Seminal plasma is a fluid that originates from the testis, epididymis,prostate, and seminal vesicles, and hence, proteomic studies may identify potential markers of infertility and other diseases of the genito-urinary tract. We profiled the proteomes of pooled seminal plasma from fertile Control and post-vasectomy (PV) men. PV seminal plasma samples are void of proteins originating from the testis and the epididymis due to ligation of the vas deferens, and hence, comparative analysis of Control and PV data sets allows for identification of proteins originating from these tissues. Utilizing offline MudPIT and high-resolution mass spectrometry, we were able to identify over 2000 proteins in Control and PV pools each and over 2300 proteins all together. With semiquantitative analysis using spectral counting, we catalogued 32 proteins unique to Control, 49 at lower abundance in PV, 3 unique to PV, and 25 at higher abundance in PV. We believe that proteins unique to Control or at lower abundance in PV have their origin in the testis and the epididymis. Public databases have confirmed that many of these proteins originate from the testis and epididymis and are linked to the reproductive tract. These proteins may serve as candidate biomarkers for future studies of infertility and urogenital diseases.

Male fertility problems range from diminished production of sperm, or oligozoospermia, to nonmeasurable levels of sperm in semen, or azoospermia, which is diagnosed in nearly 2% of men in the general population. Testicular biopsy is the only definitive diagnostic method to distinguish between obstructive (OA) and nonobstructive (NOA) azoospermia and to identify the NOA subtypes of hypospermatogenesis, maturation arrest and Sertoli cell-only syndrome. We measured by selected reaction monitoring assay 18 biomarker candidates in 119 seminal plasma samples from men with normal spermatogenesis and azoospermia, and identified two proteins, epididymis-expressed ECM1 and testis-expressed TEX101, which differentiated OA and NOA with high specificities and sensitivities. The performance of ECM1 was confirmed by enzyme-linked immunosorbent assay. On the basis of a cutoff level of 2.3 μg/ml derived from the current data, we could distinguish OA from normal spermatogenesis with 100% specificity, and OA from NOA with 73% specificity, at 100% sensitivity. Immunohistochemistry and an immunoenrichment mass spectrometry-based assay revealed the differential expression of TEX101 in distinct NOA subtypes. TEX101 semen concentrations differentiated Sertoli cell-only syndrome from the other categories of NOA. As a result, we propose a simple two-biomarker decision tree for the differential diagnosis of OA and NOA and, in addition, for the differentiation of NOA subtypes. Clinical assays for ECM1 and TEX101 have the potential to replace most of the diagnostic testicular biopsies and facilitate the prediction of outcome of sperm retrieval procedures, thus increasing the reliability and success of assisted reproduction techniques.

Current ovarian cancer biomarkers are inadequate because of their relatively low diagnostic sensitivity and specificity. There is a need to discover and validate novel ovarian cancer biomarkers that are suitable for early diagnosis, monitoring, and prediction of therapeutic response. We performed an in-depth proteomics analysis of ovarian cancer ascites fluid. Size exclusion chromatography and ultrafiltration were used to remove high abundance proteins with molecular mass >30 kDa. After trypsin digestion, the subproteome (<30 kDa) of ascites fluid was determined by two-dimensional liquid chromatography-tandem mass spectrometry. Filtering criteria were used to select potential ovarian cancer biomarker candidates. By combining data from different size exclusion and ultrafiltration fractionation protocols, we identified 445 proteins from the soluble ascites fraction using a two-dimensional linear ion trap mass spectrometer. Among these were 25 proteins previously identified as ovarian cancer biomarkers. After applying a set of filtering criteria to reduce the number of potential biomarker candidates, we identified 52 proteins for which further clinical validation is warranted. Our proteomics approach for discovering novel ovarian cancer biomarkers appears to be highly efficient because it was able to identify 25 known biomarkers and 52 new candidate biomarkers that warrant further validation. Molecular & Cellular Proteomics 8:661-669, 2009.Accounting for ϳ3% of all new cancer cases in 2008 (1) with a 1 in 59 (1.7%) lifetime probability of developing the disease, ovarian cancer is the most lethal gynecological malignancy deeming 5-6% of all cancer deaths (1). Hidden deep within the pelvis, ovarian cancer is relatively asymptomatic in early stages, and because of the lack of adequate screening, ovarian cancer has resulted in the majority of cases being presented with late stage disease in association with a low 5-year survival rate of 25-40%. When presented at an early stage, the 5-year survival rate exceeds 90% and most patients are cured by surgery alone (2). Although the most widely used serum marker for ovarian cancer is carbohydrate antigen 125 (CA125), 1 its utility as a screening marker is limited because of its high false positive rates and elevation in other malignancies such as uterine, fallopian, colon, and gastric cancer (3, 4) as well as in non-malignant conditions such as pregnancy and endometriosis (5). These reasons alone demonstrate the need and immediate benefit in using biomarkers with increased sensitivity and specificity for early diagnosis, prognosis, or monitoring of ovarian cancer.Many advanced stage ovarian cancer patients exhibit rapid growth of intraperitoneal tumors along with abdominal distention as a result of accumulation of ascites fluid in the peritoneal cavity. Mechanistically ascites formation occurs as malignant cells secrete proteins, growth factors, and cytokines that cause neovascularization, angiogenesis, increased fluid filtration, and/or lymphatic obstruction (6 -8) resulting...

Pancreatic cancer is one of the leading causes of cancerrelated deaths, for which serological biomarkers are urgently needed. Most discovery-phase studies focus on the use of one biological source for analysis. The present study details the combined mining of pancreatic cancerrelated cell line conditioned media and pancreatic juice for identification of putative diagnostic leads. Using strong cation exchange chromatography, followed by LC-MS/MS on an LTQ-Orbitrap mass spectrometer, we extensively characterized the proteomes of conditioned media from six pancreatic cancer cell lines (BxPc3, MIAPaCa2, PANC1, CAPAN1, CFPAC1, and SU.86.86), the normal human pancreatic ductal epithelial cell line HPDE, and two pools of six pancreatic juice samples from ductal adenocarcinoma patients. All samples were analyzed in triplicate. Between 1261 and 2171 proteins were identified with two or more peptides in each of the cell lines, and an average of 521 proteins were identified in the pancreatic juice pools. In total, 3479 nonredundant proteins were identified with high confidence, of which ϳ40% were extracellular or cell membrane-bound based on Genome Ontology classifications. Three strategies were employed for identification of candidate biomarkers: (1) examination of differential protein expression between the cancer and normal cell lines using label-free protein quantification, (2) integrative analysis, focusing on the overlap of proteins among the multiple biological fluids, and (3) tissue specificity analysis through mining of publically available databases. Preliminary verification of anterior gradient homolog 2, syncollin, olfactomedin-4, polymeric immunoglobulin receptor, and collagen alpha-1(VI) chain in plasma samples from pancreatic cancer patients and healthy controls using ELISA, showed a significant increase (p < 0.01) of these proteins in plasma from pancreatic cancer patients. The combination of these five proteins showed an improved area under the receiver operating characteristic curve to CA19.9 alone.