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Prediction of Breast Cancer Risk Based on Profiling With Common Genetic Variants
Abstract
Data for multiple common susceptibility alleles for breast cancer may be combined to identify women at
different levels of breast cancer risk. Such stratification could guide preventive and screening strategies. However, empirical
evidence for genetic risk stratification is lacking.
Methods: We investigated the value of using 77 breast cancer-associated single nucleotide polymorphisms (SNPs) for risk
stratification, in a study of 33 673 breast cancer cases and 33 381 control women of European origin. We tested all possible
pair-wise multiplicative interactions and constructed a 77-SNP polygenic risk score (PRS) for breast cancer overall and
by estrogen receptor (ER) status. Absolute risks of breast cancer by PRS were derived from relative risk estimates and UK
incidence and mortality rates.
Results: There was no strong evidence for departure from a multiplicative model for any SNP pair. Women in the highest 1%
of the PRS had a three-fold increased risk of developing breast cancer compared with women in the middle quintile (odds
ratio [OR] = 3.36, 95% confidence interval [CI] = 2.95 to 3.83). The ORs for ER-positive and ER-negative disease were 3.73 (95%
CI = 3.24 to 4.30) and 2.80 (95% CI = 2.26 to 3.46), respectively. Lifetime risk of breast cancer for women in the lowest and
highest quintiles of the PRS were 5.2% and 16.6% for a woman without family history, and 8.6% and 24.4% for a woman with
a first-degree family history of breast cancer.
Conclusions: The PRS stratifies breast cancer risk in women both with and without a family history of breast cancer. The
observed level of risk discrimination could inform targeted screening and prevention strategies. Further discrimination may
be achievable through combining the PRS with lifestyle/environmental factors, although these were not considered in this
report.
... 84.7 million Characterized variants in the genome of humans are single nucleotide polymorphisms [6] Most of these Single nucleotide polymorphisms are often used as biomarkers for a genetic region in genomic studies since they have not much impact on biological systems. It has been found that a collection of 77 SNPs is proven to be substantially related to the development of breast cancer [7]. These are less susceptible to homoplasy than microsatellites and mitochondrial DNAs because they results in the changes to a single base nucleotide. ...
... If an individual possesses such a variation, the likelihood of developing breast cancer is significantly greater, exceeding four times the risk typically observed in the general population. In other words, these variants greatly increase the likelihood of breast cancer [7]. • Moderate-risk variants: These variants are less common, with a low allele frequency falling within the range of 0.005 to 0.01. ...
... Individuals who possess these pathogenic variants of breast cancer risk falls 2 to 4 times the risk of those without such variants. While the risk is elevated, it is not as prominent as with high-risk variants [7]. • Low-risk variants: These variants are more prevalent within the population, exhibiting a minor allele frequency exceeding 0.05. ...
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.
... A PRS also combines relevant SNPs and therefore predicts the risk of breast cancer. Recent studies have found that the effect of PRS on absolute risks of the breast can be higher in women in the highest quartile of polygenic distribution, with at least two-fold increased risk for BC compared to those with PRS in the lowest quartile [99,100]. In the study conducted ...
... The authors estimated that female breast cancer patients had a higher mean value of PRS than non-BC patients (0.628, 95% CI = 0.620 to 0.637). Mavaddat et al.[99] did not observe a strong effect between the SNPs that modify breast cancer risks in BRCA1 and BRCA2 mutation carriers compared to the general population, consistent with the results from EIL presented here in different types of mutations. Despite the differences in methodology, Mavaddat et al.[99] demonstrated that there are inherited components associated with BC risk, which compares closely with the findings of this study. ...
... Mavaddat et al.[99] did not observe a strong effect between the SNPs that modify breast cancer risks in BRCA1 and BRCA2 mutation carriers compared to the general population, consistent with the results from EIL presented here in different types of mutations. Despite the differences in methodology, Mavaddat et al.[99] demonstrated that there are inherited components associated with BC risk, which compares closely with the findings of this study. ...
Next-generation sequencing (NGS) help to identify disease-causing genes underlying any given monogenic or complex disease. Concurrently, mathematical tools and statistical methods, includ- ing machine learning algorithms, are rapidly evolving and together, these technologies represent the new frontier of research and clinical management on a path leading toward personalised medicine.
This thesis has been divided into three main sections. Firstly, the Linkage disequilibrium (LD) patterns were observed to understand the combined impact of recombination, natural selection, genetic drift and mutation. LD is the non-random association of alleles at different loci in a given population. To this end, LD patterns were constructed using 454 whole-genome sequences (WGS) from the Wellderly study based on the Malécot Morton model (exponential distributions with restricted parameters). Therefore, the extent of the LD was computed for genic, intergenic, exon and intron regions. The main result demonstrated that significant differences between exonic, intronic and intergenic components demonstrate that fine-scale LD structure provides important insights into genome function, which cannot be revealed by LD analysis of much lower resolution array-based genotyping and conventional linkage maps.
Secondly, machine learning methodologies were applied to classify genes into four groups: essential genes, Mendelian genes, genes associated with complex disorders, and non-essential–non-disease genes. To this end, the dataset was extracted from published studies of biological and functional properties of the genes. Hence, different supervised machine learning (ML) models were studied to select the most important features relevant for classifying genes. Simultaneously, Bayesian inference in a Gaussian graphical model (BGGM) was carried out to investigate recognising the significant features to enclose genes. Once the relevant features had been selected, a proposed unsupervised ML approach was developed to cluster genes into those four groups. The combined analysis of genomic data for gradient boosting and random forest models showed that more than 50% of the variance was explained and the results from BGMM showed that the connectivity between these gene metrics was 40%. The proposed unsupervised model showed an improvement for classifying genes into Mendelian group. However, results suggested that some genes involved in developing Mendelian disorders overlap with complex disorders.
Thirdly, a polygenic risk score (PRS) was developed to quantify the cumulative effect of low- penetrance genetic variants on breast cancer (BC), following the hypothesis that the polygenic component has an important impact on BC patients, as do BRCAs variants. Genome data from POSH and WTCCC were used to generate the PRS. This score was computed based on the surprisal theory. As a result, relative genome information per individual (RGI) was estimated to understand how unusual a genome is related to the reference genome. Thus, a person with a higher RGI has a more unusual genome. Likewise, a lower RGI corresponds to having more common alleles, and therefore a less surprising genome. The PRS for women who carry BRCA1/2 mutations or intermediate-risk/common variants demonstrated the hypothesis that the BC cases contain a strong inherited polygenic component. Furthermore, the polygenic component carriers tend to have more significant changes in allele frequencies compared to BRCA1 and BRCA2 variants.
This thesis presents methodological contributions to predictive models based on machine learning techniques and mathematical programming, together with relevant insights into disease mechanisms and potential treatment options.<br/
... In recent years, some risk models have been updated to include additional genomic information, typically the effects of rare PV in other genes (ATM, CHEK2 and PALB2) [2,3] and the joined effect of single nucleotide polymorphisms (SNPs) summarized in polygenic risk scores (PRS) [4][5][6]. Indeed, in the general population, some studies suggested that stratification of women according to their risk of breast cancer based on their PRS could personalize screening and prevention strategies [7][8][9][10]. Several PRS for breast cancer have been defined and validated in women of European ancestry from the large multi-centric and multicountry studies conducted by the Breast Cancer Association Consortium (BCAC). ...
... However, even in European populations, the effect of some of these SNPs may vary from one country to another or within one country because of other factors, which may modify predictive performance of PRS. Here, we assembled a large dataset of women of European descent residing in France and representing three populations of women at different level of breast cancer risk to investigate in each group the performance of three partially overlapping PRS for breast cancer comprising 77 [10], 179 [17,18] and 313 SNPs [11]. Our goal was to assess whether these PRS modify breast cancer risk prediction in order to further assess if, when incorporated in predictive models, they could improve ability to predict breast cancer in the French population, and particularly in women attending family cancer clinics because of their personal or family history of breast cancer. ...
... PRS is the weighted combined effect of uncorrelated SNPs calculated under the hypothesis of additivity of SNP effect using SNP effect size estimated by the BCAC as weight, as described in the Supplementary data. We examined association with breast cancer and performance of the three following PRS: PRS77 which includes 77 independent SNPs identified in early GWAS on breast cancer conducted by the BCAC [10], PRS179 which includes 179 independent SNPs identified afterward in successive GWAS [17,18,31], and PRS313 which was most recently developed and validated in women of European ancestry enrolled in 10 prospective cohorts and in the UK biobank [11] (Supplementary Table S1). ...
Background
Three partially overlapping breast cancer polygenic risk scores (PRS) comprising 77, 179 and 313 SNPs have been proposed for European-ancestry women by the Breast Cancer Association Consortium (BCAC) for improving risk prediction in the general population. However, the effect of these SNPs may vary from one country to another and within a country because of other factors.
Objective
To assess their associated risk and predictive performance in French women from (1) the CECILE population-based case-control study, (2) BRCA1 or BRCA2 (BRCA1/2) pathogenic variant (PV) carriers from the GEMO study, and (3) familial breast cancer cases with no BRCA1/2 PV and unrelated controls from the GENESIS study.
Results
All three PRS were associated with breast cancer in all studies, with odds ratios per standard deviation varying from 1.7 to 2.0 in CECILE and GENESIS, and hazard ratios varying from 1.1 to 1.4 in GEMO. The predictive performance of PRS313 in CECILE was similar to that reported in BCAC but lower than that in GENESIS (area under the receiver operating characteristic curve (AUC)=0.67 and 0.75, respectively). PRS were less performant in BRCA2 and BRCA1 PV carriers (AUC = 0.58 and 0.54 respectively).
Conclusion
Our results are in line with previous validation studies in the general population and in BRCA1/2 PV carriers. Additionally, we showed that PRS may be of clinical utility for women with a strong family history of breast cancer and no BRCA1/2 PV, and for those carrying a predicted PV in a moderate-risk gene like ATM, CHEK2 or PALB2.
... A polygenic risk score (PRS) is an additive linear combination of the effects of multiple single nucleotide polymorphisms (SNPs) from GWAS and can achieve a degree of risk stratification that is useful for risk-based programs of breast cancer screening and early detection. PRSs have been developed to predict breast cancer risk in non-Hispanic white, Asian and Latin American women (5)(6)(7)(8)(9)(10). Recently, a large study has developed a 313-variant PRS for breast cancer risks in women of European ancestry (5). ...
... where β k is the per-allele log OR for breast cancer associated with SNP k and serves as the weight in PRS calculation, G k is the allele dosage for SNP k and K is the total number of SNPs included in the PRS. This form of PRS assumes a log-additive genetic model for individual SNPs, which was considered as appropriate in previous PRS development (5)(6)(7)(8)(9)(10). To find an optimal PRS, we need to determine which SNPs among all genomewide variants should be included in the PRS according to association test results from the training dataset. ...
... We calculated the lifetime and 10-year absolute risks of developing breast cancer (overall and subtype-specific disease) based on population incidence rates and relative risk estimates for different PRS categories after taking into account the competing risk of dying from causes other than breast cancer, as described previously (6). The theoretical ORs for women in different PRS categories versus women in the 40th-60th percentiles were calculated using the method of Wen et al. (9) in which PRS was modeled as continuous predictor of breast cancer risk. ...
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 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 odd ratio (OR) per standard deviation of the joint PRS in the validation set was 1.34 (95% CI: 1.27–1.42) with area under receiver operating characteristic curve (AUC) of 0.581. Compared to 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 to existing methods, the proposed joint PRSs can improve prediction of breast cancer risk in AA women.
... In the field of breast cancer, large-scale studies and reproducible methods have facilitated the development and validation of polygenic risk prediction models in European populations. Studies that examined the effect of PRS have consistently reported positive associations between PRS and breast cancer risk [16,[108][109][110][111][112][113][114]. Although several PRSs have been developed, one of the current best performing PRS incorporates information from 313 SNPs (PRS 313 ) [16]: compared with women in the middle quintile (40th-60th percentile at population average risk), those in the highest 1% of the PRS 313 distribution had approximately four-fold greater risk for breast cancer [16]. ...
... The effect of PRS on breast cancer risk is, to some extent, independent of family history. Although observed risk associated with PRS was attenuated in women with a family history of breast cancer, this association was observed for both women with and without a family history [16,114,[126][127][128][129]. PRSs have also been shown to be independent of other established risk factors for breast cancer, including mammographic density [111], lifestyle behaviors (diet, physical activity, smoking, alcohol consumption, BMI, waist circumference) [108,117,130], reproductive factors (age at menarche, parity, number of children, age at first full-term pregnancy, and breastfeeding) [108,117], and exogenous hormonal factors (use of oral contraceptives and use of postmenopausal hormone replacement therapy) [108,117]. ...
... Finally, implementation of PRS into population screening programs also requires consideration of the social, ethical, and psychological outcomes. For example, consideration needs to be given to the acceptance and adoption of new risk-stratification programs that use genetic information (particularly for those with a reduced risk), training health professionals in developing best risk communication tools, and costeffectiveness and cost-benefit evaluations of alternative prevention strategies [114,187,189]. ...
Genome-wide association studies (GWASs) have shown that the genetic architecture of cancers are highly polygenic and enabled researchers to identify genetic risk loci for cancers. The genetic variants associated with a cancer can be combined into a polygenic risk score (PRS), which captures part of an individual’s genetic susceptibility to cancer. Recently, PRSs have been widely used in cancer risk prediction and are shown to be capable of identifying groups of individuals who could benefit from the knowledge of their probabilistic susceptibility to cancer, which leads to an increased interest in understanding the potential utility of PRSs that might further refine the assessment and management of cancer risk. In this context, we provide an overview of the major discoveries from cancer GWASs. We then review the methodologies used for PRS construction, and describe steps for the development and evaluation of risk prediction models that include PRS and/or conventional risk factors. Potential utility of PRSs in cancer risk prediction, screening, and precision prevention are illustrated. Challenges and practical considerations relevant to the implementation of PRSs in health care settings are discussed.
