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

Yu Ru Su; Lori C. Sakoda; Jihyoun Jeon; Minta Thomas; Yi Lin; Jennifer L. Schneider; Natalia Udaltsova; Jeffrey K. Lee; Iris Lansdorp-Vogelaar; Elisabeth F.P. Peterse; Ann G. Zauber; Jiayin Zheng; Yingye Zheng; Elizabeth Hauser; John A. Baron; Elizabeth L. Barry; D. Timothy Bishop; Hermann Brenner; Daniel D. Buchanan; Andrea Burnett-Hartman; Peter T. Campbell; Graham Casey; Sergi Castellví-Bel; Andrew T. Chan; Jenny Chang-Claude; Jane C. Figueiredo; Steven J. Gallinger; Graham G. Giles; Stephen B. Gruber; Andrea Gsur; Marc J. Gunter; Jochen Hampe; Heather Hampel; Tabitha A. Harrison; Michael Hoffmeister; Xinwei Hua; Jeroen R. Huyghe; Mark A. Jenkins; Temitope O. Keku; Loic Le Marchand; Li Li; Annika Lindblom; Victor Moreno; Polly A. Newcomb; Paul D.P. Pharoah; Elizabeth A. Platz; John D. Potter; Conghui Qu; Gad Rennert; Robert E. Schoen; Martha L. Slattery; Mingyang Song; Fränzel J.B. van Duijnhoven; Bethany Van Guelpen; Pavel Vodicka; Alicja Wolk; Michael O. Woods; Anna H. Wu; Richard B. Hayes; Ulrike Peters; Douglas A. Corley; Li Hsu

doi:10.1158/1055-9965.EPI-22-0817

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

Yu Ru Su, Lori C. Sakoda, Jihyoun Jeon, Minta Thomas, Yi Lin, Jennifer L. Schneider, Natalia Udaltsova, Jeffrey K. Lee, Iris Lansdorp-Vogelaar, Elisabeth F.P. Peterse, Ann G. Zauber, Jiayin Zheng, Yingye Zheng, Elizabeth Hauser, John A. Baron, Elizabeth L. Barry, D. Timothy Bishop, Hermann Brenner, Daniel D. Buchanan, Andrea Burnett-HartmanPeter T. Campbell, Graham Casey, Sergi Castellví-Bel, Andrew T. Chan, Jenny Chang-Claude, Jane C. Figueiredo, Steven J. Gallinger, Graham G. Giles, Stephen B. Gruber, Andrea Gsur, Marc J. Gunter, Jochen Hampe, Heather Hampel, Tabitha A. Harrison, Michael Hoffmeister, Xinwei Hua, Jeroen R. Huyghe, Mark A. Jenkins, Temitope O. Keku, Loic Le Marchand, Li Li, Annika Lindblom, Victor Moreno, Polly A. Newcomb, Paul D.P. Pharoah, Elizabeth A. Platz, John D. Potter, Conghui Qu, Gad Rennert, Robert E. Schoen, Martha L. Slattery, Mingyang Song, Fränzel J.B. van Duijnhoven, Bethany Van Guelpen, Pavel Vodicka, Alicja Wolk, Michael O. Woods, Anna H. Wu, Richard B. Hayes, Ulrike Peters, Douglas A. Corley, Li Hsu

Research output: Contribution to journal › Article › peer-review

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.

Original language	English (US)
Pages (from-to)	353-362
Number of pages	10
Journal	Cancer Epidemiology Biomarkers and Prevention
Volume	32
Issue number	3
DOIs	https://doi.org/10.1158/1055-9965.EPI-22-0817
State	Published - Mar 1 2023
Externally published	Yes

ASJC Scopus subject areas

General Medicine

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1158/1055-9965.EPI-22-0817

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Su, Y. R., Sakoda, L. C., Jeon, J., Thomas, M., Lin, Y., Schneider, J. L., Udaltsova, N., Lee, J. K., Lansdorp-Vogelaar, I., Peterse, E. F. P., Zauber, A. G., Zheng, J., Zheng, Y., Hauser, E., Baron, J. A., Barry, E. L., Bishop, D. T., Brenner, H., Buchanan, D. D., ... Hsu, L. (2023). Validation of a Genetic-Enhanced Risk Prediction Model for Colorectal Cancer in a Large Community-Based Cohort. Cancer Epidemiology Biomarkers and Prevention, 32(3), 353-362. https://doi.org/10.1158/1055-9965.EPI-22-0817

Su, YR, Sakoda, LC, Jeon, J, Thomas, M, Lin, Y, Schneider, JL, Udaltsova, N, Lee, JK, Lansdorp-Vogelaar, I, Peterse, EFP, Zauber, AG, Zheng, J, Zheng, Y, Hauser, E, Baron, JA, Barry, EL, Bishop, DT, Brenner, H, Buchanan, DD, Burnett-Hartman, A, Campbell, PT, Casey, G, Castellví-Bel, S, Chan, AT, Chang-Claude, J, Figueiredo, JC, Gallinger, SJ, Giles, GG, Gruber, SB, Gsur, A, Gunter, MJ, Hampe, J, Hampel, H, Harrison, TA, Hoffmeister, M, Hua, X, Huyghe, JR, Jenkins, MA, Keku, TO, Le Marchand, L, Li, L, Lindblom, A, Moreno, V, Newcomb, PA, Pharoah, PDP, Platz, EA, Potter, JD, Qu, C, Rennert, G, Schoen, RE, Slattery, ML, Song, M, van Duijnhoven, FJB, Van Guelpen, B, Vodicka, P, Wolk, A, Woods, MO, Wu, AH, Hayes, RB, Peters, U, Corley, DA & Hsu, L 2023, 'Validation of a Genetic-Enhanced Risk Prediction Model for Colorectal Cancer in a Large Community-Based Cohort', Cancer Epidemiology Biomarkers and Prevention, vol. 32, no. 3, pp. 353-362. https://doi.org/10.1158/1055-9965.EPI-22-0817

