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

Clinical utility of polygenic risk scores: a critical 2023 appraisal

Since their first appearance in the context of schizophrenia and bipolar disorder in 2009, polygenic risk scores (PRSs) have been described for a large number of common complex diseases. However, the clinical utility of PRSs in disease risk assessment or therapeutic decision making is likely limited because PRSs usually only account for the heritable component of a trait and ignore the etiological role of environment and lifestyle. We surveyed the current state of PRSs for various diseases, including breast cancer, diabetes, prostate cancer, coronary artery disease, and Parkinson disease, with an extra focus upon the potential improvement of clinical scores by their combination with PRSs. We observed that the diagnostic and prognostic performance of PRSs alone is consistently low, as expected. Moreover, combining a PRS with a clinical score at best led to moderate improvement of the power of either risk marker. Despite the large number of PRSs reported in the scientific literature, prospective studies of their clinical utility, particularly of the PRS-associated improvement of standard screening or therapeutic procedures, are still rare. In conclusion, the benefit to individual patients or the health care system in general of PRS-based extensions of existing diagnostic or treatment regimens is still difficult to judge.

Combining Molecular and Radiomic Features for Risk Assessment in Breast Cancer

Breast cancer risk is highly variable within the population and current research is leading the shift toward personalized medicine. By accurately assessing an individual woman's risk, we can reduce the risk of over/undertreatment by preventing unnecessary procedures or by elevating screening procedures. Breast density measured from conventional mammography has been established as one of the most dominant risk factors for breast cancer; however, it is currently limited by its ability to characterize more complex breast parenchymal patterns that have been shown to provide additional information to strengthen cancer risk models. Molecular factors ranging from high penetrance, or high likelihood that a mutation will show signs and symptoms of the disease, to combinations of gene mutations with low penetrance have shown promise for augmenting risk assessment. Although imaging biomarkers and molecular biomarkers have both individually demonstrated improved performance in risk assessment, few studies have evaluated them together. This review aims to highlight the current state of the art in breast cancer risk assessment using imaging and genetic biomarkers.

Evaluation of polygenic risk score for risk prediction of gastric cancer

Genetic variations are associated with individual susceptibility to gastric cancer. Recently, polygenic risk score (PRS) models have been established based on genetic variants to predict the risk of gastric cancer. To assess the accuracy of current PRS models in the risk prediction, a systematic review was conducted. A total of eight eligible studies consisted of 544842 participants were included for evaluation of the performance of PRS models. The overall accuracy was moderate with Area under the curve values ranging from 0.5600 to 0.7823. Incorporation of epidemiological factors or Helicobacter pylori (H. pylori) status increased the accuracy for risk prediction, while selection of single nucleotide polymorphism (SNP) and number of SNPs appeared to have little impact on the model performance. To further improve the accuracy of PRS models for risk prediction of gastric cancer, we summarized the association between gastric cancer risk and H. pylori genomic variations, cancer associated bacteria members in the gastric microbiome, discussed the potentials for performance improvement of PRS models with these microbial factors. Future studies on comprehensive PRS models established with human SNPs, epidemiological factors and microbial factors are indicated.

Machine learning for prediction of schizophrenia using genetic and demographic factors in the UK biobank

Machine learning (ML) holds promise for precision psychiatry, but its predictive performance is unclear. We assessed whether ML provided added value over logistic regression for prediction of schizophrenia, and compared models built using polygenic risk scores (PRS) or clinical/demographic factors. LASSO and ridge-penalised logistic regression, support vector machines (SVM), random forests, boosting, neural networks and stacked models were trained to predict schizophrenia, using PRS for schizophrenia (PRS_SZ), sex, parental depression, educational attainment, winter birth, handedness and number of siblings as predictors. Models were evaluated for discrimination using area under the receiver operator characteristic curve (AUROC) and relative importance of predictors using permutation feature importance (PFI). In a secondary analysis, fitted models were tested for association with schizophrenia-related traits which had not been used in model development. Following learning curve analysis, 738 cases and 3690 randomly sampled controls were selected from the UK Biobank. ML models combining all predictors showed the highest discrimination (linear SVM, AUROC = 0.71), but did not significantly outperform logistic regression. AUROC was robust over 100 random resamples of controls. PFI identified PRS_SZ as the most important predictor. Highest variance in fitted models was explained by schizophrenia-related traits including fluid intelligence (most associated: linear SVM), digit symbol substitution (RBF SVM), BMI (XGBoost), smoking status (XGBoost) and deprivation (linear SVM). In conclusion, ML approaches did not provide substantial added value for prediction of schizophrenia over logistic regression, as indexed by AUROC; however, risk scores derived with different ML approaches differ with respect to association with schizophrenia-related traits.

Polygenic risk prediction models for colorectal cancer: a systematic review

BACKGROUND

Risk prediction models incorporating single nucleotide polymorphisms (SNPs) could lead to individualized prevention of colorectal cancer (CRC). However, the added value of incorporating SNPs into models with only traditional risk factors is still not clear. Hence, our primary aim was to summarize literature on risk prediction models including genetic variants for CRC, while our secondary aim was to evaluate the improvement of discriminatory accuracy when adding SNPs to a prediction model with only traditional risk factors.