... Recently biomedical research has addressed great efforts in evaluating the role of single-nucleotide polymorphisms (SNPs) and microRNAs (miRNAs) in BC risk. The potential use of such molecules for diagnostic/ prognostic purposes in regard to BC has been extensively evaluated and with regards to miRNAs, their stability in body fluids has opened new opportunities for anticipating BC diagnosis [15][16][17][18][19][20] with minimally invasive intervention, especially for women at higher risk [21][22][23]. ...
... With regards to the second main aim, secondary objectives are: to assess the tumor characteristics associated with miRNA levels in case subjects to understand the role of these biomarkers in cancer detection; to assess the association between the investigated BC risk factors and miRNA levels in the control group to define different thresholds for screening positivity; to evaluate the presence of 77 established SNPs associated with BC risk [18] and their impact on BC score calculation. ...
... SNP genotyping Genomic DNA will be extracted from buffy coat with High Pure PCR Template preparation kit (Roche Diagnostics®). 77 SNPs described in Mavaddat et al. [18] will be evaluated using the Personal Genome Machine (Ion Torrent) NGS apparatus. ...
... Although studies have identified more than 100 low penetrance genes as prognostic for breast cancer [19], no single SNP has shown potential for screening purposes [20,21]. Several reasons can be identified for this absence. ...
... To our knowledge, no other study has examined absolute risk estimates with a similar design. Instead, other PRS evaluations have relied on input from external sources to convert relative to absolute risks [20,21,[36][37][38][39][40]. ...
... The number of breast cancer events in the EstBB cohort may have been too small for the formal test to have sufficient power. Such a PRS-age interaction has been reported in the PRS evaluation by Mavaddat et al., 2015 and 2019 [19,21]. ...
Background
Polygenic risk scores (PRS) could potentially improve breast cancer screening recommendations. Before a PRS can be considered for implementation, it needs rigorous evaluation, using performance measures that can inform about its future clinical value.
Objectives
To evaluate the prognostic performance of a regression model with a previously developed, prevalence-based PRS and age as predictors for breast cancer incidence in women from the Estonian biobank (EstBB) cohort; to compare it to the performance of a model including age only.
Methods
We analyzed data on 30,312 women from the EstBB cohort. They entered the cohort between 2002 and 2011, were between 20 and 89 years, without a history of breast cancer, and with full 5-year follow-up by 2015. We examined PRS and other potential risk factors as possible predictors in Cox regression models for breast cancer incidence. With 10-fold cross-validation we estimated 3- and 5-year breast cancer incidence predicted by age alone and by PRS plus age, fitting models on 90% of the data. Calibration, discrimination, and reclassification were calculated on the left-out folds to express prognostic performance.
Results
A total of 101 (3.33‰) and 185 (6.1‰) incident breast cancers were observed within 3 and 5 years, respectively. For women in a defined screening age of 50–62 years, the ratio of observed vs PRS-age modelled 3-year incidence was 0.86 for women in the 75–85% PRS-group, 1.34 for the 85–95% PRS-group, and 1.41 for the top 5% PRS-group. For 5-year incidence, this was respectively 0.94, 1.15, and 1.08. Yet the number of breast cancer events was relatively low in each PRS-subgroup. For all women, the model’s AUC was 0.720 (95% CI: 0.675–0.765) for 3-year and 0.704 (95% CI: 0.670–0.737) for 5-year follow-up, respectively, just 0.022 and 0.023 higher than for the model with age alone. Using a 1% risk prediction threshold, the 3-year NRI for the PRS-age model was 0.09, and 0.05 for 5 years.
Conclusion
The model including PRS had modest incremental performance over one based on age only. A larger, independent study is needed to assess whether and how the PRS can meaningfully contribute to age, for developing more efficient screening strategies.
... Individually, each of these variants confers only a small increase in risk, but collectively, as a PRS, they account for considerable BC susceptibility. [10][11][12][13][14][15] Recent studies have demonstrated that use of PRSs enables more effective risk stratification and improves the risk-benefit ratio in population-wide BC screening. 16,17 Wolfson et al 17 showed that PRS information more effectively stratified women for BC risk than BC FH or PV alone. ...
... 2,[18][19][20][21][22][23][24][25] However, most polygenic models have been developed and validated in women of European ancestry. 2,12,18,21,22,[26][27][28][29][30][31][32][33][34] When they have been generalized to non-European populations, these PRSs typically do not provide comparable risk stratification. 34,35 These findings highlight the need to develop and validate PRSs on the basis of variants identified in women of diverse ancestries. ...
PURPOSE
To develop and validate a cross-ancestry integrated risk score (caIRS) that combines a cross-ancestry polygenic risk score (caPRS) with a clinical estimator for breast cancer (BC) risk. We hypothesized that the caIRS is a better predictor of BC risk than clinical risk factors across diverse ancestry groups.
METHODS
We used diverse retrospective cohort data with longitudinal follow-up to develop a caPRS and integrate it with the Tyrer-Cuzick (T-C) clinical model. We tested the association between the caIRS and BC risk in two validation cohorts including > 130,000 women. We compared model discrimination for 5-year and remaining lifetime BC risk between the caIRS and T-C and assessed how the caIRS would affect screening in the clinic.
RESULTS
The caIRS outperformed T-C alone for all populations tested in both validation cohorts and contributed significantly to risk prediction beyond T-C. The area under the receiver operating characteristic curve improved from 0.57 to 0.65, and the odds ratio per standard deviation increased from 1.35 (95% CI, 1.27 to 1.43) to 1.79 (95% CI, 1.70 to 1.88) in validation cohort 1 with similar improvements observed in validation cohort 2. We observed the largest gain in positive predictive value using the caIRS in Black/African American women across both validation cohorts, with an approximately two-fold increase and an equivalent negative predictive value as the T-C. In a multivariate, age-adjusted logistic regression model including both caIRS and T-C, caIRS remained significant, indicating that caIRS provides information over T-C alone.
CONCLUSION
Adding a caPRS to the T-C model improves BC risk stratification for women of multiple ancestries, which could have implications for screening recommendations and prevention.
... Beyond SNP18, SNP77 was one of the next well-validated breast cancer PRS'. In a study of ~33,000 breast cancer cases and ~33,000 controls of European origin, SNP77 was shown to stratify breast cancer risk in women with family history as well as those without [46]. Lifetime breast cancer risk was stratified as 8.6% and 24.4% in the lowest and highest quintiles respectively, for women with a first-degree relative with a history of breast cancer and 5.2% and 16.6% respectively, for women with no family history. ...
... Discrimination statistics for breast cancer PRS with increasing number of SNPs in populations of white European origin[3,33,[42][43][44][45][46][47][48][49][50][51][52][53][54]. ...
Polygenic Risk Scores (PRS) are a major component of accurate breast cancer risk prediction and have the potential to improve screening and prevention strategies. PRS combine the risk from Single nucleotide polymorphisms (SNPs) associated with breast cancer in Genome Wide Association Studies (GWAS) and explain over 30% of breast cancer heritability. When incorporated into risk models, the more personalised risk assessment derived from PRS, help identify women at higher risk of breast cancer development and enables the implementation of stratified screening and prevention approaches. This review describes the role of PRS in breast cancer risk prediction including the development of PRS and their clinical application. We have also examined the role of PRS within more well-established risk prediction models which incorporate known classic risk factors and discuss the interaction of PRS with these factors and their capacity to predict breast cancer subtypes. Before PRS can be implemented on a population-wide scale, there are several challenges that must be addressed. Perhaps the most pressing of these is the use of PRS in women of non-White European origin, where PRS have been shown to have attenuated risk prediction both in discrimination and calibration. We discuss progress in developing and applying PRS in non-white European populations. PRS represent a significant advance in breast cancer risk prediction and their further development will undoubtedly enhance personalisation.
... This was done using the UK Biobank imputed genotypes dataset of 96 million variants in chromosomes 1-22. For each disease, the PRSs used 11,23,[29][30][31][32][33] are listed in Supplementary Table S9. The rationale for using more than one PRS for each disease, when available, was to maximize the scope of information available in PRSs and avoid limiting the www.nature.com/scientificreports/ ...
Polygenic risk scores (PRSs) hold promise in their potential translation into clinical settings to improve disease risk prediction. An important consideration in integrating PRSs into clinical settings is to gain an understanding of how to identify which subpopulations of individuals most benefit from PRSs for risk prediction. In this study, using the UK Biobank dataset, we trained logistic regression models to predict the 10 year incident risk of myocardial infarction, breast cancer, and schizophrenia using either just clinical features or clinical features combined with PRSs. For each disease, we identified the top 10% subgroup with the greatest magnitude of improvement in risk prediction accuracy attributed to PRSs in the multi-modal model. Using up to ~ 3.6 k demographic, lifestyle, diagnostic, lab, and physical measurement features from the UK Biobank dataset of ~ 500 k individuals, we characterized these subgroups based on various clinical, lifestyle, and demographic characteristics. The incident cases in the top 10% subgroup for each disease represent distinct phenotypes that differ from other cases and that are strongly correlated with genetic predisposition. Our findings provide insights into disease subtypes and can encourage future studies aimed at classifying these individuals to enhance the targeting of polygenic risk scoring in practice.
... In addition, BrCa is classified into molecular subtypes based on whether BrCa cells grow in response to female hormones (i.e., estrogen, progesterone) or growth factors [5]. Stratification of women based on nongenetic risk factors for BrCa is of paramount importance for developing more effective risk reduction strategies as well as for targeted risk-stratified BrCa screening programmes [6]. ...
Background
Previous research has found associations between various non-genetic factors and breast cancer (BrCa) risk. This study summarises and appraises the credibility of the available evidence on the association between non-genetic factors and BrCa risk.
Methods
We conducted an umbrella review of meta-analyses. Medline, Scopus, and the Cochrane databases were systematically searched for meta-analyses examining non-genetic factors and BrCa incidence or mortality. The strength of the evidence was graded in four categories (i.e., weak, suggestive, highly suggestive, convincing).
Results
A total of 781 meta-analyses from 280 publications were evaluated and graded. We included exposures related to anthropometric measurements, biomarkers, breast characteristics and diseases, diet and supplements, environment, exogenous hormones, lifestyle and social factors, medical history, medication, reproductive history, and pregnancy. The largest number of examined associations was found for the category of diet and supplements and for exposures such as aspirin use and active smoking. The statistically significant (P-value < 0.05) meta-analyses were 382 (49%), of which 204 (53.4%) reported factors associated with increased BrCa risk. Most of the statistically significant evidence (n = 224, 58.6%) was graded as weak. Convincing harmful associations with heightened BrCa risk were found for increased body mass index (BMI), BMI and weight gain in postmenopausal women, oral contraceptive use in premenopausal women, increased androstenedione, estradiol, estrone, and testosterone concentrations, high Breast Imaging Reporting and Data System (BIRADS) classification, and increased breast density. Convincing protective factors associated with lower BrCa risk included high fiber intake and high sex hormone binding globulin (SHBG) levels while highly suggestive protective factors included high 25 hydroxy vitamin D [25(OH)D] levels, adherence to healthy lifestyle, and moderate-vigorous physical activity.
Conclusions
Our findings suggest some highly modifiable factors that protect from BrCa. Interestingly, while diet was the most studied exposure category, the related associations failed to reach higher levels of evidence, indicating the methodological limitations in the field. To improve the validity of these associations, future research should utilise more robust study designs and better exposure assessment techniques. Overall, our study provides knowledge that supports the development of evidence-based BrCa prevention recommendations and guidance, both at an individual level and for public health initiatives.
Trial registration
PROSPERO CRD42022370675.
... To generate polygenic scores (PGS) (Supplementary Methods) we used genome-wide association studies (GWAS) summary statistics estimated in European populations for breast cancer risk reported by Mavaddat et al. 73 . For the well-established modifiable risk factors for breast cancer, we used results from the following resources: GSCAN consortium meta-analysis of smoking initiation (ever vs never status) 74 UK biobank (UKBB) metaanalysis of body mass index (BMI) 44 , summary statistics relating to breast density reported by Chen et al. 75 , and those relating to diabetes, such as fasting glucose and fasting insulin, were obtained from UKBB studies 76 . ...
Interval breast cancers (IBCs) are cancers diagnosed between screening episodes. Understanding the biological differences between IBCs and screen-detected breast-cancers (SDBCs) has the potential to improve mammographic screening and patient management. We analysed and compared the genomic landscape of 288 IBCs and 473 SDBCs by whole genome sequencing of paired tumour-normal patient samples collected as part of the UK 100,000 Genomes Project. Compared to SDBCs, IBCs were more likely to be lobular, higher grade, and triple negative. A more aggressive clinical phenotype was reflected in IBCs displaying features of genomic instability including a higher mutation rate and number of chromosomal structural abnormalities, defective homologous recombination and TP53 mutations. We did not however, find evidence to indicate that IBCs are associated with a significantly different immune response. While IBCs do not represent a unique molecular class of invasive breast cancer they exhibit a more aggressive phenotype, which is likely to be a consequence of the timing of tumour initiation. This information is relevant both with respect to treatment as well as informing the screening interval for mammography.
... The PRSs of the study cohort were estimated using TPMI genotyping data. To evaluate the PRSs, 77 SNPs were selected as candidates, all previously identified as breast cancer susceptibility loci for various types of breast cancer, and all SNPs have reached genome-wide significance (P < 5 × 10 −8 ) 21 . The polygenic risk effects of these 77 candidate SNPs on breast cancer have been previously validated through a large European cohort study, leading to the establishment of a reported trait for breast cancer named PGS000001 (PGS Name: PRS77_BC) The candidate SNPs used in this study are listed in Supplementary Table S1. ...
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.
... To generate polygenic scores (PGS) we used genome-wide association studies (GWAS) summary statistics estimated in European populations for breast cancer risk reported by Mavaddat et al31 . For the well-established modi able risk factors for breast cancer, we used results from the following resources: ...
