Copper-based chemotherapeutic compounds Casiopeínas, have been presented as able to promote selective programmed cell death in cancer cells, thus being proper candidates for targeted cancer therapy. DNA fragmentation and apoptosis–in a process mediated by reactive oxygen species–for a number of tumor cells, have been argued to be the main mechanisms. However, a detailed functional mechanism (a model) is still to be defined and interrogated for a wide variety of cellular conditions before establishing settings and parameters needed for their wide clinical application. In order to shorten the gap in this respect, we present a model proposal centered in the role played by intrinsic (or mitochondrial) apoptosis triggered by oxidative stress caused by the chemotherapeutic agent. This model has been inferred based on genome wide expression profiling in cervix cancer (HeLa) cells, as well as statistical and computational tests, validated via functional experiments (both in the same HeLa cells and also in a Neuroblastoma model, the CHP-212 cell line) and assessed by means of data mining studies.

Metabolic transformations have been reported as involved in neoplasms survival. This suggests a role of metabolic pathways as potential cancer pharmacological targets. Modulating tumor's energy production pathways may become a substantial research area for cancer treatment. The significant role of metabolic deregulation as inducing transcriptional instabilities and consequently whole-system failure, is thus of foremost importance. By using a data integration approach that combines experimental evidence for high-throughput genome wide gene expression, a non-equilibrium thermodynamics analysis, nonlinear correlation networks as well as database mining, we were able to outline the role that transcription factors MEF2C and MNDA may have as main master regulators in primary breast cancer phenomenology, as well as the possible interrelationship between malignancy and metabolic dysfunction. The present findings are supported by the analysis of 1191 whole genome gene expression experiments, as well as probabilistic inference of gene regulatory networks, and non-equilibrium thermodynamics of such data. Other evidence sources include pathway enrichment and gene set enrichment analyses, as well as motif comparison with a comprehensive gene regulatory network (of homologue genes) in Arabidopsis thaliana. Our key finding is that the non-equilibrium free energies provide a realistic description of transcription factor activation that when supplemented with gene regulatory networks made us able to find deregulated pathways. These analyses also suggest a novel potential role of transcription factor energetics at the onset of primary tumor development. Results are important in the molecular systems biology of cancer field, since deregulation and coupling mechanisms between metabolic activity and transcriptional regulation can be better understood by taking into account the way that master regulators respond to physicochemical constraints imposed by different phenotypic conditions.

IntroductionModern high-throughput genomic technologies have allowed the large-scale characterization of living organisms, involving the generation and interpretation of data at an unprecedented scale. Computational tools and mathematical algorithms have been created aiming to integrate, organize and mine the wealth of data generated. Technologies for the detection of different types of genomic alterations have been developed and applied to the analyses of living organisms and, in particular, cancer genomes. It is clear that studies based on a single technology are limited compared with the extent of knowledge that can be acquired using different technological platforms together. Hence, there is a need for systematic methodologies facilitating data management, visualization and integration. Such methodologies should aim to permit a proper analysis of the biological implications of findings, without sacrificing computational efficiency or mathematical and statistical rigour. With these purposes in mind, we have designed and implemented a data driven 3-state model for multidimensional data integration (3-MDI). ResultsLevel 1 and Level 2 data sets were pre-processed, and genes were selected in each platform based on the summary statistics presented in Table 1. Selected targets for mRNA expression and methylation were coded with our 3-state model {1,0, −1}, with 1 for upregulated or hypermethylation, 0 for no change, and −1 for downregulated or hypomethylation.The Level 3 somatic mutation data set was re-ordered by genes and coded in a 2-state format {0,1} rather than a {1,0, −1} 3-state format in order to avoid an ad hoc threshold for the classification of hypo/hyper mutation. The 2-state format represents the presence (1) or absence (0) of a mutation in a gene.Using these 3 platforms we could find up to 18 possible scenarios for further analysis (Fig. 1). Note that a single platform reports larger gene numbers as changing (e.g., pure gene expression cases {0,0, −1} with 1274 genes and {0,0,1} with 1418 genes), and numbers of genes changing are lower when combining multiple platforms and requiring congruent behavior, which is somehow exacerbated by to the fact that not all samples have measurements in all platforms.Background: Genomic technologies have allowed a large-scale molecular characterization of living organisms, involving the generation and interpretation of data at an unprecedented scale. Advanced platforms for the detection of different types of genomic alterations have been developed and applied to analyses of living organisms and, in particular, cancer genomes. It is clear now that studies based on a single platform are limited compared with the extent of knowledge gain possible when exploiting different platforms together. There is therefore a need for systematic methodologies facilitating data management, visualization, and integration.Materials and Methods: We present a 3-state model (3-MDI) that integrates several technological platforms, visualizing and prioritizing different biological scenarios, ...