The identification of 2-(1H-indazol-4-yl)-6-(4-methanesulfonyl-piperazin-1-ylmethyl)-4-morpholin-4-yl-thieno[3,2-d]pyrimidine (GDC-0941) as a potent, selective, orally bioavailable inhibitor of class I PI3 kinase for the treatment of cancer .

Phosphatidylinositol-3-kinase (PI3K) is an important target in cancer due to the deregulation of the PI3K/ Akt signaling pathway in a wide variety of tumors. A series of thieno[3,2-d]pyrimidine derivatives were prepared and evaluated as inhibitors of PI3 kinase p110alpha. The synthesis, biological activity, and further profiling of these compounds are described. This work resulted in the discovery of 17, GDC-0941, which is a potent, selective, orally bioavailable inhibitor of PI3K and is currently being evaluated in human clinical trials for the treatment of cancer.

Structural analysis of PI3-kinase isoforms: identification of residues enabling selective inhibition by small molecule ATP-competitive inhibitors.

A series of small molecule, ATP-competitive phosphoinositide 3-kinase inhibitors have been examined in homology models of the four class I isoforms, p110alpha, p110beta, p110delta and p110gamma. This analysis provided an insight into the mode of binding of these inhibitors to the hinge and to other key regions of the ATP binding site in each of the four subtypes. Significantly, residues were identified that differ between these proteins, and which help explain the isoform-selective inhibition profiles of the compounds.

Exploring the specificity of the PI3K family inhibitor LY294002.

The PI3Ks (phosphatidylinositol 3-kinases) regulate cellular signalling networks that are involved in processes linked to the survival, growth, proliferation, metabolism and specialized differentiated functions of cells. The subversion of this network is common in cancer and has also been linked to disorders of inflammation. The elucidation of the physiological function of PI3K has come from pharmacological studies, which use the enzyme inhibitors Wortmannin and LY294002, and from PI3K genetic knockout models of the effects of loss of PI3K function. Several reports have shown that LY294002 is not exclusively selective for the PI3Ks, and could in fact act on other lipid kinases and additional apparently unrelated proteins. Since this inhibitor still remains a drug of choice in numerous PI3K studies (over 500 in the last year), it is important to establish the precise specificity of this compound. We report here the use of a chemical proteomic strategy in which an analogue of LY294002, PI828, was immobilized onto epoxy-activated Sepharose beads. This affinity material was then used as a bait to fish-out potential protein targets from cellular extracts. Proteins with high affinity for immobilized PI828 were separated by one-dimensional gel electrophoresis and identified by liquid chromatography-tandem MS. The present study reveals that LY294002 not only binds to class I PI3Ks and other PI3K-related kinases, but also to novel targets seemingly unrelated to the PI3K family.

Combinatorial use of mRNA and two-dimensional electrophoresis expression data to choose relevant features for mass spectrometric identification.

It is only recently that quantitative studies of differential proteome analysis (DPA) have become possible. In this paper the issues involved in quantitative DPA are discussed and novel tools to select features for identification by mass spectrometry (MS) are described. The problem of comparing two sets of gels on a global level is explored as well as how to find specific protein features that differentiate two sets of two-dimensional electrophoresis gels. The concept of a 'virtual' gel, derived from gene expression data, is introduced. The virtual gel enables the co-analysis of data from gene and protein expression. We discuss the value of such an approach, and consider what new information can be gained by using gene and protein expression together. These tools are illustrated by analysis of data from tandem gene and protein expression experiments. Features that are highlighted by the above methods are putative candidates for MS identification. Tools are described that integrate the process of feature selection, cutting, and MS analysis.

Evaluation of two-dimensional differential gel electrophoresis for proteomic expression analysis of a model breast cancer cell system.

The technique of fluorescent two-dimensional (2D) difference gel electrophoresis for differential protein expression analysis has been evaluated using a model breast cancer cell system of ErbB-2 overexpression. Labeling of paired cell lysate samples with N-hydroxy succinimidyl ester-derivatives of fluorescent Cy3 and Cy5 dyes for separation on the same 2D gel enabled quantitative, sensitive, and reproducible differential expression analysis of the cell lines. SyproRuby staining was shown to be a highly sensitive and 2D difference gel electrophoresis-compatible method for post-electrophoretic visualization of proteins, which could then be picked and identified by matrix-assisted laser-desorption ionization mass spectroscopy. Indeed, from these experiments, we have identified multiple proteins that are likely to be involved in ErbB-2-mediated transformation. A triple dye labeling methodology was used to identify proteins differentially expressed in the cell system over a time course of growth factor stimulation. A Cy2-labeled pool of samples was used as a standard with all Cy3- and Cy5-labeled sample pairs to facilitate cross-gel quantitative analysis. DeCyder (Amersham Biosciences, Inc.) software was used to distinguish clear statistical differences in protein expression over time and between the cell lines.

Cluster analysis of an extensive human breast cancer cell line protein expression map database.

In the current study, the protein expression maps (PEMs) of 26 breast cancer cell lines and three cell lines derived from normal breast or benign disease tissue were visualised by high resolution two-dimensional gel electrophoresis. Analysis of this data was performed with ChiClust and ChiMap, two analytical bioinformatics tools that are described here. These tools are designed to facilitate recognition of specific patterns shared by two or more (a series) PEMs. Both tools use PEMs that were matched by an image analysis program and locally written programs to create a match table that is saved in an object relational database. The ChiClust tool uses clustering and subclustering methods to extract statistically significant protein expression patterns from a large series of PEMs. The ChiMap tool calculates a differential value (either as percentage change or a fold change) and represents these graphically. All such differentials or just those identified using ChiClust can be submitted to ChiMap. These methods are not dependent on any particular commercial image analysis program, and the whole software package gives an integrated procedure for the comparison and analysis of a series of PEMs. The ChiClust tool was used here to order the breast cell lines into groups according to biological characteristics including morphology in vitro and tumour forming ability in vivo. ChiMap was then used to highlight eight major protein feature-changes detected between breast cancer cell lines that either do or do not proliferate in nude mice. Mass spectrometry was used to identify the proteins. The possible role of these proteins in cancer is discussed.

The application of 2D gel-based proteomics methods to the study of breast cancer.

The protein complement of breast cells consists of many thousands of proteins. Recent developments in 2D gel electrophoresis technology have made studies requiring the quantitative analysis of a differential proteome, such as comparison between normal and malignant cells or investigation of drug effects on cells, truly feasible. Computer software plays a central part in the comparisons between multiple gels required for such experiments. In addition, software tools allow patterns of coexpression of proteins to be studied, offering potential insights into protein regulation, interactions, and functions, especially when combined with complementary data on gene expression. In this paper, the technology and limitations of 2D gel-based proteomics are reviewed. Techniques for comparing sets of gels at a global level as well as identifying specific protein features that differentiate gels are discussed. Our own experience of studying the breast cell proteome is used to illustrate the difficulties and achievements of differential proteomics.