Evaluating the effect of multiple genetic risk score models on colorectal cancer risk prediction

Xin, Junyi; Chu, Haiyan; Ben, Shuai; Ge, Yuqiu; Shao, Wei; Zhao, Yang; Wei, Yongyue; Ma, Gaoxiang; Li, Shuwei; Gu, Dongying; Du, Mulong; Wang, Meilin

doi:10.1016/j.gene.2018.06.035

Cited by 14 publications

(25 citation statements)

References 30 publications

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“…In our systematic review [19] we identified 29 genetic only (GRS) or combined GRS and phenotypic models for CRC from 20 publications. After contacting authors if insufficient data were provided in the original publications, we excluded 11 models because either details of the risk alleles were not available [20,21], the relative risk parameters associated with predictors included in the model were not published [22][23][24], they included variables not available in UK Biobank [25][26][27] or biochemical risk factors [28], or if the model was developed separately for proximal and distal colon and rectal cancers [29].…”

Section: Selection Of Risk Prediction Modelsmentioning

confidence: 99%

“…After these exclusions, we included 17 models from our systematic review [18,22,24,[30][31][32][33][34][35][36][37][38][39].…”

Section: Selection Of Risk Prediction Modelsmentioning

confidence: 99%

“…44 among people reporting a white/European ethnic background. Five of the GRSs showed some discriminative ability among people with non-white ethnicity [22,24,29,31,35] (Table 2).…”

Section: Grs Discrimination Stratified By Ethnicitymentioning

confidence: 99%

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External Validation of Risk Prediction Models Incorporating Common Genetic Variants for Incident Colorectal Cancer Using UK Biobank

Saunders

Kilian

Thompson

et al. 2020

Cancer Prevention Research

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The aim of this study was to compare and externally validate risk scores developed to predict incident colorectal cancer (CRC) that include common genetic variants (single nucleotide polymorphisms, SNPs), with or without established lifestyle/environmental (questionnairebased/classical/phenotypic) risk factors. We externally validated 23 risk models from a previous systematic review in 443,888 participants aged 37-73 from the UK Biobank cohort who had 6 year prospective follow-up, no prior history of CRC and data for incidence of CRC through linkage to national cancer registries. There were 2,679 (0.6%) cases of incident CRC. We assessed model discrimination using the Area Under the 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%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 0.64-0.67 in women and 0.67-0.71 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 six years. Consideration should be given to exploring the feasibility of incorporating genetic and lifestyle/environmental information in any future stratified CRC screening program.

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Section: Selection Of Risk Prediction Modelsmentioning

confidence: 99%

“…After these exclusions, we included 17 models from our systematic review [18,22,24,[30][31][32][33][34][35][36][37][38][39].…”

Section: Selection Of Risk Prediction Modelsmentioning

confidence: 99%

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External Validation of Risk Prediction Models Incorporating Common Genetic Variants for Incident Colorectal Cancer Using UK Biobank

Saunders

Kilian

Thompson

et al. 2020

Cancer Prevention Research

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“…A Genetic Risk Score (GRS) summarizes the effects of multiple SNPs associated with CRC and each GRS might vary depending on the number of SNPs included, the statistical analysis used and the model used to develop the risk score 47 . The addition of a GRS to CRC risk prediction models has been shown to improve the discriminatory power in some models addressing population risk 48 – 50 . For example, Iwasaki et al 49 compared three models (amongst 349 men and 326 women with CRC compared with an equivalent number of age-matched controls without a CRC diagnosis): a non-genetic model (incorporating age, BMI, alcohol intake and smoking), a second model with age and GRS, and a third model incorporating the non-genetic model and GRS.…”

Section: Current Risk Stratificationmentioning

confidence: 99%

A risk-stratified approach to colorectal cancer prevention and diagnosis

Hull

Rees

Sharp

et al. 2020

Nat Rev Gastroenterol Hepatol

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Population screening and endoscopic surveillance are used widely to prevent the development of and death from colorectal cancer (CRC). However, CRC remains a major cause of cancer mortality and the increasing burden of endoscopic investigations threatens to overwhelm some health services. This Perspective describes the rationale for and approach to improved risk stratification and decision-making for CRC prevention and diagnosis. Limitations of current approaches will be discussed using the UK as an example of the challenges faced by a particular health-care system, followed by discussion of novel risk biomarker utilization. We explore how risk stratification will be advantageous to current health-care providers and users, enabling more efficient use of limited colonoscopy resources. We discuss risk stratification in the setting of population screening as well as the surveillance of high-risk groups and investigation of symptomatic patients. We also address challenges in the development and validation of risk stratification tools and identify key research priorities.

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“…Individual SNPs can be aggregated into a polygenic risk score (PRS). The combination of a large number of such SNPs in genetic risk scores has been demonstrated to enable relevant risk stratification (40)(41)(42)(43)(44). As such, PRS complements other factors that identify groups with modified risk and could serve as a stand-alone method for risk stratification before the diagnostic screening (45).…”

Section: Introductionmentioning

confidence: 99%

Precision Colorectal Cancer Screening with Polygenic Risk Score

Tasa¹,

Puustusmaa²,

Tõnisson

et al. 2020

Preprint

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Colorectal cancer (CRC) is the second most common cancer in women and third most common cancer in men. Genome-wide association studies have identified numerous genetic variants (SNPs) independently associated with CRC. The effects of such SNPs can be combined into a single polygenic risk score (PRS). Stratification of individuals according to PRS could be introduced to primary and secondary prevention. Our aim was to combine risk stratification of a sex-specific PRS model with recommendations for individualized CRC screening. Previously published PRS models for predicting the risk of CRC were collected from the literature. These were validated on the UK Biobank (UKBB) consisting of a total of 458 696 quality-controlled genotypes with 1810 and 1348 prevalent male cases, and 2410 and 1810 incident male and female cases. The best performing sex-specific model was selected based on the AUC in prevalent data and independently validated in the incident dataset. Using Estonian CRC background information, we performed absolute risk simulations and examined the ability of PRS in risk stratifying individual screening recommendations. The best-performing model included 91 SNPs. The C-index of the best performing model in the dataset was 0.613 (SE = 0.007) and hazard ratio (HR) per unit of PRS was 1.53 (1.47 - 1.59) for males. Respective metrics for females were 0.617 (SE = 0.006) and 1.50 (1.44 - 1.58). PRS risk simulations showed that a genetically average 50-year-old female doubles her risk by age 58 (55 in males) and triples it by age 63 (59 in males). In addition, the best performing PRS model was able to identify individuals in one of seven groups proposed by Naber et al. for different coloscopy screening recommendation regimens. We have combined PRS-based recommendations for individual screening attendance. Our approach is easily adaptable to other nationalities by using population-specific background data of other genetically similar populations.

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Evaluating the effect of multiple genetic risk score models on colorectal cancer risk prediction

Cited by 14 publications

References 30 publications

External Validation of Risk Prediction Models Incorporating Common Genetic Variants for Incident Colorectal Cancer Using UK Biobank

External Validation of Risk Prediction Models Incorporating Common Genetic Variants for Incident Colorectal Cancer Using UK Biobank

A risk-stratified approach to colorectal cancer prevention and diagnosis

Precision Colorectal Cancer Screening with Polygenic Risk Score

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