Endometrial cancer risk prediction including serum-based biomarkers: results from the EPIC cohort.
International journal of cancer 2016 ; 140: 1317-1323.
Fortner RT, Hüsing A, Kühn T, Konar M, Overvad K, Tjønneland A, Hansen L, Boutron-Ruault MC, Severi G, Fournier A, Boeing H, Trichopoulou A, Benetou V, Orfanos P, Masala G, Agnoli C, Mattiello A, Tumino R, Sacerdote C, Bueno-de-Mesquita HB, Peeters PH, Weiderpass E, Gram IT, Gavrilyuk O, Quirós JR, María Huerta J, Ardanaz E, Larrañaga N, Luján-Barroso L, Sánchez-Cantalejo E, Butt ST, Borgquist S, Idahl A, Lundin E, Khaw KT, Allen NE, Rinaldi S, Dossus L, Gunter M, Merritt MA, Tzoulaki I, Riboli E, Kaaks R
DOI : 10.1002/ijc.30560
PubMed ID : 27935083
URL : https://onlinelibrary.wiley.com/doi/full/10.1002/ijc.30560
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 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.