Showing papers by "Pablo Tamayo published in 2010"
Harvard University1, University of North Carolina at Chapel Hill2, University of California, Berkeley3, Washington University in St. Louis4, Lawrence Berkeley National Laboratory5, University of California, San Francisco6, Mayo Clinic7, Memorial Sloan Kettering Cancer Center8, SRA International9, Walter and Eliza Hall Institute of Medical Research10
TL;DR: A robust gene expression-based molecular classification of GBM into Proneural, Neural, Classical, and Mesenchymal subtypes is described and multidimensional genomic data is integrated to establish patterns of somatic mutations and DNA copy number.
5,764 citations
Harvard University1, University of North Carolina at Chapel Hill2, University of California, Berkeley3, Washington University in St. Louis4, Lawrence Berkeley National Laboratory5, University of California, San Francisco6, Mayo Clinic7, Memorial Sloan Kettering Cancer Center8, SRA International9, Walter and Eliza Hall Institute of Medical Research10
TL;DR: The Cancer Genome Atlas Network recently cataloged recurrent genomic abnormalities in glioblastoma multiforme (GBM) and proposed a robust gene expression-based molecular classification of GBM into Proneural, Neural, Classical, and Mesenchymal subtypes as discussed by the authors.
Abstract: The Cancer Genome Atlas Network recently cataloged recurrent genomic abnormalities in glioblastoma multiforme (GBM). We describe a robust gene expression-based molecular classification of GBM into Proneural, Neural, Classical, and Mesenchymal subtypes and integrate multidimensional genomic data to establish patterns of somatic mutations and DNA copy number. Aberrations and gene expression of EGFR, NF1, and PDGFRA/IDH1 each define the Classical, Mesenchymal, and Proneural subtypes, respectively. Gene signatures of normal brain cell types show a strong relationship between subtypes and different neural lineages. Additionally, response to aggressive therapy differs by subtype, with the greatest benefit in the Classical subtype and no benefit in the Proneural subtype. We provide a framework that unifies transcriptomic and genomic dimensions for GBM molecular stratification with important implications for future studies.
4,464 citations
25 Apr 2010
TL;DR: This work presents a direct multivariate finite mixture modeling approach, using skew and heavy-tailed distributions, to address the complexities of flow cytometric analysis and to deal with high-dimensional cytometric data without the need for projection or transformation.
Abstract: Flow cytometry is widely used for single cell interrogation of surface and intracellular protein expression by measuring fluorescence intensity of fluorophore-conjugated reagents We focus on the recently developed procedure of Pyne et al (2009, Proceedings of the National Academy of Sciences USA 106, 8519-8524) for automated high- dimensional flow cytometric analysis called FLAME (FLow analysis with Automated Multivariate Estimation) It introduced novel finite mixture models of heavy-tailed and asymmetric distributions to identify and model cell populations in a flow cytometric sample This approach robustly addresses the complexities of flow data without the need for transformation or projection to lower dimensions It also addresses the critical task of matching cell populations across samples that enables downstream analysis It thus facilitates application of flow cytometry to new biological and clinical problems To facilitate pipelining with standard bioinformatic applications such as high-dimensional visualization, subject classification or outcome prediction, FLAME has been incorporated with the GenePattern package of the Broad Institute Thereby analysis of flow data can be approached similarly as other genomic platforms We also consider some new work that proposes a rigorous and robust solution to the registration problem by a multi-level approach that allows us to model and register cell populations simultaneously across a cohort of high-dimensional flow samples This new approach is called JCM (Joint Clustering and Matching) It enables direct and rigorous comparisons across different time points or phenotypes in a complex biological study as well as for classification of new patient samples in a more clinical setting.
354 citations
TL;DR: The studies reveal that SNF5 is a key mediator of Hh signaling and that aberrant activation of GLI1 is a previously undescribed targetable mechanism contributing to the growth of MRT cells.
Abstract: Aberrant activation of the Hedgehog (Hh) pathway can drive tumorigenesis1. To investigate the mechanism by which glioma-associated oncogene family zinc finger-1 (GLI1), a crucial effector of Hh signaling2, regulates Hh pathway activation, we searched for GLI1-interacting proteins. We report that the chromatin remodeling protein SNF5 (encoded by SMARCB1, hereafter called SNF5), which is inactivated in human malignant rhabdoid tumors (MRTs), interacts with GLI1. We show that Snf5 localizes to Gli1-regulated promoters and that loss of Snf5 leads to activation of the Hh-Gli pathway. Conversely, re-expression of SNF5 in MRT cells represses GLI1. Consistent with this, we show the presence of a Hh-Gli–activated gene expression profile in primary MRTs and show that GLI1 drives the growth of SNF5-deficient MRT cells in vitro and in vivo. Therefore, our studies reveal that SNF5 is a key mediator of Hh signaling and that aberrant activation of GLI1 is a previously undescribed targetable mechanism contributing to the growth of MRT cells.
223 citations
TL;DR: It is found that the c-MYC oncoprotein coordinately regulates the expression of 13 different “poor-outcome” cancer signatures and functional inactivation of MYC in human breast cancer cells specifically inhibits distant metastasis in vivo and invasive behavior in vitro of these cells.
Abstract: Gene expression signatures are used in the clinic as prognostic tools to determine the risk of individual patients with localized breast tumors developing distant metastasis. We lack a clear understanding, however, of whether these correlative biomarkers link to a common biological network that regulates metastasis. We find that the c-MYC oncoprotein coordinately regulates the expression of 13 different “poor-outcome” cancer signatures. In addition, functional inactivation of MYC in human breast cancer cells specifically inhibits distant metastasis in vivo and invasive behavior in vitro of these cells. These results suggest that MYC oncogene activity (as marked by “poor-prognosis” signature expression) may be necessary for the translocation of poor-outcome human breast tumors to distant sites.
169 citations
TL;DR: This work focuses on the recently developed procedure for automated high- dimensional flow cytometry analysis called FLAME (FLow analysis with Automated Multivariate Estimation), which introduced novel finite mixture models of heavy-tailed and asymmetric distributions to identify and model cell populations in a flow cytometric sample.
Abstract: Flow cytometry is widely used for single cell interrogation of surface and intracellular protein expression by measuring fluorescence intensity of fluorophore-conjugated reagents. We focus on the recently developed procedure of Pyne et al. (2009, Proceedings of the National Academy of Sciences USA 106, 8519-8524) for automated high- dimensional flow cytometric analysis called FLAME (FLow analysis with Automated Multivariate Estimation). It introduced novel finite mixture models of heavy-tailed and asymmetric distributions to identify and model cell populations in a flow cytometric sample. This approach robustly addresses the complexities of flow data without the need for transformation or projection to lower dimensions. It also addresses the critical task of matching cell populations across samples that enables downstream analysis. It thus facilitates application of flow cytometry to new biological and clinical problems. To facilitate pipelining with standard bioinformatic applications such as high-dimensional visualization, subject classification or outcome prediction, FLAME has been incorporated with the GenePattern package of the Broad Institute. Thereby analysis of flow data can be approached similarly as other genomic platforms. We also consider some new work that proposes a rigorous and robust solution to the registration problem by a multi-level approach that allows us to model and register cell populations simultaneously across a cohort of high-dimensional flow samples. This new approach is called JCM (Joint Clustering and Matching). It enables direct and rigorous comparisons across different time points or phenotypes in a complex biological study as well as for classification of new patient samples in a more clinical setting.
122 citations