A critical assessment of SELDI-TOF-MS for biomarker discovery in serum and tissue of patients with an ovarian mass

Proteome Science

Research

Perry D Moerland¹  Wouter Wegdam²  Danielle Meijer²  Marrije R Buist²  Gemma G Kenter²  Shreyas M de Jong³  Johannes MF G Aerts³  Huub C J Hoefsloot⁴ 

[1] Bioinformatics Laboratory, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, University of Amsterdam, Meibergdreef 9, 1105, Amsterdam, AZ, the Netherlands;Netherlands Bioinformatics Centre, Geert Grooteplein 28, 6525, Nijmegen, GA, the Netherlands;Netherlands Proteomics Centre, H.R. Kruytgebouw, Padualaan 8, 3584, Utrecht, CH, the Netherlands;Department of Gynecology, Academic Medical Center, University of Amsterdam, Meibergdreef 9, 1105, Amsterdam, AZ, the Netherlands;Department of Medical Biochemistry, Academic Medical Center, University of Amsterdam, Meibergdreef 9, 1105, Amsterdam, AZ, the Netherlands;Swammerdam Institute for Life Sciences, University of Amsterdam, 1098, Amsterdam, XH, the Netherlands;

关键词: Mass spectrometry;    Microdissection;    Ovarian cancer;    SELDI;    Classification;    Biomarker;    Serum;    Tissue;   

DOI  :  10.1186/1477-5956-10-45

 received in 2012-01-21, accepted in 2012-06-29,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundLess than 25% of patients with a pelvic mass who are presented to a gynecologist will eventually be diagnosed with epithelial ovarian cancer. Since there is no reliable test to differentiate between different ovarian tumors, accurate classification could facilitate adequate referral to a gynecological oncologist, improving survival. The goal of our study was to assess the potential value of a SELDI-TOF-MS based classifier for discriminating between patients with a pelvic mass.MethodsOur study design included a well-defined patient population, stringent protocols and an independent validation cohort. We compared serum samples of 53 ovarian cancer patients, 18 patients with tumors of low malignant potential, and 57 patients with a benign ovarian tumor on different ProteinChip arrays. In addition, from a subset of 84 patients, tumor tissues were collected and microdissection was used to isolate a pure and homogenous cell population.ResultsDiagonal Linear Discriminant Analysis (DLDA) and Support Vector Machine (SVM) classification on serum samples comparing cancer versus benign tumors, yielded models with a classification accuracy of 71-81% (cross-validation), and 73-81% on the independent validation set. Cancer and benign tissues could be classified with 95-99% accuracy using cross-validation. Tumors of low malignant potential showed protein expression patterns different from both benign and cancer tissues. Remarkably, none of the peaks differentially expressed in serum samples were found to be differentially expressed in the tissue lysates of those same groups.ConclusionAlthough SELDI-TOF-MS can produce reliable classification results in serum samples of ovarian cancer patients, it will not be applicable in routine patient care. On the other hand, protein profiling of microdissected tumor tissue may lead to a better understanding of oncogenesis and could still be a source of new serum biomarkers leading to novel methods for differentiating between different histological subtypes.

【 授权许可】

CC BY   
© Wegdam et al.; licensee BioMed Central Ltd. 2012