A novel proteomic-based screening method for ovarian cancer using cervicovaginal fluids: A window into the abdomen.

OBJECTIVE: Our objective is to develop a site-specific proteomic-based screening test for ovarian cancer(OC) using the mucus of the cervix and vagina and evaluate a potential means for home testing. METHODS: Cervicovaginal fluid samples were obtained from ovarian cancer and normal control patients for LC-mass spectrometry(MS) proteomic evaluation. Statistical modeling determined the protein panel with the highest penetrance across ovarian cancer samples. A subcohort of patients consented to provide self-collected vaginal samples at home with questionnaire on feasibility. Cohen's kappa methodology was utilized to determine agreement between physician-collected and patient-collected samples. RESULTS: A total of 83 consecutive patient samples were collected prospectively (33 ovarian cancer & 50 controls). Thirty patients consented for self-collection. Using LC-MS, 30 peptides demonstrated independent statistical significance for detecting ovarian cancer. Using statistical modeling, the protein panel that determined the best predictor for detecting OC formed a "fingerprint" consisting of 5 proteins: serine proteinase inhibitor A1; periplakin; profilin1; apolipoprotein A1; and thymosin beta4-like protein. These peptides demonstrated a significant increase probability of detecting ovarian cancer with the ROC curve having an AUC of 0.86 (p = 0.00001). Physician-collected and patient-collected specimens demonstrated moderate agreement with kappa average of 0.6 with upper bound of 0.75. CONCLUSIONS: Using novel site-specific collection methods, we identified an OC "fingerprint" with adequate sensitivity and specificity to warrant further evaluation in a larger cohort. Agreement of physician-collected and patient-collected samples were encouraging and could improve access to screening with a home self-collection if this screening test is validated in future studies.

Development of a Synthetic 3-ketosteroid Δ1-dehydrogenase for the Generation of a Novel Catabolic Pathway Enabling Cholesterol Degradation in Human Cells.

Cholesterol is an essential component of membranes, which is acquired by cells via receptor-mediated endocytosis of lipoproteins or via de novo synthesis. In specialized cells, anabolic enzymes metabolize cholesterol, generating steroid hormones or bile acids. However, surplus cholesterol cannot be catabolized due to the lack of enzymes capable of degrading the cholestane ring. The inability to degrade cholesterol becomes evident in the development and progression of cardiovascular disease, where the accumulation of cholesterol/cholesteryl-esters in macrophages can elicit a maladaptive immune response leading to the development and progression of atherosclerosis. The discovery of cholesterol catabolic pathways in Actinomycetes led us to the hypothesis that if enzymes enabling cholesterol catabolism could be genetically engineered and introduced into human cells, the atherosclerotic process may be prevented or reversed. Comparison of bacterial enzymes that degrade cholesterol to obtain carbon and generate energy with the action of human enzymes revealed that humans lack a 3-ketosteroid Δ1-dehydrogenase (Δ1-KstD), which catalyzes the C-1 and C-2 desaturation of ring A. Here we describe the construction, heterologous expression, and actions of a synthetic humanized Δ1-KstD expressed in Hep3B and U-937 cells, providing proof that one of three key enzymes required for cholesterol ring opening can be functionally expressed in human cells.

Gut immunoglobulin alpha anti-glycan binding profiles as a research tool for local disease detection.

A promising approach capitalizing on the specific and highly sensitive characteristics of the body's own immune system is demonstrated in the context of revealing pancreatic ductal adenocarcinoma cancer (PDAC). IgA from a local biofluid called gastrointestinal lavage fluid (GLF) is used to investigate glycan reactivity to show the potential of this approach. IgA antibody responses, just as with IgG, result in amplification of a small signal which aids in detecting changes from a healthy state. IgA from GLF was screened against glycan arrays containing 609 glycan structures to investigate differential binding patterns associated with the disease. Samples included PDAC (n = 14) and non-PDAC (n = 6). Non-PDAC conditions included samples from healthy patients and the potentially confounding conditions of colon cancer and its precancerous lesion, colon adenoma. Results demonstrated characteristic reactivity in the PDAC sample group to a glycan structure. Also, IgA non-reactive motifs arose showing remarkable consistency within and between sample groups. While sample sizes are too small to identify putative biomarkers, these data show the use of IgA from GLF to be a promising avenue of research for local disease biomarker discovery.