Interval breast cancers (IBCs) are cancers diagnosed between screening episodes. Understanding the biological differences between IBCs and screen-detected breast-cancers (SDBCs) has the potential to improve mammographic screening and patient management. We analysed and compared the genomic landscape of 288 IBCs and 473 SDBCs by whole genome sequencing of paired tumour-normal patient samples collected as part of the UK 100,000 Genomes Project. Compared to SDBCs, IBCs were more likely to be lobular, higher grade, and triple negative. A more aggressive clinical phenotype was reflected in IBCs displaying features of genomic instability including a higher mutation rate and number of chromosomal structural abnormalities, defective homologous recombination and TP53 mutations. We did not however, find evidence to indicate that IBCs are associated with a different immune response. While IBCs do not represent a unique molecular class of invasive breast cancer they exhibit a more aggressive phenotype, which is likely to be a consequence of the timing of tumour initiation. This information is relevant both with respect to treatment as well as defining the screening interval for mammography.
... Genome-wide Association Studies (GWAS) have identified thus far a large number of common, low-risk variants that each contribute a small risk to the disease but can be combined into Polygenic Risk Scores (PRSs) with larger effect (7,8). PRSs provide a promising tool for clinical risk prediction of breast cancer by stratifying women into different categories of breast cancer risk (9)(10)(11), and may be used to inform targeted screening and prevention strategies (12)(13)(14)(15)(16)(17)(18)(19)(20). ...
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.
... Although screening can reduce the burden of breast cancer it has some drawbacks, especially over-diagnosis and high cost [23]. However, mammogram (MMG) is still considered the best screening tool for diagnosing breast cancer, if diagnosed early then chances for survival are higher for the patients [24]. Sensitivity of mammograms is about 90-95% where glandular tissue predominates whereas in high-density breast tissue sensitivity is reduced to 60-75% [25]. ...
Breast cancer is one of the most common cancers among women and the leading cause of death related to cancers. This study focuses on the screening of breast cancer for early detection of breast cancer and identification of risk factors for the development of breast cancer. This cross-sectional study was conducted among the female participants who were registered for the breast cancer screening program conducted by King Khalid University, Abha under the supervision of Family and Community Medicine, Department of College of Medicine. Convenience sampling was done and a total of 331 patients were selected for the study. The collected data were coded and entered into an Excel software (Microsoft Office Excel 2010) database. Data was analyzed using Statistical Package for Social Sciences, version 16 (SPSS, Inc., Chicago, IL, USA). p < 0.05 was considered statistically significant. Out of 331 individuals only 3 (0.9%) were diagnosed with breast cancer. Three cases of invasive ductal carcinoma were identified among individuals with breast cancer. While 4 (1.2%) individuals had benign findings, such as fibroadenoma, ductal ectasia, sclerosing adenosis, and tissue fibrosis. Past surgical history, past radiotherapy/chemotherapy, and maternal problems during pregnancy were significantly associated with varying levels of breast cancer risk. Breastcomplaints show a significant association with breast cancer indicating the need for furtherinvestigation. Findings from physical examinations, mammography reports, breast ultrasound, biopsy referrals, biopsy results, and management plans all show significant relationships with breast cancer risk. Our study findings indicate the need for health education for the prevention of breast cancer risk factors and the implementation of regular screening programs for the early detection of the disease in the community. Keywords Breast cancer; Screening; Risk factors; Outcomes
... For women who are negative or low risk for hereditary BC, we need to some tools or risk models for assessing their risk of developing BC. In the USA and Europe, BC risk predictive models, such as the Gail model and other models that have improved detection rates by incorporating whole genome analysis data are used to evaluate the risk of developing BC over a period of 5 years [6][7][8][9][10]. It is important to implement preventive strategies according to those individual risk levels. ...
The number of breast cancer patients is increasing worldwide. Furthermore, breast cancer often develops in young people, even those only in their 30s, who play a central role in their families and society. Results from many cohort studies suggest that dietary factors, alcohol consumption, lack of physical activity, obesity, nulliparity, breastfeeding, oral contraceptive use, fertility treatment and hormone replacement therapy are risk factors for breast cancer. However, the effects of lifestyle habits on the human body are complexly intertwined with various factors, and the effects vary from person to person depending on their constitution, etc., so there is no basis for this. Therefore, primary prevention of breast cancer is still not being implemented appropriately and efficiently. Furthermore, advances in genomic technology make it possible to assess the risk of developing breast cancer in some individuals. As a result, the establishment of breast cancer prevention methods has become a health priority for high-risk individuals.
Drugs such as tamoxifen and raloxifene are known to prevent the development of breast cancer, based on the results of multiple randomized controlled trials, but there are concerns regarding the side effects of these powerful agents. In addition, several clinical studies have shown that prophylactic mastectomy for women who have BRCA mutations or who are identified as being at high risk reduces the incidence of breast cancer development. However, many issues, such as changes in long-term quality of life after preventive surgery, the optimal timing of surgery and the identification of women who are at high risk but will not develop breast cancer, remain uncertain. In other words, although many researchers have focused on chemoprevention and surgical prevention and clear preventive effects of these strategies have been confirmed, it cannot be said that they are widely accepted. Therefore, the current evidence for chemoprevention and surgical prevention, as well as highlights of several interesting lines of research currently underway, are summarized in this article.
... For instance, women in the United Kingdom are invited to start mammographic screening when they turn 47 years old, which corresponds to a 2.4% 10-year risk threshold, as this is the average risk for women at this age. According to a polygenic risk score using 77 SNPs, women in the top 10% of genetic risk reach this risk threshold in their early 30s , whereas women in the bottom 10% of the polygenic risk remain below this threshold throughout their lifetime (29,30). Thus, information on genetic risk is more effective than an age-based criteria in guiding initiation of mammographic screening. ...
Context
Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease (ESRD). Measures to prevent and treat DKD require better identification of patients most at risk. In this systematic review, we summarise the existing evidence of genetic risk scores (GRSs) and their utility for predicting DKD in people with type 1 or type 2 diabetes.
Evidence Acquisition
We searched MEDLINE, Embase, Web of Science and Cochrane Reviews in June 2022 to identify all existing and relevant literature. Main data items sought were study design, sample size, population, single nucleotide polymorphisms (SNPs) of interest, DKD-related outcomes, and relevant summary measures of result. The Critical Appraisal Skills Programme checklist was used to evaluate the methodological quality of studies.
Evidence Synthesis
We identified 400 citations of which 15 were included in this review. Overall, seven studies had positive results, five had mixed results and three had negative results. Most studies with the strongest methodological quality (n = 9) reported statistically significant and favourable findings of a GRS’ association with at least one measure of DKD.
Conclusions
This systematic review presents evidence of the utility of GRSs to identify people with diabetes that are at high risk of developing DKD. In practice, a robust GRS could be used at the first clinical encounter with a person living with diabetes in order to stratify their risk of complications. Further prospective research is needed.
... We included effect allele frequencies (EAF) to adjust PRS scores to our population and calibrated the mean PRS to a mean of 1.0 as described in the literature [43,51]. We considered the OR values as reported in the most recent GWAS publications on BC associated SNPs in populations of European ancestry [27,28,52]. ...
Goals
To determine whether an 18 single nucleotide polymorphisms (SNPs) polygenic risk score (PRS18) improves breast cancer (BC) risk prediction for women at above-average risk of BC, aged 40–49, in a Central European population with BC incidence below EU average.
Methods
502 women aged 40–49 years at the time of BC diagnosis completed a questionnaire on BC risk factors (as per Tyrer-Cuzick algorithm) with data known at age 40 and before BC diagnosis. Blood samples were collected for DNA isolation. 250 DNA samples from healthy women aged 50 served as a control cohort. 18 BC-associated SNPs were genotyped in both groups and PRS18 was calculated. The predictive power of PRS18 to detect BC was evaluated using a ROC curve. 10-year BC risk was calculated using the Tyrer-Cuzick algorithm adapted to the Slovenian incidence rate (S-IBIS): first based on questionnaire-based risk factors and, second, including PRS18.
Results
The AUC for PRS18 was 0.613 (95 % CI 0.570–0.657). 83.3 % of women were classified at above-average risk for BC with S-IBIS without PRS18 and 80.7 % when PRS18 was included.
Conclusion
BC risk prediction models and SNPs panels should not be automatically used in clinical practice in different populations without prior population-based validation. In our population the addition of an 18SNPs PRS to questionnaire-based risk factors in the Tyrer-Cuzick algorithm in general did not improve BC risk stratification, however, some improvements were observed at higher BC risk scores and could be valuable in distinguishing women at intermediate and high risk of BC.
... The PRS derived from the GWAS demonstrated a significant dose-response relationship with GDM risk and glycemic traits. One methodology for quantifying genetic factors is the PRS, and this approach relies on the inclusion of diverse low penetrance variants with statistical significance derived from extensive GWAS analyses [19]. These findings provide evidence that the PRS can serve as a valuable tool for predicting GDM risk and can potentially be utilized in personalized GDM prevention strategies. ...
Our objective was to construct a polygenic risk score (PRS) and assess its utility and effectiveness in predicting the risk of gestational diabetes mellitus (GDM) in a Chinese population. We performed a case-control study involving 638 patients with GDM and 1,062 healthy controls. Genotyping was conducted utilizing a genome-wide association study (GWAS), and a PRS was constructed. We identified 12 susceptibility loci that exhibited significant associations with the risk of GDM at a p-value threshold of ≤5.0 × 10–8, of which four loci were newly discovered. A higher PRS was associated with an increased risk of GDM (OR: 1.44; 95% CI: 1.03, 2.01 for the highest quartile compared to the lowest quartile). The PRS demonstrated a clear linear relationship with the fasting plasma glucose (FPG), 1-hour postprandial glucose (1hPG), and 2-hour postprandial glucose (2hPG) levels. The maximally adjusted β coefficients and their corresponding 95% CIs were 0.181 (0.041, 0.320) for FPG, 0.225 (0.103, 0.346) for 1hPG, and 0.172 (0.036, 0.307) for 2hPG. Among the genetic variants examined, TCF7L2 rs7903146 displayed the strongest association with GDM risk (logOR = 0.18, p = 2.37 × 10–19), followed by ADAMTSL1 rs10963767 (logOR = 0.14, p = 3.58 × 10–15). The areas under the curve (AUCs) was significantly increased from 0.703 (0.678, 0.728) in the traditional risk factor model to 0.765 (0.741, 0.788) by including PRS. These findings indicate that pregnant women with a higher PRS could potentially derive considerable advantages from the implementation of a feasible PRS-based GDM screening program aimed at delivering precision prevention strategies within Chinese populations.
... PRS also could be developed using GWA (P < 5 × 10 -8 ) SNPs in core genes curated from multiple GWA studies [31][32][33], which we argue are not free from the overestimation bias. Using GWA SNPs is justified because it substitutes the P-value thresholding step required in conventional PRS studies for selecting SNPs. ...
Background
When polygenic risk score (PRS) is derived from summary statistics, independence between discovery and test sets cannot be monitored. We compared two types of PRS studies derived from raw genetic data (denoted as rPRS) and the summary statistics for IGAP (sPRS).
Results
Two variables with the high heritability in UK Biobank, hypertension, and height, are used to derive an exemplary scale effect of PRS. sPRS without APOE is derived from International Genomics of Alzheimer’s Project (IGAP), which records ΔAUC and ΔR ² of 0.051 ± 0.013 and 0.063 ± 0.015 for Alzheimer’s Disease Sequencing Project (ADSP) and 0.060 and 0.086 for Accelerating Medicine Partnership - Alzheimer’s Disease (AMP-AD). On UK Biobank, rPRS performances for hypertension assuming a similar size of discovery and test sets are 0.0036 ± 0.0027 (ΔAUC) and 0.0032 ± 0.0028 (ΔR ² ). For height, ΔR ² is 0.029 ± 0.0037.
Conclusion
Considering the high heritability of hypertension and height of UK Biobank and sample size of UK Biobank, sPRS results from AD databases are inflated. Independence between discovery and test sets is a well-known basic requirement for PRS studies. However, a lot of PRS studies cannot follow such requirements because of impossible direct comparisons when using summary statistics. Thus, for sPRS, potential duplications should be carefully considered within the same ethnic group.
... For library preparation, the Ion Total RNA-Seq kit v2 protocol (Thermo Fisher) with the recommendations for low input RNA quantity was followed as described in 25 . Barcoded primers from Ion Xpress™ RNA-Seq Barcode 01-16 Kit, Thermo Fisher, or synthesised by Eurofins Genomics as custom oligonucleotides (barcodes [17][18][19][20][21][22][23][24] were used. Differentially barcoded small-RNA libraries were pooled and checked by Bioanalyzer System and DNA 1000 Kit (Agilent Technologies) to determine the library dilution required for template preparation. ...
Mammographic breast cancer screening is effective in reducing breast cancer mortality. Nevertheless, several limitations are known. Therefore, developing an alternative or complementary non-invasive tool capable of increasing the accuracy of the screening process is highly desirable. The objective of this study was to identify circulating microRNA (miRs) ratios associated with BC in women attending mammography screening. A nested case–control study was conducted within the ANDROMEDA cohort (women of age 46–67 attending BC screening). Pre-diagnostic plasma samples, information on life-styles and common BC risk factors were collected. Small-RNA sequencing was carried out on plasma samples from 65 cases and 66 controls. miR ratios associated with BC were selected by two-sample Wilcoxon test and lasso logistic regression. Subsequent assessment by RT-qPCR of the miRs contained in the selected miR ratios was carried out as a platform validation. To identify the most promising biomarkers, penalised logistic regression was further applied to candidate miR ratios alone, or in combination with non-molecular factors. Small-RNA sequencing yielded 20 candidate miR ratios associated with BC, which were further assessed by RT-qPCR. In the resulting model, penalised logistic regression selected seven miR ratios (miR-199a-3p_let-7a-5p, miR-26b-5p_miR-142-5p, let-7b-5p_miR-19b-3p, miR-101-3p_miR-19b-3p, miR-93-5p_miR-19b-3p, let-7a-5p_miR-22-3p and miR-21-5p_miR-23a-3p), together with body mass index (BMI), menopausal status (MS), the interaction term BMI * MS, life-style score and breast density. The ROC AUC of the model was 0.79 with a sensitivity and specificity of 71.9% and 76.6%, respectively. We identified biomarkers potentially useful for BC screening measured through a widespread and low-cost technique. This is the first study reporting circulating miRs for BC detection in a screening setting. Validation in a wider sample is warranted.
