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.
Purpose Pancreatic ductal adenocarcinoma (PDAC) is a significant cause of cancer mortality. CA19.9, the only tumor marker available to detect and monitor PDAC, is not sufficiently sensitive and specific to consistently differentiate early cancer from benign disease. In this study we aimed to validate recently discovered serum protein biomarkers for the early detection of PDAC and ultimately develop a biomarker panel that could discriminate PDAC from other benign disease better than the existing marker CA19.9. Patients and Methods We performed a retrospective blinded evaluation of 400 serum samples collected from individuals recruited on a consecutive basis. The sample population consisted of 250 individuals with PDAC at various stages, 130 individuals with benign conditions and 20 healthy individuals. The serum levels of each biomarker were determined by ELISAs or automated immunoassay. Results By randomly splitting matched samples into a training (n=186) and validation (n=214) set we were able to develop and validate a biomarker panel consisting of CA19.9, CA125 and LAMC2 that significantly improved the performance of CA19.9 alone. Improved discrimination was observed in the validation set between all PDAC and benign conditions (AUCCA19.9=0.80 versus AUCCA19.9+CA125+LAMC2= 0.87; p<0.005) as well as between early-stage PDAC and benign conditions (AUCCA19.9 = 0.69 versus AUCCA19.9+CA125+LAMC2 = 0.76; p<0.05) and between early-stage PDAC and chronic pancreatitis (AUCCA19.9 = 0.59 versus AUCCA19.9+CA125+LAMC2 = 0.74; p<0.05). Conclusions The data demonstrate that a serum protein biomarker panel consisting of CA125, CA19.9 and LAMC2 is able to significantly improve upon the performance of CA19.9 alone in detecting PDAC.
Cancer-associated fibroblasts (CAFs), represent a pivotal compartment of solid cancers (desmoplasia), and are causatively implicated in cancer development and progression. CAFs are recruited by growth factors secreted by cancer cells and they present a myofibroblastic phenotype, similar to the one obtained by resident fibroblasts during wound healing. Paracrine signaling between cancer cells and CAFs results in a unique protein expression profile in areas of desmoplastic reaction, which is speculated to drive metastasis. In an attempt to decipher large-scale proteomic profiles of the cancer invasive margins, we developed an in vitro coculture model system, based on tumor-host cell interactions between colon cancer cells and CAFs. Proteomic analysis of conditioned media derived from these cocultures coupled to mass spectrometry and bioinformatic analysis was performed to uncover myofibroblastic signatures of the cancer invasion front. Our analysis resulted in the identification and generation of a desmoplastic protein dataset (DPD), consisting of 152 candidate proteins of desmoplasia. By using monoculture exclusion datasets, a secretome algorithm and gene-expression meta-analysis in DPD, we specified a 22-protein “myofibroblastic signature” with putative importance in the regulation of colorectal cancer metastasis. Of these proteins, we investigated collagen type XII by immunohistochemistry, a fibril-associated collagen with interrupted triple helices (FACIT), whose expression has not been reported in desmoplastic lesions in any type of cancer. Collagen type XII was highly expressed in desmoplastic stroma by and around alpha-smooth muscle actin (α-SMA) positive CAFs, as well as in cancer cells lining the invasion front, in a small cohort of colon cancer patients. Other stromal markers, such as collagen type III, were also expressed in stromal collagen, but not in cancer cells. In a complementary fashion, gene expression meta-analysis revealed that COL12A1 is also an upregulated gene in colorectal cancer. Our proteomic analysis identified previously documented markers of tumor invasion fronts and our DPD could serve as a pool for future investigation of the tumor microenvironment. Collagen type XII is a novel candidate marker of myofibroblasts, and/or cancer cells undergoing dedifferentiation.
The detrimental effects of the winner’s curse, including overestimation of the genetic effects of associated variants and underestimation of sufficient sample sizes for replication studies are well-recognized in genome-wide association studies (GWAS). These effects can be expected to worsen as the field moves from GWAS into whole genome sequencing. To date, few studies have reported statistical adjustments to the naive estimates, due to the lack of suitable statistical methods and computational tools. We have developed an efficient genome-wide non-parametric method that explicitly accounts for the threshold, ranking, and allele frequency effects in whole genome scans. Here, we implement the method to provide bias-reduced estimates via bootstrap re-sampling (BR-squared) for association studies of both disease status and quantitative traits, and we report the results of applying BR-squared to GWAS of psoriasis and HbA1c. We observed over 50% reduction in the genetic effect size estimation for many associated SNPs. This translates into a greater than fourfold increase in sample size requirements for successful replication studies, which in part explains some of the apparent failures in replicating the original signals. Our analysis suggests that adjusting for the winner’s curse is critical for interpreting findings from whole genome scans and planning replication and meta-GWAS studies, as well as in attempts to translate findings into the clinical setting.
To investigate the quantitative response of energy metabolic pathways in human MCF-7 breast cancer cells to hypoxia, glucose deprivation, and estradiol stimulation, we developed a targeted proteomics assay for accurate quantification of protein expression in glycolysis/gluconeogenesis, TCA cycle, and pentose phosphate pathways. Cell growth conditions were selected to roughly mimic the exposure of cells in the cancer tissue to the intermittent hypoxia, glucose deprivation, and hormonal stimulation. Targeted Adaptation of cancer cells to hypoxia, lack of nutrients, and abnormal hormonal stimulation is known to alter their metabolism (1, 2). "Aerobic glycolysis," also referred as the Warburg effect, is a unique characteristic of rapidly proliferating cancer cells (3). Deregulating cellular energetics and reprogramming of metabolism are the emerging hallmarks of cancer (4). There is also an increasing number of epidemiologic evidence that link cancer risk with metabolic disorders such as diabetes and obesity (5).Until recently, the metabolic transformation of cancer cells was studied primarily at the level of genome (6), transcriptome (7), and metabolome (8). These studies discovered new mutations (9, 10), cancer-related alternative splicing isoforms (11), and altered enzyme activities in human cancers (2). Subsequent clinical applications included diagnostic imaging (12), prognosis (13), and identification of compounds targeting tumor metabolism (14).To fully understand the events and outcomes of metabolic transformation of cancer cells, quantitative proteomic approaches are required to complement existing genomic, transcriptomic, and metabolomic approaches. Proteomic methods provide additional levels of information, such as protein abundances, post-translational modifications, dynamics of protein turn-over, which cannot be accurately predicted using other -omic approaches.Typically, expression of enzymes in cellular metabolic pathways is measured by ELISA or immunoblot assays, which provide information for a very limited number of enzymes. Multiplex proteomic assays, on the contrary, could reveal simultaneous rearrangement of protein expression in entire metabolic pathways. Mass spectrometry-based proteomics, complemented with chemical and metabolic labeling approaches, is a powerful tool for global analysis of protein expression. However, these approaches have very limited throughput and require extensive sample preparation, complex data analysis, and verification of their results by independent assays. Besides, metabolic labeling is applicable to actively dividing cell lines, but not to the primary cells.Targeted proteomic assays present an attractive complementary tool devoid of the abovementioned limitations. SeFrom the ‡Samuel Lunenfeld Research Institute, Mount Sinai Hospital,
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