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

CLC-Pred: A freely available web-service for in silico prediction of human cell line cytotoxicity for drug-like compounds

In silico methods of phenotypic screening are necessary to reduce the time and cost of the experimental in vivo screening of anticancer agents through dozens of millions of natural and synthetic chemical compounds. We used the previously developed PASS (Prediction of Activity Spectra for Substances) algorithm to create and validate the classification SAR models for predicting the cytotoxicity of chemicals against different types of human cell lines using ChEMBL experimental data. A training set from 59,882 structures of compounds was created based on the experimental data (IG50, IC50, and % inhibition values) from ChEMBL. The average accuracy of prediction (AUC) calculated by leave-one-out and a 20-fold cross-validation procedure during the training was 0.930 and 0.927 for 278 cancer cell lines, respectively, and 0.948 and 0.947 for cytotoxicity prediction for 27 normal cell lines, respectively. Using the given SAR models, we developed a freely available web-service for cell-line cytotoxicity profile prediction (CLC-Pred: Cell-Line Cytotoxicity Predictor) based on the following structural formula: http://way2drug.com/Cell-line/.

Relating Chemical Structure to Cellular Response: An Integrative Analysis of Gene Expression, Bioactivity, and Structural Data Across 11,000 Compounds

A central premise in systems pharmacology is that structurally similar compounds have similar cellular responses; however, this principle often does not hold. One of the most widely used measures of cellular response is gene expression. By integrating gene expression data from Library of Integrated Network-based Cellular Signatures (LINCS) with chemical structure and bioactivity data from PubChem, we performed a large-scale correlation analysis of chemical structures and gene expression profiles of over 11,000 compounds taking into account confounding factors such as biological conditions (e.g., cell line, dose) and bioactivities. We found that structurally similar compounds do indeed yield similar gene expression profiles. There is an ∼20% chance that two structurally similar compounds (Tanimoto Coefficient ≥ 0.85) share significantly similar gene expression profiles. Regardless of structural similarity, two compounds tend to share similar gene expression profiles in a cell line when they are administrated at a higher dose or when the cell line is sensitive to both compounds.

Profiling the kinome for drug discovery

The human kinome is made up of 518 distinctive serine/threonine and tyrosine kinases, which are key components of virtually every mammalian signal transduction pathway. Consequently, kinases provide a compelling target family for the development of small molecule inhibitors, which could be used as tools to delineate the mechanism of action for biological processes and potentially be used as therapeutics to treat human diseases such as cancer. A myriad of recent technological advances have accelerated our understanding of kinome function, its relationship to tumorigenic development, and have contributed to the progression of small molecule kinase inhibitors into the clinic. Essential to the continued growth of the field are informatics tools that can assist in interpreting disparate and voluminous data sets and correctly guide decision making processes. These advances are expected to have a dramatic impact on kinase drug development and clinical diagnoses and treatment in the near future.:

In Silico Prediction of Mechanism of Action for Cancer Therapeutics

Cancer is currently the second leading cause of death in the U.S. and is projected to become the principal cause in the near future. While radiation and surgery are common cancer treatment methods, chemotherapy remains a key treatment option, offering distinct advantages over other therapy options, especially in the management of metastasized tumors. Understanding the mechanism of action (MoA) of current and newly developed drugs is crucial to ongoing drug development research. Foreknowledge of how a candidate drug works can yield a wealth of information, including which cancers a drug may treat more effectively based on the susceptibility of the cancer to drugs with the same MoA. Previous studies concerning prediction of MoA have relied on costly experimental measurements as input for their predictions. We have developed an a priori quantitative structure-activity relationship (QSAR) for the in silico prediction of MoA without the need for experimental measurements. This model enables us to relate structural features of a chemical to its efficacy with a predictive accuracy of over 80 %, thus identifying the MoA of a candidate drug without costly, time-consuming experimental tests.

Piperlongumine induces inhibition of the ubiquitin-proteasome system in cancer cells

Piperlongumine, a natural product from the plant Piperlongum, has demonstrated selective cytotoxicity to tumor cells and to show anti-tumor activity in animal models [1]. Cytotoxicity of piperlongumine has been attributed to increase in reactive oxygen species (ROS) in cancer cells. We here report that piperlongumine is an inhibitor of the ubiquitin-proteasome system (UPS). Exposure of tumor cells to piperlongumine resulted in accumulation of a reporter substrate known to be rapidly degraded by the proteasome, and of accumulation of ubiquitin conjugated proteins. However, no inhibition of 20S proteolytic activity or 19S deubiquitinating activity was observed at concentrations inducing cytotoxicity. Consistent with previous reports, piperlongumine induced strong ROS activation which correlated closely with UPS inhibition and cytotoxicity. Proteasomal blocking could not be mimicked by agents that induce oxidative stress. Our results suggest that the anti-cancer activity of piperlongumine involves inhibition of the UPS at a pre-proteasomal step, prior to deubiquitination of malfolded protein substrates at the proteasome, and that the previously reported induction of ROS is a consequence of this inhibition.