Trial registration: The Andromeda prospective cohort study protocol was retrospectively registered on 27-11-2015 (NCT02618538).
... Such predictors are now available for nonpharmacologic phenotypes such as type 2 diabetes, 38 coronary artery disease, 39,40 neurodegenerative disease, 41,42 and some cancers. [43][44][45] Polygenic risk predictors have been demonstrated to be especially useful for those individuals at the tail ends of the distribution for a given phenotype. 46 Applied to dexmedetomidine and fentanyl clearance, polygenic risk scores may be most useful for identifying individuals at the highest risk for undersedation or oversedation with standard drug dosing regimens secondary to differences in drug clearance. ...
Despite complex pathways of drug disposition, clinical pharmacogenetic predictors currently rely on only a few high effect variants. Quantification of the polygenic contribution to variability in drug disposition is necessary to prioritize target drugs for pharmacogenomic approaches and guide analytic methods. Dexmedetomidine and fentanyl, often used in postoperative care of pediatric patients, have high rates of inter-individual variability in dosing requirements. Analyzing previously generated population pharmacokinetic parameters, we used Bayesian hierarchical mixed modeling to measure narrow-sense (additive) heritability (h2 SNP ) of dexmedetomidine and fentanyl clearance in children and identify relative contributions of small, moderate, and large effect-size variants to h2 SNP . We used genome-wide association studies (GWAS) to identify variants contributing to variation in dexmedetomidine and fentanyl clearance, followed by functional analyses to identify associated pathways. For dexmedetomidine, median clearance was 33.0 L/hr (IQR 23.8-47.9 L/hr) and h2 SNP was estimated to be 0.35 (90% credible interval 0.00 - 0.90), with 45% of h2 SNP attributed to large-, 32% to moderate-, and 23% to small-effect variants. The fentanyl cohort had median clearance of 8.2 L/hr (IQR 4.7-16.7 L/hr), with estimated h2 SNP of 0.30 (90% credible interval 0.00 - 0.84). Large-effect variants accounted for 30% of h2 SNP , while moderate- and small-effect variants accounted for 37% and 33%, respectively. As expected, given small sample sizes, no individual variants or pathways were significantly associated with dexmedetomidine or fentanyl clearance by GWAS. We conclude that clearance of both drugs is highly polygenic, motivating the future use of polygenic risk scores to guide appropriate dosing of dexmedetomidine and fentanyl. This article is protected by copyright. All rights reserved.
... zvýšeným rizikem ca prsu bez ohledu na počet SNP zahrnutých do výpočtu. Především v odlehlých percentilech hodnot PRS riziko překračuje dvojnásobek populačního průměru [44,49]. Ně kte ré z uvedených setů byly dále testovány na probandkách s jinými selekčními kritérii. ...
Background:
Breast cancer is a complex, multifactorial disease influenced by many genetic factors. Besides the relatively rare pathogenic variants in high or moderate penetrant cancer predisposition genes, breast cancer risk is modified by numerous low risk alleles considered to be polygenic genetic factors. While the risks associated with individual polygenic loci are negligible, its cumulative effect can reach clinically significant values and it can be expressed as a polygenic risk score (PRS). PRS is recently considered to be a possible tool improving assessment of absolute and cumulative risks at the individual level.
Purpose:
Several single nucleotide polymorphism sets for PRS assessment have recently been developed and prepared for their implementation into clinical practice. The following text aims to explain the fundamental principles of the PRS assessment and its interpretation as a candidate prediction tool. The use of the PRS should always depend on genetic analysis of pathogenic variants in cancer predisposition genes including its current limitations.
... Breast cancer was the first target of genetic risk scores with the discovery of BRCA1 [35]. A 2015 study that computed a polygenic risk score based on 77 SNPs found that women who scored in the top 1% had a three-fold increase in risk compared to a woman who scored in the middle quintile [36]. For comparison, in the breast cancer prediction presented based on UK Biobank data, here 8 of 9 in the top 1% had breast cancer, while 5 of 27 in the middle 3% had breast cancer. ...
Introduction
The ability to accurately predict whether a woman will develop breast cancer later in her life, should reduce the number of breast cancer deaths. Different predictive models exist for breast cancer based on family history, BRCA status, and SNP analysis. The best of these models has an accuracy (area under the receiver operating characteristic curve, AUC) of about 0.65. We have developed computational methods to characterize a genome by a small set of numbers that represent the length of segments of the chromosomes, called chromosomal-scale length variation (CSLV).
Methods
We built machine learning models to differentiate between women who had breast cancer and women who did not based on their CSLV characterization. We applied this procedure to two different datasets: the UK Biobank (1534 women with breast cancer and 4391 women who did not) and the Cancer Genome Atlas (TCGA) 874 with breast cancer and 3381 without.
Results
We found a machine learning model that could predict breast cancer with an AUC of 0.836 95% CI (0.830.0.843) in the UK Biobank data. Using a similar approach with the TCGA data, we obtained a model with an AUC of 0.704 95% CI (0.702, 0.706). Variable importance analysis indicated that no single chromosomal region was responsible for significant fraction of the model results.
Conclusion
In this retrospective study, chromosomal-scale length variation could effectively predict whether or not a woman enrolled in the UK Biobank study developed breast cancer.
... In the future, it may be possible to use these scores in the same way that we use current genetic testing, but rather than looking at one or a few genes, the whole genome will be considered [210]. While some PRS scores are currently being used for melanoma [211], coronary artery disease [212] and diabetes [213], PRS is still primarily in the research phase for ALS and many other diseases. One PRS has been completed recently in ALS but did not seem likely to have clinical utility based on the small proportion of heritability that it could explain [214]. ...
Amyotrophic lateral sclerosis (ALS) is caused by upper and lower motor neuron loss and has a fairly rapid disease progression, leading to fatality in an average of 2-5 years after symptom onset. Numerous genes have been implicated in this disease; however, many cases remain unexplained. Several technologies are being used to identify regions of interest and investigate candidate genes. Initial approaches to detect ALS genes include, among others, linkage analysis, Sanger sequencing, and genome-wide association studies. More recently, next-generation sequencing methods, such as whole-exome and whole-genome sequencing, have been introduced. While those methods have been particularly useful in discovering new ALS-linked genes, methodological advances are becoming increasingly important, especially given the complex genetics of ALS. Novel sequencing technologies, like long-read sequencing, are beginning to be used to uncover the contribution of repeat expansions and other types of structural variation, which may help explain missing heritability in ALS. In this review, we discuss how popular and/or upcoming methods are being used to discover ALS genes, highlighting emerging long-read sequencing platforms and their role in aiding our understanding of this challenging disease.
... Breast cancer was the rst target of genetic risk scores with the discovery of BRCA1 [30]. A 2015 study that computed a polygenic risk score based on 77 SNPs found that women who scored in the top 1% had a three-fold increase in risk compared to a woman who scored in the middle quintile [31]. For comparison, in the breast cancer prediction presented based on UK Biobank data, here 8 of 9 in the top 1% had breast cancer, while 5 of 27 in the middle 3% had breast cancer, for an approximate tenfold increase in risk Machine learning techniques have been used to build polygenic risk scores to predict other complex traits [32,33]. ...
Introduction.
The ability to accurately predict whether a woman will develop breast cancer later in her life, should reduce the number of breast cancer deaths. Different predictive models exist for breast cancer based on family history, BRCA status, and SNP analysis. The best of these models has an accuracy (area under the receiver operating characteristic curve, AUC) of about 0.65. We have developed computational methods to characterize a genome by a small set of numbers that represent the length of segments of the chromosomes, called chromosomal-scale length variation (CSLV).
Methods.
We built machine learning models to differentiate between women who had breast cancer and women who did not based on their CSLV characterization. We applied this procedure to two different datasets: the UK Biobank (1,534 women with breast cancer and 4,391 women who did not) and the Cancer Genome Atlas (TCGA) 874 with breast cancer and 3,381 without.
Results.
We found a machine learning model that could predict breast cancer with an AUC of 0.836 95% CI(0.830.0.843) in the UK Biobank data. Using a similar approach with the TCGA data, we obtained a model with an AUC of 0.704 95%CI(0.702,0.706). Variable importance analysis indicated that no single chromosomal region was responsible for significant fraction of the model results.
Conclusion.
Chromosomal-scale length variation can be used to effectively predict whether or not a woman will develop breast cancer.
... Details about the genotyping process can be found in the protocol [27]. The PRS was obtained using the 83 SNPs associated with breast cancer, based on Shieh et al.'s [30] or Mavaddat et al.'s [31] studies, as a composite likelihood ratio representing the individual effects of each SNP. ...
The aim of this study was to assess the acceptability and feasibility of offering risk-based breast cancer screening and its integration into regular clinical practice. A single-arm proof-of-concept trial was conducted with a sample of 387 women aged 40–50 years residing in the city of Lleida (Spain). The study intervention consisted of breast cancer risk estimation, risk communication and screening recommendations, and a follow-up. A polygenic risk score with 83 single nucleotide polymorphisms was used to update the Breast Cancer Surveillance Consortium risk model and estimate the 5-year absolute risk of breast cancer. The women expressed a positive attitude towards varying the frequency of breast screening according to individual risk and, especially, more frequently inviting women at higher-than-average risk. A lower intensity screening for women at lower risk was not as welcome, although half of the participants would accept it. Knowledge of the benefits and harms of breast screening was low, especially with regard to false positives and overdiagnosis. The women expressed a high understanding of individual risk and screening recommendations. The participants’ intention to participate in risk-based screening and satisfaction at 1-year were very high.
... Family history and gender are the leading risk factors in studies on breast cancer (19)(20)(21)(22)(23)(24)(25). In our study, gender was ...
Aim: In this retrospective observational study, it was aimed to evaluate the relationship between breast cancer deaths and demographic properties of countries. Material and Method: The research was conducted on World Health Organization (WHO) 10th International Classification of Diseases (ICD-10) mortality list and World Bank Country Reports (WBCR). Total breast cancer-related deaths, age groups and urban population rates of 14 countries between 1996 and 2017 were evaluated. Results: Both uncontrolled and controlled correlation analysis results showed that population age distribution had a significant correlation with total breast cancer-related deaths (p0.05). Generalized Linear Model (GLM) results showed that only the country had a significant effect on total breast cancer related deaths (p0.05). Conclusion: Although reasons such as age and urbanization play an important role among breast cancer risk factors, it is found that they do not affect mortality rates. A total of 22 years of WHO data and 14 country results showed that deaths due to breast cancer are only related to the country. Therefore, countries can minimize deaths due to breast cancer by carrying out more effective struggles, early diagnosis, treatment and awareness activities.
... [5] classifying women based on breast cancer risk factors can be effective in improving risk-free methods and designing targeted breast cancer screening programs. [6,7] Breast cancer is the most common cancer in women both in the developed and less developed world. it is estimated that worldwide over 508 000 women died in 2011 due to breast cancer (global Health estimates, WHO 2013). ...
Introduction; There are data on an increase in cancer cases in Albania in recent decades, this growing trend of cancer in the Albanian population is due to the rapid increase of habits or unhealthy behaviours such as; smoking, excessive alcohol consumption, unhealthy diet, high levels of obesity, and physical inactivity. However, Albanian men and women show one of the lowest values (in terms of age) in the region of Southeast Europe. In Albania still, we do not have official data regarding the number of breast cancer diagnosed patients either their characteristics, or pathological profile. Our purpose is to conduct retrospective study of number of Breast Cancer patients treated and diagnosed in Albania during 2018. Material and Methods; We have recorded and recorded all breast cancer data from the registry of the Oncology Service at University Hospital Center “Mother Teresa “and private clinics in Tirana and from the registries of the district hospitals, during 2018. Results; Total number of breast cancer patients treated and diagnosed in 2018, from them 506 patients were diagnosed and treated with breast cancer of all stages in our hospital. Since the registry of cancer is still not untirely functioning the data represents only our hospital not. Conclusions; Breast cancer remains a major public health concern worldwide. Trends in the incidence, mortality, and regulated life years of the disabled are varied across regions and countries, suggesting the allocation of appropriate health care resources for breast cancer, which should have the highest level of evaluation.
... Esta revisão buscou estudos que avaliaram aspectos clínicos, psicológicos ou sociais de pacientes com câncer no Research, Society and Development, v. 11, n. 8, e28511830635, 2022 (CC BY 4. (Simin et al., 2017), determinantes genéticos (Mavaddat et al., 2015), densidade mamográfica (Darabi et al., 2012), histórico familiar de câncer de mama (Antoniou et al., 2008). ...
É vantajoso a averiguação da qualidade de vida de indivíduos com câncer, visto que os tratamentos, sinais e sintomas perfazem a origem de algum grau de perturbação emocional, limitações físicas e funcionais que acabam interferindo na vida do indivíduo. Dessa forma, objetivou-se avaliar a qualidade de vida dos pacientes com diagnóstico de câncer no Brasil, utilizando-se estudos que fizeram uso de instrumentos validados para esse grupo de indivíduos. Sendo assim, trata-se de uma revisão sistemática realizada de acordo com o Guidelines of Transparent Reporting of Systematic Reviews and Meta-Analyses (PRISMA statement) entre janeiro de 2008 a agosto de 2019 por meio de bases de dados especializadas: PUBMED, SCOPUS, MEDLINE, LILACS e SCIELO. Foi identificado que a qualidade de vida tem sido prejudicada nos âmbitos físico e mental. Além disso, a região Sudeste apresenta o maior quantitativo de estudos. O câncer colorretal foi mais identificado nos estudos e o instrumento WHOQOL-bref o mais utilizado. Com isso, os dados encontrados podem transformar-se em subsídios para a promoção de ações que visem responder às necessidades oncológicas para o controle da doença.
... Breast cancer in women is the second most common type of cancer and accounts for about 18% of all malignant tumors in women worldwide (4). In developed countries (including BiH) it is even at the first place. ...
