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.

Tissue- and development-stage–specific mRNA and heterogeneous CNV signatures of human ribosomal proteins in normal and cancer samples

Abstract: We give results from a detailed analysis of human Ribosomal Protein (RP) levels in normal and cancer samples and cell lines from large mRNA, copy number variation and ribosome profiling datasets. After normalizing total RP mRNA levels per sample, we find highly consistent tissue specific RP mRNA signatures in normal and tumor samples. Multiple RP mRNA-subtypes exist in several cancers, with significant survival and genomic differences. Some RP mRNA variations among subtypes correlate with copy number loss of RP genes. In kidney cancer, RP subtypes map to molecular subtypes related to cell-of-origin. Pan-cancer analysis of TCGA data showed widespread single/double copy loss of RP genes, without significantly affecting survival. In several cancer cell lines, CRISPR-Cas9 knockout of RP genes did not affect cell viability. Matched RP ribosome profiling and mRNA data in humans and rodents stratified by tissue and development stage and were strongly correlated, showing that RP translation rates were proportional to mRNA levels. In a small dataset of human adult and fetal tissues, RP protein levels showed development stage and tissue specific heterogeneity of RP levels. Our results suggest that heterogeneous RP levels play a significant functional role in cellular physiology, in both normal and disease states.

Synthetic Lethal Interaction between Oncogenic KRAS Dependency and STK33 Suppression in Human Cancer Cells

Abstract: An alternative to therapeutic targeting of oncogenes is to perform "synthetic lethality" screens for genes that are essential only in the context of specific cancer-causing mutations. We used high-throughput RNA interference (RNAi) to identify synthetic lethal interactions in cancer cells harboring mutant KRAS, the most commonly mutated human oncogene. We find that cells that are dependent on mutant KRAS exhibit sensitivity to suppression of the serine/threonine kinase STK33 irrespective of tissue origin, whereas STK33 is not required by KRAS-independent cells. STK33 promotes cancer cell viability in a kinase activity-dependent manner by regulating the suppression of mitochondrial apoptosis mediated through S6K1-induced inactivation of the death agonist BAD selectively in mutant KRAS-dependent cells. These observations identify STK33 as a target for treatment of mutant KRAS-driven cancers and demonstrate the potential of RNAi screens for discovering functional dependencies created by oncogenic mutations that may enable therapeutic intervention for cancers with "undruggable" genetic alterations.

Across platform and multiple dataset molecular classification

Abstract: Systems and methods for across platform and multiple dataset classification. In one embodiment the systems combine a Large Bayes classification framework, constructed from discovered itemsets or common patterns of data, with a definition of combined relative features to represent the original values. One realization of this method is that different datasets representing the same biological system display some amount of invariant biological characteristics independent of the idiosyncrasies of sample sources, preparation and the technological platform used to obtain the measurements. These invariant biological characteristics, when captured and exposed, can provide the basis to build robust, general and accurate classification models based on reproducible biological behavior

SPT6 promotes epidermal differentiation and blockade of an intestinal-like phenotype through control of transcriptional elongation

Abstract: In adult tissue, stem and progenitor cells must tightly regulate the balance between proliferation and differentiation to sustain homeostasis. How this exquisite balance is achieved is an area of active investigation. Here, we show that epidermal genes, including ~30% of induced differentiation genes already contain stalled Pol II at the promoters in epidermal stem and progenitor cells which is then released into productive transcription elongation upon differentiation. Central to this process are SPT6 and PAF1 which are necessary for the elongation of these differentiation genes. Upon SPT6 or PAF1 depletion there is a loss of human skin differentiation and stratification. Unexpectedly, loss of SPT6 also causes the spontaneous transdifferentiation of epidermal cells into an intestinal-like phenotype due to the stalled transcription of the master regulator of epidermal fate P63. Our findings suggest that control of transcription elongation through SPT6 plays a prominent role in adult somatic tissue differentiation and the inhibition of alternative cell fate choices.

Genomic and proteomic alterations in desmoplastic small round blue- cell tumors

Abstract: PurposeDesmoplastic small round blue-cell tumors (DSRCTs) are sarcomas that contain the t(11;22) (p13;q12) translocation EWS-WT1 fusion protein. Because this is a rare tumor type, prospective clinical trials in DSRCT are challenging. Patients are treated in a manner similar to those with Ewing sarcoma; however, differences in prognosis and clinical presentation suggest fundamental differences in biology and potentially different therapeutic implications. This study aimed to characterize the molecular characteristics of DSRCT tumors to explore unique therapeutic options for this extremely rare and aggressive cancer type.MethodsThirty-five DSRCT tumors were assessed using next-generation sequencing, protein expression (immunohistochemistry), and gene amplification (chromogenic in situ hybridization or fluorescence in situ hybridization). Three patients had tumor mutational load, which was calculated as somatic nonsynonymous missense mutations sequenced with a 592-gene panel. Gene expression data were obtain...

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