SPECIFIC AIM 1: To identify aberrant expression of NF-kB regulators in ovarian cancer Rationale: In a set of cancer samples, genes with outlier expression profile are those with dramatically high or low expression in a subset of samples. Outliers may indicate oncogenic events such as translocations, amplifications or genomic deletions. One method to identify outlier genes relies on a scaling transformation of expression data to accentuate extreme values. We applied this approach to multiple myeloma, focusing on outliers associated with NF-kB activation. We identified extreme high expression of 2 oncogenes NIK and CD40, and very low expression of 3 tumor supressors TRAF3, CYLD and BIRC2/3 locus. These abnormalities occurred in myeloma cases with evidence of NF-kB activity, suggesting that the gain or loss of expression of these genes was causative. This approach has not been previously applied to the gene expression profiles of ovarian cancer, yet has the potential to identify diverse genetic abnormalities underlying the pathogenesis of the disease. Preliminary analysis of publicly available ovarian cancer datasets indicates that IGFBP2 is extremely under-expressed in the cases with highest IKKb signature gene expression. IGFBP2 negatively regulates cell growth in prostate and breast epithelia. Therefore one could hypothesize that the loss of IGFBP2 in ovarian cancer cells might allow growth-promoting effects of NF-kB cascades to ensue. Design: We will apply this method systematically to existing databases of ovarian cancer gene expression profiles (www.ncbi.nlm.nih.gov/geo/gds and internally generated data). Once outliers are identified, resources will be committed to classify and validate the target. Outliers will be correlated with the NF-kB signature expression, as an estimate of NF-kB pathway activity in individual cell lines and patients. Extreme outlier expression of a known NF-kB regulator will prompt further investigation at the genomic and proteomic levels. We will quantify the genomic DNA by quantitative PCR in order to confirm gains or losses of the locus in question. We will investigate the gene product expression at the protein level by Western blot in the cell lines or with immunohistochemistry in tissue sections from the cases. Experimental validation of the abnormality will be confirmed in ovarian cancer cell lines by either knocking down or overexpressing the gene, as appropriate.
SPECIFIC AIM 2 : To map out intersecting pathways that cooperate with NF-kB to drive the pathogenesis of ovarian cancer. Rationale: NF-kB activity is regulated directly by IkappaB kinases (IKKs). These kinases act downstream of signaling molecules that are known to be important in ovarian cancer including MAP-K, AKT, and TGF-beta. The stimulus for IKK activity, and the end result of each pathway, may be context specific. In the hematopoetic context, IKK-alpha activity has been most often described in the cytoplasm, as part of a hetero-trimeric complex regulating the activity of classical NF-kappaB components, or as a homo-dimer regulating alternative NF-kappaB transcription factors. In prostate cancer, however, IKK-alpha has a prominent role in the nucleus of the cell, de-repressing metastasis-promoting genes. In fibroblasts or macrophages, IL-1 activation can result in either classical IKK-beta cascades or alternative IKK-alpha signaling depending on the status of the IL-1 receptor associated kinase (IRAK). In non-malignant breast epithelial cells, TGF-beta is known to activate apoptosis;in the context of breast carcinoma, however, TGF-beta stimulates growth via IKK-beta that depends on TGF-beta-activated kinase binding protein 1 (TAB1). Therefore, the molecular context is key to determining which IKK is activated and how the signals will ultimately affect the cancer cell. In this Aim, we will seek to define the context in which NF-kB is activated in ovarian cancer, and the kinases that cooperate to propagate the signal. Such kinases might then be targeted in conjunction with NF-kB in order to tailor therapy for this subset of ovarian cancer. Design: Our work established the sensitivity of a subset of ovarian cancer cell lines to a small molecule inhibitor of IKK-beta. We are now using this inhibitor in combination with RNA interference (RNAi) targeting the human kinome to search for interacting pathways. We will conduct an RNAi genetic screen to uncover pathways that cooperate with IKK-beta in ovarian cancer. We will screen a library of small hairpin RNAs (shRNAs) targeting 500 protein kinases to identify genetic pathways that might potentiate or antagonize the toxic effect of the IKK-beta inhibitor. The ovarian cancer cell line, Caov3, will be transduced with a pool of retroviruses from this library and then treated with a submaximal lethal dose of IKK-beta inhibitor such that roughly 50% of the cells were killed after 7 days. Since each vector in the shRNA library possesses a unique 60-base pair molecular bar code sequence, we will compare the complement of shRNA vectors present in cultures treated with the IKK-beta inhibitor to that in parallel untreated cultures. This will allow us to identify shRNAs that increase or decrease cell death in the presence of the IKK-beta inhibitor but ignore shRNAs that are toxic in a manner that is not synergistic with IKK-beta inhibition. Each identified kinase will be validated by shRNA knockdown in IKK-beta sensitive and insensitive cell lines;by small molecule inhibition to confirm synergy with the IKK-beta inhibitor;and by re-sequencing of exons to identify potential activating mutations especially in the kinase domain.
SPECIFIC AIM 3 : To characterize IKK-epsilon in the metastatic progression of ovarian cancer. Rationale: IKKepsilon was identified as an oncogene in breast cancer and has been associated with poor clinical outcome in ovarian cancer. This IKK isoform can regulate the innate immune response via activation of interferon-response factors, or can signal through classical NF-kappaB mechanisms by inactivating CYLD, a negative regulator of the pathway. Therefore, the molecular context is key to determining which oncogenic pathway is activated and how the signals will ultimately affect the cancer cell. Understanding IKKepsilon-regulated signaling in ovarian cancer will identify novel pathway interactions for context-specific therapeutics in the poor prognostic group of women whose ovarian cancers over-express IKKepsilon. Our survey of primary ovarian tumors and solid metastases identified higher expression of IKKe in the metastatic sites. We initiated a cell line model to study IKKe signaling in ovarian cancer, using RNA interference to knock down expression of IKKe. Depletion of IKKe decreased anchorage-independent growth of ovarian cancer cell lines, and pilot xenograft experiments suggest decreased aggressiveness of IKKe-depleted ovarian cancer cells. These preliminary results led to the hypothesis that IKKe drives ovarian cancer growth and metastasis. DESIGN: We are establishing the role of IKKe in properties of cell viability, invasive capacity, and metastatic potential in a xenograft model. We will also define an ovarian cancer-specific IKKe target gene signature which will give insight in to the mechanisms of ovarian cancer pathogenesis. Finally, we will define the context in which IKKe is activated in ovarian cancer by identifying kinases that cooperate to propagate the signal.First, a select panel of small molecule kinase inhibitors will be tested for the ability to augment cytotoxicity of IKKe knock down. Simultaneously, I will use a less biased approach by screening an established shRNA library targeting the human kinome to discover kinases that cooperate with IKKe.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Investigator-Initiated Intramural Research Projects (ZIA)
Project #
1ZIABC011054-03
Application #
8157602
Study Section
Project Start
Project End
Budget Start
Budget End
Support Year
3
Fiscal Year
2010
Total Cost
$447,991
Indirect Cost
Name
National Cancer Institute Division of Basic Sciences
Department
Type
DUNS #
City
State
Country
Zip Code
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