Joseph S. Verducci

MicroRNAs modulate the chemosensitivity of tumor cells

MicroRNAs are strongly implicated in such processes as development, carcinogenesis, cell survival, and apoptosis. It is likely, therefore, that they can also modulate sensitivity and resistance to anticancer drugs in substantial ways. To test this hypothesis, we studied the pharmacologic roles of three microRNAs previously implicated in cancer biology (let-7i, mir-16, and mir-21) and also used in silico methods to test pharmacologic microRNA effects more broadly. In the experimental system, we increased the expression of individual microRNAs by transfecting their precursors (which are active) or suppressed the expression by transfection of antisense oligomers. In three NCI-60 human cancer cell lines, a panel of 60 lines used for anticancer drug discovery, we assessed the growth-inhibitory potencies of 14 structurally diverse compounds with known anticancer activities. Changing the cellular levels of let-7i, mir-16, and mir-21 affected the potencies of a number of the anticancer agents by up to 4-fold. The effect was most prominent with mir-21, with 10 of 28 cell-compound pairs showing significant shifts in growth-inhibitory activity. Varying mir-21 levels changed potencies in opposite directions depending on compound class; indicating that different mechanisms determine toxic and protective effects. In silico comparison of drug potencies with microRNA expression profiles across the entire NCI-60 panel revealed that ∼30 microRNAs, including mir-21, show highly significant correlations with numerous anticancer agents. Ten of those microRNAs have already been implicated in cancer biology. Our results support a substantial role for microRNAs in anticancer drug response, suggesting novel potential approaches to the improvement of chemotherapy. [Mol Cancer Ther 2008;7(1):1–9]

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. [Mol Cancer Ther 2007;6(5):1483–91]

MICE Models: Superior to the HERG Model in Predicting Torsade de Pointes

Drug-induced block of the cardiac hERG (human Ether-à-go-go-Related Gene) potassium channel delays cardiac repolarization and increases the risk of Torsade de Pointes (TdP), a potentially lethal arrhythmia. A positive hERG assay has been embraced by regulators as a non-clinical predictor of TdP despite a discordance of about 30%. To test whether assaying concomitant block of multiple ion channels (Multiple Ion Channel Effects or MICE) improves predictivity we measured the concentration-responses of hERG, Nav1.5 and Cav1.2 currents for 32 torsadogenic and 23 non-torsadogenic drugs from multiple classes. We used automated gigaseal patch clamp instruments to provide higher throughput along with accuracy and reproducibility. Logistic regression models using the MICE assay showed a significant reduction in false positives (Type 1 errors) and false negatives (Type 2 errors) when compared to the hERG assay. The best MICE model only required a comparison of the blocking potencies between hERG and Cav1.2.

Probability models on rankings

Abstract This paper investigates many of the probability models on permutations that have been proposed in the statistical and psychological literature. The various models are categorized into the following general classes: (1) Thurstone order statistics models, (2) ranking models induced by paired comparisons, (3) ranking models based on distances between permutations, and (4) multistage ranking models. Several thematic properties of ranking models are introduced that provide the basis for a systematic study of each of the classes of models. These properties are label-invariance, reversibility, strong unimodality, complete consensus, and L-decomposability. Next, several important subclasses of the four general classes are explored, including a determination of the pairwise intersections of the different classes of models. The paper concludes with an illustration of many of the models on a set of ranked “word association” data.

Multistage Ranking Models

Abstract Suppose that a sample of people independently examines a fixed set of k items and then ranks these items according to personal judgment. The process of ranking the items is decomposed into k −1 stages. In the forward model, the most preferred item is selected at the first stage, the best of the remaining items is selected at the second stage, and so on until the least preferred item is selected by default. Various probability models are adopted at each stage, and properties of the resulting models for randomly sampled rankings are investigated. Luce (1959) first proposed such a modeling scheme, where each item i was thought to have an intrinsic value θi , and the probability of choosing a particular item i at any stage, conditional on the set S of items not previously chosen, was given by I{i ∈ S}θ i /Σ j∈S θ j , where I{} is an indicator function. Plackett (1975) began with the same model but added interaction terms that would theoretically extend the usefulness of this approach when the basic m...

Pharmacogenomic analysis: correlating molecular substructure classes with microarray gene expression data

Genomic studies are producing large databases of molecular information on cancers and other cell and tissue types. Hence, we have the opportunity to link these accumulating data to the drug discovery processes. Our previous efforts at ‘information–intensive’ molecular pharmacology have focused on the relationship between patterns of gene expression and patterns of drug activity. In the present study, we take the process a step further—relating gene expression patterns, not just to the drugs as entities, but to ∼27 000 substructures and other chemical features within the drugs. This coupling of genomic information with structure-based data mining can be used to identify classes of compounds for which detailed experimental structure–activity studies may be fruitful. Using a systematic substructure analysis coupled with statistical correlations of compound activity with differential gene expression, we have identified two subclasses of quinones whose patterns of activity in the National Cancer Institutes 60-cell line screening panel (NCI-60) correlate strongly with the expression patterns of particular genes: (i) The growth inhibitory patterns of an electron-withdrawing subclass of benzodithiophenedione-containing compounds over the NCI-60 are highly correlated with the expression patterns of Rab7 and other melanoma-specific genes; (ii) the inhibitory patterns of indolonaphthoquinone-containing compounds are highly correlated with the expression patterns of the hematopoietic lineage-specific gene HS1 and other leukemia genes. As illustrated by these proof-of-principle examples, we introduce here a set of conceptual tools and fluent computational methods for projecting directly from gene expression patterns to drug substructures and vice versa. The analysis is presented in terms of the NCI-60 cell lines and microarray-based gene expression patterns, but the concept and methods are broadly applicable to other large-scale pharmacogenomic database sets as well. The approach (SAT for Structure-Activity-Target) provides a systematic way to mine databases for the design of further structure–activity studies, particularly to aid in target and lead identification.

