Advanced

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

Fortner, Renée T; Hüsing, Anika; Kühn, Tilman; Konar, Meric; Overvad, Kim; Tjønneland, Anne; Hansen, Louise; Boutron-Ruault, Marie-Christine; Severi, Gianluca and Fournier, Agnès, et al. (2017) In International Journal of Cancer 140(6). p.1317-1323
Abstract

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... (More)

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.

(Less)
Please use this url to cite or link to this publication:
author
, et al. (More)
(Less)
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
adipokines, cytokines, endometrial cancer, growth factors, inflammatory markers, lipids, metabolic markers, prospective cohort, risk prediction, sex steroids
in
International Journal of Cancer
volume
140
issue
6
pages
7 pages
publisher
John Wiley & Sons
external identifiers
  • scopus:85010754999
  • wos:000393979000009
ISSN
0020-7136
DOI
10.1002/ijc.30560
language
English
LU publication?
yes
id
b6e80c6b-ce46-402f-bc95-3e988c883097
date added to LUP
2017-02-16 15:42:45
date last changed
2018-04-29 04:36:11
@article{b6e80c6b-ce46-402f-bc95-3e988c883097,
  abstract     = {<p>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 &lt; 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.</p>},
  author       = {Fortner, Renée T and Hüsing, Anika and Kühn, Tilman and Konar, Meric and Overvad, Kim and Tjønneland, Anne and Hansen, Louise and Boutron-Ruault, Marie-Christine and Severi, Gianluca and Fournier, Agnès and Boeing, Heiner and Trichopoulou, Antonia and Benetou, Vasiliki and Orfanos, Philippos and Masala, Giovanna and Agnoli, Claudia and Mattiello, Amalia and Tumino, Rosario and Sacerdote, Carlotta and Bueno-de-Mesquita, H. Bas and Peeters, Petra H. M. and Weiderpass, Elisabete and Gram, Inger T and Gavrilyuk, Oxana and Quirós, J Ramón and Maria Huerta, José and Ardanaz, Eva and Larrañaga, Nerea and Lujan-Barroso, Leila and Sánchez-Cantalejo, Emilio and Butt, Salma Tunå and Borgquist, Signe and Idahl, Annika and Lundin, Eva and Khaw, Kay Tee and Allen, Naomi E and Rinaldi, Sabina and Dossus, Laure and Gunter, Marc and Merritt, Melissa A and Tzoulaki, Ioanna and Riboli, Elio and Kaaks, Rudolf},
  issn         = {0020-7136},
  keyword      = {adipokines,cytokines,endometrial cancer,growth factors,inflammatory markers,lipids,metabolic markers,prospective cohort,risk prediction,sex steroids},
  language     = {eng},
  month        = {03},
  number       = {6},
  pages        = {1317--1323},
  publisher    = {John Wiley & Sons},
  series       = {International Journal of Cancer},
  title        = {Endometrial cancer risk prediction including serum-based biomarkers : results from the EPIC cohort},
  url          = {http://dx.doi.org/10.1002/ijc.30560},
  volume       = {140},
  year         = {2017},
}