Integrating constitutive gene expression and chemoactivity: mining the NCI60 anticancer screen

Studies into the genetic origins of tumor cell chemoactivity pose significant challenges to bioinformatic mining efforts. Connections between measures of gene expression and chemoactivity have the potential to identify clinical biomarkers of compound response, cellular pathways important to efficacy and potential toxicities; all vital to anticancer drug development. An investigation has been conducted that jointly explores tumor-cell constitutive NCI60 gene expression profiles and small-molecule NCI60 growth inhibition chemoactivity profiles, viewed from novel applications of self-organizing maps (SOMs) and pathway-centric analyses of gene expressions, to identify subsets of over- and under-expressed pathway genes that discriminate chemo-sensitive and chemo-insensitive tumor cell types. Linear Discriminant Analysis (LDA) is used to quantify the accuracy of discriminating genes to predict tumor cell chemoactivity. LDA results find 15% higher prediction accuracies, using ∼30% fewer genes, for pathway-derived discriminating genes when compared to genes derived using conventional gene expression-chemoactivity correlations. The proposed pathway-centric data mining procedure was used to derive discriminating genes for ten well-known compounds. Discriminating genes were further evaluated using gene set enrichment analysis (GSEA) to reveal a cellular genetic landscape, comprised of small numbers of key over and under expressed on- and off-target pathway genes, as important for a compound’s tumor cell chemoactivity. Literature-based validations are provided as support for chemo-important pathways derived from this procedure. Qualitatively similar results are found when using gene expression measurements derived from different microarray platforms. The data used in this analysis is available at http://pubchem.ncbi.nlm.nih.gov/andhttp://www.ncbi.nlm.nih.gov/projects/geo (GPL96, GSE32474).

Versatile pathway-centric approach based on high-throughput sequencing to anticancer drug discovery

The advent of powerful genomics technologies has uncovered many fundamental aspects of biology, including the mechanisms of cancer; however, it has not been appropriately matched by the development of global approaches to discover new medicines against human diseases. Here we describe a unique high-throughput screening strategy by high-throughput sequencing, referred to as HTS(2), to meet this challenge. This technology enables large-scale and quantitative analysis of gene matrices associated with specific disease phenotypes, therefore allowing screening for small molecules that can specifically intervene with disease-linked gene-expression events. By initially applying this multitarget strategy to the pressing problem of hormone-refractory prostate cancer, which tends to be accelerated by the current antiandrogen therapy, we identify Peruvoside, a cardiac glycoside, which can potently inhibit both androgen-sensitive and -resistant prostate cancer cells without triggering severe cytotoxicity. We further show that, despite transcriptional reprogramming in prostate cancer cells at different disease stages, the compound can effectively block androgen receptor-dependent gene expression by inducing rapid androgen receptor degradation via the proteasome pathway. These findings establish a genomics-based phenotypic screening approach capable of quickly connecting pathways of phenotypic response to the molecular mechanism of drug action, thus offering a unique pathway-centric strategy for drug discovery.

Prediction of drug efficacy for cancer treatment based on comparative analysis of chemosensitivity and gene expression data

The NCI60 database is the largest available collection of compounds with measured anti-cancer activity. The strengths and limitations for using the NCI60 database as a source of new anti-cancer agents are explored and discussed in relation to previous studies. We selected a sub-set of 2333 compounds with reliable experimental half maximum growth inhibitions (GI(50)) values for 30 cell lines from the NCI60 data set and evaluated their growth inhibitory effect (chemosensitivity) with respect to tissue of origin. This was done by identifying natural clusters in the chemosensitivity data set and in a data set of expression profiles of 1901 genes for the corresponding tumor cell lines. Five clusters were identified based on the gene expression data using self-organizing maps (SOM), comprising leukemia, melanoma, ovarian and prostate, basal breast, and luminal breast cancer cells, respectively. The strong difference in gene expression between basal and luminal breast cancer cells was reflected clearly in the chemosensitivity data. Although most compounds in the data set were of low potency, high efficacy compounds that showed specificity with respect to tissue of origin could be found. Furthermore, eight potential topoisomerase II inhibitors were identified using a structural similarity search. Finally, a set of genes with expression profiles that were significantly correlated with anti-cancer drug activity was identified. Our study demonstrates that the combined data sets, which provide comprehensive information on drug activity and gene expression profiles of tumor cell lines studied, are useful for identifying potential new active compounds.