Background:
Breast cancer in women is the second most common and accounts for approximately 18% of all malignant tumors in women worldwide. The etiology of breast cancer is not clear enough. Starting from the assumption that the manifestation of breast cancer may have a multifactorial model, this article compares the population-genetic structure of patients (experimental group) with the population-genetic structure of healthy population (control group).
Objective:
The aim of the study was to examine the possible genetic basis of the Rh factor relationship with selected homozygous-recessive traits of females with breast cancer, and to diagnose the probability (assess the risk) of developing the disease in healthy women by analyzing homozygous-recessive traits (HRT).
Methods:
This are an anthroposcopic-qualitative study that included two groups of subjects, experimental and control (a total of 80 subjects). An analysis of the percentages within each group was performed using the Chi-square test. The results are presented in tables, and the accepted level of significance is at the level of p <0.05.
Results:
In the group of Rh+ subjects, the correlation of this type of Rh factor with the breast cancer was proven, given the frequency of the phenotype of homozygous-recessive traits in them. A statistically significant difference was found for 4 traits, and three are also close to the set significance level. In subjects with Rh- factor, a statistically significant difference was found for only one trait (absence of mallets on the phalanges).
Conclusion:
Although the number of subjects was relatively small, we can conclude that in the experimental group a higher frequency of recessive phenotypes for the examined traits was recorded, which indicates the genetic load of the subjects from this group. Correlation with Rh factor was observed in the case of subjects of the experimental group with Rh+ factor.
In more than 25% of all index patients who fulfil the criteria of the German Consortium of Familial Breast and Ovarian Cancer (DK) for germline testing, possible pathogenic or pathogenic germline variants (PV) in known risk genes are identified. If a germline PV is detected healthy women in a family can be offered predictive testing. In the course of personalized medicine other genetic (polygenic risk scores, PRS) and nongenetic risk factors (lifestyle, hormonal and reproductive factors, mammographic density) are increasingly receiving attention, which can significantly modulate the individual risk of disease. In this way a personalized risk prediction is possible. In healthy women the offer of risk-adapted prevention (participation in an intensified breast cancer screening) can be adjusted to the individual risk. The individualized prevention requires prospective cohort studies to evaluate a benefit for women seeking advice. The analyses should therefore be embedded in a knowledge-generating documentation and evaluation concept. Various materials for those affected have been developed in simple or plain language to address the increased complexity. In addition, patient decision aids and decision coaching support carriers of PVs in the BRCA1 and BRCA2 genes in making decisions with respect to preventive measures.
Background
The polygenic risk score (PRS) allows the quantification of the polygenic effect of many low‐penetrance alleles on the risk of breast cancer (BC). This study aimed to evaluate the performance of two sets comprising 77 or 313 low‐penetrance loci (PRS77 and PRS313) in patients with BC in the Czech population.
Methods
In a retrospective case‐control study, variants were genotyped from both the PRS77 and PRS313 sets in 1329 patients with BC and 1324 noncancer controls, all women without germline pathogenic variants in BC predisposition genes. Odds ratios (ORs) were calculated according to the categorical PRS in individual deciles. Weighted Cox regression analysis was used to estimate the hazard ratio (HR) per standard deviation (SD) increase in PRS.
Results
The distributions of standardized PRSs in patients and controls were significantly different (p < 2.2 × 10⁻¹⁶) with both sets. PRS313 outperformed PRS77 in categorical and continuous PRS analyses. For patients in the highest 2.5% of PRS313, the risk reached an OR of 3.05 (95% CI, 1.66–5.89; p = 1.76 × 10⁻⁴). The continuous risk was estimated as an HRper SD of 1.64 (95% CI, 1.49–1.81; p < 2.0 × 10⁻¹⁶), which resulted in an absolute risk of 21.03% at age 80 years for individuals in the 95th percentile of PRS313. Discordant categorization into PRS deciles was observed in 248 individuals (9.3%).
Conclusions
Both PRS77 and PRS313 are able to stratify individuals according to their BC risk in the Czech population. PRS313 shows better discriminatory ability. The results support the potential clinical utility of using PRS313 in individualized BC risk prediction.
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.
Riskadapted cancer prevention has moved to the centre of oncology through the National Decade Against Cancer. Hereditary breast and ovarian cancer have pioneered this field. By including genetic and non-genetic factors, personalised risk prediction has become possible for those affected, which forms the basis for risk-stratified screening and prevention. While in the past two decades the focus was on the about 30 % of affected women with a (high-)risk situation, the focus is now being directed towards the general population. The goal is a comprehensive personalised risk prediction for all, as a basis for the offer of risk-adapted preventive measures with a chance to improve outcome.
In more than 20% of all (index) patients who fulfil the criteria of the German Consortium of Familial Breast and Ovarian Cancer for germline testing, mutations in known risk genes are identified. If a germline mutation is detected healthy women in a family can subsequently be offered predictive testing. In the course of personalized medicine, other genetic (polygenic risk scores, PRS) and nongenetic risk factors (lifestyle, hormonal and reproductive risk factors, mammographic density) are increasingly receiving attention, which can significantly modulate the individual risk of disease. Using the CanRisk analysis in patients who already have the disease the secondary disease risk can be specified in more detail. In healthy women the offer of a risk-adjusted prevention (participation in a program for intensified detection of breast cancer) can be adapted to the individual risk. Individualized prevention requires prospective cohort studies to evaluate a benefit for women seeking advice. The analyses should therefore be embedded in a documentation and evaluation concept. Various materials have been developed in easy and plain language to address the increased complexity resulting from new developments in the field. In addition, patient decision aids support carriers of pathogenic mutations in BRCA1/2 in making decisions about preventive measures.
Background
Breast cancer (BC) screening with mammography reduces mortality but considers currently only age as a risk factor. Personalized risk-based screening has been proposed as a more efficient alternative. For that, risk prediction tools are necessary. Genome-wide association studies have identified numerous genetic variants (single-nucleotide polymorphisms [SNPs]) associated with BC. The effects of SNPs are combined into a polygenic risk score (PRS) as a risk prediction tool.
Objectives
We aimed to develop a clinical-grade PRS test suitable for BC risk-stratified screening with clinical recommendations and implementation in clinical practice.
Design and methods
In the first phase of our study, we gathered previously published PRS models for predicting BC risk from the literature and validated them using the Estonian Biobank and UK Biobank data sets. We selected the best performing model based on prevalent data and independently validated it in both incident data sets. We then conducted absolute risk simulations, developed risk-based recommendations, and implemented the PRS test in clinical practice. In the second phase, we carried out a retrospective analysis of the PRS test’s performance results in clinical practice.
Results
The best performing PRS included 2803 SNPs. The C-index of the Cox regression model associating BC status with PRS was 0.656 (SE = 0.05) with a hazard ratio of 1.66. The PRS can stratify individuals with more than a 3-fold risk increase. A total of 2637 BC PRS tests have been performed for women between the ages 30 and 83. Results in clinical use overlap well with expected PRS performance with 5.7% of women with more than 2-fold and 1.4% with more than 3-fold higher risk than the population average.
Conclusion
The PRS test separates different BC risk levels and is feasible to implement in clinical practice.
Background: To evaluate the utility of polygenic risk scores (PRS) in identifying high-risk individuals, different publicly available PRS for breast (n=85), prostate (n=37), colorectal (n=22) and lung cancers (n=11) were examined in a prospective study of 21,694 Chinese adults.
Methods: We constructed PRS using weights curated in the online PGS Catalog. PRS performance was evaluated by distribution, discrimination, predictive ability, and calibration. Hazard ratios (HR) and corresponding confidence intervals [CI] of the common cancers after 20 years of follow-up were estimated using Cox proportional hazard models for different levels of PRS.
Results: A total of 495 breast, 308 prostate, 332 female-colorectal, 409 male-colorectal, 181 female-lung and 381 male-lung incident cancers were identified. The area under receiver operating characteristic curve for the best performing site-specific PRS were 0.61 (PGS000873, breast), 0.70 (PGS00662, prostate), 0.65 (PGS000055, female-colorectal), 0.60 (PGS000734, male-colorectal) and 0.56 (PGS000721, female-lung), and 0.58 (PGS000070, male-lung), respectively. Compared to the middle quintile, individuals in the highest cancer-specific PRS quintile were 64% more likely to develop cancers of the breast, prostate, and colorectal. For lung cancer, the lowest cancer-specific PRS quintile was associated with 28-34% decreased risk compared to the middle quintile. In contrast, the hazard ratios observed for quintiles 4 (female-lung: 0.95 [0.61-1.47]; male-lung: 1.14 [0.82-1.57]) and 5 (female-lung: 0.95 [0.61-1.47]) were not significantly different from that for the middle quintile.
Conclusions: Site-specific PRSs can stratify the risk of developing breast, prostate, and colorectal cancers in this East Asian population. Appropriate correction factors may be required to improve calibration.
Funding This work is supported by the National Research Foundation Singapore (NRF-NRFF2017-02), PRECISION Health Research, Singapore (PRECISE) and the Agency for Science, Technology and Research (A*STAR). WP Koh was supported by National Medical Research Council, Singapore (NMRC/CSA/0055/2013). CC Khor was supported by National Research Foundation Singapore (NRF-NRFI2018-01). Rajkumar Dorajoo received a grant from the Agency for Science, Technology and Research Career Development Award (A*STAR CDA - 202D8090), and from Ministry of Health Healthy Longevity Catalyst Award (HLCA20Jan-0022). The Singapore Chinese Health Study was supported by grants from the National Medical Research Council, Singapore (NMRC/CIRG/1456/2016) and the U.S. National Institutes of Health [NIH] (R01 CA144034 and UM1 CA182876).
The present study examined the anticancer capabilities of Bacillus coagulans supernatant-produced copper oxide nanoparticles (BC-CuONPs) on MCF-7 and SKBR3 cancer cells. The X-ray diffraction, ultraviolet–visible spectroscopy, Fourier-transform infrared spectroscopy, X-ray photoelectron spectroscopy, transmission electron microscopy, field-emission scanning electron microscopy, energy-dispersive X-ray, dynamic light scattering, and zeta potential techniques were used to characterize BC-CuONPs. This study also investigated the cellular and molecular processes of NPs’ anti-proliferative and apoptotic properties on human breast cancer cells and compared them to the commercial pharmaceutical tamoxifen. The size of the spherical NP was from 5 to 47 nm with negative zeta potential. The MTT results showed the great cytotoxic effect of BC-CuONPs against breast cancer cells. The BC-CuONPs inhibited the growth of breast cancer cells in a time- and dose-dependent manner. The up-regulation of BCL2-associated X (BAX), cyclin dependent kinase inhibitor 1A (P21), Caspase 3 (CASP3), and Caspase 9 (CASP9), the down-regulation of BCL2 apoptosis regulator (BCL2), Annexin V-FITC/propidium iodide, and reactive oxygen species (ROS) generation results suggested that BC-CuONPs had a significant apoptotic impact when compared to the control. Scratch tests and vascular endothelial growth factor receptor gene (VEGF) down-regulation demonstrated that BC-CuONPs had anti-metastatic activity. The cell cycle analysis and down-regulation of Cyclin D1 (CCND1) and cyclin dependent kinase 4 (CDK4) revealed that cancer cells were arrested in the sub-G1 phase. Finally, the results showed that the secondary metabolites in the supernatant of Bacillus coagulans could form CuONPs, and biogenic BC-CuONPs showed anti-metastasis and anticancer properties on breast cancer cells while having less adverse effects on normal cells. Therefore, the synthesized CuONPs using B. coagulans supernatant can be shown as a potential candidate for a new therapeutic strategy in cancer management.
Background: Breast cancer accounts for a large proportion of cancer-related deaths in women. Polygenic risk score (PRS) derived from single nucleotide polymorphisms (SNP) data can evaluate the individual-level genetic risk of breast cancer and has been widely applied for risk stratification. However, standalone SNP data used for PRS may not provide satisfactory prediction accuracy. Additionally, current PRS models based on linear regression have insufficient power to leverage non-linear effects from thousands of associated SNPs.
Methods: In this study, the multiple omics data (DNA methylation data, miRNA data, mRNA data and lncRNA data) and clinical data of breast invasive carcinoma (BRCA) were collected from The Cancer Genome Atlas (TCGA). First, we developed a novel PRS model utilizing single omic data and a machine learning algorithm (LightGBM). Subsequently, we built a combination model of PRS derived from each omic data to explore whether multiple omics data can further improve the prediction accuracy of PRS. Finally, we performed association analysis and prognosis prediction of breast cancer to evaluate the utility of the PRS generated by our method.
Results: Our PRS model based on single omic data and LightGBM algorithm achieved better predictive performance than the linear models and other machine learning models. Moreover, the combination of the PRS derived from each omic data can efficiently strengthen prediction accuracy. The analysis of prevalence and the associations of the PRS with phenotypes including case-control and cancer stage status indicated that the risk of breast cancer increases with the increases of PRS. The survival analysis also suggested that PRS for the cancer stage is an effective prognostic metric of breast cancer patients.
Conclusion: Our proposed model expanded the current definition of PRS from standalone SNP data to multiple omics data and outperformed the state-of-the-art PRS models, which may provide a powerful tool for diagnostic and prognostic prediction of breast cancer.
