Polygenes, risk prediction, and targeted prevention of breast cancer

The risk stratification and predictive performance of a new combined polygenic risk score for hepatocellular carcinoma

BACKGROUND

Recent genome-wide association studies (GWASs) in liver diseases have generated some polygenic risk scores (PRSs), but their predictive effectiveness on hepatocellular carcinoma (HCC) risk assessment remains unclear.

METHODS

Here, we constructed a novel combined polygenic risk score and evaluated its increment to the well-established risk model. We used 15 HCC-associated genetic loci from two PRSs and FinnGen GWAS data to calculate a PRS-combined score and to fit the related PRS model in the UK Biobank cohort (N = 436,162). The PRS-combined score was further assessed for risk stratification for HCC integrating with the recommended clinical risk scores.

RESULTS

The PRS-combined model achieved a better AUC (0.657) than that of PRS-HFC (0.637) and PRS-cirrhosis (0.645). The top 20% of the PRS-combined distribution had a 3.25 increased risk of HCC vs. the middle decile (45-55%). At the population level, the addition of PRS-combined to the CLivD score significantly increased the C-statistic (from 0.716 to 0.746) and provided a remarkable improvement in reclassification (NRI = 0.088) at the 10-year risk threshold of 0.2%. In clinic, additional assessment of PRS-combined would reclassify 34,647 intermediate-risk participants as high genetic risk, corresponding to an increase of 63.92% (62/97) of the HCC events classified at high risk using the Fibrosis-4 alone.

CONCLUSIONS

The PRS may enhance HCC risk prediction effectiveness in the general population and refine risk stratification of the conventional clinical indicator.

Menin signaling and therapeutic targeting in breast cancer

To date, mounting evidence have shown that patients with multiple endocrine neoplasia type 1 (MEN1) may face an increased risk for breast carcinogenesis. The product of the MEN1 gene, menin, was also indicated to be an important regulator in breast cancer signaling network. Menin directly interacts with MLL, EZH2, JunD, NF-κB, PPARγ, VDR, Smad3, β-catenin and ERα to modulate gene transcriptions leading to cell proliferation inhibition. Moreover, interaction of menin-FANCD2 contributes to the enhancement of BRCA1-mediated DNA repair mechanism. Ectopic expression of menin causes Bax-, Bak- and Caspase-8-dependent apoptosis. However, despite numbers of menin inhibitors were exploited in other cancers, data on the usage of menin inhibitors in breast cancer treatment remain limited. In this review, we focused on the menin associated signaling pathways and gene transcription regulations, with the aim of elucidating its molecular mechanisms and of guiding the development of novel menin targeted drugs in breast cancer therapy.

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

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

The Role of Amino Acids in the Diagnosis, Risk Assessment, and Treatment of Breast Cancer: A Review

This review summarizes the role of amino acids in the diagnosis, risk assessment, imaging, and treatment of breast cancer. It was shown that the content of individual amino acids changes in breast cancer by an average of 10-15% compared with healthy controls. For some amino acids (Thr, Arg, Met, and Ser), an increase in concentration is more often observed in breast cancer, and for others, a decrease is observed (Asp, Pro, Trp, and His). The accuracy of diagnostics using individual amino acids is low and increases when a number of amino acids are combined with each other or with other metabolites. Gln/Glu, Asp, Arg, Leu/Ile, Lys, and Orn have the greatest significance in assessing the risk of breast cancer. The variability in the amino acid composition of biological fluids was shown to depend on the breast cancer phenotype, as well as the age, race, and menopausal status of patients. In general, the analysis of changes in the amino acid metabolism in breast cancer is a promising strategy not only for diagnosis, but also for developing new therapeutic agents, monitoring the treatment process, correcting complications after treatment, and evaluating survival rates.

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

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

Testing for Inherited Susceptibility to Breast Cancer

When BRCA1 and BRCA2 were first identified, the initial models for delivering testing were shaped by concepts of genetic exceptionalism and a lack of data regarding therapeutic implications and the effectiveness of risk reduction. Since then, interventions have been effective, and treatment implications have become clear. The sensitivity of guideline-based testing is incomplete, leading to calls for universal testing. Completely universal testing, however, is not necessary to identify the great majority of BRCA1 or BRCA2 variants. Broader testing (both in terms of eligibility and genes tested) will identify more variants, particularly in moderate penetrance genes, but the clinical implications remain less clear for these variants.

Perceptions of Breast Cancer Risks Among Women Receiving Mammograph Screening

Importance

Breast density is an independent risk factor for breast cancer. Despite the proliferation of mandated written notifications about breast density following mammography, there is little understanding of how women perceive the relative breast cancer risk associated with breast density.

Objective

To assess women's perception of breast density compared with other breast cancer risks and explore their understanding of risk reduction.

Design, Setting, and Participants

This mixed-methods qualitative study used telephone surveys and semistructured interviews to investigate perceptions about breast cancer risk among a nationally representative, population-based sample of women. Eligible study participants were aged 40 to 76 years, reported having recently undergone mammography, had no history of prior breast cancer, and had heard of breast density. Survey participants who had been informed of their personal breast density were invited for a qualitative interview. Survey administration spanned July 1, 2019, to April 30, 2020, with 2306 women completing the survey. Qualitative interviews were conducted from February 1 to May 30, 2020.

Main Outcomes and Measures

Respondents compared the breast cancer risk associated with breast density with 5 other risk factors. Participants qualitatively described what they thought contributed to breast cancer risk and ways to reduce risk.

Results

Of the 2306 women who completed the survey, 1858 (166 [9%] Asian, 503 [27%] Black, 268 [14%] Hispanic, 792 [43%] White, and 128 [7%] other race or ethnicity; 358 [19%] aged 40-49 years, 906 [49%] aged 50-64 years, and 594 [32%] aged ≥65 years) completed the revised risk perception questions and were included in the analysis. Half of respondents thought breast density to be a greater risk than not having children (957 [52%]), having more than 1 alcoholic drink per day (975 [53%]), or having a prior breast biopsy (867 [48%]). Most respondents felt breast density was a lesser risk than having a first-degree relative with breast cancer (1706 [93%]) or being overweight or obese (1188 [65%]). Of the 61 women who were interviewed, 6 (10%) described breast density as contributing to breast cancer risk, and 43 (70%) emphasized family history as a breast cancer risk factor. Of the interviewed women, 17 (28%) stated they did not know whether it was possible to reduce their breast cancer risk.

Conclusions and Relevance

In this qualitative study of women of breast cancer screening age, family history was perceived as the primary breast cancer risk factor. Most interviewees did not identify breast density as a risk factor and did not feel confident about actions to mitigate breast cancer risk. Comprehensive education about breast cancer risks and prevention strategies is needed.

Recalibrating Risk Prediction Models by Synthesizing Data Sources: Adapting the Lung Cancer PLCO Model for Taiwan

BACKGROUND

Methods synthesizing multiple data sources without prospective datasets have been proposed for absolute risk model development. This study proposed methods for adapting risk models for another population without prospective cohorts, which would help alleviate the health disparities caused by advances in absolute risk models. To exemplify, we adapted the lung cancer risk model PLCOM2012, well studied in the west, for Taiwan.

METHODS

Using Taiwanese multiple data sources, we formed an age-matched case-control study of ever-smokers (AMCCSE), estimated the number of ever-smoking lung cancer patients in 2011-2016 (NESLP2011), and synthesized a dataset resembling the population of cancer-free ever-smokers in 2010 regarding the PLCOM2012 risk factors (SPES2010). The AMCCSE was used to estimate the overall calibration slope, and the requirement that NESLP2011 equals the estimated total risk of individuals in SPES2010 was used to handle the calibration-in-the-large problem.

