Endometrial cancer risk prediction including serum-based biomarkers: results from the EPIC cohort

Endometrial cancer risk prediction models including lifestyle, anthropometric and reproductive factors have limited discrimination. Adding biomarker data to these models may improve predictive capacity; to our knowledge, this has not been investigated for endometrial cancer. Using a nested case-cont...

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Detalles Bibliográficos
Autores: Fortner, Renée T., Hüsing, Anika, Kühn, Tilman, Konar, Meric, Overvad, Kim, Tjønneland, Anne, Hansen, Louise, Boutron-Ruault, Marie-Christine, Severi, Gianluca, Fournier, Agnès, Boeing, Heiner, Trichopoulou, Antonia, Benetou, Vassiliki, Orfanos, Philippos, Masala, Giovanna, Agnoli, Claudia, Mattiello, Amalia, Tumino, Rosario, Sacerdote, Carlotta, Bueno de Mesquita, H. Bas, Peeters, Petra H. M., Weiderpass, Elisabete, Gram, Inger T., Gavrilyuk, Oxana, Quirós, José Ramón, Huerta Castaño, José María, Ardanaz, Eva, Larrañaga, Nerea, Luján Barroso, Leila, Sánchez Cantalejo, Emilio, Tunå Butt, Salma, Borgquist, Signe, Idahl, Annika, Lundin, Eva, Khaw, Kay-Tee, Allen, Naomi E., Rinaldi, Sabina, Dossus, Laure, Gunter, Marc J., Merritt, Melissa A.
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2017
País:España
Institución:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/120610
Acceso en línea:https://hdl.handle.net/2445/120610
Access Level:acceso abierto
Palabra clave:Càncer d'endometri
Marcadors bioquímics
Nutrició
Sèrum
Endometrial cancer
Biochemical markers
Nutrition
Serum
Descripción
Sumario:Endometrial cancer risk prediction models including lifestyle, anthropometric and reproductive factors have limited discrimination. Adding biomarker data to these models may improve predictive capacity; to our knowledge, this has not been investigated for endometrial cancer. Using a nested case-control study within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we investigated the improvement in discrimination gained by adding serum biomarker concentrations to risk estimates derived from an existing risk prediction model based on epidemiologic factors. Serum concentrations of sex steroid hormones, metabolic markers, growth factors, adipokines and cytokines were evaluated in a step-wise backward selection process; biomarkers were retained at p < 0.157 indicating improvement in the Akaike information criterion (AIC). Improvement in discrimination was assessed using the C-statistic for all biomarkers alone, and change in C-statistic from addition of biomarkers to preexisting absolute risk estimates. We used internal validation with bootstrapping (1000-fold) to adjust for over-fitting. Adiponectin, estrone, interleukin-1 receptor antagonist, tumor necrosis factor-alpha and triglycerides were select-ed into the model. After accounting for over-fitting, discrimination was improved by 2.0 percentage points when all evaluated biomarkers were included and 1.7 percentage points in the model including the selected biomarkers. Models including etiologic markers on independent pathways and genetic markers may further improve discrimination.

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