İstatistik biliminin kurucularından olan Ronald Aylmer Fisher 17 Şubat 1890’da Birleşik
Krallığa bağlı Doğu Finchley’de dünyaya gelmiştir. Fisher’in matematik alanındaki şaşırtıcı düzeydeki yetenekleri erken yaşlarda kendini göstermiştir. Ancak küçük yaşlarda ileri derecede miyop teşhisi konulan Fisher, yapay ışık ve kağıt - kalem kullanmadan eğitim almak zorunda kalmıştır. İlköğrenimini Stanmore Park’ta, liseyi Harrow okulu ve üniversiteyi Gonville ve Caius Kolejinde okumuştur. Fisher 1912’de korelasyon katsayısı üzerine önemli çalışmalar yapmış ve maksimum olabilirlik yöntemini ortaya koyan ilk makalesini yayımlamıştır. Aynı yıl Fisher, Gosset’in “Bir Ortalamanın Muhtemel Hatası” adlı makalesini incelerken standart sapma hesaplamalarında bir tutarsızlık tespit etmiştir. Fisher bu makalede,
standart sapma için kullanılan formülde paydada “n” kullanmak yerine “n-1” kullanılması gerektiğini bulmuştur. T dağılımındaki bu düzeltmeyi Fisher bir mektupla Gosset’en bildirmiştir. I. Dünya Savaşı’nın sonunda Fisher yeni bir iş ararken, iki iş teklifi birden almıştır. Bunlardan birisi Karl Pearson tarafından yönetilen dönemin ünlü Galton Laboratuvarı’ndan, diğeri de ülkede küçük bir tarım istasyonu olan Rothamsted Deney İstasyonu’dur. Ancak Fisher, Pearson ile gelişen rekabeti profesyonel bir engel olarak görmüş ve Galton Laboratuvarı teklifini kabul etmeyerek, özgür bir çalışma ortamı için 1919’da Rothamsted Tarım İstasyonu’nda istatistikçi olarak göreve başlamıştır. Fisher, 1921 yılında Biometrika dergisine gönderdiği makalesinde, sınıf içi korelasyon katsayısına ilişkin önerilerini belirtmiş ve z’nin örnekleme dağılımının normalliğe çok yakın olduğunu göstermiştir. Ancak Karl Pearson, Fisher’in yoluna zorluklar çıkarmak için Fisher’in makalesinin, editörlüğünü yaptığı Biometrika dergisi tarafından reddedilmesine sebep olmuştur. Fisher, 1922-1928 yılları arasında 2 istatistiği ile ilgili 5 tane makale yayımlayarak, Pearson’ın 2 istatistiğini hesaplamada kullandığı serbestlik derecesinin yanlış olduğunu vurgulayıp, farklı bir serbestlik derecesiyle yeni bir 2 tablosu oluşturmuştur. Fisher,
Pearson’ın 2 dağılımı için kullandığı (p x q - 1) serbestlik derecesi yerine bir (p x q)
tablosunda (p - 1) x (q - 1) serbestlik dereceleri için 2 dağılımı elde etmiştir. Fisher,
Rothamsted Deney İstasyonu’nda tarımsal çalışmalar sırasında değişkenler arasındaki ilişkiyi açıklamada ve sonuçların karşılaştırılması sırasında bazı zorluklarla karşılaşmıştır.
This study presents calibration results of radiation thermometer in Liquid Bath
Cavity systems low temperature range from -40 ̊C to +150 ̊C for industrial applications. The
low temperature cavity source was designed for the liquid calibration baths to better
temperature uniformity. The aim of this study is to analyse and determine the measurement
uncertainty parameters of this system. The reference temperature of the calibration is
determined by the Heitronics transfer radiation thermometer related to the spectral response of
8 - 14 µm. ITS-90 calibration technique is used and all the uncertainty parameters affecting the
measurement are evaluated and an uncertainty budget is obtained for all system. The study
shows that with this system designed for the bath, measurement uncertainty that contributes
significantly to the uncertainty of the source effect, reflected ambient radiation, temperature
gradients on the radiation source has been reduced.
Hayvan davranışlarını araştırmada ve davranışların detaylandırılarak tanımlanmasında
kullanılan yaygın yöntemlerinden birsidir. Araştırmacı araştırmak istediği davranış için hayvan sayısına bakılmaksızın grup içerisinden rast gele bir hayvan seçer, o hayvanı gözlem boyunca takip eder ve önceden belirlemiş olduğu davranışlarını kaydeder. Gözlemlere başlamadan önce gözlemciler kayıt kurallarını tanımlamalılar. Kayıt için üç farklı kayıt kuralı kullanılabilir. Sürekli kayıt (tüm gözlem süresi boyunca kaydedilen tüm davranışlar), Anlık örnekleme (gözlem başlangıcında tespit edilen davranışlar) ve Bir sıfır örnekleme ( gözlemdeki ilk oluşum). Gözlem süresi gözlemlenen davranışın türüne göre değişmektedir. Kanatlı gibi aktif hareket eden türlerde gözlem süresi kısa olurken yavaş hareket eden türler için gözlem süresi uzun tutulmalıdır. Örneğin: Tembel hayvan yavaş hareket ettiğinden gözlem süresinin uzun olarak tutulması gerekmektedir. Bunun yanı sıra Ceylanlarda emme süresi ve emme süreleri arasındaki süreler incelenirken gözlemci iki emme süresi boyunca takipte kalmalıdır. Sağlıklı veri elde etmek için. Gözlem aralığının uzunluğu belirlenen davranış parametrelerine göre değişmektedir. Çok sayıda davranış parametresi hem gözlemciyi yoracaktır hem de uzun süre gözlem yapması gerekecektir. Buna bağlı olarak verilerin doğruluk payını doğrudan etkilemektedir. Gözlemci çalışma yapacağı tür hakkında ön çalışma yapmış olmalıdır. Gözlemlenecek davranış parametrelerini önceden belirleyerek çalışmasında etkin ve doğru sonuçlara ulaşmasını sağlayacaktır. Gözlem yapılan alanın şartlarına ve imkânlarına bağlı olarak bir takım modifikasyonlar yaparak gözlemlerinde elde etmek istediği sonuçlara daha rahat ulaşmasını sağlayacaktır. Gözlem süresi boyunca birden fazla hayvan üzerinden yapılan
çalışma sürüyü temsil etmekle birlikte herhangi bir hayvana ait veri kaybı olması durumunda sadece küçük bir bilgi eksikliği oluşmaktadır. Buda çalışmanın tamamını etkilemeyecektir.
Background:
Keratinocyte cancer (KC) risk is determined by genetic and environmental factors. Genetic risk can be quantified by polygenic risk scores (PRS), which sum the combined effects of single nucleotide polymorphisms (SNPs).
Objectives:
Our objective here was to evaluate the contribution of the summed genetic score to predict the KC risk in the phenotypically well-characterised Nambour population.
Methods:
We used PLINK v1.90 to calculate PRS for 432 cases, 566 controls, using 78 genome-wide independent SNPs that are associated with KC risk. We assessed the association between PRS and KC using logistic regression, stratifying the cohort into 3 risk groups (high 20%, intermediate 60%, low 20%).
Results:
The fully adjusted model including traditional risk factors (phenotypic and sun exposure-related), showed a significant 50% increase in odds of KC per standard deviation of PRS (odds ratio (OR) =1.51; 95% confidence interval (CI) =1.30-1.76, P=5.75 × 10-8 ). Those in the top 20% PRS had over three times the risk of KC of those in the lowest 20% (OR=3.45; 95% CI=2.18-5.50, P=1.5×10-7 ) and higher absolute risk of KC per 100 person-years of 2.96 compared with 1.34. Area under the ROC curve increased from 0.72 to 0.74 on adding PRS to the fully adjusted model.
Conclusions:
These results show that PRS can enhance the prediction of KC above traditional risk factors.
Prognostic factors are important for the diagnosis of breast cancer as it helps in identification of high risk patients. The objective of the study is to assess the proliferation index, Ki-67 and correlate it with other markers. The present study was a cohort study conducted in the Department of General Surgery at Tertiary Care Teaching Hospital over a period of 1 year with a sample size of 98. All the patients meeting the inclusion and exclusion criteria are recruited sequentially by convenient sampling until the sample size is attained, with the agreement of the institutional ethics committee. A total of 98 patients with a mean age of 53.61 ± 12.48 years were studied in the final analysis. The mean duration of lump was 4.62 ± 2.18 months and only 6.12% had the complaint of pain. Majority of them had stage IIIB carcinoma at 43.88%, followed by stage IIA at 27.55%, 15.31% stage IIB, 13.27% stage IIIA. At cut off 20, 69(70.40%) had ki67 proliferation index ≥20 and 29(29.59%) had<20. Correlation of Ki-67 Index with expression of estrogen receptor status had a p value of 0.019 and with progesterone receptor status, p 0.003 which was significant.
This paper describes the present state and problems of genetic testing of monogenic and multifactorial disorders in genetic disorder. Hereditary breast and ovarian cancer syndrome (HBOC) is picked up as a sample of monogenic disorder.
Persons affected by HBOC have mutations in BRCA1/BRCA2 gene, therefore, persons who have mutations in those genes must be taken care of occurrence of HBOC. Persons who have the plural number of family history about breast or ovarian cancers had better recommended to genetic testing of BRCA1/2 gene.
Genome-wide association study (GWAS) and polygenic risk factor (PRS) study are developed rapidly but those studies about Japanese population are few for the present. Many genetic disorders are specific to populations. GWAS or PRS using Japanese population must increase from now on.
Alzheimer's disease is a multifactorial disorder and persons affected by this disease are increasing. APOE gene is one of causal genes. Many persons want to investigate this gene. We must inform the method to prevent Alzheimer's disease to persons who have mutations in APOE gene.
A PRS of coronary arterial diseases (CAD) indicates that the risk of occurrence of CAD is the same between persons who have high score of PRS and have desirable life style and persons who have low score of PRS and have undesirable life style.
Epigenome change is one cause of occurrence of genetic disorders. DNA methylation and histon modification change transcription of many genes. Many studies are necessary to clarify relation between epigenome change and life style.
Genetic disorders are very specific to individual and are difficult to understand. We must educate persons who receive health evaluation and promotion to realize genetic disorder with accuracy.
Purpose of Review
This paper discusses personalized risk assessment and screening, incorporating genetic and radiomic breast cancer risk information. The paper further explores opportunities to incorporate genomic information into traditional risk assessment tools, to improve the discriminatory accuracy of these tools, and integrate the information into clinical decision-making.
Recent Findings
We summarize screening guidelines, strategies for identifying high risk women and discuss clinical validation studies of polygenic risk scores (PRSs). We describe the options for supplemental imaging. Both sensitivity and specificity are improving in this area, and testing is more available.
Summary
Personalized screening and chemoprevention will only be possible with improved risk estimates (including breast density and PRS), meaningful, consistent shared-decision making conversations, and widespread availability of modalities for supplemental imaging. Future directions include earlier genetic testing and further validation of the PRS, particularly in non-European populations, a focus on breast density and texture in risk estimation and management and education efforts around risk communication.
The BJC is owned by Cancer Research UK, a charity dedicated to understanding the causes, prevention and treatment of cancer and to making sure that the best new treatments reach patients in the clinic as quickly as possible. The journal reflects these aims. It was founded more than fifty years ago and, from the start, its far-sighted mission was to encourage communication of the very best cancer research from laboratories and clinics in all countries. The breadth of its coverage, its editorial independence and it consistent high standards, have made BJC one of the world's premier general cancer journals. Its increasing popularity is reflected by a steadily rising impact factor.
Breast cancer is the most common cancer among women. Common variants at 27 loci have been identified as associated with susceptibility to breast cancer, and these account for ∼9% of the familial risk of the disease. We report here a meta-analysis of 9 genome-wide association studies, including 10,052 breast cancer cases and 12,575 controls of European ancestry, from which we selected 29,807 SNPs for further genotyping. These SNPs were genotyped in 45,290 cases and 41,880 controls of European ancestry from 41 studies in the Breast Cancer Association Consortium (BCAC). The SNPs were genotyped as part of a collaborative genotyping experiment involving four consortia (Collaborative Oncological Gene-environment Study, COGS) and used a custom Illumina iSelect genotyping array, iCOGS, comprising more than 200,000 SNPs. We identified SNPs at 41 new breast cancer susceptibility loci at genome-wide significance (P < 5 × 10(-8)). Further analyses suggest that more than 1,000 additional loci are involved in breast cancer susceptibility.
The PHG Foundation led a multidisciplinary program, which used results from COGS research identifying genetic variants associated with breast, ovarian and prostate cancers to model risk-stratified prevention for breast and prostate cancers. Implementing such strategies would require attention to the use and storage of genetic information, the development of risk assessment tools, new protocols for consent and programs of professional education and public engagement.
Background:
To reduce mortality, screening must detect life-threatening disease at an earlier, more curable stage. Effective cancer-screening programs therefore both increase the incidence of cancer detected at an early stage and decrease the incidence of cancer presenting at a late stage.
Methods:
We used Surveillance, Epidemiology, and End Results data to examine trends from 1976 through 2008 in the incidence of early-stage breast cancer (ductal carcinoma in situ and localized disease) and late-stage breast cancer (regional and distant disease) among women 40 years of age or older.
Results:
The introduction of screening mammography in the United States has been associated with a doubling in the number of cases of early-stage breast cancer that are detected each year, from 112 to 234 cases per 100,000 women--an absolute increase of 122 cases per 100,000 women. Concomitantly, the rate at which women present with late-stage cancer has decreased by 8%, from 102 to 94 cases per 100,000 women--an absolute decrease of 8 cases per 100,000 women. With the assumption of a constant underlying disease burden, only 8 of the 122 additional early-stage cancers diagnosed were expected to progress to advanced disease. After excluding the transient excess incidence associated with hormone-replacement therapy and adjusting for trends in the incidence of breast cancer among women younger than 40 years of age, we estimated that breast cancer was overdiagnosed (i.e., tumors were detected on screening that would never have led to clinical symptoms) in 1.3 million U.S. women in the past 30 years. We estimated that in 2008, breast cancer was overdiagnosed in more than 70,000 women; this accounted for 31% of all breast cancers diagnosed.
Conclusions:
Despite substantial increases in the number of cases of early-stage breast cancer detected, screening mammography has only marginally reduced the rate at which women present with advanced cancer. Although it is not certain which women have been affected, the imbalance suggests that there is substantial overdiagnosis, accounting for nearly a third of all newly diagnosed breast cancers, and that screening is having, at best, only a small effect on the rate of death from breast cancer.