RESULTS

The adapted model PLCOT-1 (PLCOT-2) had an AUC of 0.78 (0.75). They had high performance in calibration and clinical usefulness on subgroups of SPES2010 defined by age and smoking experience. Selecting the same number of individuals for low-dose computed tomography screening using PLCOT-1 (PLCOT-2) would have identified approximately 6% (8%) more lung cancers than the US Preventive Services Task Forces 2021 criteria. Smokers having 40+ pack-years had an average PLCOT-1 (PLCOT-2) risk of 3.8% (2.6%).

CONCLUSIONS

The adapted PLCOT models had high predictive performance.

IMPACT

The PLCOT models could be used to design lung cancer screening programs in Taiwan. The methods could be applicable to other cancer models.

Family history of breast cancer, mammographic breast density and breast cancer risk: Findings from a cohort study of Korean women

BACKGROUND

This study investigated whether the association between family history of breast cancer in first-degree relatives and breast cancer risk varies by breast density.

METHODS

Women aged 40 years and older who underwent screening between 2009 and 2010 were followed up until 2020. Family history was assessed using a self-reported questionnaire. Using Breast Imaging Reporting and Data System (BI-RADS), breast density was categorized into dense breast (heterogeneously or extremely dense) and non-dense breast (almost entirely fatty or scattered areas of fibro-glandular). Cox regression model was used to assess the association between family history and breast cancer risk.

RESULTS

Of the 4,835,507 women, 79,153 (1.6%) reported having a family history of breast cancer and 77,238 women developed breast cancer. Family history led to an increase in the 5-year cumulative incidence in women with dense- and non-dense breasts. Results from the regression model with and without adjustment for breast density yielded similar HRs in all age groups, suggesting that breast density did not modify the association between family history and breast cancer. After adjusting for breast density and other factors, family history of breast cancer was associated with an increased risk of breast cancer in all three age groups (age 40-49 years: aHR 1.96, 95% confidence interval [CI] 1.85-2.08; age 50-64 years: aHR 1.70, 95% CI 1.58-1.82, and age ≥65 years: aHR 1.95, 95% CI 1.78-2.14).

CONCLUSION

Family history of breast cancer and breast density are independently associated with breast cancer. Both factors should be carefully considered in future risk prediction models of breast cancer.

Polygenic risk scores for prediction of breast cancer risk in women of African ancestry: a cross-ancestry approach

Polygenic risk scores (PRSs) are useful for predicting breast cancer risk, but the prediction accuracy of existing PRSs in women of African ancestry (AA) remains relatively low. We aim to develop optimal PRSs for the prediction of overall and estrogen receptor (ER) subtype-specific breast cancer risk in AA women. The AA dataset comprised 9235 cases and 10 184 controls from four genome-wide association study (GWAS) consortia and a GWAS study in Ghana. We randomly divided samples into training and validation sets. We built PRSs using individual-level AA data by a forward stepwise logistic regression and then developed joint PRSs that combined (1) the PRSs built in the AA training dataset and (2) a 313-variant PRS previously developed in women of European ancestry. PRSs were evaluated in the AA validation set. For overall breast cancer, the odds ratio per standard deviation of the joint PRS in the validation set was 1.34 [95% confidence interval (CI): 1.27-1.42] with the area under receiver operating characteristic curve (AUC) of 0.581. Compared with women with average risk (40th-60th PRS percentile), women in the top decile of the PRS had a 1.98-fold increased risk (95% CI: 1.63-2.39). For PRSs of ER-positive and ER-negative breast cancer, the AUCs were 0.608 and 0.576, respectively. Compared with existing methods, the proposed joint PRSs can improve prediction of breast cancer risk in AA women.

Genetic risk score enhances the risk prediction of severe obesity in adult survivors of childhood cancer

Adult survivors of childhood cancer have high rates of obesity, which, in combination with the cardiotoxic effects of specific cancer therapies, places them at high risk for cardiovascular morbidity. Here we show the contribution of genetic risk scores (GRSs) to increase prediction of those survivors of childhood cancer who are at risk for severe obesity (body mass index ≥40 kg m-2) as an adult. Among 2,548 individuals of European ancestry from the St. Jude Lifetime Cohort Study who were 5-year survivors of childhood cancer, the GRS was found to be associated with 53-fold-higher odds of severe obesity. Addition of GRSs to risk prediction models based on cancer treatment exposures and lifestyle factors significantly improved model prediction (area under the curve increased from 0.68 to 0.75, resulting in the identification of 4.3-times more high-risk survivors), which was independently validated in 6,064 individuals from the Childhood Cancer Survivor Study. Genetic predictors improve identification of patients who could benefit from heightened surveillance and interventions to mitigate the risk of severe obesity and associated cardio-metabolic complications.

B-Cell NHL Subtype Risk Associated with Autoimmune Conditions and PRS

BACKGROUND

A previous International Lymphoma Epidemiology (InterLymph) Consortium evaluation of joint associations between five immune gene variants and autoimmune conditions reported interactions between B-cell response-mediated autoimmune conditions and the rs1800629 genotype on risk of B-cell non-Hodgkin lymphoma (NHL) subtypes. Here, we extend that evaluation using NHL subtype-specific polygenic risk scores (PRS) constructed from loci identified in genome-wide association studies of three common B-cell NHL subtypes.

METHODS

In a pooled analysis of NHL cases and controls of Caucasian descent from 14 participating InterLymph studies, we evaluated joint associations between B-cell-mediated autoimmune conditions and tertile (T) of PRS for risk of diffuse large B-cell lymphoma (DLBCL; n = 1,914), follicular lymphoma (n = 1,733), and marginal zone lymphoma (MZL; n = 407), using unconditional logistic regression.

RESULTS

We demonstrated a positive association of DLBCL PRS with DLBCL risk [T2 vs. T1: OR = 1.24; 95% confidence interval (CI), 1.08-1.43; T3 vs. T1: OR = 1.81; 95% CI, 1.59-2.07; P-trend (Ptrend) < 0.0001]. DLBCL risk also increased with increasing PRS tertile among those with an autoimmune condition, being highest for those with a B-cell-mediated autoimmune condition and a T3 PRS [OR = 6.46 vs. no autoimmune condition and a T1 PRS, Ptrend < 0.0001, P-interaction (Pinteraction) = 0.49]. Follicular lymphoma and MZL risk demonstrated no evidence of joint associations or significant Pinteraction.

CONCLUSIONS

Our results suggest that PRS constructed from currently known subtype-specific loci may not necessarily capture biological pathways shared with autoimmune conditions.

IMPACT

Targeted genetic (PRS) screening among population subsets with autoimmune conditions may offer opportunities for identifying those at highest risk for (and early detection from) DLBCL.

Embedding the Community and Individuals in Disease Prevention

The primary prevention of non-communicable diseases is one of the most challenging and exciting aspects of medicine and primary care this century. For cancer, it is an urgent matter in light of the increasing burden of the disease among younger people and the higher frequency of more aggressive forms of the disease for all ages. Most chronic disorders result from the influence of the environment on the expression of genes within an individual. The environment at-large encompasses lifestyle (including nutrition), and chemical/physical and social exposures. In cancer, the interaction between the (epi)genetic makeup of an individual and a multiplicity of environmental risk and protecting factors is considered key to disease onset. Thus, like for precision therapy developed for patients, personalized or precision prevention is envisioned for individuals at risk. Prevention means identifying people at higher risk and intervening to reduce the risk. It requires biological markers of risk and non-aggressive preventive actions for the individual, but it also involves acting on the environment and the community. Social scientists are considering micro (individual/family), meso (community), and macro (country population) levels of care to illustrate that problems and solutions exist on different scales. Ideally, the design of interventions in prevention should integrate all these levels. In this perspective article, using the example of breast cancer, we are discussing challenges and possible solutions for a multidisciplinary community of scientists, primary health care practitioners and citizens to develop a holistic approach of primary prevention, keeping in mind equitable access to care.

