Project description:Low-grade serous ovarian carcinoma is believed to arise from serous borderline ovarian tumors, yet the progression from serous borderline tumors to low-grade serous ovarian carcinoma remains poorly understood. The purpose of this study was to identify differentially expressed genes between the two groups. Expression profiles were generated from 6 human ovarian surface epithelia (HOSE), 8 serous borderline ovarian tumors (SBOT), 13 low-grade serous ovarian carcinomas (LG), and 22 high-grade serous ovarian carcinomas (HG). The anterior gradient homolog 3 (AGR3) gene was found to be highly upregulated in serous borderline ovarian tumors; this finding was validated by real-time quantitative RT-PCR, Western blotting, and immunohistochemistry. Anti-AGR3 immunohistochemistry was performed on an additional 56 LG and 103 HG tissues and the results were correlated with clinical data. Expression profiling determined that 1254 genes were differentially expressed (P < 0.005) between SBOT, LG and HG tumors. Serous borderline ovarian tumors exhibited robust positive staining for AGR3, with a lower percentage of tumor cells stained in LG and HG. Immunofluorescence staining indicated that AGR3 expression was limited to ciliated cells. Tumor samples with a high percentage (>10%) of AGR3 positively stained tumor cells were associated with improved longer median survival in both the LG (P = 0.013) and HG (P = 0.008) serous ovarian carcinoma groups. The progression of serous borderline ovarian tumors to low-grade serous ovarian carcinoma may involve the de-differentiation of ciliated cells. AGR3 could serve as a prognostic marker for survival in patients with low-grade and high-grade serous ovarian carcinomas. Total RNA were extracted from microdissected human ovarian surface epithelia (HOSE, n=6), and microdissected serous borderline ovarian tumors (LMP, n=8), low-grade serous ovarian carcinomas (LGOSC, n=13), and 22 high-grade serous ovarian carcinomas (HGOSC, n=22). Gene Expression profiles were then generated with commercial GeneChip Human Genome U133 Plus 2.0 Array. dChip was used to identify significant differentially expressed genes between LMP/LGOSC and HGOSC

Project description:To identify a prognostic gene signature accounting for the distinct clinical outcomes in ovarian cancer patients Despite the existence of morphologically indistinguishable disease, patients with advanced ovarian tumors display a broad range of survival end points. We hypothesize that gene expression profiling can identify a prognostic signature accounting for these distinct clinical outcomes. To resolve survival-associated loci, gene expression profiling was completed for an extensive set of 185(90 optimal/95 suboptimal) primary ovarian tumors using the Affymetrix human U133A microarray. Cox regression analysis identified probe sets associated with survival in optimally and suboptimally debulked tumor sets at a P value of <0.01. Leave-one-out cross-validation was applied to each tumor cohort and confirmed by a permutation test. External validation was conducted by applying the gene signature to a publicly available array database of expression profiles of advanced stage suboptimally debulked tumors. The prognostic signature successfully classified the tumors according to survival for suboptimally (P = 0.0179) but not optimally debulked (P = 0.144) patients. The suboptimal gene signature was validated using the independent set of tumors (odds ratio, 8.75; P = 0.0146). To elucidate signaling events amenable to therapeutic intervention in suboptimally debulked patients, pathway analysis was completed for the top 57 survival-associated probe sets. For suboptimally debulked patients, confirmation of the predictive gene signature supports the existence of a clinically relevant predictor, as well as the possibility of novel therapeutic opportunities. Ultimately, the prognostic classifier defined for suboptimally debulked tumors may aid in the classification and enhancement of patient outcome for this high-risk population. Gene expression profiling was completed for an extensive set of 185 primary ovarian tumors and 10 normal ovarian surface epithelium using the Affymetrix human U133A microarray

Project description:Background: Molecular profiling of diffuse gliomas has provided significant insights into the pathogenesis, classification and prognostication of these malignancies. However, previous molecular studies of glioma have largely focused on genomic readouts and targeted proteomic profiling technologies. Consequently, proteomic and downstream functional landscape of gliomas in general, and molecular subgroups in particular, remains largely unexplored. Here, we utilize liquid chromatography tandem mass spectrometry to profile genomically-defined cohorts of gliomas spanning the full range of World Health Organization (WHO) grades. Methods: Bulk frozen tissue and purified micro-dissected regions from formalin-fixed paraffin-embedded (FFPE) tissues were assembled and utilized to define robust proteomic signatures of both low grade, infiltrative and high-grade tumors. As a final analysis, primary tumor tissue was compared with both IDH-mutated and IDH-wildtype glioblastoma stem cell (GSC) lines to further overcome tissue heterogeneity and pinpoint proteins differences likely arising in the relevant glial cellular drivers of tumor development. Results: In aggregate, 5,496 unique proteins over 3 glioma cohorts were identified, and span common molecular subclasses based on IDH and 1p19q co-deletion status and all four WHO grades. Supervised clustering highlights substantial proteome and systems-level pathway differences between different genetically defined glioma subtypes and WHO grades. By using bulk tumor statistical analysis, 833 proteins distinguish different WHO grade tumors, while FFPE tumor dissection reveals 287 proteins in GBMs with abundance changes according to IDH mutation status. Using our integrative approach, calcium signaling, proteins of the endoplasmic reticulum and extracellular integrin proteins are most conserved proteomic markers that distinguish aggressive, IDH-wt, from IDH-mut GBM tumors in primary and tissue culture models gliomagenesis. Conclusions: This proteomic survey provides the largest and most diverse unbiased protein-based brain tumor resource to date. Current treatments for glial tumors are largely non-specific and overlap between genomic subtypes and WHO grades. Our analysis provides early insight into the vast downstream and epigenetic protein-level differences within this molecular framework. Given the central position proteins occupy in driving biology and phenotype, further characterization of the substantial proteomic diversity that exist between the molecular subtypes and grades of gliomas, proteomics may help define more personalized prognostic and predictive biomarkers for precision care.

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Transcription profiling of human ovarian tumors from the MALOVA (MALignant OVArian) cancer study

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