Pazopanib efficacy in renal cell carcinoma: evidence for predictive genetic markers in angiogenesis-related and exposure-related genes.

Purpose: Pazopanib, an oral angiogenesis inhibitor, is approved for the treatment of advanced renal cell carcinoma (RCC). Response to pazopanib monotherapy varies between patients, and no validated biomarkers predictive of treatment outcome have been identified. We tested the hypothesis that this variability is partially dependent on germline genetic variants that may affect pazopanib exposure or angiogenesis pathways.

Plasma cytokine and angiogenic factor profiling identifies markers associated with tumor shrinkage in early-stage non-small cell lung cancer patients treated with pazopanib.

There is an unmet need for pharmacodynamic and predictive biomarkers for antiangiogenic agents. Recent studies have shown that soluble vascular endothelial growth factor receptor 2 (sVEGFR2), VEGF, and several other soluble factors may be modulated by VEGF pathway inhibitors. We conducted a broad profiling of cytokine and angiogenic factors (CAF) to investigate the relationship between baseline CAF levels, CAF changes during treatment, and tumor shrinkage in early-stage non-small cell lung cancer (NSCLC) patients treated with pazopanib, an oral angiogenesis inhibitor targeting VEGFR, platelet-derived growth factor receptor, and c-kit.

The role of positive selection in determining the molecular cause of species differences in disease.

Background: Related species, such as humans and chimpanzees, often experience the same disease with varying degrees of pathology, as seen in the cases of Alzheimer's disease, or differing symptomatology as in AIDS. Furthermore, certain diseases such as schizophrenia, epithelial cancers and autoimmune disorders are far more frequent in humans than in other species for reasons not associated with lifestyle. Genes that have undergone positive selection during species evolution are indicative of functional adaptations that drive species differences.

A global pathway crosstalk network.

Motivation: Given the complex nature of biological systems, pathways often need to function in a coordinated fashion in order to produce appropriate physiological responses to both internal and external stimuli. Therefore, understanding the interaction and crosstalk between pathways is important for understanding the function of both cells and more complex systems.

Results: We have developed a computational approach to detect crosstalk among pathways based on protein interactions between the pathway components.

A comparison of statistical tests for detecting differential expression using Affymetrix oligonucleotide microarrays.

Signal quantification and detection of differential expression are critical steps in the analysis of Affymetrix microarray data. Many methods have been proposed in the literature for each of these steps. The goal of this paper is to evaluate several signal quantification methods (GCRMA, RSVD, VSN, MAS5, and Resolver) and statistical methods for differential expression (t test, Cyber-T, SAM, LPE, RankProducts, Resolver RatioBuild).

Inferring pathways from gene lists using a literature-derived network of biological relationships.

Motivation: A number of omic technologies such as transcriptional profiling, proteomics, literature searches, genetic association, etc. help in the identification of sets of important genes. A subset of these genes may act in a coordinated manner, possibly because they are part of the same biological pathway.

A comparison of statistical methods for analysis of high density oligonucleotide array data.

Motivation: Gene expression profiling has become an invaluable tool in functional genomics. A wide variety of statistical methods have been employed to analyze the data generated in experiments using Affymetrix GeneChip microarrays. It is important to understand the relative performance of these methods in terms of accuracy in detecting and quantifying relative gene expression levels and changes in gene expression.