Genetic variants associated with sepsis

BACKGROUND

The variable presentations and different phenotypes of sepsis suggest that risk of sepsis comes from many genes each having a small effect. The cumulative effect can be used to create individual risk profile. The purpose of this study was to create a polygenic risk score and determine the genetic variants associated with sepsis.

METHODS

We sequenced ~14 million single nucleotide polymorphisms with a minimac imputation quality R2>0.3 and minor allele frequency >10-6 in patients with Sepsis-2 or Sepsis-3. Genome-wide association was performed using Firth bias-corrected logistic regression. Semi-parsimonious logistic regression was used to create polygenic risk scores and reduced regression to determine the genetic variants independently associated with sepsis.

FINDINGS

2261 patients had sepsis and 13,068 control patients did not. The polygenic risk scores had good discrimination: c-statistic = 0.752 ± 0.005 for Sepsis-2 and 0.752 ± 0.007 for Sepsis-3. We found 772 genetic variants associated with Sepsis-2 and 442 with Sepsis-3, p<0.01. After multivariate adjustment, 100 variants on 85 genes were associated with Sepsis-2 and 69 variants in 54 genes with Sepsis-3. Twenty-five variants were present in both the Sepsis-2 and Sepsis-3 groups out of 32 genes that were present in both groups. The other 7 genes had different variants present. Most variants had small effect sizes.

CONCLUSIONS

Sepsis-2 and Sepsis-3 have both separate and shared genetic variants. Most genetic variants have small effects sizes, but cumulatively, the polygenic risk scores have good discrimination.

Association of VEGF haplotypes with breast cancer risk in North-West Indians

BACKGROUND

Angiogenesis is a complex and coordinated process regulated by different growth factors and is one of the hallmark features of cancer. VEGF is one of the most important endothelial cell mitogen and has a critical role in normal physiological and tumor angiogenesis. The objective of this study was to investigate the potential association of haplotypes of six VEGF polymorphisms with breast cancer risk in North-West Indians.

METHODS

Samples of 250 breast cancer patients and 250 age and sex matched controls were genotyped for VEGF -2578C/A, -2549I/D, -460T/C, +405C/G, -7C/T and +936C/T polymorphisms. Haplotypes were generated to determine the better contribution of VEGF polymorphisms to breast cancer risk.

RESULTS

Haplotypes CDTCCC (OR = 0.56, 95%CI, 0.38-0.81; p = 0.003) and CDTGCC (OR = 0.63, 95%CI, 0.44-0.92; p = 0.018) of VEGF -2578C/A, -2549I/D, -460T/C, +405C/G, -7C/T and +936C/T polymorphisms were significantly associated with decreased risk of breast cancer. CDTCCC haplotype was also significantly associated with reduced risk of breast cancer in pre and post menopausal as well as both obese and non obese patients. Haplotype CDTGCC was marginally associated (p = 0.07) with reduced risk of breast cancer in non-obese patients as compared with non-obese controls where as haplotype AICGTC was marginally associated (p = 0.09) with reduced risk of breast cancer in obese patients when compared with non-obese patients. The CDTGCC haplotype was significantly associated with increased risk of breast cancer in premenopausal obese patients (OR = 1.98, 95%CI, 1.10-3.56; p = 0.02).

CONCLUSIONS

Our data indicated that CDTCCC and CDTGCC haplotypes of VEGF -2578C/A, -2549I/D, -460T/C, +405C/G, -7C/T and +936C/T polymorphisms were significantly associated with breast cancer risk in North-West Indians. Further studies on multiethnic groups with larger sample size are required to confirm our results.

Risks and Function of Breast Cancer Susceptibility Alleles

Family history remains one of the strongest risk factors for breast cancer. It is well established that women with a first-degree relative affected by breast cancer are twice as likely to develop the disease themselves. Twins studies indicate that this is most likely due to shared genetics rather than shared epidemiological/lifestyle risk factors. Linkage and targeted sequencing studies have shown that rare high- and moderate-penetrance germline variants in genes involved in the DNA damage response (DDR) including BRCA1, BRCA2, PALB2, ATM, and TP53 are responsible for a proportion of breast cancer cases. However, breast cancer is a heterogeneous disease, and there is now strong evidence that different risk alleles can predispose to different subtypes of breast cancer. Here, we review the associations between the different genes and subtype-specificity of breast cancer based on the most comprehensive genetic studies published. Genome-wide association studies (GWAS) have also been used to identify an additional hereditary component of breast cancer, and have identified hundreds of common, low-penetrance susceptibility alleles. The combination of these low penetrance risk variants, summed as a polygenic risk score (PRS), can identify individuals across the spectrum of disease risk. However, there remains a substantial bottleneck between the discovery of GWAS-risk variants and their contribution to tumorigenesis mainly because the majority of these variants map to the non-protein coding genome. A range of functional genomic approaches are needed to identify the causal risk variants and target susceptibility genes and establish their underlying role in disease biology. We discuss how the application of these multidisciplinary approaches to understand genetic risk for breast cancer can be used to identify individuals in the population that may benefit from clinical interventions including screening for early detection and prevention, and treatment strategies to reduce breast cancer-related mortalities.

Genetic Influences on Disease Subtypes

Disease classification, or nosology, was historically driven by careful examination of clinical features of patients. As technologies to measure and understand human phenotypes advanced, so too did classifications of disease, and the advent of genetic data has led to a surge in genetic subtyping in the past decades. Although the fundamental process of refining disease definitions and subtypes is shared across diverse fields, each field is driven by its own goals and technological expertise, leading to inconsistent and conflicting definitions of disease subtypes. Here, we review several classical and recent subtypes and subtyping approaches and provide concrete definitions to delineate subtypes. In particular, we focus on subtypes with distinct causal disease biology, which are of primary interest to scientists, and subtypes with pragmatic medical benefits, which are of primary interest to physicians. We propose genetic heterogeneity as a gold standard for establishing biologically distinct subtypes of complex polygenic disease. We focus especially on methods to find and validate genetic subtypes, emphasizing common pitfalls and how to avoid them.

Prediction and clinical utility of a liver cancer risk model in Chinese adults: A prospective cohort study of 0.5 million people

China has made rapid progress in reducing the incidence of HBV infection in the past three decades, along with a rapidly changing lifestyle and aging population. We aimed to develop and validate an up-to-date liver cancer risk prediction model with routinely available predictors and evaluate its applicability for screening guidance. Using data from the China Kadoorie Biobank, we included 486 285 participants in this analysis. Fifteen risk factors were included in the model. Flexible parametric survival models were used to estimate the 10-year absolute risk of liver cancer. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility. A total of 2706 participants occurred liver cancer over the 4 814 320 person-years of follow-up. Excellent discrimination of the model was observed in both development and validation datasets, with c-statistics (95% CI) of 0.80 (0.79-0.81) and 0.80 (0.78-0.82) respectively, as well as excellent calibration of observed and predicted risks. Decision curve analysis revealed that use of the model in selecting participants for screening improved benefit at a threshold of 2% 10-year risk, compared to current guideline of screening all HBsAg carriers. Our model was more sensitive than current guideline for cancer screening (28.17% vs 25.96%). We developed and validated a CKB-PLR (Prediction for Liver cancer Risk Based on the China Kadoorie Biobank Study) model to predict the absolute risk of liver cancer for both HBsAg seropositive and seronegative populations. Application of the model is beneficial for precisely identifying the high-risk groups among the general population.

