HLA class I and II genotype of the NCI-60 cell lines

Sixty cancer cell lines have been extensively characterized and used by the National Cancer Institute's Developmental Therapeutics Program (NCI-60) since the early 90's as screening tools for anti-cancer drug development. An extensive database has been accumulated that could be used to select individual cells lines for specific experimental designs based on their global genetic and biological profile. However, information on the human leukocyte antigen (HLA) genotype of these cell lines is scant and mostly antiquated since it was derived from serological typing. We, therefore, re-typed the NCI-60 panel of cell lines by high-resolution sequence-based typing. This information may be used to: 1) identify and verify the identity of the same cell lines at various institutions; 2) check for possible contaminant cell lines in culture; 3) adopt individual cell lines for experiments in which knowledge of HLA molecule expression is relevant. Since genome-based typing does not guarantee actual surface protein expression, further characterization of relevant cell lines should be entertained to verify surface expression in experiments requiring correct antigen presentation

Dinuclear gold(III) complexes as potential anticancer agents: structure, reactivity and biological profile of a series of gold(III) oxo-bridged derivatives

Six homologous gold(III) dinuclear oxo-bridged complexes, of the type [(bipynR)Au(μ-O)2Au(bipynR)][PF6]2, bearing variously substituted 2,2'-bipyridine ligands (bipynR = 2,2'-bipyridine, 4,4'-di-tert-butyl-, 6-methyl-, 6-neopentyl-, 6-o-xylyl- and 6,6'-dimethyl-2,2'-bipyridine), here called Auoxos, were prepared, characterised and recently tested as potential anticancer agents. Crystal structures were obtained for five members of the series that allowed us to perform detailed comparative analyses. Interestingly, the various Auoxos showed an acceptable stability profile in buffer solution and turned out to manifest outstanding antitumor properties in vitro. In particular, one member of this family, Auoxo6 (bipynR = 6,6'-dimethyl-2,2'-bipyridine), produced more selective and far greater antiproliferative effects than all other tested Auoxos, qualifying itself as the best “drug candidate”. In turn, COMPARE analysis of the cytotoxicity profiles of five Auoxos, toward an established panel of thirty-six human tumor cell lines, revealed important mechanistic differences; a number of likely biomolecular targets could thus be proposed such as HDAC and PKC. Biophysical studies revealed markedly different modes of interaction with calf thymus DNA for two representative Auoxo compounds. In addition, a peculiar reactivity with model proteins was documented on the ground of spectrophotometric and ESI MS data, most likely as the result of redox processes. In view of the several experimental evidences gathered so far, it can be stated that Auoxos constitute a novel family of promising cytotoxic gold compounds with an innovative mechanism of action that merit a more extensive pharmacological evaluation

miRConnect: Identifying Effector Genes of miRNAs and miRNA Families in Cancer Cells

micro(mi)RNAs are small non-coding RNAs that negatively regulate expression of most mRNAs. They are powerful regulators of various differentiation stages, and the expression of genes that either negatively or positively correlate with expressed miRNAs is expected to hold information on the biological state of the cell and, hence, of the function of the expressed miRNAs. We have compared the large amount of available gene array data on the steady state system of the NCI60 cell lines to two different data sets containing information on the expression of 583 individual miRNAs. In addition, we have generated custom data sets containing expression information of 54 miRNA families sharing the same seed match. We have developed a novel strategy for correlating miRNAs with individual genes based on a summed Pearson Correlation Coefficient (sPCC) that mimics an in silico titration experiment. By focusing on the genes that correlate with the expression of miRNAs without necessarily being direct targets of miRNAs, we have clustered miRNAs into different functional groups. This has resulted in the identification of three novel miRNAs that are linked to the epithelial-to-mesenchymal transition (EMT) in addition to the known EMT regulators of the miR-200 miRNA family. In addition, an analysis of gene signatures associated with EMT, c-MYC activity, and ribosomal protein gene expression allowed us to assign different activities to each of the functional clusters of miRNAs. All correlation data are available via a web interface that allows investigators to identify genes whose expression correlates with the expression of single miRNAs or entire miRNA families. miRConnect.org will aid in identifying pathways regulated by miRNAs without requiring specific knowledge of miRNA targets

SMARTS Approach to Chemical Data Mining and Physicochemical Property Prediction.

The calculation of physicochemical and biological properties is essential in order to facilitate modern drug discovery. Chemical spaces dimensionalized by these descriptors have been used to scaffold-hop in order to discover new lead and drug-like molecules. Broadening the boundaries of structure based drug design, these molecules are expected to share the same physiological target and have similar efficacy, as do known drug molecules sharing the same region in chemical property space. In the past few decades physicochemical and ADMET (absorption, distribution, metabolism, elimination, and toxicity) property predictors have been the subject of increased focus in academia and the pharmaceutical industry. Due to the ever increasing attention given to data mining and property predictions, we first discuss the sources of experimental pKa values and current methodologies used for pKa prediction in proteins and small molecules. Of particular concern is an analysis of the scope, statistical validity, overall accuracy, and predictive power of these methods. The expressed concerns are not limited to predicting pKa, but apply to all empirical predictive methodologies. In a bottom-up approach, we explored the influence of freely generated SMARTS string representations of molecular fragments on chelation and cytotoxicity. Later investigations, involving the derivation of predictive models, use stepwise regression to determine the optimal pool of SMARTS strings having the greatest influence over the property of interest. By applying a unique scoring system to sets of highly generalized SMARTS strings, we have constructed well balanced regression trees with predictive accuracy exceeding that of many published and commercially available models for cytotoxicity, pKa, and aqueous solubility. The methodology is robust, extremely adaptable, and can handle any molecular dataset with experimental data. This story details our struggles of data gathering, curation, and the development of a machine learning methodology able to derive and validate highly accurate regression trees capable of extremely fast property predictions. Regression trees created by our method are well suited to calculate descriptors for large in silico molecular libraries, facilitating data mining of chemical spaces in search of new lead molecules in drug discovery.Ph.D.Medicinal ChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/64627/1/adamclee_1.pd