@article{95ac8b52426341be88835bde8d21c7fd,

title = "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.",

author = "Su, {Yu Ru} and Sakoda, {Lori C.} and Jihyoun Jeon and Minta Thomas and Yi Lin and Schneider, {Jennifer L.} and Natalia Udaltsova and Lee, {Jeffrey K.} and Iris Lansdorp-Vogelaar and Peterse, {Elisabeth F.P.} and Zauber, {Ann G.} and Jiayin Zheng and Yingye Zheng and Elizabeth Hauser and Baron, {John A.} and Barry, {Elizabeth L.} and Bishop, {D. Timothy} and Hermann Brenner and Buchanan, {Daniel D.} and Andrea Burnett-Hartman and Campbell, {Peter T.} and Graham Casey and Sergi Castellv{\'i}-Bel and Chan, {Andrew T.} and Jenny Chang-Claude and Figueiredo, {Jane C.} and Gallinger, {Steven J.} and Giles, {Graham G.} and Gruber, {Stephen B.} and Andrea Gsur and Gunter, {Marc J.} and Jochen Hampe and Heather Hampel and Harrison, {Tabitha A.} and Michael Hoffmeister and Xinwei Hua and Huyghe, {Jeroen R.} and Jenkins, {Mark A.} and Keku, {Temitope O.} and {Le Marchand}, Loic and Li Li and Annika Lindblom and Victor Moreno and Newcomb, {Polly A.} and Pharoah, {Paul D.P.} and Platz, {Elizabeth A.} and Potter, {John D.} and Conghui Qu and Gad Rennert and Schoen, {Robert E.} and Slattery, {Martha L.} and Mingyang Song and {van Duijnhoven}, {Fr{\"a}nzel J.B.} and {Van Guelpen}, Bethany and Pavel Vodicka and Alicja Wolk and Woods, {Michael O.} and Wu, {Anna H.} and Hayes, {Richard B.} and Ulrike Peters and Corley, {Douglas A.} and Li Hsu",

note = "Publisher Copyright: {\textcopyright} 2023 American Association for Cancer Research.",

year = "2023",

month = mar,

day = "1",

doi = "10.1158/1055-9965.EPI-22-0817",

language = "English (US)",

volume = "32",

pages = "353--362",

journal = "Cancer Epidemiology Biomarkers and Prevention",

issn = "1055-9965",

publisher = "American Association for Cancer Research Inc.",

number = "3",

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TY - JOUR

T1 - Validation of a Genetic-Enhanced Risk Prediction Model for Colorectal Cancer in a Large Community-Based Cohort

AU - Su, Yu Ru

AU - Sakoda, Lori C.

AU - Jeon, Jihyoun

AU - Thomas, Minta

AU - Lin, Yi

AU - Schneider, Jennifer L.

AU - Udaltsova, Natalia

AU - Lee, Jeffrey K.

AU - Lansdorp-Vogelaar, Iris

AU - Peterse, Elisabeth F.P.

AU - Zauber, Ann G.

AU - Zheng, Jiayin

AU - Zheng, Yingye

AU - Hauser, Elizabeth

AU - Baron, John A.

AU - Barry, Elizabeth L.

AU - Bishop, D. Timothy

AU - Brenner, Hermann

AU - Buchanan, Daniel D.

AU - Burnett-Hartman, Andrea

AU - Campbell, Peter T.

AU - Casey, Graham

AU - Castellví-Bel, Sergi

AU - Chan, Andrew T.

AU - Chang-Claude, Jenny

AU - Figueiredo, Jane C.

AU - Gallinger, Steven J.

AU - Giles, Graham G.

AU - Gruber, Stephen B.

AU - Gsur, Andrea

AU - Gunter, Marc J.

AU - Hampe, Jochen

AU - Hampel, Heather

AU - Harrison, Tabitha A.

AU - Hoffmeister, Michael

AU - Hua, Xinwei

AU - Huyghe, Jeroen R.

AU - Jenkins, Mark A.

AU - Keku, Temitope O.

AU - Le Marchand, Loic

AU - Li, Li

AU - Lindblom, Annika

AU - Moreno, Victor

AU - Newcomb, Polly A.

AU - Pharoah, Paul D.P.

AU - Platz, Elizabeth A.

AU - Potter, John D.

AU - Qu, Conghui

AU - Rennert, Gad

AU - Schoen, Robert E.

AU - Slattery, Martha L.

AU - Song, Mingyang

AU - van Duijnhoven, Fränzel J.B.

AU - Van Guelpen, Bethany

AU - Vodicka, Pavel

AU - Wolk, Alicja

AU - Woods, Michael O.

AU - Wu, Anna H.

AU - Hayes, Richard B.

AU - Peters, Ulrike

AU - Corley, Douglas A.

AU - Hsu, Li

PY - 2023/3/1

Y1 - 2023/3/1

N2 - 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.

AB - 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.

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JO - Cancer Epidemiology Biomarkers and Prevention

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ER -

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

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