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Relevant molecular markers and targets.

ABSTRACT: Ovarian cancer is a heterogeneous disease with extensive cytogenetic and molecular heterogeneity including aneuploidy, chromosomal alterations, mutations and overexpression as well as a natural propensity to disseminate and spread, making it difficult to diagnose at an early stage. Insights into the molecular mechanisms operative in cancer development, progression and metastasis have uncovered a wide array of targets for therapeutic intervention. In the absence of a common driving oncogene in ovarian cancer, single targeted therapy for this disease is unlikely to yield significant clinical benefit. Tailored approaches that combine molecular targeting agents with cytotoxic regimens hold great promise when used in primary treatment, during consolidation and maintenance therapy, and in the treatment of persistent or recurrent disease. The most promising treatment strategies are those that target the drivers of tumorigenesis and enhance the activity of cytotoxic agents. Receptor tyrosine kinases, non-receptor tyrosine kinases, serine/threonine kinases, transferases, proteases and deacetylases are among the relevant molecular markers and targets for ovarian cancer that are discussed. Collaboration, coordination, creativity and aggressive outreach to patients and their advocates are essential for success in running the concurrent trials with multiple clinical end points and embedded translational research that are needed to evaluate the array of promising targeted therapeutics and combinations. Validated biomarkers, surrogate specimens and end points, and additional clinically relevant in vitro and in vivo models for ovarian cancer are needed to facilitate the drug development and evaluation process, and ultimately to make meaningful improvements in the diagnosis, prevention and management of ovarian cancer.

SUBMITTER: Darcy KM

PROVIDER: S-EPMC1868014 | biostudies-literature | 2006 Nov

REPOSITORIES: biostudies-literature

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Relevant molecular markers and targets.

Darcy Kathleen M KM Schilder Russel J RJ

Gynecologic oncology 20061010 2 Suppl 1

Ovarian cancer is a heterogeneous disease with extensive cytogenetic and molecular heterogeneity including aneuploidy, chromosomal alterations, mutations and overexpression as well as a natural propensity to disseminate and spread, making it difficult to diagnose at an early stage. Insights into the molecular mechanisms operative in cancer development, progression and metastasis have uncovered a wide array of targets for therapeutic intervention. In the absence of a common driving oncogene in ov ...[more]

PMID: 17034839

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Project description:BACKGROUND:Protein-protein recognition is of fundamental importance in the vast majority of biological processes. However, it has already been demonstrated that it is very hard to distinguish true complexes from false complexes in so-called cross-docking experiments, where binary protein complexes are separated and the isolated proteins are all docked against each other and scored. Does this result, at least in part, reflect a physical reality? False complexes could reflect possible nonspecific or weak associations. RESULTS:In this paper, we investigate the twilight zone of protein-protein interactions, building on an interesting outcome of cross-docking experiments: false complexes seem to favor residues from the true interaction site, suggesting that randomly chosen partners dock in a non-random fashion on protein surfaces. Here, we carry out arbitrary docking of a non-redundant data set of 198 proteins, with more than 300 randomly chosen "probe" proteins. We investigate the tendency of arbitrary partners to aggregate at localized regions of the protein surfaces, the shape and compositional bias of the generated interfaces, and the potential of this property to predict biologically relevant binding sites. We show that the non-random localization of arbitrary partners after protein-protein docking is a generic feature of protein structures. The interfaces generated in this way are not systematically planar or curved, but tend to be closer than average to the center of the proteins. These results can be used to predict biological interfaces with an AUC value up to 0.69 alone, and 0.72 when used in combination with evolutionary information. An appropriate choice of random partners and number of docking models make this method computationally practical. It is also noted that nonspecific interfaces can point to alternate interaction sites in the case of proteins with multiple interfaces. We illustrate the usefulness of arbitrary docking using PEBP (Phosphatidylethanolamine binding protein), a kinase inhibitor with multiple partners. CONCLUSIONS:An approach using arbitrary docking, and based solely on physical properties, can successfully identify biologically pertinent protein interfaces.

Project description:BackgroundAn important goal in post-genomic research is discovering the network of interactions between transcription factors (TFs) and the genes they regulate. We have previously reported the development of a supervised-learning approach to TF target identification, and used it to predict targets of 104 transcription factors in yeast. We now include a new sequence conservation measure, expand our predictions to include 59 new TFs, introduce a web-server, and implement an improved ranking method to reveal the biological features contributing to regulation. The classifiers combine 8 genomic datasets covering a broad range of measurements including sequence conservation, sequence overrepresentation, gene expression, and DNA structural properties.Principal findings(1) Application of the method yields an amplification of information about yeast regulators. The ratio of total targets to previously known targets is greater than 2 for 11 TFs, with several having larger gains: Ash1(4), Ino2(2.6), Yaf1(2.4), and Yap6(2.4). (2) Many predicted targets for TFs match well with the known biology of their regulators. As a case study we discuss the regulator Swi6, presenting evidence that it may be important in the DNA damage response, and that the previously uncharacterized gene YMR279C plays a role in DNA damage response and perhaps in cell-cycle progression. (3) A procedure based on recursive-feature-elimination is able to uncover from the large initial data sets those features that best distinguish targets for any TF, providing clues relevant to its biology. An analysis of Swi6 suggests a possible role in lipid metabolism, and more specifically in metabolism of ceramide, a bioactive lipid currently being investigated for anti-cancer properties. (4) An analysis of global network properties highlights the transcriptional network hubs; the factors which control the most genes and the genes which are bound by the largest set of regulators. Cell-cycle and growth related regulators dominate the former; genes involved in carbon metabolism and energy generation dominate the latter.ConclusionPostprocessing of regulatory-classifier results can provide high quality predictions, and feature ranking strategies can deliver insight into the regulatory functions of TFs. Predictions are available at an online web-server, including the full transcriptional network, which can be analyzed using VisAnt network analysis suite.

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Relevant molecular markers and targets.

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Relevant molecular markers and targets.

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