METHODS

We conducted a systematic review on prediction models incorporating multiple SNPs for CRC risk prediction. We tested whether a significant trend in the increase of Area Under Curve (AUC) according to the number of SNPs could be observed, and estimated the correlation between AUC improvement and number of SNPs. We estimated pooled AUC improvement for SNP-enhanced models compared with non-SNP-enhanced models using random effects meta-analysis, and conducted meta-regression to investigate the association of specific factors with AUC improvement.

RESULTS

We included 33 studies, 78.79% using genetic risk scores to combine genetic data. We found no significant trend in AUC improvement according to the number of SNPs (p for trend = 0.774), and no correlation between the number of SNPs and AUC improvement (p = 0.695). Pooled AUC improvement was 0.040 (95% CI: 0.035, 0.045), and the number of cases in the study and the AUC of the starting model were inversely associated with AUC improvement obtained when adding SNPs to a prediction model. In addition, models constructed in Asian individuals achieved better AUC improvement with the incorporation of SNPs compared with those developed among individuals of European ancestry.

CONCLUSIONS

Though not conclusive, our results provide insights on factors influencing discriminatory accuracy of SNP-enhanced models. Genetic variants might be useful to inform stratified CRC screening in the future, but further research is needed.

Preventive population genomics: The model of BRCA related cancers

Preventive population genomics offers the prospect of population stratification for targeting screening and prevention and tailoring care to those at greatest risk. Within cancer, this approach is now within reach, given our expanding knowledge of its heritable components, improved ability to predict risk, and increasing availability of effective preventive strategies. Advances in technology and bioinformatics has made population-testing technically feasible. The BRCA model provides 30 years of insight and experience of how to conceive of and construct care and serves as an initial model for preventive population genomics. Population-based BRCA-testing in the Jewish population is feasible, acceptable, reduces anxiety, does not detrimentally affect psychological well-being or quality of life, is cost-effective and is now beginning to be implemented. Population-based BRCA-testing and multigene panel testing in the wider general population is cost-effective for numerous health systems and can save thousands more lives than the current clinical strategy. There is huge potential for using both genetic and non-genetic information in complex risk prediction algorithms to stratify populations for risk adapted screening and prevention. While numerous strides have been made in the last decade several issues need resolving for population genomics to fulfil its promise and potential for maximizing precision prevention. Healthcare systems need to overcome significant challenges associated with developing delivery pathways, infrastructure expansion including laboratory services, clinical workforce training, scaling of management pathways for screening and prevention. Large-scale real world population studies are needed to evaluate context specific population-testing implementation models for cancer risk prediction, screening and prevention.

Preparation of yolk-shell structure NH2-MIL-125 magnetic nanoparticles for the selective extraction of nucleotides

Yolk-shell structure magnetic metal-organic framework nanoparticles were prepared via post solvothermal method and employed as a magnetic solid-phase extraction adsorbent for selective pre-concentration of 5'-ribonucleotides by π stacking interaction, hydrogen bonding, and the strong interaction between titanium ions (Ti⁴⁺) and phosphate group. The properties of the materials were confirmed by scanning electron microscopy, transmission electron microscopy, X-ray diffraction, X-ray photoelectron spectrometry, vibrating sample magnetometer, infrared spectroscopy, thermogravimetric analysis, and Brunauer-Emmett-Teller analysis. The main parameters affecting the adsorption-desorption process, including adsorbent amount, incubation time, incubation temperature, sample pH, shaking speed, elution solution, and elution time, were systematically optimized. Finally, 1.0 mg of adsorbent mixed with 1.0 mL sample solution (10.0 mmol⋅L^-1 NaCl, pH 3.0) and shaken at 135 rpm for 5 min at 40 °C, washed with 1.0 mL Na₃PO₄-NH₃∙H₂O under vortex for 5 min were selected as optimized adsorption-desorption conditions. The binding performance of adsorbent towards five nucleotides was evaluated by static adsorption experiments. The data are well-fitted to the Langmuir isotherm model and the maximum adsorption capacity is 27.8 mg g^-1 for adenosine 5'-monophosphate. The limit of detection of the method is 19.44-38.41 ng mL^-1. Under the optimal conditions, the adsorbent was successfully applied to magnetic solid-phase extraction and high performance liquid chromatography determination of five nucleotides in octopus, chicken, fish, and pork samples.

Cases in Precision Medicine: The Role of Polygenic Risk Scores in Breast Cancer Risk Assessment

Polygenic risk scores (PRSs) have been consistently associated with elevated breast cancer risk in cohort studies and are associated with risk in both women with and those without a family history of breast cancer. However, before clinical implementation, several issues must be addressed, including understanding the potential clinical utility and optimal method to communicate personalized screening recommendations that incorporate the PRS. Several trials are under way to answer some of these questions and facilitate clinical implementation. Because these PRSs have been developed in women of European ancestry, it is important to understand the limitations of their predictive ability in other ancestral groups. Finally, the value of the PRS will lie in considering it along with other clinical, familial, and rare genetic factors that are currently used in personalized risk assessment of breast cancer.