PURPOSEGenome-wide association studies have identified common genomic variants associated with increased susceptibility to breast cancer. In the general population, the risk associated with these known variants seems insufficient to inform clinical management. Their contribution to the development of familial breast cancer is less clear.Patients AndmethodsWe studied 1,143 women with breast cancer who had completed BRCA1 and BRCA2 mutation screening as a result of a high risk for hereditary breast cancer. Genotyping of 22 breast cancer-associated genomic variants was performed. A polygenic risk score (PRS), calculated as the sum of the log odds ratios for each allele, was compared with the same metric in 892 controls from the Australian Ovarian Cancer Study. The clinical features associated with the high and low ends of the polygenic risk distribution were compared.ResultsWomen affected by familial breast cancer had a highly significant excess of risk alleles compared with controls (P = 1.0 × 10(-16)). Polygenic risk (measured by the PRS) was greater in women who tested negative for a BRCA1 or BRCA2 mutation compared with mutation carriers (P = 2.3 × 10(-6)). Non-BRCA1/2 women in the top quartile of the polygenic risk distribution were more likely to have had early-onset breast cancer (< 30 years of age, odds ratio [OR] 3.37, P = .03) and had a higher rate of second breast cancer (OR 1.96, P = .02) compared with women with low polygenic risk. CONCLUSION
Genetic testing for common risk variants in women undergoing assessment for familial breast cancer may identify a distinct group of high-risk women in whom the role of risk-reducing interventions should be explored.
Objective There is increasing interest in adding common genetic variants identified through genome wide association studies (GWAS) to breast cancer risk prediction models. First results from such models showed modest benefits in terms of risk discrimination. Heterogeneity of breast cancer as defined by hormone-receptor status has not been considered in this context. In this study we investigated the predictive capacity of 32 GWAS-detected common variants for breast cancer risk, alone and in combination with classical risk factors, and for tumours with different hormone receptor status.
Material and methods Within the Breast and Prostate Cancer Cohort Consortium, we analysed 6009 invasive breast cancer cases and 7827 matched controls of European ancestry, with data on classical breast cancer risk factors and 32 common gene variants identified through GWAS. Discriminatory ability with respect to breast cancer of specific hormone receptor-status was assessed with the age adjusted and cohort-adjusted concordance statistic (AUROCa). Absolute risk scores were calculated with external reference data. Integrated discrimination improvement was used to measure improvements in risk prediction.
Results We found a small but steady increase in discriminatory ability with increasing numbers of genetic variants included in the model (difference in AUROCa going from 2.7% to 4%). Discriminatory ability for all models varied strongly by hormone receptor status.
Discussion and conclusions Adding information on common polymorphisms provides small but statistically significant improvements in the quality of breast cancer risk prediction models. We consistently observed better performance for receptor-positive cases, but the gain in discriminatory quality is not sufficient for clinical application.
Over the last decade several breast cancer risk alleles have been identified which has led to an increased interest in individualised risk prediction for clinical purposes.
We investigate the performance of an up-to-date 18 breast cancer risk single-nucleotide polymorphisms (SNPs), together with mammographic percentage density (PD), body mass index (BMI) and clinical risk factors in predicting absolute risk of breast cancer, empirically, in a well characterised Swedish case-control study of postmenopausal women. We examined the efficiency of various prediction models at a population level for individualised screening by extending a recently proposed analytical approach for estimating number of cases captured.
The performance of a risk prediction model based on an initial set of seven breast cancer risk SNPs is improved by additionally including eleven more recently established breast cancer risk SNPs (P = 4.69 × 10-4). Adding mammographic PD, BMI and all 18 SNPs to a Swedish Gail model improved the discriminatory accuracy (the AUC statistic) from 55% to 62%. The net reclassification improvement was used to assess improvement in classification of women into low, intermediate, and high categories of 5-year risk (P = 8.93 × 10-9). For scenarios we considered, we estimated that an individualised screening strategy based on risk models incorporating clinical risk factors, mammographic density and SNPs, captures 10% more cases than a screening strategy using the same resources, based on age alone. Estimates of numbers of cases captured by screening stratified by age provide insight into how individualised screening programs might appear in practice.
Taken together, genetic risk factors and mammographic density offer moderate improvements to clinical risk factor models for predicting breast cancer.
Risk prediction based on genomic profiles has raised a lot of attention recently. However, family history is usually ignored in genetic risk prediction. In this study we proposed a statistical framework for risk prediction given an individual's genotype profile and family history. Genotype information about the relatives can also be incorporated. We allow risk prediction given the current age and follow-up period and consider competing risks of mortality. The framework allows easy extension to any family size and structure. In addition, the predicted risk at any percentile and the risk distribution graphs can be computed analytically. We applied the method to risk prediction for breast and prostate cancers by using known susceptibility loci from genome-wide association studies. For breast cancer, in the population the 10-year risk at age 50 ranged from 1.1% at the 5th percentile to 4.7% at the 95th percentile. If we consider the average 10-year risk at age 50 (2.39%) as the threshold for screening, the screening age ranged from 62 at the 20th percentile to 38 at the 95th percentile (and some never reach the threshold). For women with one affected first-degree relative, the 10-year risks ranged from 2.6% (at the 5th percentile) to 8.1% (at the 95th percentile). For prostate cancer, the corresponding 10-year risks at age 60 varied from 1.8% to 14.9% in the population and from 4.2% to 23.2% in those with an affected first-degree relative. We suggest that for some diseases genetic testing that incorporates family history can stratify people into diverse risk categories and might be useful in targeted prevention and screening.
We modelled the efficiency of a personalised approach to screening for prostate and breast cancer based on age and polygenic risk-profile compared with the standard approach based on age alone.
We compared the number of cases potentially detectable by screening in a population undergoing personalised screening with a population undergoing screening based on age alone. Polygenic disease risk was assumed to have a log-normal relative risk distribution predicted for the currently known prostate or breast cancer susceptibility variants (N=31 and N=18, respectively).
Compared with screening men based on age alone (aged 55-79: 10-year absolute risk ≥2%), personalised screening of men age 45-79 at the same risk threshold would result in 16% fewer men being eligible for screening at a cost of 3% fewer screen-detectable cases, but with added benefit of detecting additional cases in younger men at high risk. Similarly, compared with screening women based on age alone (aged 47-79: 10-year absolute risk ≥2.5%), personalised screening of women age 35-79 at the same risk threshold would result in 24% fewer women being eligible for screening at a cost of 14% fewer screen-detectable cases.
Personalised screening approach could improve the efficiency of screening programmes. This has potential implications on informing public health policy on cancer screening.
The Gail model is widely used for the assessment of risk of invasive breast cancer based on recognized clinical risk factors. In recent years, a substantial number of single-nucleotide polymorphisms (SNPs) associated with breast cancer risk have been identified. However, it remains unclear how to effectively integrate clinical and genetic risk factors for risk assessment.
Seven SNPs associated with breast cancer risk were selected from the literature and genotyped in white non-Hispanic women in a nested case-control cohort of 1664 case patients and 1636 control subjects within the Women's Health Initiative Clinical Trial. SNP risk scores were computed based on previously published odds ratios assuming a multiplicative model. Combined risk scores were calculated by multiplying Gail risk estimates by the SNP risk scores. The independence of Gail risk and SNP risk was evaluated by logistic regression. Calibration of relative risks was evaluated using the Hosmer-Lemeshow test. The performance of the combined risk scores was evaluated using receiver operating characteristic curves. The net reclassification improvement (NRI) was used to assess improvement in classification of women into low (<1.5%), intermediate (1.5%-2%), and high (>2%) categories of 5-year risk. All tests of statistical significance were two-sided.
The SNP risk score was nearly independent of Gail risk. There was good agreement between predicted and observed SNP relative risks. In the analysis for receiver operating characteristic curves, the combined risk score was more discriminating, with area under the curve of 0.594 compared with area under the curve of 0.557 for Gail risk alone (P < .001). Classification also improved for 5.6% of case patients and 2.9% of control subjects, showing an NRI value of 0.085 (P = 1.0 × 10⁻⁵). Focusing on women with intermediate Gail risk resulted in an improved NRI of 0.195 (P = 8.6 × 10⁻⁵).
Combining validated common genetic risk factors with clinical risk factors resulted in modest improvement in classification of breast cancer risks in white non-Hispanic postmenopausal women. Classification performance was further improved by focusing on women at intermediate risk.
Genomewide association studies have identified multiple genetic variants associated with breast cancer. The extent to which these variants add to existing risk-assessment models is unknown.
We used information on traditional risk factors and 10 common genetic variants associated with breast cancer in 5590 case subjects and 5998 control subjects, 50 to 79 years of age, from four U.S. cohort studies and one case-control study from Poland to fit models of the absolute risk of breast cancer. With the use of receiver-operating-characteristic curve analysis, we calculated the area under the curve (AUC) as a measure of discrimination. By definition, random classification of case and control subjects provides an AUC of 50%; perfect classification provides an AUC of 100%. We calculated the fraction of case subjects in quintiles of estimated absolute risk after the addition of genetic variants to the traditional risk model.
The AUC for a risk model with age, study and entry year, and four traditional risk factors was 58.0%; with the addition of 10 genetic variants, the AUC was 61.8%. About half the case subjects (47.2%) were in the same quintile of risk as in a model without genetic variants; 32.5% were in a higher quintile, and 20.4% were in a lower quintile.
The inclusion of newly discovered genetic factors modestly improved the performance of risk models for breast cancer. The level of predicted breast-cancer risk among most women changed little after the addition of currently available genetic information.
Estrogen receptor (ER)-negative tumors represent 20–30% of all breast cancers, with a higher proportion occurring in younger women and women of African ancestry1 . The etiology2 and clinical behavior3 of ER-negative tumors are different from those of tumors expressing ER (ER positive), including differences in genetic predisposition4 . To identify susceptibility loci specific to ER-negative disease, we combined in a meta-analysis 3 genome-wide association studies of 4,193 ER-negative breast cancer cases and 35,194 controls with a series of 40 follow-up studies (6,514 cases and 41,455 controls), genotyped using a custom Illumina array, iCOGS, developed by the Collaborative Oncological Gene-environment Study (COGS). SNPs at four loci, 1q32.1 (MDM4, P = 2.1 × 10−12 and LGR6, P = 1.4 × 10−8), 2p24.1 (P = 4.6 × 10−8) and 16q12.2 (FTO, P = 4.0 × 10−8), were associated with ER-negative but not ER-positive breast cancer (P > 0.05). These findings provide further evidence for distinct etiological pathways associated with invasive ER-positive and ER-negative breast cancers.
Various common genetic susceptibility loci have been identified for breast cancer; however, it is unclear how they combine with lifestyle/environmental risk factors to influence risk. We undertook an international collaborative study to assess gene-environment interaction for risk of breast cancer. Data from 24 studies of the Breast Cancer Association Consortium were pooled. Using up to 34,793 invasive breast cancers and 41,099 controls, we examined whether the relative risks associated with 23 single nucleotide polymorphisms were modified by 10 established environmental risk factors (age at menarche, parity, breastfeeding, body mass index, height, oral contraceptive use, menopausal hormone therapy use, alcohol consumption, cigarette smoking, physical activity) in women of European ancestry. We used logistic regression models stratified by study and adjusted for age and performed likelihood ratio tests to assess gene–environment interactions. All statistical tests were two-sided. We replicated previously reported potential interactions between LSP1-rs3817198 and parity (Pinteraction = 2.4×10−6) and between CASP8-rs17468277 and alcohol consumption (Pinteraction = 3.1×10−4). Overall, the per-allele odds ratio (95% confidence interval) for LSP1-rs3817198 was 1.08 (1.01–1.16) in nulliparous women and ranged from 1.03 (0.96–1.10) in parous women with one birth to 1.26 (1.16–1.37) in women with at least four births. For CASP8-rs17468277, the per-allele OR was 0.91 (0.85–0.98) in those with an alcohol intake of <20 g/day and 1.45 (1.14–1.85) in those who drank ≥20 g/day. Additionally, interaction was found between 1p11.2-rs11249433 and ever being parous (Pinteraction = 5.3×10−5), with a per-allele OR of 1.14 (1.11–1.17) in parous women and 0.98 (0.92–1.05) in nulliparous women. These data provide first strong evidence that the risk of breast cancer associated with some common genetic variants may vary with environmental risk factors.
Breast cancers demonstrate substantial biological, clinical and etiological heterogeneity. We investigated breast cancer risk associations of eight susceptibility loci identified in GWAS and two putative susceptibility loci in candidate genes in relation to specific breast tumor subtypes. Subtypes were defined by five markers (ER, PR, HER2, CK5/6, EGFR) and other pathological and clinical features. Analyses included up to 30 040 invasive breast cancer cases and 53 692 controls from 31 studies within the Breast Cancer Association Consortium. We confirmed previous reports of stronger associations with ER+ than ER− tumors for six of the eight loci identified in GWAS: rs2981582 (10q26) (P-heterogeneity = 6.1 × 10−18), rs3803662 (16q12) (P = 3.7 × 10−5), rs13281615 (8q24) (P = 0.002), rs13387042 (2q35) (P = 0.006), rs4973768 (3p24) (P = 0.003) and rs6504950 (17q23) (P = 0.002). The two candidate loci, CASP8 (rs1045485, rs17468277) and TGFB1 (rs1982073), were most strongly related with the risk of PR negative tumors (P = 5.1 × 10−6 and P = 4.1 × 10−4, respectively), as previously suggested. Four of the eight loci identified in GWAS were associated with triple negative tumors (P ≤ 0.016): rs3803662 (16q12), rs889312 (5q11), rs3817198 (11p15) and rs13387042 (2q35); however, only two of them (16q12 and 2q35) were associated with tumors with the core basal phenotype (P ≤ 0.002). These analyses are consistent with different biological origins of breast cancers, and indicate that tumor stratification might help in the identification and characterization of novel risk factors for breast cancer subtypes. This may eventually result in further improvements in prevention, early detection and treatment.
