Even though treatment of several types of solid tumors has improved in the past few years with the introduction of molecular targeted agents in the therapeutic armamentarium of the medical oncologist, response rates to these agents are generally modest. Increasing evidence is now revealing that genetic factors are affecting patients' response to these therapeutic agents as well as the frequency and intensity of toxic reactions. Importantly, pharmacogenetic analysis is now required for the administration of several molecular targeted agents in clinical practice. For the vast majority of these agents, however, data remain purely experimental. Herein, we provide an overview of the genetic changes (mutations and polymorphisms) that have been associated with response to treatment with anticancer molecular targeted agents. Special emphasis is given on molecules (monoclonal antibodies and tyrosine kinase inhibitors) that target critical mediators in the epidermal growth factor receptor (EGFR), the human epidermal growth factor receptor 2 (HER2/ERBB2/NEU) and the vascular endothelial growth factor receptor (VEGFR) pathways. The true clinical utility of these applications remains to be proven in future prospective, randomized clinical trials in large patient cohorts of all different ethnic backgrounds.

Lung carcinogenesis is considered to be the result of composite environmental, genetic and epigenetic changes. Despite the fact that many of the genetic alterations, including loss of heterozygocity in the 3p chromosome locus and point mutations in the tumor-suppressor genes TP53 and retinoblastoma (RB1), occur in nearly all histopathologic types of lung cancer, the frequency and the "timing" of their occurrence seems to differ between small-cell lung cancer (SCLC) cells, that are characterized by neuroendocrine differentiation, and non-small-cell lung cancer (NSCLC) cells. Although loss of cell-cycle control is the crucial molecular event in both types, the mechanism by which it provokes oncogenesis differs significantly between SCLC and NSCLC. Importantly, some of these molecular events, including DNA-damage response and epidermal growth factor receptor (EGFR) mutations are valuable in predicting response to conventional chemotherapy or molecular-targeted agents as well as in the prognosis of patients that harbor these alterations. In the current review we report on the best characterized histopathologic and genetic changes in NSCLC and SCLC in relation to each histological subtype and we discuss their predictive and prognostic implications.

Batch effects are the systematic non-biological differences between batches (groups) of samples in microarray experiments due to various causes such as differences in sample preparation and hybridization protocols. Previous work focused mainly on the development of methods for effective batch effects removal. However, their impact on cross-batch prediction performance, which is one of the most important goals in microarray-based applications, has not been addressed. This paper uses a broad selection of data sets from the Microarray Quality Control Phase II (MAQC-II) effort, generated on three microarray platforms with different causes of batch effects to assess the efficacy of their removal. Two data sets from cross-tissue and cross-platform experiments are also included. Of the 120 cases studied using Support vector machines (SVM) and K nearest neighbors (KNN) as classifiers and Matthews correlation coefficient (MCC) as performance metric, we find that Ratio-G, Ratio-A, EJLR, mean-centering and standardization methods perform better or equivalent to no batch effect removal in 89, 85, 83, 79 and 75% of the cases, respectively, suggesting that the application of these methods is generally advisable and ratio-based methods are preferred.

Systems biology has emerged as a major trend in biological research during the past decade. As living organisms are described in more and more detail, it aims at filling the gap between understanding basic molecular processes and complex biological systems in which new properties often emerge from the combination of these elementary processes. This approach culminates in the development of computer-based mathematical models of physiological and pathophysiological processes. We review the state of the art in dynamic modelling, with emphasis on two complementary approaches: the modelling of small systems that is mostly developed by academic teams and aims at understanding generic biological properties, and the modelling of large systems that is mostly implemented by industrial companies and aims at the generation of new therapeutic strategies. We also provide an example of such large-scale modelling applied to the identification of drug targets for neurodegeneration.

Heart disease represents the primary cause of death worldwide, with mortality rates being predicted to remain constant within the next couple of decades. Cardiac disease treatment currently includes the administration of drugs, predominantly aiming at improving heart performance, through controlling heart rhythm, blood pressure, as well as reducing cholesterol and blood clotting. Despite, however, the medical advances that have lead towards a better understanding of heart disease pathophysiology and the development of new therapeutic approaches, the degree of success of the available drug therapies varies among patients. The existence of polymorphisms in a number of genes has been shown to result in differences in pharmacokinetics, pharmacodynamics and drug metabolism and have therefore been associated with response to drug treatment. The occurrence of adverse drug reactions that may lead to drug-induced toxicity represents another factor influencing outcome of therapeutic treatment. While the influence of genetic polymorphisms in patient's response to heart disease drugs is being unveiled, the rapidly evolving field of pharmacogenetics is promising to aid clinicians in choosing the best suited drug/dose for each patient and the pharmaceutical companies in the design of better targeted, more effective new chemical compounds. In the near future individualized, targeted therapy will become part of clinical care routine maximizing patient therapeutic benefits and minimizing risks of adverse effects.

We have used a new ApoA-I transgenic mouse model to identify by global gene expression profiling, candidate genes that affect lipid and lipoprotein metabolism in response to fenofibrate treatment. Multilevel bioinformatical analysis and stringent selection criteria (2-fold change, 0% false discovery rate) identified 267 significantly changed genes involved in several molecular pathways. The fenofibrate-treated group did not have significantly altered levels of hepatic human APOA-I mRNA and plasma ApoA-I compared with the control group. However, the treatment increased cholesterol levels to 1.95-fold mainly due to the increase in high-density lipoprotein (HDL) cholesterol. The observed changes in HDL are associated with the upregulation of genes involved in phospholipid biosynthesis and lipid hydrolysis, as well as phospholipid transfer protein. Significant upregulation was observed in genes involved in fatty acid transport and beta-oxidation, but not in those of fatty acid and cholesterol biosynthesis, Krebs cycle and gluconeogenesis. Fenofibrate changed significantly the expression of seven transcription factors. The estrogen receptor-related gamma gene was upregulated 2.36-fold and had a significant positive correlation with genes of lipid and lipoprotein metabolism and mitochondrial functions, indicating an important role of this orphan receptor in mediating the fenofibrate-induced activation of a specific subset of its target genes.