Using drug response data to identify molecular effectors, and molecular "omic" data to identify candidate drugs in cancer.

The current convergence of molecular and pharmacological data provides unprecedented opportunities to gain insights into the relationships between the two types of data. Multiple forms of large-scale molecular data, including but not limited to gene and microRNA transcript expression, DNA somatic and germline variations from next-generation DNA and RNA sequencing, and DNA copy number from array comparative genomic hybridization are all potentially informative when one attempts to recognize the panoply of potentially influential events both for cancer progression and therapeutic outcome. Concurrently, there has also been a substantial expansion of the pharmacological data being accrued in a systematic fashion. For cancer cell lines, the National Cancer Institute cell line panel (NCI-60), the Cancer Cell Line Encyclopedia (CCLE), and the collaborative Genomics of Drug Sensitivity in Cancer (GDSC) databases all provide subsets of these forms of data. For the patient-derived data, The Cancer Genome Atlas (TCGA) provides analogous forms of genomic information along with treatment histories. Integration of these data in turn relies on the fields of statistics and statistical learning. Multiple algorithmic approaches may be chosen, depending on the data being considered, and the nature of the question being asked. Combining these algorithms with prior biological knowledge, the results of molecular biological studies, and the consideration of genes as pathways or functional groups provides both the challenge and the potential of the field. The ultimate goal is to provide a paradigm shift in the way that drugs are selected to provide a more targeted and efficacious outcome for the patient.

RedundancyMiner: De-replication of redundant GO categories in microarray and proteomics analysis.

BACKGROUND: The Gene Ontology (GO) Consortium organizes genes into hierarchical categories based on biological process, molecular function and subcellular localization. Tools such as GoMiner can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. Two or more of the categories are often redundant, in the sense that identical or nearly-identical sets of genes map to the categories. The redundancy might typically inflate the report of significant categories by a factor of three-fold, create an illusion of an overly long list of significant categories, and obscure the relevant biological interpretation. RESULTS: We now introduce a new resource, RedundancyMiner, that de-replicates the redundant and nearly-redundant GO categories that had been determined by first running GoMiner. The main algorithm of RedundancyMiner, MultiClust, performs a novel form of cluster analysis in which a GO category might belong to several category clusters. Each category cluster follows a "complete linkage" paradigm. The metric is a similarity measure that captures the overlap in gene mapping between pairs of categories. CONCLUSIONS: RedundancyMiner effectively eliminated redundancies from a set of GO categories. For illustration, we have applied it to the clarification of the results arising from two current studies: (1) assessment of the gene expression profiles obtained by laser capture microdissection (LCM) of serial cryosections of the retina at the site of final optic fissure closure in the mouse embryos at specific embryonic stages, and (2) analysis of a conceptual data set obtained by examining a list of genes deemed to be "kinetochore" genes.

Detailed DNA methylation profiles of the E-cadherin promoter in the NCI-60 cancer cells.

E-cadherin (E-cad) is a transmembrane adhesion glycoprotein, the expression of which is often reduced in invasive or metastatic tumors. To assess E-cad's distribution among different types of cancer cells, we used bisulfite-sequencing for detailed, base-by-base measurement of CpG methylation in E-cad's promoter region in the NCI-60 cell lines. The mean methylation levels of the cell lines were distributed bimodally, with values pushed toward either the high or low end of the methylation scale. The 38 epithelial cell lines showed substantially lower (28%) mean methylation levels compared with the nonepithelial cell lines (58%). The CpG site at -143 with respect to the transcriptional start was commonly methylated at intermediate levels, even in cell lines with low overall DNA methylation. We also profiled the NCI-60 cell lines using Affymetrix U133 microarrays and found E-cad expression to be correlated with E-cad methylation at highly statistically significant levels. Above a threshold of approximately 20% to 30% mean methylation, the expression of E-cad was effectively silenced. Overall, this study provides a type of detailed analysis of methylation that can also be applied to other cancer-related genes. As has been shown in recent years, DNA methylation status can serve as a biomarker for use in choosing therapy.

Effects of acetylation, polymerase phosphorylation, and DNA unwinding in glucocorticoid receptor transactivation.

Varying the concentration of selected factors alters the induction properties of steroid receptors by changing the position of the dose-response curve (or the value for half-maximal induction=EC(50)) and the amount of partial agonist activity of antisteroids. We now describe a rudimentary mathematical model that predicts a simple Michaelis-Menten curve for the multi-step process of steroid-regulated gene induction. This model suggests that steps far downstream from receptor binding to steroid can influence the EC(50) of agonist-complexes and partial agonist activity of antagonist-complexes. We therefore asked whether inhibitors of three possible downstream steps can reverse the effects of increased concentrations of two factors: glucocorticoid receptors (GRs) and Ubc9. The downstream steps (with inhibitors in parentheses) are protein deacetylation (TSA and VPA), DNA unwinding (CPT), and CTD phosphorylation of RNA polymerase II (DRB and H8). None of the inhibitors mimic or prevent the effects of added GRs. However, inhibitors of DNA unwinding and CTD phosphorylation do reverse the effects of Ubc9 with high GR concentrations. These results support our earlier conclusion that different rate-limiting steps operate at low and high GR concentrations versus high GR with Ubc9. The present data also suggest that downstream steps can modulate the EC(50) of GR-mediated induction, thus both supporting the utility of our mathematical model and widening the field of biochemical processes that can modify the EC(50).

Multifactorial regulation of E-cadherin expression: an integrative study.

E-cadherin (E-cad) is an adhesion molecule associated with tumor invasion and metastasis. Its down-regulation is associated with poor prognosis for many epithelial tumor types. We have profiled E-cad in the NCI-60 cancer cell lines at the DNA, RNA, and protein levels using six different microarray platforms plus bisulfite sequencing. Here we consider the effects on E-cad expression of eight potential regulatory factors: E-cad promoter DNA methylation, the transcript levels of six transcriptional repressors (SNAI1, SNAI2, TCF3, TCF8, TWIST1, and ZFHX1B), and E-cad DNA copy number. Combined bioinformatic and pharmacological analyses indicate the following ranking of influence on E-cad expression: (1) E-cad promoter methylation appears predominant, is strongly correlated with E-cad expression, and shows a 20% to 30% threshold above which E-cad expression is silenced; (2) TCF8 expression levels correlate with (-0.62) and predict (P < 0.00001) E-cad expression; (3) SNAI2 and ZFHX1B expression levels correlate positively with each other (+0.83) and also correlate with (-0.32 and -0.30, respectively) and predict (P = 0.03 and 0.01, respectively) E-cad expression; (4) TWIST1 correlates with (-0.34) but does not predict E-cad expression; and (5) SNAI1 expression, TCF3 expression, and E-cad DNA copy number do not correlate with or predict E-cad expression. Predictions of E-cad regulation based on the above factors were tested and verified by demethylation studies using 5-aza-2'-deoxycytidine treatment; siRNA knock-down of TCF8, SNAI2, or ZFHX1B expression; and combined treatment with 5-aza-2'-deoxycytidine and TCF8 siRNA. Finally, levels of cellular E-cad expression are associated with levels of cell-cell adhesion and response to drug treatment.