Association of Family History with the Development of Breast Cancer: A Cohort Study of 129,374 Women in KoGES Data

Background: Breast cancer is the most common cancer among women. The Korean Genome and Epidemiology Study (KoGES) is a large cohort study that is available to the public. Using this large cohort study, we aimed to unravel the relationship between breast cancer development and a family history of breast cancer in Korea. Methods: This cohort study relied on data from the KoGES from 2001 through 2013. A total of 211,725 participants were screened. Of these, 129,374 women were evaluated. They were divided into two groups, including participants with and without breast cancer. A logistic regression model was used to retrospectively analyze the odds ratio of breast cancer history in families of women with and without breast cancer. Results: Of 129,374 women, 981 had breast cancer. The breast cancer group had more mothers and siblings with histories of breast cancer (p < 0.001). A history of breast cancer in the participant’s mother resulted in an odds ratio of 3.12 (1.75–5.59), and a history of breast cancer in the participant’s sibling resulted in an odds ratio of 2.63 (1.85–3.74). There was no interaction between the history of maternal breast cancer and the history of sibling breast cancer. Based on the subgroup analysis, family history was a stronger factor in premenopausal women than in menopausal and postmenopausal women. Conclusions: A family history of breast cancer is a significant risk factor for breast cancer in Korea. Premenopausal women with a maternal history of breast cancer are of particular concern. Intensive screening and risk-reducing strategies should be considered for this vulnerable subpopulation.

Personalized Risk Assessment for Prevention and Early Detection of Breast Cancer: Integration and Implementation (PERSPECTIVE I&I)

Early detection of breast cancer through screening reduces breast cancer mortality. The benefits of screening must also be considered within the context of potential harms (e.g., false positives, overdiagnosis). Furthermore, while breast cancer risk is highly variable within the population, most screening programs use age to determine eligibility. A risk-based approach is expected to improve the benefit-harm ratio of breast cancer screening programs. The PERSPECTIVE I&I (Personalized Risk Assessment for Prevention and Early Detection of Breast Cancer: Integration and Implementation) project seeks to improve personalized risk assessment to allow for a cost-effective, population-based approach to risk-based screening and determine best practices for implementation in Canada. This commentary describes the four inter-related activities that comprise the PERSPECTIVE I&I project. 1: Identification and validation of novel moderate to high-risk susceptibility genes. 2: Improvement, validation, and adaptation of a risk prediction web-tool for the Canadian context. 3: Development and piloting of a socio-ethical framework to support implementation of risk-based breast cancer screening. 4: Economic analysis to optimize the implementation of risk-based screening. Risk-based screening and prevention is expected to benefit all women, empowering them to work with their healthcare provider to make informed decisions about screening and prevention.

Breast Cancer Predisposition Genes and Synthetic Lethality

BRCA1 and BRCA2 are tumor suppressor genes with pivotal roles in the development of breast and ovarian cancers. These genes are essential for DNA double-strand break repair via homologous recombination (HR), which is a virtually error-free DNA repair mechanism. Following BRCA1 or BRCA2 mutations, HR is compromised, forcing cells to adopt alternative error-prone repair pathways that often result in tumorigenesis. Synthetic lethality refers to cell death caused by simultaneous perturbations of two genes while change of any one of them alone is nonlethal. Therefore, synthetic lethality can be instrumental in identifying new therapeutic targets for BRCA1/2 mutations. PARP is an established synthetic lethal partner of the BRCA genes. Its role is imperative in the single-strand break DNA repair system. Recently, Olaparib (a PARP inhibitor) was approved for treatment of BRCA1/2 breast and ovarian cancer as the first successful synthetic lethality-based therapy, showing considerable success in the development of effective targeted cancer therapeutics. Nevertheless, the possibility of drug resistance to targeted cancer therapy based on synthetic lethality necessitates the development of additional therapeutic options. This literature review addresses cancer predisposition genes, including BRCA1, BRCA2, and PALB2, synthetic lethality in the context of DNA repair machinery, as well as available treatment options.

Personalizing Breast Cancer Screening Based on Polygenic Risk and Family History

BACKGROUND

We assessed the clinical utility of a first-degree breast cancer family history and polygenic risk score (PRS) to inform screening decisions among women aged 30-50 years.

METHODS

Two established breast cancer models evaluated digital mammography screening strategies in the 1985 US birth cohort by risk groups defined by family history and PRS based on 313 single nucleotide polymorphisms. Strategies varied in initiation age (30, 35, 40, 45, and 50 years) and interval (annual, hybrid, biennial, triennial). The benefits (breast cancer deaths averted, life-years gained) and harms (false-positive mammograms, overdiagnoses) were compared with those seen with 3 established screening guidelines.

RESULTS

Women with a breast cancer family history who initiated biennial screening at age 40 years (vs 50 years) had a 36% (model range = 29%-40%) increase in life-years gained and 20% (model range = 16%-24%) more breast cancer deaths averted, but 21% (model range = 17%-23%) more overdiagnoses and 63% (model range = 62%-64%) more false positives. Screening tailored to PRS vs biennial screening from 50 to 74 years had smaller positive effects on life-years gained (20%) and breast cancer deaths averted (11%) but also smaller increases in overdiagnoses (10%) and false positives (26%). Combined use of family history and PRS vs biennial screening from 50 to 74 years had the greatest increase in life-years gained (29%) and breast cancer deaths averted (18%).

CONCLUSIONS

Our results suggest that breast cancer family history and PRS could guide screening decisions before age 50 years among women at increased risk for breast cancer but expected increases in overdiagnoses and false positives should be expected.

Combined Associations of a Polygenic Risk Score and Classical Risk Factors With Breast Cancer Risk

We evaluated the joint associations between a new 313-variant PRS (PRS313) and questionnaire-based breast cancer risk factors for women of European ancestry, using 72 284 cases and 80 354 controls from the Breast Cancer Association Consortium. Interactions were evaluated using standard logistic regression and a newly developed case-only method for breast cancer risk overall and by estrogen receptor status. After accounting for multiple testing, we did not find evidence that per-standard deviation PRS313 odds ratio differed across strata defined by individual risk factors. Goodness-of-fit tests did not reject the assumption of a multiplicative model between PRS313 and each risk factor. Variation in projected absolute lifetime risk of breast cancer associated with classical risk factors was greater for women with higher genetic risk (PRS313 and family history) and, on average, 17.5% higher in the highest vs lowest deciles of genetic risk. These findings have implications for risk prevention for women at increased risk of breast cancer.

Introducing a low-risk breast screening pathway into the NHS Breast Screening Programme: Views from healthcare professionals who are delivering risk-stratified screening

OBJECTIVES

Proposals to stratify breast screening by breast cancer risk aim to produce a better balance of benefits to harms. Notably, risk estimation calculated from common risk factors and a polygenic risk score would enable high-risk women to benefit from more frequent screening or preventive medication. This service would also identify low-risk women who experience fewer benefits from attending, as lower grade and in situ cancers may be treated unnecessarily. It may therefore be appropriate for low-risk women to attend screening less. This study aimed to elicit views regarding implementing less frequent screening for low-risk women from healthcare professionals who implement risk-stratified screening.

