Diet- and Lifestyle-Based Prediction Models to Estimate Cancer Recurrence and Death in Patients With Stage III Colon Cancer (CALGB 89803/Alliance).

Autor:	Cheng E; Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT.; Division of Research, Kaiser Permanente Northern California, Oakland, CA., Ou FS; Alliance Statistics and Data Management Center, Mayo Clinic, Rochester, MN., Ma C; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA., Spiegelman D; Department of Biostatistics, Yale School of Public Health, New Haven, CT.; Center on Methods for Implementation and Prevention Science, Yale School of Public Health, New Haven, CT., Zhang S; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA., Zhou X; Department of Biostatistics, Yale School of Public Health, New Haven, CT.; Center on Methods for Implementation and Prevention Science, Yale School of Public Health, New Haven, CT., Bainter TM; Alliance Statistics and Data Management Center, Mayo Clinic, Rochester, MN., Saltz LB; Memorial Sloan Kettering Cancer Center, New York, NY., Niedzwiecki D; Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC., Mayer RJ; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA., Whittom R; Hôpital du Sacré-Coeur de Montréal, Montreal, Quebec, Canada., Hantel A; Loyola University, Stritch School of Medicine, Naperville, IL., Benson A; Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL., Atienza D; Virginia Oncology Associates, Norfolk, VA., Messino M; Messino Cancer Centers, Asheville, NC., Kindler H; University of Chicago, Chicago, IL., Giovannucci EL; Department of Epidemiology, and Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA., Van Blarigan EL; Department of Epidemiology and Biostatistics, and Urology, University of California, San Francisco, CA., Brown JC; Cancer Metabolism Program, Pennington Biomedical Research Center, Baton Rouge, LA., Ng K; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA., Gross CP; Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT.; Section of General Internal Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT.; Cancer Outcomes, Public Policy, and Effectiveness Research Center, Yale Cancer Center, New Haven, CT., Meyerhardt JA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA., Fuchs CS; Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT.; Division of Hematology and Medical Oncology, Department of Internal Medicine, Yale School of Medicine, New Haven, CT.; Yale Cancer Center, Smilow Cancer Hospital, New Haven, CT.; Hematology and Oncology Product Development, Genentech & Roche, South San Francisco, CA.
Jazyk:	angličtina
Zdroj:	Journal of clinical oncology : official journal of the American Society of Clinical Oncology [J Clin Oncol] 2022 Mar 01; Vol. 40 (7), pp. 740-751. Date of Electronic Publication: 2022 Jan 07.
DOI:	10.1200/JCO.21.01784
Abstrakt:	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. Competing Interests: En ChengStock and Other Ownership Interests: Pfizer Fang-Shu OuConsulting or Advisory Role: SC Liver Research Consortium Leonard B. SaltzResearch Funding: Taiho Pharmaceutical Robert J. MayerConsulting or Advisory Role: Bayer Renaud WhittomConsulting or Advisory Role: AstraZenecaResearch Funding: Canadian Cancer Trials Group (Inst) Al BensonConsulting or Advisory Role: Merck Sharp & Dohme, Array BioPharma, Bristol Myers Squibb, Samsung Bioepis, Pfizer, HalioDx, AbbVie, Janssen Oncology, Natera, Apexigen, Artemida Pharma, Xencor, Therabionic, Mirati Therapeutics, Boston ScientificResearch Funding: Infinity Pharmaceuticals (Inst), Merck Sharp & Dohme (Inst), Taiho Pharmaceutical (Inst), Bristol Myers Squibb (Inst), Celgene (Inst), Rafael Pharmaceuticals (Inst), MedImmune (Inst), Xencor (Inst), Astellas Pharma (Inst), Amgen (Inst), Syncore (Inst), Elevar Therapeutics (Inst), Tyme Inc (Inst), ST Pharm (Inst), ITM Solucin (Inst) Daniel AtienzaEmployment: US Oncology Hedy KindlerHonoraria: AstraZenecaConsulting or Advisory Role: AstraZeneca, Bristol Myers Squibb, Deciphera, Novocure, Seattle Genetics, InhibrxResearch Funding: Aduro Biotech (Inst), AstraZeneca (Inst), GlaxoSmithKline (Inst), Merck (Inst), Verastem (Inst), Bristol Myers Squibb (Inst), Polaris (Inst), Deciphera (Inst), Inhibrx (Inst), Roche/Genentech (Inst), Tesaro (Inst), Macrogenics (Inst), Leap Therapeutics (Inst), Fibrogen (Inst), Vivace Therapeutics (Inst), Constellation Pharmaceuticals (Inst), Harpoon therapeutics (Inst), Bayer (Inst), Seattle Genetics (Inst), Blueprint Medicines (Inst)Travel, Accommodations, Expenses: AstraZeneca Kimmie NgConsulting or Advisory Role: Seattle Genetics, X-Biotix Therapeutics, Array BioPharma, BiomX, Bicara TherapeuticsResearch Funding: Pharmavite (Inst), Revolution Medicines (Inst), Evergrande Group (Inst), Janssen (Inst) Cary P. GrossResearch Funding: Johnson & Johnson (Inst), AstraZeneca (Inst), GenentechUncompensated Relationships: Genentech (Inst) Jeffrey A. MeyerhardtHonoraria: Cota Healthcare, Taiho PharmaceuticalResearch Funding: Boston Biomedical (Inst) Charles S. FuchsEmployment: Genentech/RocheLeadership: CytomX Therapeutics, Evolveimmune TherapeuticsStock and Other Ownership Interests: CytomX Therapeutics, Entrinsic Health, Evolveimmune Therapeutics, Roche/GenentechConsulting or Advisory Role: Sanofi, Merck, Entrinsic Health, Agios, Taiho Pharmaceutical, Genentech/Roche, CytomX Therapeutics, Unum Therapeutics, Bain Capital, Lilly, Amylin, Daiichi-Sankyo, Evolveimmune Therapeutics, AstraZeneca, AstraZenecaExpert Testimony: Lilly, AmylinNo other potential conflicts of interest were reported.
Databáze:	MEDLINE
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