Ruili Huang

Linking tumor cell cytotoxicity to mechanism of drug action: an integrated analysis of gene expression, small-molecule screening and structural databases.

An integrated, bioinformatic analysis of three databases comprising tumor‐cell‐based small molecule screening data, gene expression measurements, and PDB (Protein Data Bank) ligand–target structures has been developed for probing mechanism of drug action (MOA). Clustering analysis of GI50 profiles for the NCIs database of compounds screened across a panel of tumor cells (NCI60) was used to select a subset of unique cytotoxic responses for about 4000 small molecules. Drug–gene–PDB relationships for this test set were examined by correlative analysis of cytotoxic response and differential gene expression profiles within the NCI60 and structural comparisons with known ligand–target crystallographic complexes. A survey of molecular features within these compounds finds thirteen conserved Compound Classes, each class exhibiting chemical features important for interactions with a variety of biological targets. Protein targets for an additional twelve Compound Classes could be directly assigned using drug‐protein interactions observed in the crystallographic database. Results from the analysis of constitutive gene expressions established a clear connection between chemo‐resistance and overexpression of gene families associated with the extracellular matrix, cytoskeletal organization, and xenobiotic metabolism. Conversely, chemo‐sensitivity implicated overexpression of gene families involved in homeostatic functions of nucleic acid repair, aryl hydrocarbon metabolism, heat shock response, proteasome degradation and apoptosis. Correlations between chemo‐responsiveness and differential gene expressions identified chemotypes with nonselective (i.e., many) molecular targets from those likely to have selective (i.e., few) molecular targets. Applications of data mining strategies that jointly utilize tumor cell screening, genomic, and structural data are presented for hypotheses generation and identifying novel anticancer candidates. Proteins 2005. Published 2005 Wiley‐Liss, Inc.

Linking pathway gene expressions to the growth inhibition response from the National Cancer Institute's anticancer screen and drug mechanism of action

Novel strategies are proposed to quantitatively analyze and relate biological pathways to drug responses using gene expression and small-molecule growth inhibition data (GI50) derived from the National Cancer Institutes 60 cancer cells (NCI60). We have annotated groups of drug GI50 responses with pathways defined by the Kyoto Encyclopedia of Genes and Genomes (KEGG) and BioCarta, and functional categories defined by Gene Ontology (GO), through correlations between pathway gene expression patterns and drug GI50 profiles. Drug–gene-pathway relationships may then be utilized to find drug targets or target-specific drugs. Significantly correlated pathways and the gene products involved represent interesting targets for further exploration, whereas drugs that are significantly correlated with only certain pathways are more likely to be target specific. Separate pathway clustering finds that pathways engaged in the same biological process tend to have similar drug correlation patterns. The biological and statistical significances of our method are established by comparison to known small-molecule inhibitor–gene target relationships reported in the literature and by standard randomization procedures. The results of our pathway, gene expression and drug-induced growth inhibition associations, can serve as a basis for proposing testable hypotheses about potential anticancer drugs, their targets, and mechanisms of action.

Targeting changes in cancer: assessing pathway stability by comparing pathway gene expression coherence levels in tumor and normal tissues

The purpose of this study is to examine gene expression changes occurring in cancer from a pathway perspective by analyzing the level of pathway coherence in tumor tissues in comparison with their normal counterparts. Instability in pathway regulation patterns can be considered either as a result of or as a contributing factor to genetic instability and possibly cancer. Our analysis has identified pathways that show a significant change in their coherence level in tumor tissues, some of which are tumor type specific, indicating novel targets for cancer type–specific therapies. Pathways are found to have a general tendency to lose their gene expression coherence in tumor tissues when compared with normal tissues, especially for signaling pathways. The selective growth advantage of cancer cells over normal cells seems to originate from their preserved control over vital pathways to ensure survival and altered signaling, allowing excessive proliferation. We have additionally investigated the tissue-related instability of pathways, providing valuable clues to the cellular processes underlying the tumorigenesis and/or growth of specific cancer types. Pathways that contain known cancer genes (i.e., “cancer pathways”) show significantly greater instability and are more likely to become incoherent in tumor tissues. Finally, we have proposed strategies to target instability (i.e., pathways that are prone to changes) by identifying compound groups that show selective activity against pathways with a detectable coherence change in cancer. These results can serve as guidelines for selecting novel agents that have the potential to specifically target a particular pathway that has relevance in cancer. [Mol Cancer Ther 2006;5(9):2417–27]

