Risk Prediction Models for Colorectal Cancer Incorporating Common Genetic Variants: A Systematic Review.
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ABSTRACT:
SUBMITTER: McGeoch L
PROVIDER: S-EPMC7610631 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The aim of this study was to compare and externally validate risk scores developed to predict incident colorectal cancer that include common genetic variants (SNPs), with or without established lifestyle/environmental (questionnaire-based/classical/phenotypic) risk factors. We externally validated 23 risk models from a previous systematic review in 443,888 participants ages 37 to 73 from the UK Biobank cohort who had 6-year prospective follow-up, no prior history of colorectal cancer, and data for incidence of colorectal cancer through linkage to national cancer registries. There were 2,679 (0.6%) cases of incident colorectal cancer. We assessed model discrimination using the area under the operating characteristic curve (AUC) and relative risk calibration. The AUC of models including only SNPs increased with the number of included SNPs and was similar in men and women: the model by Huyghe with 120 SNPs had the highest AUC of 0.62 [95% confidence interval (CI), 0.59-0.64] in women and 0.64 (95% CI, 0.61-0.66) in men. Adding phenotypic risk factors without age improved discrimination in men but not in women. Adding phenotypic risk factors and age increased discrimination in all cases (P < 0.05), with the best performing models including SNPs, phenotypic risk factors, and age having AUCs between 0.64 and 0.67 in women and 0.67 and 0.71 in men. Relative risk calibration varied substantially across the models. Among middle-aged people in the UK, existing polygenic risk scores discriminate moderately well between those who do and do not develop colorectal cancer over 6 years. Consideration should be given to exploring the feasibility of incorporating genetic and lifestyle/environmental information in any future stratified colorectal cancer screening program.
| S-EPMC7610623 | biostudies-literature
Project description:Colorectal cancer is the second leading cause of cancer-related death in Europe and the United States. Survival is strongly related to stage at diagnosis and population-based screening reduces colorectal cancer incidence and mortality. Stratifying the population by risk offers the potential to improve the efficiency of screening. In this systematic review we searched Medline, EMBASE, and the Cochrane Library for primary research studies reporting or validating models to predict future risk of primary colorectal cancer for asymptomatic individuals. A total of 12,808 papers were identified from the literature search and nine through citation searching. Fifty-two risk models were included. Where reported (n = 37), half the models had acceptable-to-good discrimination (the area under the receiver operating characteristic curve, AUROC >0.7) in the derivation sample. Calibration was less commonly assessed (n = 21), but overall acceptable. In external validation studies, 10 models showed acceptable discrimination (AUROC 0.71-0.78). These include two with only three variables (age, gender, and BMI; age, gender, and family history of colorectal cancer). A small number of prediction models developed from case-control studies of genetic biomarkers also show some promise but require further external validation using population-based samples. Further research should focus on the feasibility and impact of incorporating such models into stratified screening programmes.
| S-EPMC7610622 | biostudies-literature
Project description:BackgroundRisk 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.MethodsWe 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.ResultsWe 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.ConclusionsThough 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.
| S-EPMC8760647 | biostudies-literature
Project description:Cancer risk prediction models are important in identifying individuals at high risk of developing cancer, which could result in targeted screening and interventions to maximize the treatment benefit and minimize the burden of cancer. The cancer-associated genetic variants identified in genome-wide or candidate gene association studies have been shown to collectively enhance cancer risk prediction, improve our understanding of carcinogenesis, and possibly result in the development of targeted treatments for patients. In this article, we review the cancer risk prediction models that have been developed for popular cancers and assess their applicability, strengths, and weaknesses. We also discuss the factors to be considered for future development and improvement of models for cancer risk prediction.
| S-EPMC4179686 | biostudies-literature
Project description:BackgroundGenomewide 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.MethodsWe 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.ResultsThe 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.ConclusionsThe 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.
| S-EPMC2921181 | biostudies-literature
Project description:To further dissect the genetic architecture of colorectal cancer (CRC), we performed whole-genome sequencing of 1,439 cases and 720 controls, imputed discovered sequence variants and Haplotype Reference Consortium panel variants into genome-wide association study data, and tested for association in 34,869 cases and 29,051 controls. Findings were followed up in an additional 23,262 cases and 38,296 controls. We discovered a strongly protective 0.3% frequency variant signal at CHD1. In a combined meta-analysis of 125,478 individuals, we identified 40 new independent signals at P?<?5?×?10-8, bringing the number of known independent signals for CRC to ~100. New signals implicate lower-frequency variants, Krüppel-like factors, Hedgehog signaling, Hippo-YAP signaling, long noncoding RNAs and somatic drivers, and support a role for immune function. Heritability analyses suggest that CRC risk is highly polygenic, and larger, more comprehensive studies enabling rare variant analysis will improve understanding of biology underlying this risk and influence personalized screening strategies and drug development.
| S-EPMC6358437 | biostudies-literature
Project description:Despite the many successes of genome-wide association studies (GWAS), the known susceptibility variants identified by GWAS have modest effect sizes, leading to notable skepticism about the effectiveness of building a risk prediction model from large-scale genetic data. However, in contrast to genetic variants, the family history of diseases has been largely accepted as an important risk factor in clinical diagnosis and risk prediction. Nevertheless, the complicated structures of the family history of diseases have limited their application in clinical practice. Here, we developed a new method that enables incorporation of the general family history of diseases with a liability threshold model, and propose a new analysis strategy for risk prediction with penalized regression analysis that incorporates both large numbers of genetic variants and clinical risk factors. Application of our model to type 2 diabetes in the Korean population (1846 cases and 1846 controls) demonstrated that single-nucleotide polymorphisms accounted for 32.5% of the variation explained by the predicted risk scores in the test data set, and incorporation of family history led to an additional 6.3% improvement in prediction. Our results illustrate that family medical history provides valuable information on the variation of complex diseases and improves prediction performance.
