Pablo Tamayo

Bio: Pablo Tamayo is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Gene expression profiling & Cancer. The author has an hindex of 72, co-authored 177 publications receiving 97318 citations. Previous affiliations of Pablo Tamayo include University of California, Berkeley & Harvard University.

Modelling bistable tumour population dynamics to design effective treatment strategies

Abstract: Despite recent advances in targeted drugs and immunotherapy, cancer remains "the emperor of all maladies" due to inevitable emergence of resistance. Drug resistance is thought to be driven by mutations and/or dynamic plasticity that deregulate pathway activities and regulatory programs of a highly heterogeneous tumour. In this study, we propose a modelling framework to simulate population dynamics of heterogeneous tumour cells with reversible drug resistance. Drug sensitivity of a tumour cell is determined by its internal states, which are demarcated by coordinated activities of multiple interconnected oncogenic pathways. Transitions between cellular states depend on the effects of targeted drugs and regulatory relations between the pathways. Under this framework, we build a simple model to capture drug resistance characteristics of BRAF-mutant melanoma, where two cell states are described by two mutually inhibitory - main and alternative - pathways. We assume that cells with an activated main pathway are proliferative yet sensitive to the BRAF inhibitor, and cells with an activated alternative pathway are quiescent but resistant to the drug. We describe a dynamical process of tumour growth under various drug regimens using the explicit solution of mean-field equations. Based on these solutions, we compare efficacy of three treatment strategies: static treatments with continuous and constant dosages, periodic treatments with regular intermittent phases and drug holidays, and treatments derived from optimal control theory (OCT). Based on these analysis, periodic treatments outperform static treatments with a considerable margin, while treatments based on OCT outperform the best periodic treatment. Our results provide insights regarding optimal cancer treatment modalities for heterogeneous tumours, and may guide the development of optimal therapeutic strategies to circumvent drug resistance and due to tumour plasticity.

The multikinase inhibitor RXDX-105 is effective against neuroblastoma in vitro and in vivo.

Abstract: Neuroblastoma is the most common extracranial solid tumor of childhood and accounts for 15% of all pediatric cancer-related deaths. New therapies are needed to improve outcomes for children with high-risk and relapsed tumors. Inhibitors of the RET kinase and the RAS-MAPK pathway have previously been shown to be effective against neuroblastoma, suggesting that combined inhibition may have increased efficacy. RXDX-105 is a small molecule inhibitor of multiple kinases, including the RET and BRAF kinases. We found that treatment of neuroblastoma cells with RXDX-105 resulted in a significant decrease in cell viability and proliferation in vitro and in tumor growth and tumor vascularity in vivo. Treatment with RXDX-105 inhibited RET phosphorylation and phosphorylation of the MEK and ERK kinases in neuroblastoma cells and xenograft tumors, and RXDX-105 treatment induced both apoptosis and cell cycle arrest. RXDX-105 also showed enhanced efficacy in combination with 13-cis-retinoic acid, which is currently a component of maintenance therapy for children with high-risk neuroblastoma. Our results demonstrate that RXDX-105 shows promise as a novel therapeutic agent for children with high-risk and relapsed neuroblastoma.

Classification of samples, and identification of unknown class

Abstract: PROBLEM TO BE SOLVED: To provide a method for identifying an unknown class basing on the gene expression by sorting genes depending on a degree of their expression in a sample correlating to the class identification, determining whether the correlation is stronger than that which can be obtained by an accident, followed by identifying a set of genes. SOLUTION: This method comprises the steps of sorting genes depending on a degree of their expression in a sample correlating to the class identification such as known class identification and disease class identification such as cancer class identification, determining whether or not the correlation is stronger than that which can be obtained by an accident, wherein a gene whose expression is correlated to stronger class identification than that which can be obtained by an accident is a useful gene which gives information, and identifying a set of useful genes which give information. The cancer class identification is selected from leukemia class identification, intracranial tumor class identification, and lymphoma class identification. A useful gene which gives information is C-myb, proteasome-iota and the like.

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks

Abstract: Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

Abstract: DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.

limma powers differential expression analyses for RNA-sequencing and microarray studies

Abstract: limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

Regularization and variable selection via the elastic net

Abstract: Summary. We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, where strongly correlated predictors tend to be in or out of the model together.The elastic net is particularly useful when the number of predictors (p) is much bigger than the number of observations (n). By contrast, the lasso is not a very satisfactory variable selection method in the