanisms, screening and preventive medicine/surgery, oncology genetics is currently also involved in new medical/surgical approaches, the long-term benefits/risks of which will need to be monitored.

The interaction of diet, alcohol, genetic predisposition, and the risk of breast cancer: a cohort study from the UK Biobank

BACKGROUND

Dietary factors have consistently been associated with breast cancer risk. However, there is limited evidence regarding their associations in women with different genetic susceptibility to breast cancer, and their interaction with alcohol consumption is also not well understood.

METHODS

We analyzed data from 261,853 female participants in the UK Biobank. Multivariable adjusted Cox proportional hazards models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for associations between dietary factors and breast cancer risk. Additionally, we assessed the interaction of dietary factors with alcohol consumption and polygenic risk score (PRS) for breast cancer.

RESULTS

A moderately higher risk of breast cancer was associated with the consumption of processed meat (HR = 1.10, 95% CI 1.03, 1.18, p-trend = 0.016). Higher intake of raw vegetables and fresh fruits, and adherence to a healthy dietary pattern were inversely associated with breast cancer risk [HR (95% CI):0.93 (0.88-0.99), 0.87 (0.81, 0.93) and 0.93 (0.86-1.00), p for trend: 0.025, < 0.001, and 0.041, respectively]. Furthermore, a borderline significant interaction was found between alcohol consumption and the intake of processed meat with regard to breast cancer risk (P for interaction = 0.065). No multiplicative interaction was observed between dietary factors and PRS.

CONCLUSION

Processed meat was positively associated with breast cancer risk, and vegetables, fruits, and healthy dietary patterns were negatively associated with breast cancer risk. We found no strong interaction of dietary factors with alcohol consumption and genetic predisposition for risk of breast cancer.

Polygenic risk score comparator (PRScomp): Test population vs. worldwide populations

BACKGROUND

Polygenic risk scores (PRS) are a powerful tool for predicting an individual's genetic risk for complex diseases.

METHODS

We have developed a web service (PRScomp) as a user-friendly tool to evaluate PRS of the user own population and compare it with worldwide populations.

RESULTS

A disease/trait database has been constructed from GWAS Catalog summary statistics. Genotype data of test population is uploaded and merged with the reference dataset (1000 Genome Project and Human Genome Diversity Project) to obtain a file including the common SNPs. The user can select a disease/trait from the database and a curated set of risk markers is used to calculate summatory PRS. Distribution of z-scored PRS values is presented in publication-ready plots and text files that can be downloaded.

DISCUSSION

PRScomp can be useful for public health decision-making by identifying population-specific genetic risk factors and informing the development of targeted interventions for at-risk populations.

Differences in polygenic score distributions in European ancestry populations: implications for breast cancer risk prediction

The 313-variant polygenic risk score (PRS313) provides a promising tool for breast cancer risk prediction. However, evaluation of the PRS313 across different European populations which could influence risk estimation has not been performed. Here, we explored the distribution of PRS313 across European populations using genotype data from 94,072 females without breast cancer, of European-ancestry from 21 countries participating in the Breast Cancer Association Consortium (BCAC) and 225,105 female participants from the UK Biobank. The mean PRS313 differed markedly across European countries, being highest in south-eastern Europe and lowest in north-western Europe. Using the overall European PRS313 distribution to categorise individuals leads to overestimation and underestimation of risk in some individuals from south-eastern and north-western countries, respectively. Adjustment for principal components explained most of the observed heterogeneity in mean PRS. Country-specific PRS distributions may be used to calibrate risk categories in individuals from different countries.