Showing papers by "Pablo Tamayo published in 2013"
TL;DR: It is shown that 5q amplification leads to overexpression of the SQSTM1 oncogene in ccRCC lines and tumors, and the selection pressure to amplify 5q inccRCC is driven, at least partly, by SQ STM1.
134 citations
TL;DR: This work uses a computational method, Analytic Technique for Assessment of RNAi by Similarity (ATARiS), that takes advantage of patterns in RNAi data across multiple samples in order to enrich for RNAi reagents whose phenotypic effects relate to suppression of their intended targets and produces quantitative, gene-level phenotype values, which provide an intuitive measure of the effect of gene suppression in each sample.
Abstract: Genome-scale RNAi libraries enable the systematic interrogation of gene function. However, the interpretation of RNAi screens is complicated by the observation that RNAi reagents designed to suppress the mRNA transcripts of the same gene often produce a spectrum of phenotypic outcomes due to differential on-target gene suppression or perturbation of off-target transcripts. Here we present a computational method, Analytic Technique for Assessment of RNAi by Similarity (ATARiS), that takes advantage of patterns in RNAi data across multiple samples in order to enrich for RNAi reagents whose phenotypic effects relate to suppression of their intended targets. By summarizing only such reagent effects for each gene, ATARiS produces quantitative, gene-level phenotype values, which provide an intuitive measure of the effect of gene suppression in each sample. This method is robust for data sets that contain as few as 10 samples and can be used to analyze screens of any number of targeted genes. We used this analytic approach to interrogate RNAi data derived from screening more than 100 human cancer cell lines and identified HNF1B as a transforming oncogene required for the survival of cancer cells that harbor HNF1B amplifications. ATARiS is publicly available at http://broadinstitute.org/ataris.
132 citations
TL;DR: The development of a high-throughput platform for measuring radiation survival in vitro and its validation in comparison with conventional clonogenic radiation survival analysis is reported and pathways implicated in cell survival, genotoxic stress, detoxification, and innate and adaptive immunity are identified as key correlates of radiation sensitivity.
Abstract: Radiation therapy is one of the mainstays of anti-cancer treatment, but the relationship between the radiosensitivity of cancer cells and their genomic characteristics is still not well-defined. Here we report the development of a high-throughput platform for measuring radiation survival in vitro and its validation by comparison to conventional clonogenic radiation survival analysis. We combined results from this high-throughput assay with genomic parameters in cell lines from squamous cell lung carcinoma, which is standardly treated by radiation therapy, to identify parameters that predict radiation sensitivity. We showed that activation of NFE2L2, a frequent event in lung squamous cancers, confers radiation resistance. An expression-based, in silico screen nominated inhibitors of PI3K as NFE2L2 antagonists. We showed that the selective PI3K inhibitor, NVP-BKM120, both decreased NRF2 protein levels and sensitized NFE2L2 or KEAP1 mutant cells to radiation. We then combined results from this high-throughput assay with single-sample gene set enrichment analysis (ssGSEA) of gene expression data. The resulting analysis identified pathways implicated in cell survival, genotoxic stress, detoxification, and innate and adaptive immunity as key correlates of radiation sensitivity. The integrative, high-throughput methods shown here for large-scale profiling of radiation survival and genomic features of solid-tumor derived cell lines should facilitate tumor radiogenomics and the discovery of genotype-selective radiation sensitizers and protective agents.
113 citations
TL;DR: These studies identify TLOC1 and SKIL as driver genes in 3q26 and suggest that cooperating genes may be coamplified in other regions with somatic copy number gain, providing evidence that regions of somaticcopy number gain may harbor cooperating genes of different but complementary functions.
Abstract: 3q26 is frequently amplified in several cancer types with a common amplified region containing 20 genes. To identify cancer driver genes in this region, we interrogated the function of each of these genes by loss- and gain-of-function genetic screens. Specifically, we found that TLOC1 (SEC62) was selectively required for the proliferation of cell lines with 3q26 amplification. Increased TLOC1 expression induced anchorage independent growth and a second 3q26 gene, SKIL (SNON), facilitated cell invasion in immortalized human mammary epithelial cells. Expression of both TLOC1 and SKIL induced subcutaneous tumor growth. Proteomic studies demonstrated that TLOC1 binds to DDX3X, which is essential for TLOC1-induced transformation and affected protein translation. SKIL induced invasion through up-regulation of SLUG (SNAI2) expression. Together, these studies identify TLOC1 and SKIL as driver genes at 3q26 and more broadly suggest that cooperating genes may be co-amplified in other regions with somatic copy number gain.
76 citations
TL;DR: The data indicate high ATad2 expression is a marker of aggressive endometrial cancers, and suggest specific inhibitors of ATAD2 may have therapeutic utility in these and other MYC-dependent cancers.
Abstract: Chromosome 8q24 is the most commonly amplified region across multiple cancer types, and the typical length of the amplification suggests that it may target additional genes to MYC. To explore the roles of the genes most frequently included in 8q24 amplifications, we analyzed the relation between copy number alterations and gene expression in three sets of endometrial cancers (N = 252); and in glioblastoma, ovarian, and breast cancers profiled by TCGA. Among the genes neighbouring MYC, expression of the bromodomain-containing gene ATAD2 was the most associated with amplification. Bromodomain-containing genes have been implicated as mediators of MYC transcriptional function, and indeed ATAD2 expression was more closely associated with expression of genes known to be upregulated by MYC than was MYC itself. Amplifications of 8q24, expression of genes downstream from MYC, and overexpression of ATAD2 predicted poor outcome and increased from primary to metastatic lesions. Knockdown of ATAD2 and MYC in seven endometrial and 21 breast cancer cell lines demonstrated that cell lines that were dependent on MYC also depended upon ATAD2. These same cell lines were also the most sensitive to the histone deacetylase (HDAC) inhibitor Trichostatin-A, consistent with prior studies identifying bromodomain-containing proteins as targets of inhibition by HDAC inhibitors. Our data indicate high ATAD2 expression is a marker of aggressive endometrial cancers, and suggest specific inhibitors of ATAD2 may have therapeutic utility in these and other MYC-dependent cancers.
75 citations
Patent•
01 May 2013TL;DR: In this paper, the authors propose a method for projection mining, which consists of performing a first projection on a first data object of a first type comprising a plurality of data entries and a second data object consisting of a second type including data entries, and then performing a second projection of the definitions of the attributes of the first object and the definition of the second object into a space of meta-attributes based on semantic relationships among them.
Abstract: A method for projection mining comprises performing a first projection on a first data object of a first type comprising a plurality of data entries and a second data object of a second type comprising a plurality of data entries to create definitions of attributes of the first data object and definitions of attributes of the second data object, performing a second projection of the definitions of the attributes of the first data object and the definitions of the attributes of the second data object into a space of meta-attributes based on semantic relationships among the attributes of the first data object and the second data object, learning relationships between the space of meta-attributes formed by the projections of the first data object and the second data object and a space of meta-attributes relating to new data not included in the first data object and the second data object, and generating at least one new data object of the first or second type based on the new data using the learned relationships.
15 citations