RESUMEN
BACKGROUND: Less 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. METHODS: Our 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. RESULTS: Diagonal 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. CONCLUSION: Although 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.
RESUMEN
Gaucher disease is an inherited lysosomal storage disorder, characterized by massive accumulation of glucosylceramide-laden macrophages in the spleen, liver and bone marrow as a consequence of deficient activity of glucocerebrosidase. Gaucher disease has been the playground to develop new therapeutic interventions such as enzyme-replacement therapy and substrate-reduction therapy. The availability of these costly therapies has stimulated research regarding suitable biomarkers to monitor onset and progression of disease, as well as the efficacy of therapeutic intervention. Given the important role of storage cells in the pathology, various attempts have been made to identify proteins in plasma or serum reflecting the body burden of these pathological cells. In this review, the existing data regarding biomarkers for Gaucher disease, as well as the current application of biomarkers in clinical management of Gaucher patients are discussed. Moreover, the use of several modern proteomic technologies for the identification of Gaucher biomarkers is reviewed.
