Peter Salzman

Synergistic response to oncogenic mutations defines gene class critical to cancer phenotype

Understanding the molecular underpinnings of cancer is of critical importance to the development of targeted intervention strategies. Identification of such targets, however, is notoriously difficult and unpredictable. Malignant cell transformation requires the cooperation of a few oncogenic mutations that cause substantial reorganization of many cell features and induce complex changes in gene expression patterns. Genes critical to this multifaceted cellular phenotype have therefore only been identified after signalling pathway analysis or on an ad hoc basis. Our observations that cell transformation by cooperating oncogenic lesions depends on synergistic modulation of downstream signalling circuitry suggest that malignant transformation is a highly cooperative process, involving synergy at multiple levels of regulation, including gene expression. Here we show that a large proportion of genes controlled synergistically by loss-of-function p53 and Ras activation are critical to the malignant state of murine and human colon cells. Notably, 14 out of 24 ‘cooperation response genes’ were found to contribute to tumour formation in gene perturbation experiments. In contrast, only 1 in 14 perturbations of the genes responding in a non-synergistic manner had a similar effect. Synergistic control of gene expression by oncogenic mutations thus emerges as an underlying key to malignancy, and provides an attractive rationale for identifying intervention targets in gene networks downstream of oncogenic gain- and loss-of-function mutations.

A MULTIVARIATE EXTENSION OF THE GENE SET ENRICHMENT ANALYSIS

A test-statistic typically employed in the gene set enrichment analysis (GSEA) prevents this method from being genuinely multivariate. In particular, this statistic is insensitive to changes in the correlation structure of the gene sets of interest. The present paper considers the utility of an alternative test-statistic in designing the confirmatory component of the GSEA. This statistic is based on a pertinent distance between joint distributions of expression levels of genes included in the set of interest. The null distribution of the proposed test-statistic, known as the multivariate N-statistic, is obtained by permuting group labels. Our simulation studies and analysis of biological data confirm the conjecture that the N-statistic is a much better choice for multivariate significance testing within the framework of the GSEA. We also discuss some other aspects of the GSEA paradigm and suggest new avenues for future research.

Utility of correlation measures in analysis of gene expression.

SummaryThe role of the correlation structure of gene expression data are two-fold: It is a source of complications and useful information at the same time. Ignoring the strong stochastic dependence between gene expression levels in statistical methodologies for microarray data analysis may deteriorate their performance. However, there is a host of valuable information in the correlation structure that deserves a closer look. A proper use of correlation measures can remedy deficiencies of currently practiced methods that are focused too heavily on strong effects in terms of differential expression of genes. The present paper discusses the utility of correlation measures in microarray data analysis and gene regulatory network reconstruction, along with various pitfalls in both research areas that have been uncovered in methodological studies. These issues have broad applicability to all genomic studies examining the biology, diagnosis, and treatment of neurological disorders.

Second harmonic generation microscopy reveals altered collagen microstructure in usual interstitial pneumonia versus healthy lung

BackgroundIt is not understood why some pulmonary fibroses such as cryptogenic organizing pneumonia (COP) respond well to treatment, while others like usual interstitial pneumonia (UIP) do not. Increased understanding of the structure and function of the matrix in this area is critical to improving our understanding of the biology of these diseases and developing novel therapies. The objectives herein are to provide new insights into the underlying collagen- and matrix-related biological mechanisms driving COP versus UIP.MethodsTwo-photon second harmonic generation (SHG) and excitation fluorescence microscopies were used to interrogate and quantify differences between intrinsic fibrillar collagen and elastin matrix signals in healthy, COP, and UIP lung.ResultsCollagen microstructure was different in UIP versus healthy lung, but not in COP versus healthy, as indicated by the ratio of forward-to-backward propagating SHG signal (FSHG/BSHG). This collagen microstructure as assessed by FSHG/BSHG was also different in areas with preserved alveolar architecture adjacent to UIP fibroblastic foci or honeycomb areas versus healthy lung. Fibrosis was evidenced by increased col1 and col3 content in COP and UIP versus healthy, with highest col1:col3 ratio in UIP. Evidence of elastin breakdown (i.e. reduced mature elastin fiber content), and increased collagen:mature elastin ratios, were seen in COP and UIP versus healthy.ConclusionsFibrillar collagen’s subresolution structure (i.e. “microstructure”) is altered in UIP versus COP and healthy lung, which may provide novel insights into the biological reasons why unlike COP, UIP is resistant to therapies, and demonstrates the ability of SHG microscopy to potentially distinguish treatable versus intractable pulmonary fibroses.