METHODS

Healthcare professionals involved in the delivery of risk-stratified breast screening were invited to participate in a focus group within the screening setting in which they work or have a telephone interview. Primary care staff were also invited to provide their perspective. Three focus groups and two telephone interviews were conducted with 28 healthcare professionals. To identify patterns across the sample, data were analysed as a single dataset using reflexive thematic analysis.

RESULTS

Analysis yielded three themes: Reservations concerning the introduction of less frequent screening, highlighting healthcare professionals' unease and concerns towards implementing less frequent screening; Considerations for the management of public knowledge, providing views on media impact on public opinion and the potential for a low-risk pathway to cause confusion and raise suspicion regarding implementation motives; and Deliberating service implications and reconfiguration management, where the practicalities of implementation are discussed.

CONCLUSIONS

Healthcare professionals broadly supported less frequent screening but had concerns about implementation. It will be essential to address concerns regarding risk estimate accuracy, healthcare professional confidence, service infrastructure and public communication prior to introducing less frequent screening for low-risk women.

Combination of phenotype and polygenic risk score in breast cancer risk evaluation in the Spanish population: a case -control study

Background

In recent years, the identification of genetic and phenotypic biomarkers of cancer for prevention, early diagnosis and patient stratification has been a main objective of research in the field. Different multivariable models that use biomarkers have been proposed for the evaluation of individual risk of developing breast cancer.

Methods

This is a case control study based on a population-based cohort. We describe and evaluate a multivariable model that incorporates 92 Single-nucleotide polymorphisms (SNPs) (Supplementary Table S1) and five different phenotypic variables and which was employed in a Spanish population of 642 healthy women and 455 breast cancer patients.

Results

Our model allowed us to stratify two groups: high and low risk of developing breast cancer. The 9th decile included 1% of controls vs 9% of cases, with an odds ratio (OR) of 12.9 and a p-value of 3.43E-07. The first decile presented an inverse proportion: 1% of cases and 9% of controls, with an OR of 0.097 and a p-value of 1.86E-08.

Conclusions

These results indicate the capacity of our multivariable model to stratify women according to their risk of developing breast cancer. The major limitation of our analysis is the small cohort size. However, despite the limitations, the results of our analysis provide proof of concept in a poorly studied population, and opens up the possibility of using this method in the routine screening of the Spanish population.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-020-07584-9.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-020-07584-9.

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.

Penalized regression and model selection methods for polygenic scores on summary statistics

Polygenic scores quantify the genetic risk associated with a given phenotype and are widely used to predict the risk of complex diseases. There has been recent interest in developing methods to construct polygenic risk scores using summary statistic data. We propose a method to construct polygenic risk scores via penalized regression using summary statistic data and publicly available reference data. Our method bears similarity to existing method LassoSum, extending their framework to the Truncated Lasso Penalty (TLP) and the elastic net. We show via simulation and real data application that the TLP improves predictive accuracy as compared to the LASSO while imposing additional sparsity where appropriate. To facilitate model selection in the absence of validation data, we propose methods for estimating model fitting criteria AIC and BIC. These methods approximate the AIC and BIC in the case where we have a polygenic risk score estimated on summary statistic data and no validation data. Additionally, we propose the so-called quasi-correlation metric, which quantifies the predictive accuracy of a polygenic risk score applied to out-of-sample data for which we have only summary statistic information. In total, these methods facilitate estimation and model selection of polygenic risk scores on summary statistic data, and the application of these polygenic risk scores to out-of-sample data for which we have only summary statistic information. We demonstrate the utility of these methods by applying them to GWA studies of lipids, height, and lung cancer.

Identification of profiles of volatile organic compounds in exhaled breath by means of an electronic nose as a proposal for a screening method for breast cancer: a case-control study

The objective of the present study was to identify volatile prints from exhaled breath, termed breath-print, from breast cancer (BC) patients and healthy women by means of an electronic nose and to evaluate its potential use as a screening method. A cross-sectional study was performed on 443 exhaled breath samples from women, of whom 262 had been diagnosed with BC by biopsy and 181 were healthy women (control group). Breath-print analysis was performed utilizing the Cyranose 320 electronic nose. Group data were evaluated by principal component analysis (PCA), canonical discriminant analysis (CDA), and support vector machine (SVM), and the test's diagnostic power was evaluated by means of receiver operating characteristic (ROC) curves. The results obtained using the model generated from the CDA, which best describes the behavior of the assessed groups, indicated that the breath-print of BC patients was different from that of healthy women and that they presented with a variability of up to 98.8% and a correct classification of 98%. The sensitivity, specificity, negative predictive value, and positive predictive value reached 100% according to the ROC curve. The present study demonstrates the capability of the electronic nose to separate between healthy subjects and BC patients. This research could have a beneficial impact on clinical practice as we consider that this test could probably be used at the first point before the application of established gold tests (mammography, ultrasound, and biopsy) and substantially improve screening tests in the general population.

Polygenic background modifies penetrance of monogenic variants for tier 1 genomic conditions

Genetic variation can predispose to disease both through (i) monogenic risk variants that disrupt a physiologic pathway with large effect on disease and (ii) polygenic risk that involves many variants of small effect in different pathways. Few studies have explored the interplay between monogenic and polygenic risk. Here, we study 80,928 individuals to examine whether polygenic background can modify penetrance of disease in tier 1 genomic conditions — familial hypercholesterolemia, hereditary breast and ovarian cancer, and Lynch syndrome. Among carriers of a monogenic risk variant, we estimate substantial gradients in disease risk based on polygenic background — the probability of disease by age 75 years ranged from 17% to 78% for coronary artery disease, 13% to 76% for breast cancer, and 11% to 80% for colon cancer. We propose that accounting for polygenic background is likely to increase accuracy of risk estimation for individuals who inherit a monogenic risk variant.

Genetic variation predisposes to disease via monogenic and polygenic risk variants. Here, the authors assess the interplay between these types of variation on disease penetrance in 80,928 individuals. In carriers of monogenic variants, they show that disease risk is a gradient influenced by polygenic background.

Independent Validation of Early-Stage Non-Small Cell Lung Cancer Prognostic Scores Incorporating Epigenetic and Transcriptional Biomarkers With Gene-Gene Interactions and Main Effects

BACKGROUND

DNA methylation and gene expression are promising biomarkers of various cancers, including non-small cell lung cancer (NSCLC). Besides the main effects of biomarkers, the progression of complex diseases is also influenced by gene-gene (G×G) interactions.

RESEARCH QUESTION

Would screening the functional capacity of biomarkers on the basis of main effects or interactions, using multiomics data, improve the accuracy of cancer prognosis?

STUDY DESIGN AND METHODS

Biomarker screening and model validation were used to construct and validate a prognostic prediction model. NSCLC prognosis-associated biomarkers were identified on the basis of either their main effects or interactions with two types of omics data. A prognostic score incorporating epigenetic and transcriptional biomarkers, as well as clinical information, was independently validated.

RESULTS

Twenty-six pairs of biomarkers with G×G interactions and two biomarkers with main effects were significantly associated with NSCLC survival. Compared with a model using clinical information only, the accuracy of the epigenetic and transcriptional biomarker-based prognostic model, measured by area under the receiver operating characteristic curve (AUC), increased by 35.38% (95% CI, 27.09%-42.17%; P = 5.10 × 10-17) and 34.85% (95% CI, 26.33%-41.87%; P = 2.52 × 10-18) for 3- and 5-year survival, respectively, which exhibited a superior predictive ability for NSCLC survival (AUC3 year, 0.88 [95% CI, 0.83-0.93]; and AUC5 year, 0.89 [95% CI, 0.83-0.93]) in an independent Cancer Genome Atlas population. G×G interactions contributed a 65.2% and 91.3% increase in prediction accuracy for 3- and 5-year survival, respectively.

