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Proteomic biomarkers predicting lymph node involvement in serum of cervical cancer patients. Limitations of SELDI-TOF MS

Toon Van Gorp12*, Isabelle Cadron13, Anneleen Daemen145, Bart De Moor4, Etienne Waelkens6 and Ignace Vergote1

Author Affiliations

1 Department of Obstetrics and Gynaecology, Leuven Cancer Institute, Universitaire Ziekenhuizen Leuven, KU Leuven, Herestraat 49, 3000, Leuven, Belgium

2 Department of Obstetrics and Gynaecology, MUMC+, GROW – School for Oncology and Developmental Biology, P. Debyelaan 25, 6229 HX, Maastricht, The Netherlands

3 Department of Obstetrics and Gynaecology, AZ Turnhout, Steenweg op Merksplas 44, 2300, Turnhout, Belgium

4 Department Laboratory Medicine, UCSF School of Medicine, Box 0808, 2340 Sutter Street, MZ, San Francisco, CA, 94143, USA

5 Department of Electrical Engineering, ESAT-SCD/SISTA, Kasteelpark Arenberg 10, PO box 2446, 3001, Heverlee, Belgium

6 Department of Molecular Cell Biology, Universitaire Ziekenhuizen Leuven, Katholieke Universiteit Leuven, Herestraat 49, 3000, Leuven, Belgium

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Proteome Science 2012, 10:41  doi:10.1186/1477-5956-10-41

Published: 13 June 2012

Abstract

Background

Lymph node status is not part of the staging system for cervical cancer, but provides important information for prognosis and treatment. We investigated whether lymph node status can be predicted with proteomic profiling.

Material & methods

Serum samples of 60 cervical cancer patients (FIGO I/II) were obtained before primary treatment. Samples were run through a HPLC depletion column, eliminating the 14 most abundant proteins ubiquitously present in serum. Unbound fractions were concentrated with spin filters. Fractions were spotted onto CM10 and IMAC30 surfaces and analyzed with surface-enhanced laser desorption time of flight (SELDI-TOF) mass spectrometry (MS). Unsupervised peak detection and peak clustering was performed using MASDA software. Leave-one-out (LOO) validation for weighted Least Squares Support Vector Machines (LSSVM) was used for prediction of lymph node involvement. Other outcomes were histological type, lymphvascular space involvement (LVSI) and recurrent disease.

Results

LSSVM models were able to determine LN status with a LOO area under the receiver operating characteristics curve (AUC) of 0.95, based on peaks with m/z values 2,698.9, 3,953.2, and 15,254.8. Furthermore, we were able to predict LVSI (AUC 0.81), to predict recurrence (AUC 0.92), and to differentiate between squamous carcinomas and adenocarcinomas (AUC 0.88), between squamous and adenosquamous carcinomas (AUC 0.85), and between adenocarcinomas and adenosquamous carcinomas (AUC 0.94).

Conclusions

Potential markers related with lymph node involvement were detected, and protein/peptide profiling support differentiation between various subtypes of cervical cancer. However, identification of the potential biomarkers was hampered by the technical limitations of SELDI-TOF MS.

Keywords:
Cervical cancer; Biomarker; Recurrence; Lymph node; SELDI-TOF MS