Plasminogen Activator Inhibitor-2 Polymorphism Associates with Recurrent Coronary Event Risk in Patients with High HDL and C-Reactive Protein Levels

The objective of this work was to investigate whether fibrinolysis plays a role in establishing recurrent coronary event risk in a previously identified group of postinfarction patients. This group of patients was defined as having concurrently high levels of high-density lipoprotein cholesterol (HDL-C) and C-reactive protein (CRP) and was previously demonstrated to be at high-risk for recurrent coronary events. Potential risk associations of a genetic polymorphism of plasminogen activator inhibitor-2 (PAI-2) were probed as well as potential modulatory effects on such risk of a polymorphism of low-density lipoprotein receptor related protein (LRP-1), a scavenger receptor known to be involved in fibrinolysis in the context of cellular internalization of plasminogen activator/plansminogen activator inhibitor complexes. To this end, Cox multivariable modeling was performed as a function of genetic polymorphisms of PAI-2 (SERPINB, rs6095) and LRP-1 (LRP1, rs1800156) as well as a set of clinical parameters, blood biomarkers, and genetic polymorphisms previously demonstrated to be significantly and independently associated with risk in the study population including cholesteryl ester transfer protein (CETP, rs708272), p22phox (CYBA, rs4673), and thrombospondin-4 (THBS4, rs1866389). Risk association was demonstrated for the reference allele of the PAI-2 polymorphism (hazard ratio 0.41 per allele, 95% CI 0.20-0.84, p=0.014) along with continued significant risk associations for the p22phox and thrombospondin-4 polymorphisms. Additionally, further analysis revealed interaction of the LRP-1 and PAI-2 polymorphisms in generating differential risk that was illustrated using Kaplan-Meier survival analysis. We conclude from the study that fibrinolysis likely plays a role in establishing recurrent coronary risk in postinfarction patients with concurrently high levels of HDL-C and CRP as manifested by differential effects on risk by polymorphisms of several genes linked to key actions involved in the fibrinolytic process.

Gene signature critical to cancer phenotype as a paradigm for anticancer drug discovery.

Malignant cell transformation commonly results in the deregulation of thousands of cellular genes, an observation that suggests a complex biological process and an inherently challenging scenario for the development of effective cancer interventions. To better define the genes/pathways essential to regulating the malignant phenotype, we recently described a novel strategy based on the cooperative nature of carcinogenesis that focuses on genes synergistically deregulated in response to cooperating oncogenic mutations. These so-called ‘cooperation response genes’ (CRGs) are highly enriched for genes critical for the cancer phenotype, thereby suggesting their causal role in the malignant state. Here, we show that CRGs have an essential role in drug-mediated anticancer activity and that anticancer agents can be identified through their ability to antagonize the CRG expression profile. These findings provide proof-of-concept for the use of the CRG signature as a novel means of drug discovery with relevance to underlying anticancer drug mechanisms.