Targeting karyotypic complexity and chromosomal instability of cancer cells

Multiple karyotypic abnormalities and chromosomal instability are characteristic features of many cancers that are relatively resistant to chemotherapeutic agents currently used in the clinic. These same features represent potentially targetable "states" that are essentially tumor specific. The assessment of the chromosomal state of a cancer cell population may provide a guide for the selection or development of drugs active against aggressive and intractable cancers.

Centchroman inhibits proliferation of head and neck cancer cells through the modulation of PI3K/mTOR pathway

Centchroman (CC; 67/20; INN: Ormeloxifene) is a non-steroidal antiestrogen extensively used as a female contraceptive in India. In the present study, we report the anti-proliferative effect of CC in head and neck squamous cell carcinoma (HNSCC) cells. CC inhibited cell proliferation in a dose dependent manner at 24 h of treatment. Further studies showed that CC treatment induced apoptosis, inhibited Akt/mTOR and signal transducers and activators of transcription protein 3 (STAT3) signaling, altered proteins associated with cell cycle regulation and DNA damage and inhibited colony forming efficiency of HNSCC cells. In addition, CC displayed anti-proliferative activity against a variety of non-HNSCC cell lines of diverse origin. The ability of CC to serve as a dual-inhibitor of Akt/mTOR and STAT3 signaling warrants further studies into its role as a therapeutic strategy against HNSCC.

The synthetic caged garcinia xanthone cluvenone induces cell stress and apoptosis and has immune modulatory activity

Several caged Garcinia xanthone natural products have potent bioactivity and a documented value in traditional Eastern medicine. Previous synthesis and structure activity relationship studies of these natural products resulted in the identification of the pharmacophore represented by the structure of cluvenone. In the current study, we examined the anticancer activity of cluvenone and conducted gene expression profiling and pathway analyses. Cluvenone was found to induce apoptosis in T-cell acute lymphoblastic leukemia cells (EC₅₀ = 0.25 μmol/L) and had potent growth-inhibitory activity against the NCI60 cell panel, including those that are multidrug-resistant, with a GI₅₀ range of 0.1 to 2.7 μmol/L. Importantly, cluvenone was approximately 5-fold more potent against a primary B-cell acute lymphoblastic leukemia compared with peripheral blood mononuclear cells from normal donors, suggesting that it has significant tumor selectivity. Comparison of cluvenone's growth-inhibitory profile to those in the National Cancer Institute database revealed that compounds with a similar profile to cluvenone were mechanistically unlike known agents, but were associated with cell stress and survival signaling. Gene expression profiling studies determined that cluvenone induced the activation of mitogen-activated protein kinase and NrF2 stress response pathways. Furthermore, cluvenone was found to induce intracellular reactive oxygen species formation. Lastly, the modulation in the expression of several genes associated with T cell and natural killer cell activation and function by cluvenone suggests a role as an immune-modulator. The current work highlights the potential of cluvenone as a chemotherapeutic agent and provides support for further investigation of these intriguing molecules with regard to mechanism and targets.

Bicyclic triterpenoid Iripallidal induces apoptosis and inhibits Akt/mTOR pathway in glioma cells

BACKGROUND

The highly resistant nature of glioblastoma multiforme (GBM) to chemotherapy prompted us to evaluate the efficacy of bicyclic triterpenoid Iripallidal against GBM in vitro.

METHODS

The effect of Iripallidal on proliferation and apoptosis in glioma cell lines was evaluated by MTS, colony formation and caspase-3 activity. The effect of iripallidal to regulate (i) Akt/mTOR and STAT3 signaling (ii) molecules associated with cell cycle and DNA damage was evaluated by Western blot analysis. The effect of Iripallidal on telomerase activity was also determined.

RESULTS

Iripallidal (i) induced apoptosis, (ii) inhibited Akt/mTOR and STAT3 signaling, (iii) altered molecules associated with cell cycle and DNA damage, (iv) inhibited telomerase activity and colony forming efficiency of glioma cells. In addition, Iripallidal displayed anti-proliferative activity against non-glioma cancer cell lines of diverse origin.

