Prediction model for pancreatic cancer risk in the general Japanese population

Abstract

Genome-wide association studies (GWASs) have identified many single nucleotide polymorphisms (SNPs) that are significantly associated with pancreatic cancer susceptibility. We sought to replicate the associations of 61 GWAS-identified SNPs at 42 loci with pancreatic cancer in Japanese and to develop a risk model for the identification of individuals at high risk for pancreatic cancer development in the general Japanese population. The model was based on data including directly determined or imputed SNP genotypes for 664 pancreatic cancer case and 664 age- and sex-matched control subjects. Stepwise logistic regression uncovered five GWAS-identified SNPs at five loci that also showed significant associations in our case-control cohort. These five SNPs were included in the risk model and also applied to calculation of the polygenic risk score (PRS). The area under the curve determined with the leave-one-out cross-validation method was 0.63 (95% confidence interval, 0.60–0.66) or 0.61 (0.58–0.64) for versions of the model that did or did not include cigarette smoking and family history of pancreatic cancer in addition to the five SNPs, respectively. Individuals in the lowest and highest quintiles for the PRS had odds ratios of 0.62 (0.42–0.91) and 1.98 (1.42–2.76), respectively, for pancreatic cancer development compared with those in the middle quintile. We have thus developed a risk model for pancreatic cancer that showed moderately good discriminatory ability with regard to differentiation of pancreatic cancer patients from control individuals. Our findings suggest the potential utility of a risk model that incorporates replicated GWAS-identified SNPs and established demographic or environmental factors for the identification of individuals at increased risk for pancreatic cancer development.

Pancreatic Cancer Prediction Through an Artificial Neural Network

“…Our ANN's weights were fit on the training dataset and if more risk factors are added, updating the weights to include the new factors can be done quickly by re-fitting the ANN. Nakatochi et al (2018) presented a PC risk prediction model in the general population in Japan with AUC of 0.63. However, their model was based on data including directly determined or imputed single nucleotide polymorphisms (SNPs) genotypes.…”

Development of PancRISK, a urine biomarker-based risk score for stratified screening of pancreatic cancer patients

“…Since the Framingham study in 1976, yielding a first risk prediction model for coronary heart disease, a number of prediction models have been reported for various medical conditions, including cancer. [1][2][3][4][5] In pancreatic ductal adenocarcinoma (PDAC), few such models have been designed, including the ones for absolute risk prediction [6][7][8][9][10][11][12] and gene carrier status prediction, 13 as well as prediction models in groups at risk. 14,15 Recently, two independent models to determine the risk of PDAC in patients in new-onset diabetes (NOD) cohort have also been reported.…”

Susceptibility Loci for Pancreatic Cancer in the Brazilian Population

“…In our control group this value was increased to 22% and, among PA patients this frequency increased to 37% and was associated with high cancer risk. The minor allele was also associated with increased PA risk in European [22] and Japanese populations [45]. In European populations, it was highlighted in PA susceptibility only in the largest pancreatic cancer GWAS to date, including 11,537 patients and 17,107 controls from the Pancreatic Cancer Cohort Consortium (PanScan I+II, III), Pancreatic Cancer Case-Control Consortium (PanC4) and…”

“…PANcreatic Disease ReseArch (PANDoRA) consortium [22]. In the Japanese population, 664 pancreatic cancer cases and 664 controls were analyzed and this SNP was highlighted as PA risk factor [45]. This SNP is mapped at 1p36.33, in the rst intron of the NOC2L gene and probably in uences the host expression.…”