TY - JOUR
T1 - Targeted proteomics identifies proteomic signatures in liquid biopsies of the endometrium to diagnose endometrial cancer and assist in the prediction of the optimal surgical treatment
AU - Martinez-Garcia, Elena
AU - Lesur, Antoine
AU - Devis, Laura
AU - Cabrera, Silvia
AU - Matias-Guiu, Xavier
AU - Hirschfeld, Marc
AU - Asberger, Jasmin
AU - Van Oostrum, Jan
AU - Casares de Cal, María De Los Angeles
AU - Gomez-Tato, Antonio
AU - Reventos, Jaume
AU - Domon, Bruno
AU - Colas, Eva
AU - Gil-Moreno, Antonio
N1 - Funding Information:
This work was supported by the Spanish Ministry of Health (RD12/0036/ 0035), the Spanish Ministry of Economy and Competitivity (PI14/02043), the "Fondo Europeo de Desarrollo Regional" FEDER (RTC-2014-3110-1), the AECC (Grupos Estables de Investigacion 2011-AECC-GCB 110333 REVE), the Fundació La Marató TV3 (2/C/2013), the CIRIT Generalitat de Catalunya (2014 SGR 1330), the Fundación DEXEUS Salud de la Mujer (FSDF-2013-03). The Spanish Ministry of Economy and Competitiveness (IJCI-2015-25000), and the PERIS grant (Generalitat de Catalunya) granted Dr. Colás. The present work has been also funded by the "Fonds National de la Recherche du Luxembourg" (FNR) via the PEARL-CPIL program to B. Domon and an AFR grant to A. Lesur (PDR 2013-2, Project Reference 6835664).
Publisher Copyright:
©2017 AACR.
PY - 2017/11/1
Y1 - 2017/11/1
N2 - Purpose: Endometrial cancer (EC) diagnosis relies on the observation of tumor cells in endometrial biopsies obtained by aspiration (i.e., uterine aspirates), but it is associated with 22% undiagnosed patients and up to 50% of incorrectly assigned EC histotype and grade. We aimed to identify biomarker signatures in the fluid fraction of these biopsies to overcome these limitations. Experimental Design: The levels of 52 proteins were measured in the fluid fraction of uterine aspirates from 116 patients by LC-PRM, the latest generation of targeted mass-spectrometry acquisition. A logistic regression model was used to assess the power of protein panels to differentiate between EC and non-EC patients and between EC histologic subtypes. The robustness of the panels was assessed by the "leave-one-out" cross-validation procedure performed within the same cohort of patients and an independent cohort of 38 patients. Results: The levels of 28 proteins were significantly higher in patients with EC (n ¼ 69) compared with controls (n ¼ 47). The combination of MMP9 and KPYM exhibited 94% sensitivity and 87% specificity for detecting EC cases. This panel perfectly complemented the standard diagnosis, achieving 100% of correct diagnosis in this dataset. Nine proteins were significantly increased in endometrioid EC (n ¼ 49) compared with serous EC (n ¼ 20). The combination of CTNB1, XPO2, and CAPG achieved 95% sensitivity and 96% specificity for the discrimination of these subtypes. Conclusions: We developed two uterine aspirate-based signatures to diagnose EC and classify tumors in the most prevalent histologic subtypes. This will improve diagnosis and assist in the prediction of the optimal surgical treatment.
AB - Purpose: Endometrial cancer (EC) diagnosis relies on the observation of tumor cells in endometrial biopsies obtained by aspiration (i.e., uterine aspirates), but it is associated with 22% undiagnosed patients and up to 50% of incorrectly assigned EC histotype and grade. We aimed to identify biomarker signatures in the fluid fraction of these biopsies to overcome these limitations. Experimental Design: The levels of 52 proteins were measured in the fluid fraction of uterine aspirates from 116 patients by LC-PRM, the latest generation of targeted mass-spectrometry acquisition. A logistic regression model was used to assess the power of protein panels to differentiate between EC and non-EC patients and between EC histologic subtypes. The robustness of the panels was assessed by the "leave-one-out" cross-validation procedure performed within the same cohort of patients and an independent cohort of 38 patients. Results: The levels of 28 proteins were significantly higher in patients with EC (n ¼ 69) compared with controls (n ¼ 47). The combination of MMP9 and KPYM exhibited 94% sensitivity and 87% specificity for detecting EC cases. This panel perfectly complemented the standard diagnosis, achieving 100% of correct diagnosis in this dataset. Nine proteins were significantly increased in endometrioid EC (n ¼ 49) compared with serous EC (n ¼ 20). The combination of CTNB1, XPO2, and CAPG achieved 95% sensitivity and 96% specificity for the discrimination of these subtypes. Conclusions: We developed two uterine aspirate-based signatures to diagnose EC and classify tumors in the most prevalent histologic subtypes. This will improve diagnosis and assist in the prediction of the optimal surgical treatment.
UR - http://www.scopus.com/inward/record.url?scp=85033576947&partnerID=8YFLogxK
U2 - 10.1158/1078-0432.CCR-17-0474
DO - 10.1158/1078-0432.CCR-17-0474
M3 - Article
C2 - 28790116
AN - SCOPUS:85033576947
SN - 1078-0432
VL - 23
SP - 6458
EP - 6467
JO - Clinical Cancer Research
JF - Clinical Cancer Research
IS - 21
ER -