Performance of Single-Nucleotide Polymorphisms in Breast Cancer Risk Prediction Models: A Systematic Review and Meta-analysis

Fung, Si Ming; Wong, Xin Yi; Lee, Shi Xun; Miao, Hui; Hartman, Mikael; Wee, Hwee Lin

doi:10.1158/1055-9965.epi-18-0810

Cited by 16 publications

(24 citation statements)

References 97 publications

(248 reference statements)

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“…To the best of our knowledge, there is no established cut-offs for low, moderate and high quality. Hence, we have relied on previous literature [18] to define low quality as a score ≤ 5, moderate quality as a score between 6 and 7, and high quality as a score between 8 and 9.…”

Section: Data Extraction and Quality Assessmentmentioning

confidence: 99%

Association of microRNA biosynthesis genes XPO5 and RAN polymorphisms with cancer susceptibility: Bayesian hierarchical meta-analysis

Shao¹,

Shen²,

Zhao³

et al. 2020

J. Cancer

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XPO5/RAN-GTP complex mediates the nuclear transport of pre-miRNAs in the miRNA processing system, its altered expression is indicated to be correlated with cancer risk. Several studies have inspected the association between XPO5 or RAN polymorphisms and the risk of various cancers, but the findings remain controversial. A Bayesian hierarchical meta-analysis was carried out to review and analyze the effect of XPO5 and RAN polymorphisms on cancer risk. The association was estimated by calculating the logarithm of odds ratio (Log OR) and 95% credible interval (95% CrI). The expression quantitative trait loci (eQTL) analysis was used for in silico functional validation of the identified significant susceptibility loci. Consequently, 38 case-control studies (from 27 citations) with 27,459 cancer cases and 25,151controls were included in the meta-analysis of the five most prevalent SNPs (rs11077 A/C, rs2257082 G/A, rs3803012 A/G, rs14035 C/T, rs3809142 C/T). In the XPO5 gene rs11077 SNP, the minor C allele significantly increased the risk of cancer (Log OR = 0.120, 95% CrI = 0.013, 0.241), and a strong association between rs11077 SNP and cancer risk was also found in the dominant model (CC + AC vs. AA: Log OR = 0.132, 95% CrI = 0.009, 0.275). In addition, the minor GG genotype allele of the RAN gene rs3803012 SNP significantly increased the cancer risk (Log OR = 0.707, 95% CrI = 0.059, 1.385). Statistically significant associations between rs3803012 SNP and cancer risk were also observed in the recessive model (GG vs. AG + AA: Log OR = 0.708, 95% CrI = 0.059, 1.359). Furthermore, the eQTL analysis revealed that rs11077 SNP was significantly correlated with XPO5 mRNA expression, which provided additional biological basis for the observed positive association. Our results suggest that XPO5 rs11077 may be a possible functional susceptibility locus for cancer risk.

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Section: Data Extraction and Quality Assessmentmentioning

confidence: 99%

Association of microRNA biosynthesis genes XPO5 and RAN polymorphisms with cancer susceptibility: Bayesian hierarchical meta-analysis

Shao¹,

Shen²,

Zhao³

et al. 2020

J. Cancer

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“…A recent comparison of several of the most commonly used clinical models (i.e., Gail, BRCAPRO, BCSC, Claus, IBIS) in a large, predominantly White US screening population indicated that the models were generally well-calibrated (O/E range: 0.78-0.97) but with only moderate discrimination (AUC range: 0.61-0.64) [304]. Expansion of these models to include multiple biomarkers (e.g., mammographic density and/or other imaging features, polygenic risk scores, endogenous hormones, epigenetics, metabolomics), and the development and validation of models across race/ethnicity and by tumor subtype is ongoing, and likely to lead to model improvement [305][306][307][308][309][310][311][312][313][314][315][316].…”