A Modification of the Jaccard–Tanimoto Similarity Index for Diverse Selection of Chemical Compounds Using Binary Strings

Determination of molecular similarity plays an important role in analyzing large compound databases in chemical and pharmaceutical research. When molecules are described by binary vectors with bits corresponding to the presence or absence of structural features, the Tanimoto association coefficient is the most commonly used measure of similarity or chemical distance between two compounds. However, when used to select compounds for an optimal spread design, the Tanimoto coefficient produces an intrinsic bias toward smaller compounds. We have developed a new association coefficient that overcomes this bias. This article gives details of the new coefficient and contrasts the two coefficients for selecting diverse sets of compounds from a large collection. When the Tanimoto coefficient is modified as suggested to select a diverse set in the National Cancer Institute and Registry of Toxic Effects of Chemical Substances databases, the average number of features among the selected compounds increases by more than 50%.

Probability Models and Statistical Analyses for Ranking Data

This book of edited contributions provides a wide-ranging survey of the use of probability models for ranking data and it introduces new methods for the statistical analysis of ranking data. The contributors are drawn from a variety of fields including psychology, sociology, and the health sciences as well as statistics. Consequently, many researchers whose work involves the study of ranked data will find much of practical interest here. The papers cover the following topics: basic models and mixture models; inference from full and partial ranking; amalgamation and consensus; and paired ranking and unfolding. A foreward by Persi Diaconis draws together some of the mathematical ideas underlying this subject and explores its links with the statistical analysis of permutations.

Analysis of Variance

I would like to hear comments from more experienced experimental researchers about standard practices for recruiting and compensating participants in consumer and marketing experiments. What are the pros and cons of using student participants? (I know there was a debate about this in the literature a few years ago, but what is the current prevailing opinion?) Is there a difference between using undergraduate students (business majors or nonbusiness majors) and graduate students? When using student participants, is it better to compensate them with extra course credit or to pay them? And, is one time of the semester or quarter (i.e., beginning, middle, or end) preferable for using student participants? I am especially interested to know if anyone has conducted a systematic study of these last two issues. I have recently run experiments using student samples from the same population, but paying one sample and giving extra credit to the other, which definitely affected the rate at which students showed up for their assigned sessions. It may also have affected the variance in the quality of students that chose to participate. Also, in an experiment that I recently ran at the end of a semester (during the last week and a half of class meetings before the final exam week), I collected informal statements from participants in debriefing sessions that indicated that they were no busier or more distracted than they would have been in the middle of the semester. Also, what are the standard practices for recruiting and compensating nonstudent participants (e.g., ordinary folks off the street)? And, for experimental marketing and organizational research (on which I am presently embarking), what are the equivalent standards for industry-based samples (i.e., executives, managers, executive MBA students)? (This information is critical for budgeting grant proposals. I recently called the National Science Foundation and they could not offer much help on this point.) Also, does anyone have any great suggestions for increasing our success rate for getting such populations to participate in experimental research? I was discouraged by a recent conversation with George Day and David Montgomery, who said that even they are finding it increasingly difficult to recruit managerial research participants in the executive courses at the Wharton School and Stanford University. (So, where does that leave the rest of us?) Professor Prashant Malaviya University of Illinois at ChicagoIn the spring of 1990, the Hubble Space Telescope was put into orbit, the culmination of work by a multitude of astronomers, engineers, technicians, and researchers over a period of many years. Its proponents hail it as a key tool to understanding the universe, while its critics write it off as a monumental waste of resources that will never fulfill the expectations of those who designed it. Almost immediately after it went on-line, concern arose about the robustness of its inner workings, yet the demand for access to this device is immense.This special issue poses anonymous questions, then provides the answers and a discussion of the issues by the expert who responded. The answers are not anonymous--partly to give credit to the experts and partly to encourage future communication and debate on whatever lingering controversies may arise. After a number of questions, the special issue concludes with a discussion by the guest editor that summarizes the answers and provides straightforward answers to questions that were not addressed by the experts.

On combining recursive partitioning and simulated annealing to detect groups of biologically active compounds.

Statistical data mining methods have proven to be powerful tools for investigating correlations between molecular structure and biological activity. Recursive partitioning (RP), in particular, offers several advantages in mining large, diverse data sets resulting from high throughput screening. When used with binary molecular descriptors, the standard implementation of RP splits on single descriptors. We use simulated annealing (SA) to find combinations of molecular descriptors whose simultaneous presence best separates off the most active, chemically similar group of compounds. The search is incorporated into a recursive partitioning design to produce a regression tree for biological activity on the space of structural fingerprints. Each node is characterized by a specific combination of structural features, and the terminal nodes with high average activities correspond, roughly, to different classes of compounds. Using LeadScope structural features as descriptors to mine a database from the National Cancer Institute, the merging of RP and SA consistently identifies structurally homogeneous classes of highly potent anticancer agents.