A colorectal cancer prediction model using traditional and genetic risk scores in Koreans

Abstract

BackgroundGenome-wide association studies have identified numerous single nucleotide polymorphisms (SNPs) as associated with colorectal cancer (CRC) risk in populations of European descent. However, their utility for predicting risk to CRC in Asians remains unknown. A case-cohort study (random sub-cohort N = 1,685) from the Korean Cancer Prevention Study-II (KCPS-II) (N = 145,842) was used. Twenty-three SNPs identified in previous 47 studies were genotyped on the KCPS-II sub-cohort members. A genetic risk score (GRS) was calculated by summing the number of risk alleles over all SNPs. Prediction models with or without GRS were evaluated in terms of the area under the receiver operating characteristic curve (AUROC) and the continuous net reclassification index (NRI).ResultsSeven of 23 SNPs showed significant association with CRC and rectal cancer in Koreans, but not with colon cancer alone. AUROCs (95% CI) for traditional risk score (TRS) alone and TRS plus GRS were 0.73 (0.69–0.78) and 0.74 (0.70–0.78) for CRC, and 0.71 (0.65–0.77) and 0.74 (0.68–0.79) for rectal cancer, respectively. The NRI (95% CI) for a prediction model with GRS compared to the model with TRS alone was 0.17 (-0.05-0.37) for CRC and 0.41 (0.10–0.68) for rectal cancer alone.ConclusionOur results indicate genetic variants may be useful for predicting risk to CRC in the Koreans, especially risk for rectal cancer alone. Moreover, this study suggests effective prediction models for colon and rectal cancer should be developed separately.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-015-0207-y) contains supplementary material, which is available to authorized users.

A panel of DNA methylation signature from peripheral blood may predict colorectal cancer susceptibility

“…For example, Cho et al (39), reported a higher GRS that was associated with CRC (OR, 2.57; 95% CI, 1.89, 3.49) using thirteen SNPs. In addition, Jung et al (40) in a casecohort study, demonstrated that participants in the highest quartiles of the genetic risk score had an increased risk of CRC (hazard ratio, 2.65; 95% CI, 1.43 to 4.91) compared with those in the lowest quartile using seven SNPs.…”

Risk prediction models for colorectal cancer: A Scoping review

“…Socio-demographic characteristics of the study populations A majority of the risk prediction models have been developed in United States of America [18,22,33,38,39,42,44,45,48,49,50,51,53,54] followed by Korea [14,17,20,35]. Three models have been developed in China [16,23,36] and Japan [40] while, Netherlands [25,26], Germany [21], Taiwan [32,46] and United Kingdom [31,34] have developed two models each.…”

“…The questionnaires range from those with only one or two simple questions to those with detailed questions on factors such as diet, physical activity, alcohol and smoking habits and past medical facts. Three purely genetic models have used only a blood test for genetic biomarkers [32,34,39], while thirteen models have used data from a questionnaire and the results of blood tests for genetic biomarkers and other biochemical tests [16,17,20,27,31,35,47]. One model has obtained data both from a questionnaire and medical records [15].…”

“…A majority of the studies have used multiple logistic regression to identify the risk predictors [16,27,28,30,33,34,35,41,43,47,50,52] followed by the allocation of the risk points, based on the values of beta-coefficients [14,15,18,21,23,24,36,37,39,45,46,48]. Fourteen models have incorporated Cox proportional hazards regression to develop the risk model and the score [17,19,20,22,38,40]. One model [30] was developed from meta-analysis of various studies, one [31] used risk modelling software in a simulated population and one [26] used pure bivariate analysis.…”

“…Among the models four were purely genetic [17,32,34,47], seven models have both components [16,27,31,35,39] while the rest of the models are non-genetic (n=48). The authors of the 58 risk prediction models have considered 77 different risk factors (excluding genetic factors) as shown in Table 3.…”