Section: Risk Prediction Modelsmentioning

confidence: 99%

Cancer Progress and Priorities: Breast Cancer

Houghton

Hankinson

2021

Cancer Epidemiology, Biomarkers &Amp; Prevention

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DCIS) is more common than lobular carcinoma in situ (LCIS), and while both are considered risk factors for invasive breast cancer, LCIS is not considered to be a lesion capable of becoming malignant [19,20]. However, the etiology and natural history of in situ tumors is not well known.Invasive breast cancer is classified by histology, to guide clinical treatment, into invasive ductal (70-80% of breast cancer), invasive lobular (5-15% of breast cancer), and other less common types such as papillary tumors [17,19]. Immunohistochemical (IHC) staining for the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) guide the use of targeted therapies. The use of other tests such as gene array profiling or other IHC markers are not as commonly used clinically [17,[21][22][23]. Several main intrinsic molecular subtypes have been identified using gene micro-arrays: Luminal A, Luminal B, Basal Like, and Normal Like [17,[24][25][26]. Further classifications of primarily triple negative tumors have identified the Claudin-low subtype and six triple-negative molecular subtypes [27,28]. Several gene panels (e.g., PAM50) have been developed as less expensive options with a similar ability to classify tumors into molecular subtypes [17,29,30]. In 2013, the St. Gallen consensus agreed on surrogate definitions of the intrinsic subtypes that could be approximated by IHC staining of ER, PR, and HER2 as well as grade and proliferation [31] (Table 1), though several studies have noted that the agreement may be low [18,32]. Disparities in the United StatesPrior to age 40, US Black women have the highest breast cancer incidence rates, after which rates are highest among White women (Figure 2). American Indian/ Alaska Native women have the lowest rates until age 74. Asian/ Pacific Islander women have similar incidence rates as White and Black women until age 45, after which they have the lowest incidence rates. Black women have the highest mortality rates at all ages followed by White women (e.g., 68.2 per 100,000 in Blacks vs 46.5 per 100,000 in Whites at age 60-64). Mortality rates for American Indian/ Alaska Native and Asian/ Pacific Islander women are similar until about age 60, at which point Asian/ Pacific Islander women have the lowest mortality rates (e.g., 30.0 per 100,000 in Asian/ Pacific Islander vs 33.2 per 100,000 in American Indian/ Alaska Native at age 60-64).While breast cancer incidence rates have either declined or remained stable since the early 2000s among White women, incidence rates among Black women have continued to increase (Figure 3) [33]. In 2016, the age-standardized incidence rate was 128.2 per 100,000 among Black women versus 132.7 per 100,000 among White women [4]. While incidence rates are lower, Black women experience higher breast cancer mortality than White women; further, that gap has continued to widen even as survival has increased overall (Figure 3) [33]. The 2016 age-standardized mortality rate was 27.3 per 100,000 Black women versus 19.6 per 100,00...

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“…Modeling suggests this approach could be cost-effective (53). The maximum improvement of breast cancer risk with SNP addition probably comes in the intermediate-risk women, with only small impacts reported in the overall AUC (54). Machine-learning algorithms may be better at handling multi-dimensional data with increased predictive abilities for complex disease risk than current polygenic risk scores (55).…”

Section: Testing Low-prevalence Populations: the General Population Modelmentioning

confidence: 99%

Population-based Genetic Testing for Precision Prevention

Evans

Manchanda

2020

Cancer Prevention Research

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Global interest in genetic testing for cancer susceptibility genes (CSG) has surged with falling costs, increasing awareness, and celebrity endorsement. Current access to genetic testing is based on clinical criteria/risk model assessment which uses family history as a surrogate. However, this approach is fraught with inequality, massive underutilization, and misses 50% CSG carriers. This reflects huge missed opportunities for precision prevention. Early CSG identification enables uptake of risk-reducing strategies in unaffected individuals to reduce cancer risk. Population-based genetic testing (PGT) can overcome limitations of clinical criteria/family history-based testing. Jewish population studies show population-based BRCA testing is feasible, acceptable, has high satisfaction, does not harm psychologic well-being/quality of life, and is extremely costeffective, arguing for changing paradigm to PGT in the Jewish population. Innovative approaches for delivering pretest information/education are needed to facilitate informed decision-making for PGT. Different health systems will need context-specific implementation strategies and management pathways, while maintaining principles of population screening. Data on general population PGT are beginning to emerge, prompting evaluation of wider implementation. Sophisticated risk prediction models incorporating genetic and nongenetic data are being used to stratify populations for ovarian cancer and breast cancer risk and risk-adapted screening/prevention. PGT is potentially cost-effective for panel testing of breast and ovarian CSGs and for risk-adapted breast cancer screening. Further research/implementation studies evaluating the impact, clinical efficacy, psychologic and socio-ethical consequences, and cost-effectiveness of PGT are needed.

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Performance of Single-Nucleotide Polymorphisms in Breast Cancer Risk Prediction Models: A Systematic Review and Meta-analysis

Cited by 16 publications

References 97 publications

Association of microRNA biosynthesis genes XPO5 and RAN polymorphisms with cancer susceptibility: Bayesian hierarchical meta-analysis

Association of microRNA biosynthesis genes XPO5 and RAN polymorphisms with cancer susceptibility: Bayesian hierarchical meta-analysis

Cancer Progress and Priorities: Breast Cancer

Population-based Genetic Testing for Precision Prevention

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