BACKGROUND: Microarrays used for gene expression studies yield large amounts of data. The processing of such data typically leads to lists of differentially-regulated genes. A common terminal data analysis step is to map pathways of potentially interrelated genes. METHODS: We applied a transcriptomics analysis tool to elucidate the underlying pathways of leukocyte maturation at the genomic level in an established cellular model of leukemia by examining time-course data in two subclones of U-937 cells. Leukemias such as Acute Promyelocytic Leukemia (APL) are characterized by a block in the hematopoietic stem cell maturation program at a point when expansion of clones which should be destined to mature into terminally-differentiated effector cells get locked into endless proliferation with few cells reaching maturation. Treatment with retinoic acid, depending on the precise genomic abnormality, often releases the responsible promyelocytes from this blockade but clinically can yield adverse sequellae in terms of potentially lethal side effects, referred to as retinoic acid syndrome. RESULTS: Briefly, the list of genes for temporal patterns of expression was pasted into the ABCC GRID Promoter TFSite Comparison Page website tool and the outputs for each pattern were examined for possible coordinated regulation by shared regelems (regulatory elements). We found it informative to use this novel web tool for identifying, on a genomic scale, genes regulated by drug treatment. CONCLUSION: Improvement is needed in understanding the nature of the mutations responsible for controlling the maturation process and how these genes regulate downstream effects if there is to be better targeting of chemical interventions. Expanded implementation of the techniques and results reported here may better direct future efforts to improve treatment for diseases not restricted to APL.

MOTIVATION: Data mining tools are proposed to establish mechanistic connections between chemotypes and specific cellular functions. Drawing on a previous study that classified the cellular response patterns of growth inhibition measurements log( GI(50)) from the National Cancer Institute's (NCI's) anticancer screen, we have examined additional data for mRNA expression, sets of known molecular targets and mutational status against these same tumor cell lines to relate chemosensitivity more precisely to biochemical pathways. RESULTS: Our analysis finds that gene expression levels do not, in general, correlate with log(GI(50)) measurements, instead they reflect a generic toxic condition. Within the remaining set of non-generic conditions, examples were found where a correlation suggesting a biochemical basis for cellular cytotoxicity could be supported. These included reconfirmation of previously observed associations between mutant and wild-type status of p53, and chemosensitivity to alkylating agents, while extending these results to reveal associations with gamma-induced expressions of MDM2, WAF1 and GADD45, signals that were not apparent in measurements of basal mRNA expression levels for any of these genes. Additional examinations revealed that mRNA expression levels directly correlated with paclitaxel chemosensitivity to mitosis, while also identifying additional chemotypes as P-glycoprotein substrates. Our analysis revealed well-known direct associations between p16 mutant status and chemotypes implicated in cell cycle control, and extended these results to include expression levels for three additional tyrosine kinase proteins (TEK, transgelin and hCdc4). Links were also found that suggested associations between chemosensitivity and the endocrine, paracrine ligand-receptor loops, via expression of the adrenergic receptor, calcium second messenger pathways via expression levels of carbonic anhydrase and cellular communication pathways via fibrillin.