CONCLUSION

The ability of Iripallidal to serve as a dual-inhibitor of Akt/mTOR and STAT3 signaling warrants further investigation into its role as a therapeutic strategy against GBM.

Gene expression associations with the growth inhibitory effects of small molecules on live cells: specificity of effects and uniformity of mechanisms

The NCI60 human tumor cell line screen is a public resource for studying selective and non-selective growth inhibition of small molecules against cancer cells. By coupling growth inhibition screening data with biological characterizations of the different cell lines, it becomes possible to infer mechanisms of action underlying some of the observable patterns of selective activity. Using these data, mechanistic relationships have been identified including specific associations between single genes and small families of closely related compounds, and less specific relationships between biological processes involving several cooperating genes and broader families of compounds. Here we aim to characterize the degree to which such specific and general relationships are present in these data. A related question is whether genes tend to act with a uniform mechanism for all associated compounds, or whether multiple mechanisms are commonly involved. We address these two issues in a statistical framework placing special emphasis on the effects of measurement error in the gene expression and chemical screening data. We find that as measurement accuracy increases, the pattern of apparent associations shifts from one dominated by isolated gene/compound pairs, to one in which families consisting of an average of 25 compounds are associated to the same gene. At the same time, the number of genes that appear to play a role in influencing compound activities decreases. For less than half of the genes, the presence of both positive and negative correlations indicates pleiotropic associations with molecules via different mechanisms of action.

Gene expression patterns within cell lines are predictive of chemosensitivity

Background

The NCI has undertaken a twenty-year project to characterize compound sensitivity patterns in a selected set of sixty tumor derived cell lines. Previous studies have explored the relationship between compound sensitivity patterns to gene expression, protein expression, and DNA copy number for these same cell lines. A strong correlation between the pattern of expression of a biomarker and sensitivity to a compound could suggest a clinically interesting biological relationship between the two.

Results

We isolated RNA's and measured expression of 40000 genes using cDNA microarrays from the fifty-nine publicly available cell lines. Analysis of this data set in comparison with published gene expression data sets demonstrates a high degree of reproducibility in expression level measurements even using completely independent RNA preparations and array technologies. Using the fifty-nine cell lines for discovery and an additional seven cell lines for which extensive compound sensitivity data were available as a test set, we determined that gene-compound pairs with a correlation coefficient above 0.6 had a false discovery rate of approximately 5%. Large scale features of the gene expression and chemosensitivity data, such as tissue of origin and other physiological factors, did not seem to explain the majority of correlations between gene and compound patterns.

Conclusion

A comparison of gene expression and compound sensitivity in panels of cell lines was demonstrated to have a relatively high validation and low false discovery rate supporting the use of this approach and datasets for identifying candidate biomarkers and targeted biologically active compounds.

Connecting chemosensitivity, gene expression and disease

Omics-based investigations offer potentially powerful readouts that might be useful for probing the underlying biology of normal and diseased states, identifying novel therapeutic targets and proposing relevant markers for designing treatment strategies. A vital component of these investigations involves a systematic analysis of gene expression and chemosensitivity data in the context of disease states and small molecule probes into the function of targets responsible for a disease phenotype. Systematic analysis of chemical and pharmacogenetics data offers a possible means to identify novel, small-molecule, potentially therapeutic, agents that affect the phenotype of a particular target. Elegantly simple in concept, the covariation of genetic and chemosensitivity readouts provide a hypothetical link for relating compounds through genomic expression profiles to underlying biology.

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.

Atypical cytostatic mechanism of N-1-sulfonylcytosine derivatives determined by in vitro screening and computational analysis

We have previously shown that N-1-sulfonylpyrimidine derivatives have strong antiproliferative activity on human tumor cell lines, whereby 1-(p-toluenesulfonyl)cytosine showed good selectivity with regard to normal cells and was easily synthesized on a large scale. In the present work we have used an interdisciplinary approach to elucidate the compounds' mechanistic class. An augmented number of cell lines (11) has allowed a computational search for compounds with similar activity profiles and/or mechanistic class by integrating our data with the comprehensive DTP-NCI database. We applied supervised machine learning methodology (Random Forest classifier), which offers information complementary to unsupervised algorithms commonly used for analysis of cytostatic activity profiles, such as self-organizing maps. The computational results taken together with cell cycle perturbation and apoptosis analysis of the cell lines point to an unusual mechanism of cytostatic action, possibly a combination of nucleic acid antimetabolite activity and a novel molecular mechanism.

