Colorectal cancer screening reduces colorectal cancer incidence and mortality. Risk models based on phenotypic variables have relatively good discrimination in external validation and may improve ...efficiency of screening. Models incorporating genetic variables may perform better. In this review, we updated our previous review by searching Medline and EMBASE from the end date of that review (January 2014) to February 2019 to identify models incorporating at least one SNP and applicable to asymptomatic individuals in the general population. We identified 23 new models, giving a total of 29. Of those in which the SNP selection was on the basis of published genome-wide association studies, in external or split-sample validation the AUROC was 0.56 to 0.57 for models that included SNPs alone, 0.61 to 0.63 for SNPs in combination with other risk factors, and 0.56 to 0.70 when age was included. Calibration was only reported for four. The addition of SNPs to other risk factors increases discrimination by 0.01 to 0.06. Public health modeling studies suggest that, if determined by risk models, the range of starting ages for screening would be several years greater than using family history alone. Further validation and calibration studies are needed alongside modeling studies to assess the population-level impact of introducing genetic risk-based screening programs.
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.
Abstract Breast cancer risks in older BRCA2 pathogenic variant carriers are understudied. Recent studies show a marked decline in the relative risk at older ages. We used data from two large studies ...to update the breast cancer risks in the BOADICEA model for BRCA2 carriers 60 years and older.
Recently, RAD51C mutations were identified in families with breast and ovarian cancer. This observation prompted us to investigate the role of RAD51D in cancer susceptibility. We identified eight ...inactivating RAD51D mutations in unrelated individuals from 911 breast-ovarian cancer families compared with one inactivating mutation identified in 1,060 controls (P = 0.01). The association found here was principally with ovarian cancer, with three mutations identified in the 59 pedigrees with three or more individuals with ovarian cancer (P = 0.0005). The relative risk of ovarian cancer for RAD51D mutation carriers was estimated to be 6.30 (95% CI 2.86-13.85, P = 4.8 × 10−6). By contrast, we estimated the relative risk of breast cancer to be 1.32 (95% CI 0.59-2.96, P = 0.50). These data indicate that RAD51D mutation testing may have clinical utility in individuals with ovarian cancer and their families. Moreover, we show that cells deficient in RAD51D are sensitive to treatment with a PARP inhibitor, suggesting a possible therapeutic approach for cancers arising in RAD51D mutation carriers.
Prostate cancer (PCa) is highly heritable. No validated PCa risk model currently exists. We therefore sought to develop a genetic risk model that can provide personalized predicted PCa risks on the ...basis of known moderate- to high-risk pathogenic variants, low-risk common genetic variants, and explicit cancer family history, and to externally validate the model in an independent prospective cohort.
We developed a risk model using a kin-cohort comprising individuals from 16,633 PCa families ascertained in the United Kingdom from 1993 to 2017 from the UK Genetic Prostate Cancer Study, and complex segregation analysis adjusting for ascertainment. The model was externally validated in 170,850 unaffected men (7,624 incident PCas) recruited from 2006 to 2010 to the independent UK Biobank prospective cohort study.
The most parsimonious model included the effects of pathogenic variants in
,
, and
, and a polygenic score on the basis of 268 common low-risk variants. Residual familial risk was modeled by a hypothetical recessively inherited variant and a polygenic component whose standard deviation decreased log-linearly with age. The model predicted familial risks that were consistent with those reported in previous observational studies. In the validation cohort, the model discriminated well between unaffected men and men with incident PCas within 5 years (C-index, 0.790; 95% CI, 0.783 to 0.797) and 10 years (C-index, 0.772; 95% CI, 0.768 to 0.777). The 50% of men with highest predicted risks captured 86.3% of PCa cases within 10 years.
To our knowledge, this is the first validated risk model offering personalized PCa risks. The model will assist in counseling men concerned about their risk and can facilitate future risk-stratified population screening approaches.
BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) for breast cancer and the epithelial tubo-ovarian cancer (EOC) models included in the CanRisk tool ...(www.canrisk.org) provide future cancer risks based on pathogenic variants in cancer-susceptibility genes, polygenic risk scores, breast density, questionnaire-based risk factors and family history. Here, we extend the models to include the effects of pathogenic variants in recently established breast cancer and EOC susceptibility genes, up-to-date age-specific pathology distributions and continuous risk factors.
BOADICEA was extended to further incorporate the associations of pathogenic variants in
,
and
with breast cancer risk. The EOC model was extended to include the association of
pathogenic variants with EOC risk. Age-specific distributions of oestrogen-receptor-negative and triple-negative breast cancer status for pathogenic variant carriers in these genes and
and
were also incorporated. A novel method to include continuous risk factors was developed, exemplified by including adult height as continuous.
,
and
explain 0.31% of the breast cancer polygenic variance. When incorporated into the multifactorial model, 34%-44% of these carriers would be reclassified to the near-population and 15%-22% to the high-risk categories based on the UK National Institute for Health and Care Excellence guidelines. Under the EOC multifactorial model, 62%, 35% and 3% of
carriers have lifetime EOC risks of <5%, 5%-10% and >10%, respectively. Including height as continuous, increased the breast cancer relative risk variance from 0.002 to 0.010.
These extensions will allow for better personalised risks for
,
,
and
pathogenic variant carriers and more informed choices on screening, prevention, risk factor modification or other risk-reducing options.
Germline mutations in BRCA1 and BRCA2 (BRCA1/2) genes considerably increase breast and ovarian cancer risk. Given that tumors with these mutations have elevated genomic instability, they exhibit ...relative vulnerability to certain chemotherapies and targeted treatments based on poly (ADP-ribose) polymerase (PARP) inhibition. However, the molecular mechanisms that influence cancer risk and therapeutic benefit or resistance remain only partially understood. BRCA1 and BRCA2 have also been implicated in the suppression of R-loops, triple-stranded nucleic acid structures composed of a DNA:RNA hybrid and a displaced ssDNA strand. Here, we report that loss of RNF168, an E3 ubiquitin ligase and DNA double-strand break (DSB) responder, remarkably protected Brca1-mutant mice against mammary tumorigenesis. We demonstrate that RNF168 deficiency resulted in accumulation of R-loops in BRCA1/2-mutant breast and ovarian cancer cells, leading to DSBs, senescence, and subsequent cell death. Using interactome assays, we identified RNF168 interaction with DHX9, a helicase involved in the resolution and removal of R-loops. Mechanistically, RNF168 directly ubiquitylated DHX9 to facilitate its recruitment to R-loop-prone genomic loci. Consequently, loss of RNF168 impaired DHX9 recruitment to R-loops, thereby abrogating its ability to resolve R-loops. The data presented in this study highlight a dependence of BRCA1/2-defective tumors on factors that suppress R-loops and reveal a fundamental RNF168-mediated molecular mechanism that governs cancer development and vulnerability.
Advances in knowledge about breast cancer risk factors have led to the development of more comprehensive risk models. These integrate information on a variety of risk factors such as lifestyle, ...genetics, family history, and breast density. These risk models have the potential to deliver more personalised breast cancer prevention. This is through improving accuracy of risk estimates, enabling more effective targeting of preventive options and creating novel prevention pathways through enabling risk estimation in a wider variety of populations than currently possible. The systematic use of risk tools as part of population screening programmes is one such example. A clear understanding of how such tools can contribute to the goal of personalised prevention can aid in understanding and addressing barriers to implementation. In this paper we describe how emerging models, and their associated tools can contribute to the goal of personalised healthcare for breast cancer through health promotion, early disease detection (screening) and improved management of women at higher risk of disease. We outline how addressing specific challenges on the level of communication, evidence, evaluation, regulation, and acceptance, can facilitate implementation and uptake.
•Breast cancer prediction models can impact on healthcare pathways in different ways.•We need to understand how current research aligns with practice and policy needs.•Wider stakeholder engagement is needed for implementation of accessible programmes.•Wider engagement can inform evidence needs.•Evidence will be incomplete – but policy decisions still need to be made.
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