En Cheng1,2, Fang-Shu Ou3, Chao Ma4, Donna Spiegelman5,6, Sui Zhang4, Xin Zhou5,6, Tiffany M Bainter3, Leonard B Saltz7, Donna Niedzwiecki8, Robert J Mayer4, Renaud Whittom9, Alexander Hantel10, Al Benson11, Daniel Atienza12, Michael Messino13, Hedy Kindler14, Edward L Giovannucci15, Erin L Van Blarigan16, Justin C Brown17, Kimmie Ng4, Cary P Gross1,18,19, Jeffrey A Meyerhardt4, Charles S Fuchs1,20,21,22. 1. Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT. 2. Division of Research, Kaiser Permanente Northern California, Oakland, CA. 3. Alliance Statistics and Data Management Center, Mayo Clinic, Rochester, MN. 4. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA. 5. Department of Biostatistics, Yale School of Public Health, New Haven, CT. 6. Center on Methods for Implementation and Prevention Science, Yale School of Public Health, New Haven, CT. 7. Memorial Sloan Kettering Cancer Center, New York, NY. 8. Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC. 9. Hôpital du Sacré-Coeur de Montréal, Montreal, Quebec, Canada. 10. Loyola University, Stritch School of Medicine, Naperville, IL. 11. Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL. 12. Virginia Oncology Associates, Norfolk, VA. 13. Messino Cancer Centers, Asheville, NC. 14. University of Chicago, Chicago, IL. 15. Department of Epidemiology, and Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA. 16. Department of Epidemiology and Biostatistics, and Urology, University of California, San Francisco, CA. 17. Cancer Metabolism Program, Pennington Biomedical Research Center, Baton Rouge, LA. 18. Section of General Internal Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT. 19. Cancer Outcomes, Public Policy, and Effectiveness Research Center, Yale Cancer Center, New Haven, CT. 20. Division of Hematology and Medical Oncology, Department of Internal Medicine, Yale School of Medicine, New Haven, CT. 21. Yale Cancer Center, Smilow Cancer Hospital, New Haven, CT. 22. Hematology and Oncology Product Development, Genentech & Roche, South San Francisco, CA.
Abstract
PURPOSE: Current tools in predicting survival outcomes for patients with colon cancer predominantly rely on clinical and pathologic characteristics, but increasing evidence suggests that diet and lifestyle habits are associated with patient outcomes and should be considered to enhance model accuracy. METHODS: Using an adjuvant chemotherapy trial for stage III colon cancer (CALGB 89803), we developed prediction models of disease-free survival (DFS) and overall survival by additionally incorporating self-reported nine diet and lifestyle factors. Both models were assessed by multivariable Cox proportional hazards regression and externally validated using another trial for stage III colon cancer (CALGB/SWOG 80702), and visual nomograms of prediction models were constructed accordingly. We also proposed three hypothetical scenarios for patients with (1) good-risk, (2) average-risk, and (3) poor-risk clinical and pathologic features, and estimated their predictive survival by considering clinical and pathologic features with or without adding self-reported diet and lifestyle factors. RESULTS: Among 1,024 patients (median age 60.0 years, 43.8% female), we observed 394 DFS events and 311 deaths after median follow-up of 7.3 years. Adding self-reported diet and lifestyle factors to clinical and pathologic characteristics meaningfully improved performance of prediction models (c-index from 0.64 [95% CI, 0.62 to 0.67] to 0.69 [95% CI, 0.67 to 0.72] for DFS, and from 0.67 [95% CI, 0.64 to 0.70] to 0.71 [95% CI, 0.69 to 0.75] for overall survival). External validation also indicated good performance of discrimination and calibration. Adding most self-reported favorable diet and lifestyle exposures to multivariate modeling improved 5-year DFS of all patients and by 6.3% for good-risk, 21.4% for average-risk, and 42.6% for poor-risk clinical and pathologic features. CONCLUSION: Diet and lifestyle factors further inform current recurrence and survival prediction models for patients with stage III colon cancer.
PURPOSE: Current tools in predicting survival outcomes for patients with colon cancer predominantly rely on clinical and pathologic characteristics, but increasing evidence suggests that diet and lifestyle habits are associated with patient outcomes and should be considered to enhance model accuracy. METHODS: Using an adjuvant chemotherapy trial for stage III colon cancer (CALGB 89803), we developed prediction models of disease-free survival (DFS) and overall survival by additionally incorporating self-reported nine diet and lifestyle factors. Both models were assessed by multivariable Cox proportional hazards regression and externally validated using another trial for stage III colon cancer (CALGB/SWOG 80702), and visual nomograms of prediction models were constructed accordingly. We also proposed three hypothetical scenarios for patients with (1) good-risk, (2) average-risk, and (3) poor-risk clinical and pathologic features, and estimated their predictive survival by considering clinical and pathologic features with or without adding self-reported diet and lifestyle factors. RESULTS: Among 1,024 patients (median age 60.0 years, 43.8% female), we observed 394 DFS events and 311 deaths after median follow-up of 7.3 years. Adding self-reported diet and lifestyle factors to clinical and pathologic characteristics meaningfully improved performance of prediction models (c-index from 0.64 [95% CI, 0.62 to 0.67] to 0.69 [95% CI, 0.67 to 0.72] for DFS, and from 0.67 [95% CI, 0.64 to 0.70] to 0.71 [95% CI, 0.69 to 0.75] for overall survival). External validation also indicated good performance of discrimination and calibration. Adding most self-reported favorable diet and lifestyle exposures to multivariate modeling improved 5-year DFS of all patients and by 6.3% for good-risk, 21.4% for average-risk, and 42.6% for poor-risk clinical and pathologic features. CONCLUSION: Diet and lifestyle factors further inform current recurrence and survival prediction models for patients with stage III colon cancer.
