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Objective Previously, it has been shown that KIF14 mRNA is overexpressed in ovarian cancer (OvCa), regardless of histological subtype. KIF14 levels are independently predictive of poor outcome and increased rates of recurrence in serous OvCa patients. Furthermore, it has been shown that KIF14 also controls the in vivo tumorigenicity of OvCa cell lines. In this study, we evaluate the potential of KIF14 as a therapeutic target through selective inhibition of KIF14 in primary high-grade serous patient-derived OvCa cells. Methods To assess the dependence of primary serous OvCa cultures on KIF14, protein levels in 11 prospective high grade serous ovarian cancer samples were increased (KIF14 overexpression by transfection) or decreased (anti-KIF14 shRNA) in vitro, and proliferative capacity, anchorage independence and xenograft growth were assessed. Results Seven of eleven samples demonstrated increased/decreased in vitro proliferation in response to KIF14 overexpression/knockdown, respectively. When examining in vitro tumorigenicity (colony formation) and in vivo growth (subcutaneous xenografts) in response to KIF14 manipulation, none of the samples demonstrated growth in soft agar (11 samples), or xenograft growth (4 samples). Conclusions Although primary high-grade serous OvCa cells may depend on KIF14 for in vitro proliferation we were unable to demonstrate a role for KIF14 on tumorigenicity or develop an in vivo model for assessment. We have, however developed an effective in vitro method to evaluate the effect of target gene manipulation on the proliferative capacity of primary OvCa cultures. [ABSTRACT FROM AUTHOR]

Ovarian cancer (OvCa) is the most lethal gynecological malignancy. The emergence of highthroughput technologies, such as mass spectrometry, has allowed for a paradigm shift in the way we search for novel biomarkers. Urine-based peptidomic profiling is a novel approach that may result in the discovery of noninvasive biomarkers for diagnosing patients with OvCa. In this study, the peptidome of urine from 6 ovarian cancer patients and 6 healthy controls was deciphered.Urine samples underwent ultrafiltration and the filtrate was subjected to solid phase extraction, followed by fractionation using strong cation exchange chromatography. These fractions were analyzed using an Orbitrap mass spectrometer. Over 4600 unique endogenous urine peptides arising from 713 proteins were catalogued, representing the largest urine peptidome reported to date. Each specimen was processed in triplicate and reproducibility at the protein (69-76%) and peptide (58-63%) levels were noted. More importantly, over 3100 unique peptides were detected solely in OvCa specimens. One such promising biomarker was leucine-rich alpha-2-glycoprotein (LRG1), where multiple peptides were found in all urines from OvCa patients, but only one peptide was found in one healthy control urine sample. Conclusions Mining the urine peptidome may yield highly promising novel OvCa biomarkers. [ABSTRACT FROM AUTHOR]

Background: Mutations in the TP53 gene are extremely common and occur very early in the progression of serous ovarian cancers. Gene expression patterns that relate to mutational status may provide insight into the etiology and biology of the disease. Methods: The TP53 coding region was sequenced in 89 frozen serous ovarian cancers, 40 early stage (I/II) and 49 advanced stage (III/IV). Affymetrix U133A expression data was used to define gene expression patterns by mutation, type of mutation, and cancer stage. Results: Missense or chain terminating (null) mutations in TP53 were found in 59/89 (66%) ovarian cancers. Early stage cancers had a significantly higher rate of null mutations than late stage disease (38% vs. 8%, p < 0.03). In advanced stage cases, mutations were more prevalent in short term survivors than long term survivors (81% vs. 30%, p = 0.0004). Gene expression patterns had a robust ability to predict TP53 status within training data. By using early versus late stage disease for out of sample predictions, the signature derived from early stage cancers could accurately (86%) predict mutation status of late stage cancers. Conclusions: This represents the first attempt to define a genomic signature of TP53 mutation in ovarian cancer. Patterns of gene expression characteristic of TP53 mutation could be discerned and included several genes that are known p53 targets or have been described in the context of expression signatures of TP53 mutation in breast cancer. [ABSTRACT FROM AUTHOR]