INTERPRETATION

The integration of epigenetic and transcriptional biomarkers with main effects and G×G interactions significantly improves the accuracy of prognostic prediction of early-stage NSCLC survival.

Precision Science on Incidence and Progression of Early-Detected Small Breast Invasive Cancers by Mammographic Features

The aim was to evaluate how the inter-screening interval affected the performance of screening by mammographic appearances. This was a Swedish retrospective screening cohort study with information on screening history and mammography features in two periods (1977-1985 and 1996-2010). The pre-clinical incidence and the mean sojourn time (MST) for small breast cancer allowing for sensitivity by mammographic appearances were estimated. The percentage of interval cancer against background incidence (I/E ratio) was used to assess the performance of mammography screening by different inter-screening intervals. The sensitivity-adjusted MSTs (in years) were heterogeneous with mammographic features, being longer for powdery and crushed stone-like calcifications (4.26, (95% CI, 3.50-5.26)) and stellate masses (3.76, (95% CI, 3.15-4.53)) but shorter for circular masses (2.65, (95% CI, 2.06-3.55)) in 1996-2010. The similar trends, albeit longer MSTs, were also noted in 1977-1985. The I/E ratios for the stellate type were 23% and 32% for biennial and triennial screening, respectively. The corresponding figures were 32% and 43% for the circular type and 21% and 29% for powdery and crushed stone-like calcifications, respectively. Mammography-featured progressions of small invasive breast cancer provides a new insight into personalized quality assurance, surveillance, treatment and therapy of early-detected breast cancer.

Polygenic and multifactorial scores for pancreatic ductal adenocarcinoma risk prediction

BACKGROUND

Most cases of pancreatic ductal adenocarcinoma (PDAC) are asymptomatic in early stages, and the disease is typically diagnosed in advanced phases, resulting in very high mortality. Tools to identify individuals at high risk of developing PDAC would be useful to improve chances of early detection.

OBJECTIVE

We generated a polygenic risk score (PRS) for PDAC risk prediction, combining the effect of known risk SNPs, and carried out an exploratory analysis of a multifactorial score.

METHODS

We tested the associations of the individual known risk SNPs on up to 2851 PDAC cases and 4810 controls of European origin from the PANcreatic Disease ReseArch (PANDoRA) consortium. Thirty risk SNPs were included in a PRS, which was computed on the subset of subjects that had 100% call rate, consisting of 839 cases and 2040 controls in PANDoRA and 6420 cases and 4889 controls from the previously published Pancreatic Cancer Cohort Consortium I-III and Pancreatic Cancer Case-Control Consortium genome-wide association studies. Additional exploratory multifactorial scores were constructed by complementing the genetic score with smoking and diabetes.

RESULTS

The scores were associated with increased PDAC risk and reached high statistical significance (OR=2.70, 95% CI 1.99 to 3.68, p=2.54×10^-10 highest vs lowest quintile of the weighted PRS, and OR=14.37, 95% CI 5.57 to 37.09, p=3.64×10^-8, highest vs lowest quintile of the weighted multifactorial score).

CONCLUSION

We found a highly significant association between a PRS and PDAC risk, which explains more than individual SNPs and is a step forward in the direction of the construction of a tool for risk stratification in the population.

iCARE: An R package to build, validate and apply absolute risk models

This report describes an R package, called the Individualized Coherent Absolute Risk Estimator (iCARE) tool, that allows researchers to build and evaluate models for absolute risk and apply them to estimate an individual's risk of developing disease during a specified time interval based on a set of user defined input parameters. An attractive feature of the software is that it gives users flexibility to update models rapidly based on new knowledge on risk factors and tailor models to different populations by specifying three input arguments: a model for relative risk, an age-specific disease incidence rate and the distribution of risk factors for the population of interest. The tool can handle missing information on risk factors for individuals for whom risks are to be predicted using a coherent approach where all estimates are derived from a single model after appropriate model averaging. The software allows single nucleotide polymorphisms (SNPs) to be incorporated into the model using published odds ratios and allele frequencies. The validation component of the software implements the methods for evaluation of model calibration, discrimination and risk-stratification based on independent validation datasets. We provide an illustration of the utility of iCARE for building, validating and applying absolute risk models using breast cancer as an example.

Variable prediction accuracy of polygenic scores within an ancestry group

Fields as diverse as human genetics and sociology are increasingly using polygenic scores based on genome-wide association studies (GWAS) for phenotypic prediction. However, recent work has shown that polygenic scores have limited portability across groups of different genetic ancestries, restricting the contexts in which they can be used reliably and potentially creating serious inequities in future clinical applications. Using the UK Biobank data, we demonstrate that even within a single ancestry group (i.e., when there are negligible differences in linkage disequilibrium or in causal alleles frequencies), the prediction accuracy of polygenic scores can depend on characteristics such as the socio-economic status, age or sex of the individuals in which the GWAS and the prediction were conducted, as well as on the GWAS design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use.

Variable prediction accuracy of polygenic scores within an ancestry group

Fields as diverse as human genetics and sociology are increasingly using polygenic scores based on genome-wide association studies (GWAS) for phenotypic prediction. However, recent work has shown that polygenic scores have limited portability across groups of different genetic ancestries, restricting the contexts in which they can be used reliably and potentially creating serious inequities in future clinical applications. Using the UK Biobank data, we demonstrate that even within a single ancestry group (i.e., when there are negligible differences in linkage disequilibrium or in causal alleles frequencies), the prediction accuracy of polygenic scores can depend on characteristics such as the socio-economic status, age or sex of the individuals in which the GWAS and the prediction were conducted, as well as on the GWAS design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use.

The Menstrual Cycle and Risk of Breast Cancer: A Review

Cyclic hormonal stimulation of the breast tissue plays a significant role in breast carcinogenesis. Current risk factor models do not include direct measures of cycle characteristics although the effects of possible surrogates of cycle activity such as age at menarche and menopause, parity, and nursing time have been investigated. Future risk models should also include menstrual cycle length, regularity, number of cycles before first full-term pregnancy, and life-time number of cycles. New risk factor models for pre- and postmenopausal breast cancer are proposed here. Furthermore, there is a need for more long-term, prospective studies investigating menstrual cycle characteristics as data currently available are primarily retrospective and collected at one time-point only.

Establishing a valid approach for estimating familial risk of cancer explained by common genetic variants

We critically examined existing approaches for the estimation of the excess familial risk of cancer that can be attributed to identified common genetic risk variants and propose an alternative, more straightforward approach for calculating this proportion using well-established epidemiological methodology. We applied the underlying equations of the traditional approaches and the new epidemiological approach for colorectal cancer (CRC) in a large population-based case-control study in Germany with 4,447 cases and 3,480 controls, who were recruited from 2003 to 2016 and for whom interview, medical and genomic data were available. Having a family history of CRC (FH) was associated with a 1.77-fold risk increase in our study population (95% CI 1.52-2.07). Traditional approaches yielded estimates of the FH-associated risk explained by 97 common genetics variants from 9.6% to 23.1%, depending on various assumptions. Our alternative approach resulted in smaller and more consistent estimates of this proportion, ranging from 5.4% to 14.3%. Commonly employed methods may lead to strongly divergent and possibly exaggerated estimates of excess familial risk of cancer explained by associated known common genetic variants. Our results suggest that familial risk and risk associated with known common genetic variants might reflect two complementary major sources of risk.

