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

Renée T. Fortner; Anika Hüsing; Tilman Kühn; Meric Konar; Kim Overvad; Anne Tjønneland; Louise Hansen; Marie Christine Boutron-Ruault; Gianluca Severi; Agnès Fournier; Heiner Boeing; Antonia Trichopoulou; Vasiliki Benetou; Philippos Orfanos; Giovanna Masala; Claudia Agnoli; Amalia Mattiello; Rosario Tumino; Carlotta Sacerdote; H. Bas Bueno-de-Mesquita; Petra H.M. Peeters; Elisabete Weiderpass; Inger T. Gram; Oxana Gavrilyuk; J. Ramón Quirós; José Maria Huerta; Eva Ardanaz; Nerea Larrañaga; Leila Lujan-Barroso; Emilio Sánchez-Cantalejo; Salma Tunå Butt; Signe Borgquist; Annika Idahl; Eva Lundin; Kay Tee Khaw; Naomi E. Allen; Sabina Rinaldi; Laure Dossus; Marc Gunter; Melissa A. Merritt; Ioanna Tzoulaki; Elio Riboli; Rudolf Kaaks

doi:10.1002/ijc.30560

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

Renée T. Fortner, Anika Hüsing, Tilman Kühn, Meric Konar, Kim Overvad, Anne Tjønneland, Louise Hansen, Marie Christine Boutron-Ruault, Gianluca Severi, Agnès Fournier, Heiner Boeing, Antonia Trichopoulou, Vasiliki Benetou, Philippos Orfanos, Giovanna Masala, Claudia Agnoli, Amalia Mattiello, Rosario Tumino, Carlotta Sacerdote, H. Bas Bueno-de-MesquitaPetra H.M. Peeters, Elisabete Weiderpass, Inger T. Gram, Oxana Gavrilyuk, J. Ramón Quirós, José Maria Huerta, Eva Ardanaz, Nerea Larrañaga, Leila Lujan-Barroso, Emilio Sánchez-Cantalejo, Salma Tunå Butt, Signe Borgquist, Annika Idahl, Eva Lundin, Kay Tee Khaw, Naomi E. Allen, Sabina Rinaldi, Laure Dossus, Marc Gunter, Melissa A. Merritt, Ioanna Tzoulaki, Elio Riboli, Rudolf Kaaks

Résultats de recherche: Contribution à un journal › Article › Revue par des pairs

33 Citations (Scopus)

Résumé

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.

langue originale	Anglais
Pages (de - à)	1317-1323
Nombre de pages	7
journal	International Journal of Cancer
Volume	140
Numéro de publication	6
Les DOIs	https://doi.org/10.1002/ijc.30560
état	Publié - 15 mars 2017

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10.1002/ijc.30560

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Fortner, R. T., Hüsing, A., Kühn, T., Konar, M., Overvad, K., Tjønneland, 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{13e2f91d0a084300a400559b424fe564,

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.",

keywords = "adipokines, cytokines, endometrial cancer, growth factors, inflammatory markers, lipids, metabolic markers, prospective cohort, risk prediction, sex steroids",

author = "Fortner, {Ren{\'e}e T.} and Anika H{\"u}sing and Tilman K{\"u}hn and Meric Konar and Kim Overvad and Anne Tj{\o}nneland and Louise Hansen and Boutron-Ruault, {Marie Christine} and Gianluca Severi and Agn{\`e}s Fournier and Heiner Boeing and Antonia Trichopoulou and Vasiliki Benetou and Philippos Orfanos and Giovanna Masala and Claudia Agnoli and Amalia Mattiello and Rosario Tumino and Carlotta Sacerdote and Bueno-de-Mesquita, {H. Bas} and Peeters, {Petra H.M.} and Elisabete Weiderpass and Gram, {Inger T.} and Oxana Gavrilyuk and Quir{\'o}s, {J. Ram{\'o}n} and {Maria Huerta}, Jos{\'e} and Eva Ardanaz and Nerea Larra{\~n}aga and Leila Lujan-Barroso and Emilio S{\'a}nchez-Cantalejo and Butt, {Salma Tun{\aa}} and Signe Borgquist and Annika Idahl and Eva Lundin and Khaw, {Kay Tee} and Allen, {Naomi E.} and Sabina Rinaldi and Laure Dossus and Marc Gunter and Merritt, {Melissa A.} and Ioanna Tzoulaki and Elio Riboli and Rudolf Kaaks",

note = "Publisher Copyright: {\textcopyright} 2016 UICC",

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",

number = "6",

}

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, PHM, Weiderpass, E, Gram, IT, Gavrilyuk, O, Quirós, JR, Maria Huerta, J, Ardanaz, E, Larrañaga, N, Lujan-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 2017, 'Endometrial cancer risk prediction including serum-based biomarkers: results from the EPIC cohort', International Journal of Cancer, VOL. 140, Numéro 6, p. 1317-1323. https://doi.org/10.1002/ijc.30560

TY - JOUR

T1 - Endometrial cancer risk prediction including serum-based biomarkers

T2 - results from the EPIC cohort

AU - Fortner, Renée T.

AU - Hüsing, Anika

AU - Kühn, Tilman

AU - Konar, Meric

AU - Overvad, Kim

AU - Tjønneland, Anne

AU - Hansen, Louise

AU - Boutron-Ruault, Marie Christine

AU - Severi, Gianluca

AU - Fournier, Agnès

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. Bas

AU - Peeters, Petra H.M.

AU - Weiderpass, Elisabete

AU - Gram, Inger T.

AU - Gavrilyuk, Oxana

AU - Quirós, J. Ramón

AU - Maria Huerta, José

AU - Ardanaz, Eva

AU - Larrañaga, Nerea

AU - Lujan-Barroso, Leila

AU - Sánchez-Cantalejo, Emilio

AU - Butt, Salma Tunå

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 - 2017/3/15

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.

KW - adipokines

KW - cytokines

KW - endometrial cancer

KW - growth factors

KW - inflammatory markers

KW - lipids

KW - metabolic markers

KW - prospective cohort

KW - risk prediction

KW - sex steroids

UR - http://www.scopus.com/inward/record.url?scp=85010754999&partnerID=8YFLogxK

U2 - 10.1002/ijc.30560

DO - 10.1002/ijc.30560

M3 - Article

C2 - 27935083

AN - SCOPUS:85010754999

SN - 0020-7136

VL - 140

SP - 1317

EP - 1323

JO - International Journal of Cancer

JF - International Journal of Cancer

IS - 6

ER -