Dataset Information

Genetic and Circulating Biomarker Data Improve Risk Prediction for Pancreatic Cancer in the General Population.

ABSTRACT:

Background

Pancreatic cancer is the third leading cause of cancer death in the United States, and 80% of patients present with advanced, incurable disease. Risk markers for pancreatic cancer have been characterized, but combined models are not used clinically to identify individuals at high risk for the disease.

Methods

Within a nested case-control study of 500 pancreatic cancer cases diagnosed after blood collection and 1,091 matched controls enrolled in four U.S. prospective cohorts, we characterized absolute risk models that included clinical factors (e.g., body mass index, history of diabetes), germline genetic polymorphisms, and circulating biomarkers.

Results

Model discrimination showed an area under ROC curve of 0.62 via cross-validation. Our final integrated model identified 3.7% of men and 2.6% of women who had at least 3 times greater than average risk in the ensuing 10 years. Individuals within the top risk percentile had a 4% risk of developing pancreatic cancer by age 80 years and 2% 10-year risk at age 70 years.

Conclusions

Risk models that include established clinical, genetic, and circulating factors improved disease discrimination over models using clinical factors alone.

Impact

Absolute risk models for pancreatic cancer may help identify individuals in the general population appropriate for disease interception.

SUBMITTER: Kim J

PROVIDER: S-EPMC8020898 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Genetic and Circulating Biomarker Data Improve Risk Prediction for Pancreatic Cancer in the General Population.

Kim Jihye J Yuan Chen C Babic Ana A Bao Ying Y Clish Clary B CB Pollak Michael N MN Amundadottir Laufey T LT Klein Alison P AP Stolzenberg-Solomon Rachael Z RZ Pandharipande Pari V PV Brais Lauren K LK Welch Marisa W MW Ng Kimmie K Giovannucci Edward L EL Sesso Howard D HD Manson JoAnn E JE Stampfer Meir J MJ Fuchs Charles S CS Wolpin Brian M BM Kraft Peter P

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20200422 5

<h4>Background</h4>Pancreatic cancer is the third leading cause of cancer death in the United States, and 80% of patients present with advanced, incurable disease. Risk markers for pancreatic cancer have been characterized, but combined models are not used clinically to identify individuals at high risk for the disease.<h4>Methods</h4>Within a nested case-control study of 500 pancreatic cancer cases diagnosed after blood collection and 1,091 matched controls enrolled in four U.S. prospective coh ...[more]

PMID: 32321713

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Project description:Objective To provide an overview of prediction models for risk of cardiovascular disease (CVD) in the general population.Design Systematic review.Data sources Medline and Embase until June 2013.Eligibility criteria for study selection Studies describing the development or external validation of a multivariable model for predicting CVD risk in the general population.Results 9965 references were screened, of which 212 articles were included in the review, describing the development of 363 prediction models and 473 external validations. Most models were developed in Europe (n=167, 46%), predicted risk of fatal or non-fatal coronary heart disease (n=118, 33%) over a 10 year period (n=209, 58%). The most common predictors were smoking (n=325, 90%) and age (n=321, 88%), and most models were sex specific (n=250, 69%). Substantial heterogeneity in predictor and outcome definitions was observed between models, and important clinical and methodological information were often missing. The prediction horizon was not specified for 49 models (13%), and for 92 (25%) crucial information was missing to enable the model to be used for individual risk prediction. Only 132 developed models (36%) were externally validated and only 70 (19%) by independent investigators. Model performance was heterogeneous and measures such as discrimination and calibration were reported for only 65% and 58% of the external validations, respectively.Conclusions There is an excess of models predicting incident CVD in the general population. The usefulness of most of the models remains unclear owing to methodological shortcomings, incomplete presentation, and lack of external validation and model impact studies. Rather than developing yet another similar CVD risk prediction model, in this era of large datasets, future research should focus on externally validating and comparing head-to-head promising CVD risk models that already exist, on tailoring or even combining these models to local settings, and investigating whether these models can be extended by addition of new predictors.

Dataset Information

Genetic and Circulating Biomarker Data Improve Risk Prediction for Pancreatic Cancer in the General Population.

Background

Methods

Results

Conclusions

Impact

Publications

Genetic and Circulating Biomarker Data Improve Risk Prediction for Pancreatic Cancer in the General Population.

Similar Datasets

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