TY - JOUR
T1 - Endometrial cancer risk prediction including serum-based biomarkers: results from the EPIC cohort.
AU - Turzanski-Fortner, Renee
AU - Husing, Anika
AU - Kuhn, Tilman
AU - Konar, Meric
AU - Overvad, Kim
AU - Tjonneland, Anne
AU - Hansen, Louise
AU - Boutron-Ruault, Marie Christine
AU - Severi, Gianluca
AU - Fournier, Agnes
AU - Boeing, Heiner
AU - Trichopoulou, Antonia
AU - Benetou, Vasiliki
AU - Orfanos, Philippos
AU - Masala, Giovanna
AU - Agnoli, Claudia
AU - Mattiello, Amalia
AU - Tumino, Rosario
AU - Sacerdote, Carlotta
AU - Bueno-de-Mesquita, H B as
AU - Peeters, Petra H M
AU - Weiderpass, Elisabete
AU - Gram, Inger Torhild
AU - Gavrilyuk, Oxana
AU - Quiros, J. Ramon
AU - Maria Huerta, Jose
AU - Ardanaz, Eva
AU - Larranaga, Nerea
AU - Lujan-Barroso, Leila
AU - Sanchez-Cantalejo, Emilio
AU - Butt, Salma Tuna
AU - Borgquist, Signe
AU - Idahl, Annika
AU - Lundin, Eva
AU - Khaw, Kay Tee
AU - Allen, Naomi E.
AU - Rinaldi, Sabina
AU - Dossus, Laure
AU - Gunter, Marc
AU - Merritt, Melissa A.
AU - Tzoulaki, Ioanna
AU - Riboli, Elio
AU - Kaaks, Rudolf
PY - 2017/3/1
Y1 - 2017/3/1
N2 - 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 textless 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 selected 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.
AB - 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 textless 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 selected 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.
KW - adipokines, cytokines, endometrial cancer, growth factors, inflammatory markers, lipids, metabolic markers, prospective cohort, risk prediction, sex steroids
U2 - 10.1002/ijc.30560
DO - 10.1002/ijc.30560
M3 - Articolo
VL - 140
SP - 1317
EP - 1323
JO - International Journal of Cancer
JF - International Journal of Cancer
SN - 0020-7136
IS - 6
ER -