Anticancer medicines in development: assessment of bioactivity profiles within the National Cancer Institute anticancer screening data

We present an analysis of current anticancer compounds that are in phase I, II, or III clinical trials and their structural analogues that have been screened in the National Cancer Institute (NCI) anticancer screening program. Bioactivity profiles, measured across the NCI 60 cell lines, were examined for a correspondence between the type of cancer proposed for clinical testing and selective sensitivity to appropriately matched tumor subpanels in the NCI screen. These results find strongest support for using the NCI anticancer screen to select analogue compounds with selective sensitivity to the leukemia, colon, central nervous system, melanoma, and ovarian panels, but not for renal, prostate, and breast panels. These results are extended to applications of two-dimensional structural features to further refine compound selections based on tumor panel sensitivity obtained from tumor screening results. [Mol Cancer Ther 2007;6(8):2261–70]

Evaluating chemical structure similarity as an indicator of cellular growth inhibition

Chemical variations of small compounds are commonly used to probe biological systems and potentially discover lead-like compounds with selective target activity. Molecular probes are either generated by synthesis or acquired through directed searches of commercially available compound libraries. The data generated when testing the probes in various biological systems constitutes a structure/activity analysis. The ability to detect variations and classify biological responses requires the analysis of a compound in multiple assays. While the concept of a structure/activity relationship is straightforward, its implementation can vary considerably depending on the biological system under study and the probe library selected for testing. The analysis presented here will focus on the accumulated compound library used to screen for growth inhibition across the National Cancer Institutes panel of 60 tumor cells. The considerable chemical and biological diversity inherent in these data offers an opportunity to establish a quantifiable connection between chemical structure and biological activity. We find that the connection between structure and biological response is not symmetric, with biological response better at predicting chemical structure than vice versa. Structurally and functionally similar compounds can have distinguishable biological responses reflecting different mechanisms of action.

Cytotoxicity of RH1: NAD(P)H:quinone acceptor oxidoreductase (NQO1)-independent oxidative stress and apoptosis induction.

The elevated expression of the flavoprotein NAD(P)H:quinone acceptor oxidoreductase (NQO1) (EC 1.6.99.2) in many human solid tumors, along with its ability to activate quinone-based anticancer agents, makes it an excellent target for enzyme-directed drug development. Previous studies have shown a significant statistical correlation between NQO1 enzymatic activity and the cytotoxicity of certain antitumor quinones. RH1 [2,5-diaziridinyl-3-(hydroxymethyl)-6-methyl-1,4-benzoquinone], presently in late preclinical and entering early clinical development, has been previously considered to be an excellent substrate for activation by NQO1. In this study we investigate the cytotoxicity of RH1 in cell lines selected from the NCIs 60 tumor cell line panel, expressing varying levels of NQO1 activity. Exposure time- and concentration-dependent cytotoxicity was seen, apparently independent from levels of NQO1 activity in these cells. Furthermore, the NQO1 inhibitor dicoumarol had no impact on the sensitivity profiles of RH1 response. The HL-60 myeloid leukemia cells, which do not have detectable NQO1 activity, were further investigated. RH1 treatment of HL-60 cells generated high levels of free radicals, which was accompanied by robust redox cycling, oxygen consumption and induction of apoptosis. These results are in agreement with previous data suggesting that, in addition to its activation by NQO1, RH1-induced cytotoxicity might involve alternative pathways for activation of this compound. Furthermore, the high cytotoxicity of RH1 in the leukemia/lymphoma subpanel of the NCI in vitro cell line screen would suggest an empirical rationale for the utilization of this compound in the treatment of these malignancies.