Authors: Cheryl L Rock; Colleen Doyle; Wendy Demark-Wahnefried; Jeffrey Meyerhardt; Kerry S Courneya; Anna L Schwartz; Elisa V Bandera; Kathryn K Hamilton; Barbara Grant; Marji McCullough; Tim Byers; Ted Gansler Journal: CA Cancer J Clin Date: 2012-04-26 Impact factor: 508.702
Authors: Jeffrey A Meyerhardt; Denise Heseltine; Donna Niedzwiecki; Donna Hollis; Leonard B Saltz; Robert J Mayer; James Thomas; Heidi Nelson; Renaud Whittom; Alexander Hantel; Richard L Schilsky; Charles S Fuchs Journal: J Clin Oncol Date: 2006-07-05 Impact factor: 44.544
Authors: Mai Yamauchi; Paul Lochhead; Yu Imamura; Aya Kuchiba; Xiaoyun Liao; Zhi Rong Qian; Reiko Nishihara; Teppei Morikawa; Kaori Shima; Kana Wu; Edward Giovannucci; Jeffrey A Meyerhardt; Charles S Fuchs; Andrew T Chan; Shuji Ogino Journal: Cancer Epidemiol Biomarkers Prev Date: 2013-04-29 Impact factor: 4.254
Authors: Erin L Van Blarigan; Charles S Fuchs; Donna Niedzwiecki; Xing Ye; Sui Zhang; Mingyang Song; Leonard B Saltz; Robert J Mayer; Rex B Mowat; Renaud Whittom; Alexander Hantel; Al Benson; Daniel Atienza; Michael Messino; Hedy Kindler; Alan Venook; Shuji Ogino; Edward L Giovannucci; Jeffrey A Meyerhardt Journal: Cancer Epidemiol Biomarkers Prev Date: 2018-01-22 Impact factor: 4.254
Authors: Jeffrey A Meyerhardt; Donna Niedzwiecki; Donna Hollis; Leonard B Saltz; Frank B Hu; Robert J Mayer; Heidi Nelson; Renaud Whittom; Alexander Hantel; James Thomas; Charles S Fuchs Journal: JAMA Date: 2007-08-15 Impact factor: 56.272
Authors: Sonia Y Angell; Michael V McConnell; Cheryl A M Anderson; Kirsten Bibbins-Domingo; Douglas S Boyle; Simon Capewell; Majid Ezzati; Sarah de Ferranti; Darrell J Gaskin; Ron Z Goetzel; Mark D Huffman; Marsha Jones; Yosef M Khan; Sonia Kim; Shiriki K Kumanyika; Alexa T McCray; Robert K Merritt; Bobby Milstein; Dariush Mozaffarian; Tyler Norris; Gregory A Roth; Ralph L Sacco; Jorge F Saucedo; Christina M Shay; David Siedzik; Somava Saha; John J Warner Journal: Circulation Date: 2020-01-29 Impact factor: 29.690
Authors: M A Fuchs; C Yuan; K Sato; D Niedzwiecki; X Ye; L B Saltz; R J Mayer; R B Mowat; R Whittom; A Hantel; A Benson; D Atienza; M Messino; H Kindler; A Venook; F Innocenti; R S Warren; M M Bertagnolli; S Ogino; E L Giovannucci; E Horvath; J A Meyerhardt; K Ng Journal: Ann Oncol Date: 2017-06-01 Impact factor: 32.976
Authors: Jeffrey A Meyerhardt; Qian Shi; Charles S Fuchs; Jeffrey Meyer; Donna Niedzwiecki; Tyler Zemla; Priya Kumthekar; Katherine A Guthrie; Felix Couture; Philip Kuebler; Johanna C Bendell; Pankaj Kumar; Dequincy Lewis; Benjamin Tan; Monica Bertagnolli; Axel Grothey; Howard S Hochster; Richard M Goldberg; Alan Venook; Charles Blanke; Eileen M O'Reilly; Anthony F Shields Journal: JAMA Date: 2021-04-06 Impact factor: 56.272
Authors: Michael A Fuchs; Kaori Sato; Donna Niedzwiecki; Xing Ye; Leonard B Saltz; Robert J Mayer; Rex B Mowat; Renaud Whittom; Alexander Hantel; Al Benson; Daniel Atienza; Michael Messino; Hedy Kindler; Alan Venook; Shuji Ogino; Kana Wu; Walter C Willett; Edward L Giovannucci; Jeffrey A Meyerhardt Journal: PLoS One Date: 2014-06-17 Impact factor: 3.240
Authors: Agata Lewandowska; Urszula Religioni; Aleksandra Czerw; Andrzej Deptała; Beata Karakiewicz; Olga Partyka; Monika Pajewska; Katarzyna Sygit; Elżbieta Cipora; Kamila Kmieć; Anna Augustynowicz; Dominika Mękal; Michał Waszkiewicz; Agnieszka Barańska; Daniela Mináriková; Peter Minárik; Piotr Merks Journal: Int J Environ Res Public Health Date: 2022-06-04 Impact factor: 4.614