Clinical and Economic Impact of Tailoring Screening to Predicted Colorectal Cancer Risk: A Decision Analytic Modeling Study

BACKGROUND

Global increases in colorectal cancer risk have spurred debate about optimal use of screening resources. We explored the potential clinical and economic impact of colorectal cancer screening tailored to predicted colorectal cancer risk.

METHODS

We compared screening tailored to predicted risk versus uniform screening in a validated decision analytic model, considering the average risk population's actual colorectal cancer risk distribution, and a risk-prediction tool's discriminatory ability and cost. Low, moderate, and high risk tiers were identified as colorectal cancer risk after age 50 years of ≤3%, >3 to <12%, and ≥12%, respectively, based on threshold analyses with willingness-to-pay <$50,000/quality-adjusted life-year (QALY) gained. Tailored colonoscopy (once at age 60 years for low risk, every 10 years for moderate risk, and every 5 years for high risk) was compared with colonoscopy every 10 years for all. Tailored fecal immunochemical testing (FIT)/colonoscopy (annual FIT for low and moderate risk, colonoscopy every 5 years for high risk) was compared with annual FIT for all.

RESULTS

Assuming no colorectal cancer risk misclassification or risk-prediction tool costs, tailored screening was preferred over uniform screening. Tailored colonoscopy was minimally less effective than uniform colonoscopy, but saved $90,200-$889,000/QALY; tailored FIT/colonoscopy yielded more QALYs/person than annual FIT at $10,600-$60,000/QALY gained. Relatively modest colorectal cancer risk misclassification rates or risk-prediction tool costs resulted in uniform screening as the preferred approach.

CONCLUSIONS

Current risk-prediction tools may not yet be accurate enough to optimize colorectal cancer screening.

IMPACT

Uniform screening is likely to be preferred over tailored screening if a risk-prediction tool is associated with even modest misclassification rates or costs.

Germline variants in cancer genes in high-risk non-BRCA patients from Puerto Rico

Inherited pathogenic variants in genes that confer moderate to high risk of breast cancer may explain up to 50% of familial breast cancer. This study aimed at identifying inherited pathogenic variants in breast cancer cases from Puerto Rico that were not linked to BRCA1 or BRCA2. Forty-eight breast cancer patients that met the clinical criteria for BRCA testing but had received a negative BRCA1/2 result were recruited. Fifty-three genes previously implicated in hereditary cancer predisposition were captured using the BROCA Agilent cancer risk panel followed by massively parallel sequencing. Missense variants of uncertain clinical significance in CHEK2 were evaluated using an in vitro kinase assays to determine their impact on function. Pathogenic variants were identified in CHEK2, MUTYH, and RAD51B in four breast cancer patients, which represented 8.3% of the cohort. We identified three rare missense variants of uncertain significance in CHEK2 and two variants (p.Pro484Leu and p.Glu239Lys) showed markedly decreased kinase activity in vitro comparable to a known pathogenic variant. Interestingly, the local ancestry at the RAD51B locus in the carrier of p.Arg47* was predicted to be of African origin. In this cohort, 12.5% of the BRCA-negative breast cancer patients were found to carry a known pathogenic variant or a variant affecting protein activity. This study reveals an unmet clinical need of genetic testing that could benefit a significant proportion of at-risk Latinas. It also highlights the complexity of Hispanic populations as pathogenic factors may originate from any of the ancestral populations that make up their genetic backgrounds.

Identification of four novel associations for B-cell acute lymphoblastic leukaemia risk

There is increasing evidence for a strong inherited genetic basis of susceptibility to acute lymphoblastic leukaemia (ALL) in children. To identify new risk variants for B-cell ALL (B-ALL) we conducted a meta-analysis with four GWAS (genome-wide association studies), totalling 5321 cases and 16,666 controls of European descent. We herein describe novel risk loci for B-ALL at 9q21.31 (rs76925697, P = 2.11 × 10-8), for high-hyperdiploid ALL at 5q31.1 (rs886285, P = 1.56 × 10-8) and 6p21.31 (rs210143 in BAK1, P = 2.21 × 10-8), and ETV6-RUNX1 ALL at 17q21.32 (rs10853104 in IGF2BP1, P = 1.82 × 10-8). Particularly notable are the pleiotropic effects of the BAK1 variant on multiple haematological malignancies and specific effects of IGF2BP1 on ETV6-RUNX1 ALL evidenced by both germline and somatic genomic analyses. Integration of GWAS signals with transcriptomic/epigenomic profiling and 3D chromatin interaction data for these leukaemia risk loci suggests deregulation of B-cell development and the cell cycle as central mechanisms governing genetic susceptibility to ALL.

Genomic Prediction of 16 Complex Disease Risks Including Heart Attack, Diabetes, Breast and Prostate Cancer

We construct risk predictors using polygenic scores (PGS) computed from common Single Nucleotide Polymorphisms (SNPs) for a number of complex disease conditions, using L1-penalized regression (also known as LASSO) on case-control data from UK Biobank. Among the disease conditions studied are Hypothyroidism, (Resistant) Hypertension, Type 1 and 2 Diabetes, Breast Cancer, Prostate Cancer, Testicular Cancer, Gallstones, Glaucoma, Gout, Atrial Fibrillation, High Cholesterol, Asthma, Basal Cell Carcinoma, Malignant Melanoma, and Heart Attack. We obtain values for the area under the receiver operating characteristic curves (AUC) in the range ~0.58-0.71 using SNP data alone. Substantially higher predictor AUCs are obtained when incorporating additional variables such as age and sex. Some SNP predictors alone are sufficient to identify outliers (e.g., in the 99th percentile of polygenic score, or PGS) with 3-8 times higher risk than typical individuals. We validate predictors out-of-sample using the eMERGE dataset, and also with different ancestry subgroups within the UK Biobank population. Our results indicate that substantial improvements in predictive power are attainable using training sets with larger case populations. We anticipate rapid improvement in genomic prediction as more case-control data become available for analysis.

Genome-wide association analysis identifies a meningioma risk locus at 11p15.5

Background

Meningiomas are adult brain tumors originating in the meningeal coverings of the brain and spinal cord, with significant heritable basis. Genome-wide association studies (GWAS) have previously identified only a single risk locus for meningioma, at 10p12.31.

Methods

To identify a susceptibility locus for meningioma, we conducted a meta-analysis of 2 GWAS, imputed using a merged reference panel from the 1000 Genomes Project and UK10K data, with validation in 2 independent sample series totaling 2138 cases and 12081 controls.

Results

We identified a new susceptibility locus for meningioma at 11p15.5 (rs2686876, odds ratio = 1.44, P = 9.86 × 10^–9). A number of genes localize to the region of linkage disequilibrium encompassing rs2686876, including RIC8A, which plays a central role in the development of neural crest-derived structures, such as the meninges.

Conclusions

This finding advances our understanding of the genetic basis of meningioma development and provides additional support for a polygenic model of meningioma.

A flexible and parallelizable approach to genome-wide polygenic risk scores

The heritability of most complex traits is driven by variants throughout the genome. Consequently, polygenic risk scores, which combine information on multiple variants genome-wide, have demonstrated improved accuracy in genetic risk prediction. We present a new two-step approach to constructing genome-wide polygenic risk scores from meta-GWAS summary statistics. Local linkage disequilibrium (LD) is adjusted for in Step 1, followed by, uniquely, long-range LD in Step 2. Our algorithm is highly parallelizable since block-wise analyses in Step 1 can be distributed across a high-performance computing cluster, and flexible, since sparsity and heritability are estimated within each block. Inference is obtained through a formal Bayesian variable selection framework, meaning final risk predictions are averaged over competing models. We compared our method to two alternative approaches: LDPred and lassosum using all seven traits in the Welcome Trust Case Control Consortium as well as meta-GWAS summaries for type 1 diabetes (T1D), coronary artery disease, and schizophrenia. Performance was generally similar across methods, although our framework provided more accurate predictions for T1D, for which there are multiple heterogeneous signals in regions of both short- and long-range LD. With sufficient compute resources, our method also allows the fastest runtimes.