Radiation decreases murine small intestinal HCO3− secretion

Purpose: While secretagogue-induced diarrhea is rich in chloride (Cl−) and bicarbonate (HCO3 −) anions, little is known about diarrhea or its anionic composition following irradiation. We performed studies to characterize the differences between cyclic adenosine monophosphate (cAMP)-stimulated anion secretions in irradiated and non-irradiated mice. Materials and methods: HCO3 − secretion was examined in basal, cAMP-stimulated, and irradiated jejunal tissues from BALB/c (Bagg albino) mice. The abdomens of the mice were γ-irradiated using a caesium-137 source. Results: Ussing-chamber experiments performed in an HCO3−-containing, Cl−-free solution on the bath side showed inhibition of HCO3− in irradiated mice. Non-irradiated mice exhibited bumetanide-sensitive and insensitive current, while irradiated mice displayed bumetanide-sensitive current. pH-stat experiments showed inhibition of basal and cAMP-stimulated HCO3− secretions in irradiated mice. Immunohistochemistry and Western blot analysis displayed a sodium-bicarbonate cotransporter expression in the villus and not the crypt of non-irradiated mice, while its expression and protein levels decreased in irradiated mice. Conclusions: Anion secretions in irradiated mice, being primarily Cl− and minimally HCO3−, differ from that of secretagogue-induced anion secretions. Understanding anion loss will help us correct electrolyte imbalances, while reduced HCO3− secretion in the upper-gastrointestinal tract might also have implications for irradiation-induced nausea and vomiting.

Using Complexity for the Estimation of Bayesian Networks

Statistical inference of graphical models has become an important tool in the reconstruction of biological networks of the type which model, for example, gene regulatory interactions. In particular, the construction of a score-based Bayesian posterior density over the space of models provides an intuitive and computationally feasible method of assessing model uncertainty and of assigning statistical confidence to structural features. One problem which frequently occurs with this approach is the tendency to overestimate the degree of model complexity. Spurious graphical features obtained in this way may affect the inference in unpredictable ways, even when using scoring techniques, such as the Bayesian Information Criterion (BIC), that are specifically designed to compensate for overfitting.In this article we propose a simple adjustment to a BIC-based scoring procedure. The method proceeds in two steps. In the first step we derive an independent estimate of the parametric complexity of the model. In the second we modify the BIC score so that the mean parametric complexity of the posterior density is equal to the estimated value. The method is applied to a set of test networks, and to a collection of genes from the yeast genome known to possess regulatory relationships. A Bayesian network model with binary responses is employed. In the examples considered, we find that the number of spurious graph edges inferred is reduced, while the effect on the identification of true edges is minimal.

Using a Bayesian Posterior Density in the Design of Perturbation Experiments for Network Reconstruction

Gene perturbation experiments are commonly used in the reconstruction of gene regulatory networks. Because such experiments are often difficult to perform, it is important to predict on a rational basis those experiments likely to result in the greatest resolution of model uncertainty. When a method for constructing Bayesian posterior densities on the space of network models is available, this provides a means with which to estimate the expected reduction in entropy that would result from a given perturbation experiment. We define an algorithm for selecting perturbation experiments based on this idea, and demonstrate it using a simulation study using a Bayesian network model.

Influences on plasminogen activator inhibitor-2 polymorphism-associated recurrent cardiovascular disease risk in patients with high HDL cholesterol and inflammation

BACKGROUND AND AIMS Evidence continues to accumulate that athero-protective effects of high-density lipoprotein (HDL) depend to some degree on effective HDL functionality and that such functionality can become degraded in the setting of chronic inflammation. To investigate this issue, we have studied a group of post-myocardial infarction patients with high levels of C-reactive protein as an indicator of chronic inflammation and with concurrently high levels of HDL cholesterol. For these patients we have demonstrated high-risk for recurrent cardiac events as well as a strong association of risk with a polymorphism of the gene (SERPINB2) for plasminogen activator inhibitor-2 (PAI-2) presumptively reflective of an important role for fibrinolysis in risk. However, additional processes might be involved. The current work sought to characterize processes underlying how PAI-2 might be involved in the generation of risk. METHODS Multivariate population data were leveraged using Bayesian network modeling, a graphical probabilistic approach for knowledge discovery, to generate networks reflective of influences on PAI-2 polymorphism-associated risk. RESULTS Modeling results revealed three individual networks centering on the PAI-2 polymorphism with specific features providing information relating to how the polymorphism might associate with risk. These included racial dependency, platelet clot initiation and propagation, oxidative stress, inflammation effects on HDL metabolism and coagulation, and induction and termination of fibrinolysis. CONCLUSIONS Beyond direct association of a PAI-2 polymorphism with recurrent risk in post-myocardial infarction patients, results suggest that PAI-2 likely plays a key role leading to risk through multiple pathophysiologic processes. Such knowledge could potentially be valuable with individualization of patient care.