RESUMEN
With multiple omics strategies being applied to several cancer genomics projects, researchers have the opportunity to develop a rational planning of targeted cancer therapy. The investigation of such numerous and diverse pharmacogenomic datasets is a complex task. It requires biological knowledge and skills on a set of tools to accurately predict signaling network and clinical outcomes. Herein, we describe Web-based in silico approaches user friendly for exploring integrative studies on cancer biology and pharmacogenomics. We briefly explain how to submit a query to cancer genome databases to predict which genes are significantly altered across several types of cancers using CBioPortal. Moreover, we describe how to identify clinically available drugs and potential small molecules for gene targeting using CellMiner. We also show how to generate a gene signature and compare gene expression profiles to investigate the complex biology behind drug response using Connectivity Map. Furthermore, we discuss on-going challenges, limitations and new directions to integrate molecular, biological and epidemiological information from oncogenomics platforms to create hypothesis-driven projects. Finally, we discuss the use of Patient-Derived Xenografts models (PDXs) for drug profiling in vivo assay. These platforms and approaches are a rational way to predict patient-targeted therapy response and to develop clinically relevant small molecules drugs.
RESUMEN
Many molecular mechanisms in complex biological processes of diseases cannot be fully understood without direct visualization. In the last decades, advances in imaging principles and technologies have expanded our ability to capture and analyze the morphology of tissues and cells' components on conventional widefield optical and fluorescence microscopies and their derivatives confocal and multiphoton fluorescence laser-scanning microscopes, as well as transmission electron microscopes. Innovative imaging technologies are now emerging for constructing fine structural features, precise localization and the dynamic interplay of single and macromolecular assemblies that drive cell growth, differentiation and cell death as well as stromal and chromatin remodeling within many cellular context. The newer super-resolution microscopies capture images with unprecedented sensitivity and clarity allowing the exploration of interactions between individual molecules with a distance resolution as low as 20 nm. But these techniques are not robust enough to quantitate molecules on a genome-wide scale. Mass spectrometry imaging is a high-throughput chemical imaging technique for the identification, quantitation and distribution of proteins, lipids and chemical metabolites at picomol level within a single-cell and complex multicellular tissue. Here we provide an overview on imaging instrumentations and computational platforms to store, data mining, analyze and retrieving genomic, proteomic and immunohistochemistry digital image information which are available for multilevel academic-private collaborations. The expansion of these data sets will lead to a merge picture from it we will retrieve knowledge for more rational-design systems to basic and clinical research in near future.