Dataset Information

Validation of a Genetic-Enhanced Risk Prediction Model for Colorectal Cancer in a Large Community-Based Cohort.

ABSTRACT:

Background

Polygenic risk scores (PRS) which summarize individuals' genetic risk profile may enhance targeted colorectal cancer screening. A critical step towards clinical implementation is rigorous external validations in large community-based cohorts. This study externally validated a PRS-enhanced colorectal cancer risk model comprising 140 known colorectal cancer loci to provide a comprehensive assessment on prediction performance.

Methods

The model was developed using 20,338 individuals and externally validated in a community-based cohort (n = 85,221). We validated predicted 5-year absolute colorectal cancer risk, including calibration using expected-to-observed case ratios (E/O) and calibration plots, and discriminatory accuracy using time-dependent AUC. The PRS-related improvement in AUC, sensitivity and specificity were assessed in individuals of age 45 to 74 years (screening-eligible age group) and 40 to 49 years with no endoscopy history (younger-age group).

Results

In European-ancestral individuals, the predicted 5-year risk calibrated well [E/O = 1.01; 95% confidence interval (CI), 0.91-1.13] and had high discriminatory accuracy (AUC = 0.73; 95% CI, 0.71-0.76). Adding the PRS to a model with age, sex, family and endoscopy history improved the 5-year AUC by 0.06 (P < 0.001) and 0.14 (P = 0.05) in the screening-eligible age and younger-age groups, respectively. Using a risk-threshold of 5-year SEER colorectal cancer incidence rate at age 50 years, adding the PRS had a similar sensitivity but improved the specificity by 11% (P < 0.001) in the screening-eligible age group. In the younger-age group it improved the sensitivity by 27% (P = 0.04) with similar specificity.

Conclusions

The proposed PRS-enhanced model provides a well-calibrated 5-year colorectal cancer risk prediction and improves discriminatory accuracy in the external cohort.

Impact

The proposed model has potential utility in risk-stratified colorectal cancer prevention.

SUBMITTER: Su YR

PROVIDER: S-EPMC9992158 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Publications

Validation of a Genetic-Enhanced Risk Prediction Model for Colorectal Cancer in a Large Community-Based Cohort.

Su Yu-Ru YR Sakoda Lori C LC Jeon Jihyoun J Thomas Minta M Lin Yi Y Schneider Jennifer L JL Udaltsova Natalia N Lee Jeffrey K JK Lansdorp-Vogelaar Iris I Peterse Elisabeth F P EFP Zauber Ann G AG Zheng Jiayin J Zheng Yingye Y Hauser Elizabeth E Baron John A JA Barry Elizabeth L EL Bishop D Timothy DT Brenner Hermann H Buchanan Daniel D DD Burnett-Hartman Andrea A Campbell Peter T PT Casey Graham G Castellví-Bel Sergi S Chan Andrew T AT Chang-Claude Jenny J Figueiredo Jane C JC Gallinger Steven J SJ Giles Graham G GG Gruber Stephen B SB Gsur Andrea A Gunter Marc J MJ Hampe Jochen J Hampel Heather H Harrison Tabitha A TA Hoffmeister Michael M Hua Xinwei X Huyghe Jeroen R JR Jenkins Mark A MA Keku Temitope O TO Marchand Loic Le LL Li Li L Lindblom Annika A Moreno Victor V Newcomb Polly A PA Pharoah Paul D P PDP Platz Elizabeth A EA Potter John D JD Qu Conghui C Rennert Gad G Schoen Robert E RE Slattery Martha L ML Song Mingyang M van Duijnhoven Fränzel J B FJB Van Guelpen Bethany B Vodicka Pavel P Wolk Alicja A Woods Michael O MO Wu Anna H AH Hayes Richard B RB Peters Ulrike U Corley Douglas A DA Hsu Li L

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20230301 3

<h4>Background</h4>Polygenic risk scores (PRS) which summarize individuals' genetic risk profile may enhance targeted colorectal cancer screening. A critical step towards clinical implementation is rigorous external validations in large community-based cohorts. This study externally validated a PRS-enhanced colorectal cancer risk model comprising 140 known colorectal cancer loci to provide a comprehensive assessment on prediction performance.<h4>Methods</h4>The model was developed using 20,338 i ...[more]

PMID: 36622766

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Dataset Information

Validation of a Genetic-Enhanced Risk Prediction Model for Colorectal Cancer in a Large Community-Based Cohort.

Background

Methods

Results

Conclusions

Impact

Publications

Validation of a Genetic-Enhanced Risk Prediction Model for Colorectal Cancer in a Large Community-Based Cohort.

Similar Datasets

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