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

R. T. Fortner; A. Husing; T. Kuhn; M. Konar; K. Overvad; A. Tjonneland; L. Hansen; M. C. Boutron-Ruault; G. Severi; A. Fournier; H. Boeing; A. Trichopoulou; V. Benetou; P. Orfanos; G. Masala; C. Agnoli; A. Mattiello; R. Tumino; C. Sacerdote; H. B. Bueno-de-Mesquita; P. H. Peeters; E. Weiderpass; I. T. Gram; O. Gavrilyuk; J. R. Quiros; J. Maria Huerta; E. Ardanaz; N. Larranaga; L. Lujan-Barroso; E. Sanchez-Cantalejo; S. T. Butt; S. Borgquist; A. Idahl; E. Lundin; K. T. Khaw; S. Rinaldi; L. Dossus; M. Gunter; M. A. Merritt; I. Tzoulaki; E. Riboli; R. Kaaks

doi:10.1002/ijc.30560

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

R. T. Fortner, A. Husing, T. Kuhn, M. Konar, K. Overvad, A. Tjonneland, L. Hansen, M. C. Boutron-Ruault, G. Severi, A. Fournier, H. Boeing, A. Trichopoulou, V. Benetou, P. Orfanos, G. Masala, C. Agnoli, A. Mattiello, R. Tumino, C. Sacerdote, H. B. Bueno-de-MesquitaP. H. Peeters, E. Weiderpass, I. T. Gram, O. Gavrilyuk, J. R. Quiros, J. Maria Huerta, E. Ardanaz, N. Larranaga, L. Lujan-Barroso, E. Sanchez-Cantalejo, S. T. Butt, S. Borgquist, A. Idahl, E. Lundin, K. T. Khaw, S. Rinaldi, L. Dossus, M. Gunter, M. A. Merritt, I. Tzoulaki, E. Riboli, R. Kaaks

School of Biological Sciences

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33 Citations (Scopus)

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 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.

Original language	English
Pages (from-to)	1317-1323
Number of pages	7
Journal	International Journal of Cancer
Volume	140
Issue number	6
Early online date	09 Dec 2016
DOIs	https://doi.org/10.1002/ijc.30560
Publication status	Published - 15 Mar 2017

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Fortner, R. T., Husing, A., Kuhn, T., Konar, M., Overvad, K., Tjonneland, A., Hansen, L., Boutron-Ruault, M. C., Severi, G., Fournier, A., Boeing, H., Trichopoulou, A., Benetou, V., Orfanos, P., Masala, G., Agnoli, C., Mattiello, A., Tumino, R., Sacerdote, C., ... Kaaks, R. (2017). Endometrial cancer risk prediction including serum-based biomarkers: results from the EPIC cohort. International Journal of Cancer, 140(6), 1317-1323. https://doi.org/10.1002/ijc.30560

@article{2578bb064924414cb70ad35b7037eec1,

title = "Endometrial cancer risk prediction including serum-based biomarkers: results from the EPIC cohort",

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 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.",

author = "Fortner, {R. T.} and A. Husing and T. Kuhn and M. Konar and K. Overvad and A. Tjonneland and L. Hansen and Boutron-Ruault, {M. C.} and G. Severi and A. Fournier and H. Boeing and A. Trichopoulou and V. Benetou and P. Orfanos and G. Masala and C. Agnoli and A. Mattiello and R. Tumino and C. Sacerdote and Bueno-de-Mesquita, {H. B.} and Peeters, {P. H.} and E. Weiderpass and Gram, {I. T.} and O. Gavrilyuk and Quiros, {J. R.} and {Maria Huerta}, J. and E. Ardanaz and N. Larranaga and L. Lujan-Barroso and E. Sanchez-Cantalejo and Butt, {S. T.} and S. Borgquist and A. Idahl and E. Lundin and Khaw, {K. T.} and S. Rinaldi and L. Dossus and M. Gunter and Merritt, {M. A.} and I. Tzoulaki and E. Riboli and R. Kaaks",

year = "2017",

month = mar,

day = "15",

doi = "10.1002/ijc.30560",

language = "English",

volume = "140",

pages = "1317--1323",

journal = "International Journal of Cancer",

issn = "0020-7136",

publisher = "John Wiley & Sons Inc.",

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Fortner, RT, Husing, A, Kuhn, T, Konar, M, Overvad, K, Tjonneland, 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, Quiros, JR, Maria Huerta, J, Ardanaz, E, Larranaga, N, Lujan-Barroso, L, Sanchez-Cantalejo, E, Butt, ST, Borgquist, S, Idahl, A, Lundin, E, Khaw, KT, Rinaldi, S, Dossus, L, Gunter, M, Merritt, MA, Tzoulaki, I, Riboli, E & Kaaks, R 2017, 'Endometrial cancer risk prediction including serum-based biomarkers: results from the EPIC cohort', International Journal of Cancer, vol. 140, no. 6, pp. 1317-1323. https://doi.org/10.1002/ijc.30560

TY - JOUR

T1 - Endometrial cancer risk prediction including serum-based biomarkers: results from the EPIC cohort

AU - Fortner, R. T.

AU - Husing, A.

AU - Kuhn, T.

AU - Konar, M.

AU - Overvad, K.

AU - Tjonneland, A.

AU - Hansen, L.

AU - Boutron-Ruault, M. C.

AU - Severi, G.

AU - Fournier, A.

AU - Boeing, H.

AU - Trichopoulou, A.

AU - Benetou, V.

AU - Orfanos, P.

AU - Masala, G.

AU - Agnoli, C.

AU - Mattiello, A.

AU - Tumino, R.

AU - Sacerdote, C.

AU - Bueno-de-Mesquita, H. B.

AU - Peeters, P. H.

AU - Weiderpass, E.

AU - Gram, I. T.

AU - Gavrilyuk, O.

AU - Quiros, J. R.

AU - Maria Huerta, J.

AU - Ardanaz, E.

AU - Larranaga, N.

AU - Lujan-Barroso, L.

AU - Sanchez-Cantalejo, E.

AU - Butt, S. T.

AU - Borgquist, S.

AU - Idahl, A.

AU - Lundin, E.

AU - Khaw, K. T.

AU - Rinaldi, S.

AU - Dossus, L.

AU - Gunter, M.

AU - Merritt, M. A.

AU - Tzoulaki, I.

AU - Riboli, E.

AU - Kaaks, R.

PY - 2017/3/15

Y1 - 2017/3/15

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 < 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 < 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.

U2 - 10.1002/ijc.30560

DO - 10.1002/ijc.30560

M3 - Article

SN - 0020-7136

VL - 140

SP - 1317

EP - 1323

JO - International Journal of Cancer

JF - International Journal of Cancer

IS - 6

ER -

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

Abstract

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