Breast cancer exhibits familial aggregation, consistent with variation in genetic susceptibility to the disease. Known susceptibility genes account for less than 25% of the familial risk of breast cancer, and the residual genetic variance is likely to be due to variants conferring more moderate risks. To identify further susceptibility alleles, we conducted a two-stage genome-wide association study in 4,398 breast cancer cases and 4,316 controls, followed by a third stage in which 30 single nucleotide polymorphisms (SNPs) were tested for confirmation in 21,860 cases and 22,578 controls from 22 studies. We used 227,876 SNPs that were estimated to correlate with 77% of known common SNPs in Europeans at r(2) > 0.5. SNPs in five novel independent loci exhibited strong and consistent evidence of association with breast cancer (P < 10(-7)). Four of these contain plausible causative genes (FGFR2, TNRC9, MAP3K1 and LSP1). At the second stage, 1,792 SNPs were significant at the P < 0.05 level compared with an estimated 1,343 that would be expected by chance, indicating that many additional common susceptibility alleles may be identifiable by this approach.
Genome-wide association studies (GWAS) have identified hundreds of genetic susceptibility loci for cancers and other complex
diseases. However, the public health and clinical relevance of these discoveries is unclear. Evaluating the combined associations
of genetic and environmental risk factors, particularly those that can be modified, will be critical in assessing the utility
of genetic information for risk stratified prevention. In this commentary, using breast cancer as a model, we show that genetic
information in combination with other risk factors can provide levels of risk stratification that could be useful for individual
decision-making or population-based prevention programs. Our projections are theoretical and rely on a number of assumptions,
including multiplicative models for the combined associations of the different risk factors, which need confirmation. Thus,
analyses of epidemiological studies with high-quality risk factor information, as well as prevention trials, are needed to
empirically assess the impact of genetics in risk stratified prevention.
Genetic susceptibility to breast cancer in women is conferred by a large number of genes, of which six have so far been identified. In the context of multiple-case families, BRCA1 and BRCA2 are the most important. Mutations in these genes confer high lifetime risks of breast cancer and ovarian cancer, and more moderate risks of prostate cancer and some other cancer types. Mutations in the CHEK2 and ATM genes, by contrast, cause much more modest (2–4 fold) risks of breast cancer. Genes so far identified explain approximately 20% of the familial aggregation of breast cancer. The remaining susceptibility genes have, so far, proved illusive, suggesting that they are numerous and confer moderate risks. A variety of techniques including genome-wide association studies, use of quantitative intermediate endpoints, and resequencing of genes may be required to identify them. The identification of such genes can provide a basis for targeted prevention of breast cancer.
Background
Tamoxifen and raloxifene reduce the risk of breast cancer in women at elevated risk of disease, but the duration of the effect is unknown. We assessed the effectiveness of selective oestrogen receptor modulators (SERMs) on breast cancer incidence.
Methods
We did a meta-analysis with individual participant data from nine prevention trials comparing four selective oestrogen receptor modulators (SERMs; tamoxifen, raloxifene, arzoxifene, and lasofoxifene) with placebo, or in one study with tamoxifen. Our primary endpoint was incidence of all breast cancer (including ductal carcinoma in situ) during a 10 year follow-up period. Analysis was by intention to treat.
Results
We analysed data for 83 399 women with 306 617 women-years of follow-up. Median follow-up was 65 months (IQR 54–93). Overall, we noted a 38% reduction (hazard ratio [HR] 0·62, 95% CI 0·56–0·69) in breast cancer incidence, and 42 women would need to be treated to prevent one breast cancer event in the first 10 years of follow-up. The reduction was larger in the first 5 years of follow-up than in years 5–10 (42%, HR 0·58, 0·51–0·66; p<0·0001 vs 25%, 0·75, 0·61–0·93; p=0·007), but we noted no heterogeneity between time periods. Thromboembolic events were significantly increased with all SERMs (odds ratio 1·73, 95% CI 1·47–2·05; p<0·0001). We recorded a significant reduction of 34% in vertebral fractures (0·66, 0·59–0·73), but only a small effect for non-vertebral fractures (0·93, 0·87–0·99).
Interpretation
For all SERMs, incidence of invasive oestrogen (ER)-positive breast cancer was reduced both during treatment and for at least 5 years after completion. Similar to other preventive interventions, careful consideration of risks and benefits is needed to identify women who are most likely to benefit from these drugs.
Funding
Cancer Research UK.
Analysis of 4,405 variants in 89,050 European subjects from 41 case-control studies identified three independent association signals for estrogen-receptor-positive tumors at 11q13. The strongest signal maps to a transcriptional enhancer element in which the G allele of the best candidate causative variant rs554219 increases risk of breast cancer, reduces both binding of ELK4 transcription factor and luciferase activity in reporter assays, and may be associated with low cyclin D1 protein levels in tumors. Another candidate variant, rs78540526, lies in the same enhancer element. Risk association signal 2, rs75915166, creates a GATA3 binding site within a silencer element. Chromatin conformation studies demonstrate that these enhancer and silencer elements interact with each other and with their likely target gene, CCND1.
Breast cancer is not only increasing in the West but also particularly rapidly in Eastern countries where traditionally the incidence has been low. The rise in incidence is mainly related to changes in reproductive patterns and lifestyle. These trends could potentially be reversed by defining women at greatest risk and offering appropriate preventive measures. A model for this approach was the establishment of Family History Clinics (FHCs), which have resulted in improved survival in younger women at high risk. New predictive models of risk that include reproductive and lifestyle factors, mammographic density and measurement of risk-associated single nucleotide polymorphisms (SNPs) may give more precise information concerning risk and enable better targeting for mammographic screening programmes and of preventive measures. Endocrine prevention using anti-oestrogens and aromatase inhibitors is effective, and observational studies suggest lifestyle modification may also be effective. However, referral to FHCs is opportunistic and predominantly includes younger women. A better approach for identifying older women at risk may be to use national breast screening programmes. Here were described pilot studies to assess whether the routine assessment of breast cancer risk is feasible within a population-based screening programme, whether the feedback and advice on risk-reducing interventions would be welcomed and taken up, and to consider whether the screening interval should be modified according to breast cancer risk. © 2012 The Association for the Publication of the Journal of Internal Medicine.
Genome wide association studies have identified several single nucleotide polymorphisms (SNPs) that are independently associated with small increments in risk of prostate cancer, opening up the possibility for using such variants in risk prediction. Using segregation analysis of population-based samples of 4,390 families of prostate cancer patients from the UK and Australia, and assuming all familial aggregation has genetic causes, we previously found that the best model for the genetic susceptibility to prostate cancer was a mixed model of inheritance that included both a recessive major gene component and a polygenic component (P) that represents the effect of a large number of genetic variants each of small effect, where . Based on published studies of 26 SNPs that are currently known to be associated with prostate cancer, we have extended our model to incorporate these SNPs by decomposing the polygenic component into two parts: a polygenic component due to the known susceptibility SNPs, , and the residual polygenic component due to the postulated but as yet unknown genetic variants, . The resulting algorithm can be used for predicting the probability of developing prostate cancer in the future based on both SNP profiles and explicit family history information. This approach can be applied to other diseases for which population-based family data and established risk variants exist.
There is limited evidence on how the risk of breast cancer and its subtypes depend on low-penetrance susceptibility loci, individually or in combination.
To analyze breast cancer risk, overall and by tumor subtype, in relation to 14 individual single-nucleotide polymorphisms (SNPs) previously linked to the disease, and in relation to a polygenic risk score.
Study of 10,306 women with breast cancer (mean age at diagnosis, 58 years) and 10,393 women without breast cancer who in 2005-2008 provided blood samples for genotyping in a large prospective study of UK women; and meta-analysis of these results and of other published results.
Estimated per-allele odds ratio (OR) for individual SNPs, and cumulative incidence of breast cancer to age 70 years in relation to a polygenic risk score based on the 4, 7, or 10 SNPs most strongly associated with risk.
Odds ratios for breast cancer were greatest for FGFR2-rs2981582 and TNRC9-rs3803662 and, for these 2 SNPs, were significantly greater for estrogen receptor (ER)-positive than for ER-negative disease, both in our data and in meta-analyses of all published data (pooled per-allele ORs [95% confidence intervals] for ER-positive vs ER-negative disease: 1.30 [1.26-1.33] vs 1.05 [1.01-1.10] for FGFR2; interaction P < .001; and 1.24 [1.21-1.28] vs 1.12 [1.07-1.17] for TNRC9; interaction P < .001). The next strongest association was for 2q-rs13387042, for which the per-allele OR was significantly greater for bilateral than unilateral disease (1.39 [1.21-1.60] vs 1.15 [1.11-1.20]; interaction P = .008) and for lobular than ductal tumors (1.35 [1.23-1.49] vs 1.10 [1.05-1.15]; interaction P < .001). The estimated cumulative incidence (95% confidence interval) of breast cancer to age 70 years among women in the top and bottom fifths of a polygenic risk score based on 7 SNPs was 8.8% (8.3%-9.4%) and 4.4% (4.2%-4.8%), respectively. For ER-positive disease the corresponding risks were 7.4% (6.9%-8.0%) and 3.4% (3.1%-3.8%), respectively; while for ER-negative disease they were 1.4% (1.2%-1.6%) and 1.0% (0.8%-1.2%). The findings did not differ materially according to the number of SNPs included in the polygenic risk model.
The polygenic risk score was substantially more predictive of ER-positive than of ER-negative breast cancer, particularly for absolute risk.
Information is scarce about the combined effects on breast cancer incidence of low-penetrance genetic susceptibility polymorphisms and environmental factors (reproductive, behavioural, and anthropometric risk factors for breast cancer). To test for evidence of gene-environment interactions, we compared genotypic relative risks for breast cancer across the other risk factors in a large UK prospective study.
We tested gene-environment interactions in 7610 women who developed breast cancer and 10 196 controls without the disease, studying the effects of 12 polymorphisms (FGFR2-rs2981582, TNRC9-rs3803662, 2q35-rs13387042, MAP3K1-rs889312, 8q24-rs13281615, 2p-rs4666451, 5p12-rs981782, CASP8-rs1045485, LSP1-rs3817198, 5q-rs30099, TGFB1-rs1982073, and ATM-rs1800054) in relation to prospectively collected information about ten established environmental risk factors (age at menarche, parity, age at first birth, breastfeeding, menopausal status, age at menopause, use of hormone replacement therapy, body-mass index, height, and alcohol consumption).
After allowance for multiple testing none of the 120 comparisons yielded significant evidence of a gene-environment interaction. By contrast with previous suggestions, there was little evidence that the genotypic relative risks were affected by use of hormone replacement therapy, either overall or for oestrogen-receptor-positive disease. Only one of the 12 polymorphisms was correlated with any of the ten other risk factors: carriers of the high-risk C allele of MAP3K1-rs889312 were significantly shorter than non-carriers (mean height 162.4 cm [95% CI 162.1-162.7] vs 163.1 cm [162.9-163.2]; p=0.01 after allowance for multiple testing).
Risks of breast cancer associated with low-penetrance susceptibility polymorphisms do not vary significantly with these ten established environmental risk factors.
Cancer Research UK and the UK Medical Research Council.
Genetic and lifestyle/environmental factors are implicated in the aetiology of breast cancer. This review summarizes the current state of knowledge on rare high penetrance mutations, as well as moderate and low-penetrance genetic variants implicated in breast cancer aetiology. We summarize recent discoveries from large collaborative efforts to combine data from candidate gene studies, and to conduct genome-wide association studies (GWAS), primarily in breast cancers in the general population. These findings are compared with results from collaborative efforts aiming to identify genetic modifiers in BRCA1 and BRCA2 carriers. Breast cancer is a heterogeneous disease, and tumours from BRCA1 and BRCA2 carriers display distinct pathological characteristics when compared with tumours unselected for family history. The relationship between genetic variants and pathological subtypes of breast cancer, and the implication of discoveries of novel genetic variants to risk prediction in BRCA1/2 mutation carriers and in populations unselected for mutation carrier status, are discussed.
New developments in the search for susceptibility alleles in complex disorders provide support for the possibility of a polygenic approach to the prevention and treatment of common diseases.
We examined the implications, both for individualized disease prevention and for public health policy, of findings concerning the risk of breast cancer that are based on common genetic variation.
Our analysis suggests that the risk profile generated by the known, common, moderate-risk alleles does not provide sufficient discrimination to warrant individualized prevention. However, useful risk stratification may be possible in the context of programs for disease prevention in the general population.
The clinical use of single, common, low-penetrance genes is limited, but a few susceptibility alleles may distinguish women who are at high risk for breast cancer from those who are at low risk, particularly in the context of population screening.
Heidelberg, Germany: UH; Institute for Prevention and Occupational Medicine of the German Social Accident Insurance
- Hans-Peter Fischer
Institute of Pathology, Medical Faculty University of Bonn,
Germany: Hans-Peter Fischer; Molecular Genetics of Breast
Cancer, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg,
Germany: UH; Institute for Prevention and Occupational
Medicine of the German Social Accident Insurance, Institute of
the Ruhr University Bochum (IPA), Bochum, Germany: TB, Beate
Pesch, Sylvia Rabstein, Anne Lotz; Institute of Occupational
Medicine and Maritime Medicine, University Medical Center
Hamburg-Eppendorf, Germany: Volker Harth.
HEBON
Netherlands Cancer Institute, Amsterdam: Senno Verhoef,
Martijn Verheus, Laura J. van't Veer, Flora E. van Leeuwen;
Cancer Incidence and Mortality Worldwide: IARC CancerBase No
- J Ferlay
- I Soerjomataram
- M Ervik
Ferlay J, Soerjomataram I, Ervik M, et al. GLOBOCAN 2012 v1.0, Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11. Lyon, France, International Agency for Research on Cancer. http://globocan.iarc.fr/. Accessed
February 3, 2014.
Independent UK Panel on Breast Cancer Screening. The benefits and harms of breast cancer screening: an independent review
Independent UK Panel on Breast Cancer Screening. The benefits and harms of
breast cancer screening: an independent review. Lancet. 2012;380(9855):1778-1786.
Familial breast cancer: collaborative reanalysis of individual data from 52 epidemiological studies including 58,209 women with breast cancer and 101,986 women without the disease
Familial breast cancer: collaborative reanalysis of individual data from 52
epidemiological studies including 58,209 women with breast cancer and
101,986 women without the disease. Lancet. 2001;358(9291):1389-1399.