MicroRNA expression profiles for the NCI-60 cancer cell panel

Advances in the understanding of cancer cell biology and response to drug treatment have benefited from new molecular technologies and methods for integrating information from multiple sources. The NCI-60, a panel of 60 diverse human cancer cell lines, has been used by the National Cancer Institute to screen >100,000 chemical compounds and natural product extracts for anticancer activity. The NCI-60 has also been profiled for mRNA and protein expression, mutational status, chromosomal aberrations, and DNA copy number, generating an unparalleled public resource for integrated chemogenomic studies. Recently, microRNAs have been shown to target particular sets of mRNAs, thereby preventing translation or accelerating mRNA turnover. To complement the existing NCI-60 data sets, we have measured expression levels of microRNAs in the NCI-60 and incorporated the resulting data into the CellMiner program package for integrative analysis. Cell line groupings based on microRNA expression were generally consistent with tissue type and with cell line clustering based on mRNA expression. However, mRNA expression seemed to be somewhat more informative for discriminating among tissue types than was microRNA expression. In addition, we found that there does not seem to be a significant correlation between microRNA expression patterns and those of known target transcripts. Comparison of microRNA expression patterns and compound potency patterns showed significant correlations, suggesting that microRNAs may play a role in chemoresistance. Combined with gene expression and other biological data using multivariate analysis, microRNA expression profiles may provide a critical link for understanding mechanisms involved in chemosensitivity and chemoresistance.

Characterization of microRNA expression levels and their biological correlates in human cancer cell lines

MicroRNAs are small noncoding RNAs that function by regulating target gene expression posttranscriptionally. They play a critical role in developmental and physiologic processes and are implicated in the pathogenesis of several human diseases including cancer. We examined the expression profiles of 241 human microRNAs in normal tissues and the NCI-60 panel of human tumor-derived cell lines. To quantify microRNA expression, we employed a highly sensitive technique that uses stem-loop primers for reverse transcription followed by real-time PCR. Most microRNAs were expressed at lower levels in tumor-derived cell lines compared with the corresponding normal tissue. Agglomerative hierarchical clustering analysis of microRNA expression revealed four groups among the NCI-60 cell lines consisting of hematologic, colon, central nervous system, and melanoma tumor-derived cell lines clustered in a manner that reflected their tissue of origin. We identified specific subsets of microRNAs that provide candidate molecular signatures characteristic of the tumor-derived cell lines belonging to these four clusters. We also identified specific microRNA expression patterns that correlated with the proliferation indices of the NCI-60 cell lines, and we developed evidence for the identification of specific microRNAs as candidate oncogenes and tumor suppressor genes in different tumor types. Our results provide evidence that microRNA expression patterns may mark specific biological characteristics of tumors and/or mediate biological activities important for the pathobiology of malignant tumors. These findings call attention to the potential of microRNAs to provide etiologic insights as well as to serve as both diagnostic markers and therapeutic targets for many different tumor types.

A Cheminformatic Toolkit for Mining Biomedical Knowledge

PURPOSE

Cheminformatics can be broadly defined to encompass any activity related to the application of information technology to the study of properties, effects and uses of chemical agents. One of the most important current challenges in cheminformatics is to allow researchers to search databases of biomedical knowledge, using chemical structures as input.

MATERIALS AND METHODS

An important step towards this goal was the establishment of PubChem, an open, centralized database of small molecules accessible through the World Wide Web. While PubChem is primarily intended to serve as a repository for high throughput screening data from federally-funded screening centers and academic research laboratories, the major impact of PubChem could also reside in its ability to serve as a chemical gateway to biomedical databases such as PubMed.

CONCLUSION

This article will review cheminformatic tools that can be applied to facilitate annotation of PubChem through links to the scientific literature; to integrate PubChem with transcriptomic, proteomic, and metabolomic datasets; to incorporate results of numerical simulations of physiological systems into PubChem annotation; and ultimately, to translate data of chemical genomics screening efforts into information that will benefit biomedical researchers and physician scientists across all therapeutic areas.