Inclusion of a gene-environment interaction between alcohol consumption and the aldehyde dehydrogenase 2 genotype in a risk prediction model for upper aerodigestive tract cancer in Japanese men

The well-known gene-environment interaction between alcohol consumption and aldehyde dehydrogenase 2 (ALDH2) genotype in upper aerodigestive tract cancer risk may improve our ability to identify high-risk subjects. Here, we developed and validated risk prediction models for this cancer in Japanese men and evaluated whether adding the gene-environment interaction to the model improved the predictive performance. We developed two case-cohort datasets in the Japan Public Health Center-based Prospective Study: one from subjects in the baseline survey for model development (108 cases and 4049 subcohort subjects) and the second from subjects in the 5-year follow-up survey for model validation (31 cases and 1527 subcohort subjects). We developed an environmental model including age, smoking status, and alcohol consumption, and a gene-environment interaction model including age, smoking status, and the combination of alcohol consumption and the ALDH2 genotype. We found a statistically significant gene-environment interaction for alcohol consumption and the ALDH2 genotype. The c-index for the gene-environment interaction model (0.71) was slightly higher than that for the environmental model (0.67). The values of integrated discrimination improvement and net reclassification improvement for the gene-environment interaction model were also slightly higher than those for the environmental model. Goodness-of-fit tests suggested that the models were well calibrated. Results from external model validation by the 5-year follow-up survey were consistent with those from the model development by the baseline survey. The addition of a gene-environment interaction to a lifestyle-based model might improve the performance to estimate the probability of developing upper aerodigestive tract cancer for Japanese men.

Population-based Genetic Testing for Precision Prevention

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 cost-effective, 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.

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

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.

Exon1 and -116 C/G Promoter Polymorphism on the X-Box DNA Binding Protein- 1 Gene is not Associated with Breast Cancer among Jordanian Women

Background: Human X -box binding protein 1 (XBP1), a critical gene in the endoplasmic reticulum stress response, is located on chromosome 22q12, which has been linked with the pathogenesis of many diseases, particularly cancers such as breast cancer (BC). Single nucleotide polymorphisms (SNPs) in the XBP1 gene can alter structure and function of the gene. In this study, polymorphism in the promoter region and exon1 of the gene XBP1 and its association with BC in Jordanian women was investigated. Methods: Polymorphism in the promoter and exon1 of XBP1 was analyzed in 100 subjects (controls: n=40; BC patients=60). −116 C/G SNP was genotyped by Polymerase Chain Reaction (PCR)-sequence specific primer technique. The odd ratios (ORs) at 95% confidence intervals (CIs) were computed to assess the strength of this association. Results: The three genotypes of the SNP (GG, GC, CC) and their allelic frequencies have nonsignificant differences between patients and control group. It was noticed that the frequencies of the mutant allele (G) were (75.8% versus 24.2%)) in the patients and control groups, respectively, while those of the normal allele (C) were (67.5% versus 32.5%). XBP1 (-116 G→C) G allele did not show significant association with BC risk (confidence interval = 0.3534- 1.2395, odds ratio = 0.6619, P= 0.197). Moreover, there were no significant mutations in the XBP1 exon1 neither in BC subjects nor control subjects. Conclusions: This is the first study to evaluate the effect of polymorphism in the promoter and exon1 of XBP1 gene in the pathogenesis of BC in Jordanian women. The results do not support a role for polymorphism in development of BC and further studies with a larger sample size and detailed data should be performed in other populations.

Towards clinical utility of polygenic risk scores

Prediction of disease risk is an essential part of preventative medicine, often guiding clinical management. Risk prediction typically includes risk factors such as age, sex, family history of disease and lifestyle (e.g. smoking status); however, in recent years, there has been increasing interest to include genomic information into risk models. Polygenic risk scores (PRS) aggregate the effects of many genetic variants across the human genome into a single score and have recently been shown to have predictive value for multiple common diseases. In this review, we summarize the potential use cases for seven common diseases (breast cancer, prostate cancer, coronary artery disease, obesity, type 1 diabetes, type 2 diabetes and Alzheimer’s disease) where PRS has or could have clinical utility. PRS analysis for these diseases frequently revolved around (i) risk prediction performance of a PRS alone and in combination with other non-genetic risk factors, (ii) estimation of lifetime risk trajectories, (iii) the independent information of PRS and family history of disease or monogenic mutations and (iv) estimation of the value of adding a PRS to specific clinical risk prediction scenarios. We summarize open questions regarding PRS usability, ancestry bias and transferability, emphasizing the need for the next wave of studies to focus on the implementation and health-economic value of PRS testing. In conclusion, it is becoming clear that PRS have value in disease risk prediction and there are multiple areas where this may have clinical utility.