| S-EPMC5676245 | biostudies-literature
Project description:ContextEarly detection of kidney cancer improves survival; however, low prevalence means that population-wide screening may be inefficient. Stratification of the population into risk categories could allow for the introduction of a screening programme tailored to individuals.ObjectiveThis review will identify and compare published models that predict the risk of developing kidney cancer in the general population.Evidence acquisitionA search identified primary research reporting or validating models predicting the risk of kidney cancer in Medline and EMBASE. After screening identified studies for inclusion, we extracted data onto a standardised form. The risk models were classified using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines and evaluated using the PROBAST assessment tool.Evidence synthesisThe search identified 15 281 articles. Sixty-two satisfied the inclusion criteria; performance measures were provided for 11 models. Some models predicted the risk of prevalent undiagnosed disease and others future incident disease. Six of the models had been validated, two using external populations. The most commonly included risk factors were age, smoking status, and body mass index. Most of the models had acceptable-to-good discrimination (area under the receiver-operating curve >0.7) in development and validation. Many models also had high specificity; however, several had low sensitivity. The highest performance was seen for the models using only biomarkers to detect kidney cancer; however, these were developed and validated in small case-control studies.ConclusionsWe identified a small number of risk models that could be used to stratify the population according to the risk of kidney cancer. Most exhibit reasonable discrimination, but a few have been validated externally in population-based studies.Patient summaryIn this review, we looked at mathematical models predicting the likelihood of an individual developing kidney cancer. We found several suitable models, using a range of risk factors (such as age and smoking) to predict the risk for individuals. Most of the models identified require further testing in the general population to confirm their usefulness.
| S-EPMC8642244 | biostudies-literature
Project description:ObjectivesTo look at the available literature on validated prediction models for contrast induced nephropathy and describe their characteristics.DesignSystematic review.Data sourcesMedline, Embase, and CINAHL (cumulative index to nursing and allied health literature) databases.Review methodsDatabases searched from inception to 2015, and the retrieved reference lists hand searched. Dual reviews were conducted to identify studies published in the English language of prediction models tested with patients that included derivation and validation cohorts. Data were extracted on baseline patient characteristics, procedural characteristics, modelling methods, metrics of model performance, risk of bias, and clinical usefulness. Eligible studies evaluated characteristics of predictive models that identified patients at risk of contrast induced nephropathy among adults undergoing a diagnostic or interventional procedure using conventional radiocontrast media (media used for computed tomography or angiography, and not gadolinium based contrast).Results16 studies were identified, describing 12 prediction models. Substantial interstudy heterogeneity was identified, as a result of different clinical settings, cointerventions, and the timing of creatinine measurement to define contrast induced nephropathy. Ten models were validated internally and six were validated externally. Discrimination varied in studies that were validated internally (C statistic 0.61-0.95) and externally (0.57-0.86). Only one study presented reclassification indices. The majority of higher performing models included measures of pre-existing chronic kidney disease, age, diabetes, heart failure or impaired ejection fraction, and hypotension or shock. No prediction model evaluated its effect on clinical decision making or patient outcomes.ConclusionsMost predictive models for contrast induced nephropathy in clinical use have modest ability, and are only relevant to patients receiving contrast for coronary angiography. Further research is needed to develop models that can better inform patient centred decision making, as well as improve the use of prevention strategies for contrast induced nephropathy.
| S-EPMC4784870 | biostudies-literature
Project description:IntroductionKnown prediction models for breast cancer can potentially by improved by the addition of mammographic density and common genetic variants identified in genome-wide associations studies known to be associated with risk of the disease. We evaluated the benefit of including mammographic density and the cumulative effect of genetic variants in breast cancer risk prediction among women in a Singapore population.MethodsWe estimated the risk of breast cancer using a prospective cohort of 24,161 women aged 50 to 64 from Singapore with available mammograms and known risk factors for breast cancer who were recruited between 1994 and 1997. We measured mammographic density using the medio-lateral oblique views of both breasts. Each woman's genotype for 75 SNPs was simulated based on the genotype frequency obtained from the Breast Cancer Association Consortium data and the cumulative effect was summarized by a genetic risk score (GRS). Any improvement in the performance of our proposed prediction model versus one containing only variables from the Gail model was assessed by changes in receiver-operating characteristic and predictive values.ResultsDuring 17 years of follow-up, 680 breast cancer cases were diagnosed. The multivariate-adjusted hazard ratios (95% confidence intervals) were 1.60 (1.22-2.10), 2.20 (1.65-2.92), 2.33 (1.71-3.20), 2.12 (1.43-3.14), and 3.27 (2.24-4.76) for the corresponding mammographic density categories: 11-20cm2, 21-30cm2, 31-40cm2, 41-50cm2, 51-60cm2, and 1.10 (1.03-1.16) for GRS. At the predicted absolute 10-year risk thresholds of 2.5% and 3.0%, a model with mammographic density and GRS could correctly identify 0.9% and 0.5% more women who would develop the disease compared to a model using only the Gail variables, respectively.ConclusionMammographic density and common genetic variants can improve the discriminatory power of an established breast cancer risk prediction model among females in Singapore.
| S-EPMC4581713 | biostudies-literature