Learning from the data: mining of large high-throughput screening databases

High-throughput screening (HTS) campaigns in pharmaceutical companies have accumulated a large amount of data for several million compounds over a couple of hundred assays. Despite the general awareness that rich information is hidden inside the vast amount of data, little has been reported for a systematic data mining method that can reliably extract relevant knowledge of interest for chemists and biologists. We developed a data mining approach based on an algorithm called ontology-based pattern identification (OPI) and applied it to our in-house HTS database. We identified nearly 1500 scaffold families with statistically significant structure-HTS activity profile relationships. Among them, dozens of scaffolds were characterized as leading to artifactual results stemming from the screening technology employed, such as assay format and/or readout. Four types of compound scaffolds can be characterized based on this data mining effort: tumor cytotoxic, general toxic, potential reporter gene assay artifact, and target family specific. The OPI-based data mining approach can reliably identify compounds that are not only structurally similar but also share statistically significant biological activity profiles. Statistical tests such as Kruskal-Wallis test and analysis of variance (ANOVA) can then be applied to the discovered scaffolds for effective assignment of relevant biological information. The scaffolds identified by our HTS data mining efforts are an invaluable resource for designing SAR-robust diversity libraries, generating in silico biological annotations of compounds on a scaffold basis, and providing novel target family specific scaffolds for focused compound library design.

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.

Identification of clinically significant tumor antigens by selecting phage antibody library on tumor cells in situ using laser capture microdissection

Much work has been done to develop tumor-targeting antibodies by selecting a phage antibody library on cancer cell lines. However, when tumor cells are removed from their natural environment, they may undergo genetic and epigenetic changes yielding different surface antigens than those seen in actual cases of cancer. We developed a strategy that allows selection of phage antibodies against tumor cells in situ on both fresh frozen and paraffin-embedded tissues using laser capture microdissection. By restricting antibody selection to binders of internalizing epitopes, we generated a panel of phage antibodies that target clinically represented prostate cancer antigens. We identified ALCAM/MEMD/CD166, a newly discovered prostate cancer marker, as the target for one of the selected antibodies, demonstrating the effectiveness of our approach. We further conjugated two single chain Fv fragments to liposomes and demonstrated that these nanotargeting devices were efficiently delivered to the interior of prostate cancer cells. The ability to deliver payload intracellularly and to recognize tumor cells in situ makes these antibodies attractive candidates for the development of targeted cancer therapeutics.

Assessment of in vitro and in vivo activities in the National Cancer Institute's anticancer screen with respect to chemical structure, target specificity, and mechanism of action

This paper examines two biological models of anticancer activity, cytotoxicity and hollow fiber (HF) activity, for chemotherapeutic agents evaluated as part of the National Cancer Institute's (NCI's) drug screening effort. Our analysis proposes strategies to globally assess compounds tested in the NCI's 60-cell (NCI60) in vitro anticancer screen in terms of structural features, biological activity, target specificity, and mechanism of action by data integration via our self-organizing maps of structural and biological response patterns. We have built statistical models to predict compound potency and HF activity based on physicochemical properties. Our results find that it is the combination of different structural properties that determines a compound's biological activity. A direct correlation is also found between compound potency and specificity, indicating that specific targeting, rather than promiscuous poisoning, gives rise to potency. Finally, we offer a strategy to exploit this relationship for future mining of novel anticancer candidates.

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 Institute's 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.

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 (GI(50)) derived from the National Cancer Institute's 60 cancer cells (NCI(60)). We have annotated groups of drug GI(50) 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 GI(50) 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.

Anticancer metal compounds in NCI's tumor-screening database: putative mode of action

Clustering analysis of tumor cell cytotoxicity profiles for the National Cancer Institute (NCI)'s open compound repository has been used to catalog over 1100 metal or metalloid containing compounds with potential anticancer activity. The molecular features and corresponding reactivity of these compounds have been analyzed in terms of properties of their metals, their associated organic components (ligands) and their capacity to inhibit tumor cell growth. Cytotoxic responses are influenced by both the identity of the metal and the properties of its coordination ligand, with clear associations between structural similarities and cytotoxicity. Assignments of mechanisms of action (MOAs) for these compounds could be segregated into four broad response classes according to preference for binding to biological sulfhydryl groups, chelation, generation of reactive oxygen species (ROS), and production of lipophilic ions. Correlations between specific cytotoxic responses and differential gene expression profiles within the NCI's tumor cell panel serve as a validation for candidate biological targets and putative MOA classes. In addition, specific sensitivity toward subsets of metal containing agents has been found for certain tumor cell panels. Taken together, our results expand the knowledge base available for evaluating, designing and developing new metal-based anticancer drugs that may provide the basis for target-specific therapeutics.