Drugs aimed at targeting characteristic karyotypic phenotypes of cancer cells

The karyotypic features of cancer cells have not been a particular focus of anticancer drug targeting either as guidance for treatment or as specific drug targets themselves. Cancer cell lines typically have considerable, characteristic, and variable chromosomal aberrations. Here, we consider small-molecule screening data across the National Cancer Institutes 60 tumor cell line drug screening panel (NCI-60) analyzed for specific association with karyotypic variables (numerical and structural complexity and heterogeneity) determined for these same cell lines. This analysis is carried out with the aid of a self-organizing map allowing for a simultaneous assessment of all screened compounds, revealing an association between karyotypic variables and a unique part of the cytotoxic response space. Thirteen groups of compounds based on related specific chemical structural motifs are identified as possible leads for anticancer drug discovery. These compounds form distinct groups of molecules associated with relatively unexplored regions of the NCI-60 self-organizing map where anticancer agents currently standard in the clinic are not present. We suggest that compounds identified in this study may represent new classes of potential anticancer agents.

Chemoinformatic analysis of NCI preclinical tumor data : Evaluating compound efficacy from mouse xenograft data, NCI-60 screening data, and compound descriptors

We provide a chemoinformatic examination of the NCI public human tumor xenograft data to explore relationships between small molecules, treatment modality, efficacy, and toxicity. Efficacy endpoints of tumor weight reduction (TW) and survival time increase (ST) compared to tumor bearing control mice were augmented by a toxicity measure, defined as the survival advantage of treated versus control animals (TX). These endpoints were used to define two independent therapeutic indices (TIs) as the ratio of efficacy (TW or ST) to toxicity (TX). Linear models predictive of xenograft endpoints were successfully constructed (0.67 < r(2) < or = 0.74)(observed_versus_predicted) using a model comprised of variables in treatment modality, chemoinformatic descriptors, and in vitro cell growth inhibition in the NCI 60-cell assay. Cross-validation analysis based on randomly chosen training subsets found these predictive correlations to be robust. Model-based sensitivity analysis found chemistry and growth inhibition to provide the best, and treatment modality the worst, indicators of xenograft endpoint. The poor predictive power derived from treatment alone appears to be of less importance to xenograft outcome for compounds having strongly similar chemical and biological features. ROC-based model validation found a 70% positive predictive value for distinguishing FDA approved oncology agents from available xenograft tested compounds. Additional chemoinformatic applications are provided that relate xenograft outcome to biological pathways and putative mechanism of compound action. These results find a strong relationship between xenograft efficacy and pathways comprised of genes having highly correlated mRNA expressions. Our analysis demonstrates that chemoinformatic studies utilizing a combination of xenograft data and in vitro preclinical testing offer an effective means to identify compound classes with superior efficacy and reduced toxicity.

Pharmacogenomics of the National Cancer Institute’s 60-Tumor Cell Panel

One of the important goals of cancer research is to understand the nature of gene expression regulation and biological pathways and to apply this knowledge to find the mechanism by which small drug molecules interfere with the biological system through interactions with gene products and pathways. We have utilized the gene expression and small molecule screening data available at the National Cancer Institute (NCI) for 60 immortalized cell lines representing a range of major cancers. This extensive data set potentially contains the complete information necessary to understand and target cancer cells. In our experience it is most fruitful to adopt systems biology and pharmacogenomic approaches to deconvolute the necessary chemistry and biology in order to conduct a rational anti-cancer drug design effort. In this undertaking, existing biological pathway and gene expression information is merged with drug chemosensitivity data to both elucidate a drug’s mechanism of action and to find cancer-specific targets. This framework offers a rational design strategy to mine novel anti-cancer candidates that are both potent and show specificity to targets in cancer pathways.