AA genotype of cyclin D1 G870A polymorphism increases breast cancer risk: Findings of a case-control study and meta-analysis

BACKGROUND

Cyclin D1 (CCND1) polymorphisms, a regulator of the cell cycle progress from G1 to the S phase, may lead to uncontrolled cell proliferation and lack of apoptosis. G870A, a common single-nucleotide polymorphism in CCND1 influences breast cancer risk. However, the association between G870A polymorphism and breast cancer risk is ambiguous so far.

MATERIALS AND METHODS

In this case-control study, we analyzed the role of G870A polymorphism with breast cancer risk in Indian women. A meta-analysis of 18 studies was also performed to elucidate this association by increasing statistical power.

RESULTS

In our case-control study, significant risk association of the CCND1 G870A AA genotype with breast cancer in total cohort (odds ratio [OR], 2.98; 95% confidence interval [CI], 1.64-5.42; P value, 4.96e-04) and premenopausal women (OR, 3.31; 95% CI, 1.54-7.08; P value, .003) was found. The results of the meta-analysis showed that AA genotype of the CCND1 G870A polymorphism significantly increases breast cancer risk in total pooled data (AA vs GG+GA: OR = 1.20; 95% CI = 1.03 to 1.39; P value, 0.016*) and Caucasian (AA vs GG+GA: OR = 1.22; 95% CI = 0.99 to 1.51; P value, .056*) but not in Asian population. Further, a significant protective association with breast cancer was also found in the GA vs AA comparison model in pooled data (OR = 0.73; 95% CI = 0.58 to 0.92; P value, .007*) as well as in Caucasian subgroup (OR = 0.62; 95% CI = 0.49 to 0.94; P value, .022*).

CONCLUSION

CCND1 G870A AA genotype was found associated with breast cancer risk. Future association studies considering the environmental impact on gene expression are required to validate/explore this association.

Association analyses identify 31 new risk loci for colorectal cancer susceptibility

Colorectal cancer (CRC) is a leading cause of cancer-related death worldwide, and has a strong heritable basis. We report a genome-wide association analysis of 34,627 CRC cases and 71,379 controls of European ancestry that identifies SNPs at 31 new CRC risk loci. We also identify eight independent risk SNPs at the new and previously reported European CRC loci, and a further nine CRC SNPs at loci previously only identified in Asian populations. We use in situ promoter capture Hi-C (CHi-C), gene expression, and in silico annotation methods to identify likely target genes of CRC SNPs. Whilst these new SNP associations implicate target genes that are enriched for known CRC pathways such as Wnt and BMP, they also highlight novel pathways with no prior links to colorectal tumourigenesis. These findings provide further insight into CRC susceptibility and enhance the prospects of applying genetic risk scores to personalised screening and prevention.

Comparison of Questionnaire-Based Breast Cancer Prediction Models in the Nurses' Health Study

BACKGROUND

The Gail model and the model developed by Tyrer and Cuzick are two questionnaire-based approaches with demonstrated ability to predict development of breast cancer in a general population.

METHODS

We compared calibration, discrimination, and net reclassification of these models, using data from questionnaires sent every 2 years to 76,922 participants in the Nurses' Health Study between 1980 and 2006, with 4,384 incident invasive breast cancers identified by 2008 (median follow-up, 24 years; range, 1-28 years). In a random one third sample of women, we also compared the performance of these models with predictions from the Rosner-Colditz model estimated from the remaining participants.

RESULTS

Both the Gail and Tyrer-Cuzick models showed evidence of miscalibration (Hosmer-Lemeshow P < 0.001 for each) with notable (P < 0.01) overprediction in higher-risk women (2-year risk above about 1%) and underprediction in lower-risk women (risk below about 0.25%). The Tyrer-Cuzick model had slightly higher C-statistics both overall (P < 0.001) and in age-specific comparisons than the Gail model (overall C, 0.63 for Tyrer-Cuzick vs. 0.61 for the Gail model). Evaluation of net reclassification did not favor either model. In the one third sample, the Rosner-Colditz model had better calibration and discrimination than the other two models. All models had C-statistics <0.60 among women ages ≥70 years.

CONCLUSIONS

Both the Gail and Tyrer-Cuzick models had some ability to discriminate breast cancer cases and noncases, but have limitations in their model fit.

IMPACT

Refinements may be needed to questionnaire-based approaches to predict breast cancer in older and higher-risk women.

Polygenic Prediction of Weight and Obesity Trajectories from Birth to Adulthood

Severe obesity is a rapidly growing global health threat. Although often attributed to unhealthy lifestyle choices or environmental factors, obesity is known to be heritable and highly polygenic; the majority of inherited susceptibility is related to the cumulative effect of many common DNA variants. Here we derive and validate a new polygenic predictor comprised of 2.1 million common variants to quantify this susceptibility and test this predictor in more than 300,000 individuals ranging from middle age to birth. Among middle-aged adults, we observe a 13-kg gradient in weight and a 25-fold gradient in risk of severe obesity across polygenic score deciles. In a longitudinal birth cohort, we note minimal differences in birthweight across score deciles, but a significant gradient emerged in early childhood and reached 12 kg by 18 years of age. This new approach to quantify inherited susceptibility to obesity affords new opportunities for clinical prevention and mechanistic assessment.

Factors associated with intentions for breast cancer risk management: Does risk group matter?

OBJECTIVE

National guidelines provide breast cancer (BC) risk management recommendations based on estimated lifetime risk. Despite this specificity, it is unclear if women's risk management intentions are or are not guideline concordant. To address this knowledge gap, women at varying risk levels reported intentions for risk-reducing behaviors. Factors associated with intentions, informed by the Health Beliefs Model, were also studied.

METHODS

Women with elevated BC risk (N = 103) were studied and categorized by risk level: moderate (15%-20%), high (greater than or equal to 20%), or very high (BRCA1/2 positive). Participants self-reported BC susceptibility, self-efficacy, and benefits, barriers, and intentions for risk-reducing mastectomy (RRM), risk-reducing salpingo-oophorectomy (RRSO), chemoprevention, improving diet or physical activity, and reducing alcohol use.

RESULTS

Groups significantly differed in RRSO intentions (P < .01); BRCA1/2 positive women had greater intentions for RRSO. Groups did not differ in intentions for RRM, chemoprevention, or lifestyle changes (Ps > .28). In hierarchical linear regression models examining Health Belief Model (HBM) factors, perceived susceptibility was associated with intentions for RRM (β = .169, P = .08). Perceived benefits was associated with intentions for RRM (β = .237, P = .02) and chemoprevention (β = .388, P < .01). Self-efficacy was associated with intentions for physical activity (β = .286, P < .01).

CONCLUSIONS

Consistent with guidelines, BRCA1/2 positive women reported greater intentions for RRSO, and risk groups did not differ in intentions for lifestyle changes. Notably, women's intentions for RRM and chemoprevention were guideline discordant; groups did not differ in intentions for these behaviors. Accounting for the effects of risk group, modifiable health beliefs were also associated with risk management intentions; these may represent targets for decision support interventions.