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Results: 1 to 10 of 22

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Oncogenic Signaling Pathways in The Cancer Genome Atlas.
Sanchez-Vega F, Mina M, Armenia J, Chatila WK, Luna A, La KC, Dimitriadoy S, Liu DL, Kantheti HS, Saghafinia S, Chakravarty D, Daian F, Gao Q, Bailey MH, Liang WW, Foltz SM, Shmulevich I, Ding L, Heins Z, Ochoa A, Gross B, Gao J, Zhang H, Kundra R, Kandoth C, Bahceci I, Dervishi L, Dogrusoz U, Zhou W, Shen H, Laird PW, Way GP, Greene CS, Liang H, Xiao Y, Wang C, Iavarone A, Berger AH, Bivona TG, Lazar AJ, Hammer GD, Giordano T, Kwong LN, McArthur G, Huang C, Tward AD, Frederick MJ, McCormick F, Meyerson M, Cancer Genome Atlas Research Network, Van Allen EM, Cherniack AD, Ciriello G, Sander C, Schultz N
(2018) Cell 173: 321-337.e10
MeSH Terms: Databases, Genetic, Genes, Neoplasm, Humans, Neoplasms, Phosphatidylinositol 3-Kinases, Signal Transduction, Transforming Growth Factor beta, Tumor Suppressor Protein p53, Wnt Proteins
Show Abstract · Added October 30, 2019
Genetic alterations in signaling pathways that control cell-cycle progression, apoptosis, and cell growth are common hallmarks of cancer, but the extent, mechanisms, and co-occurrence of alterations in these pathways differ between individual tumors and tumor types. Using mutations, copy-number changes, mRNA expression, gene fusions and DNA methylation in 9,125 tumors profiled by The Cancer Genome Atlas (TCGA), we analyzed the mechanisms and patterns of somatic alterations in ten canonical pathways: cell cycle, Hippo, Myc, Notch, Nrf2, PI-3-Kinase/Akt, RTK-RAS, TGFβ signaling, p53 and β-catenin/Wnt. We charted the detailed landscape of pathway alterations in 33 cancer types, stratified into 64 subtypes, and identified patterns of co-occurrence and mutual exclusivity. Eighty-nine percent of tumors had at least one driver alteration in these pathways, and 57% percent of tumors had at least one alteration potentially targetable by currently available drugs. Thirty percent of tumors had multiple targetable alterations, indicating opportunities for combination therapy.
Copyright © 2018. Published by Elsevier Inc.
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MeSH Terms
Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics.
Ding L, Bailey MH, Porta-Pardo E, Thorsson V, Colaprico A, Bertrand D, Gibbs DL, Weerasinghe A, Huang KL, Tokheim C, Cortés-Ciriano I, Jayasinghe R, Chen F, Yu L, Sun S, Olsen C, Kim J, Taylor AM, Cherniack AD, Akbani R, Suphavilai C, Nagarajan N, Stuart JM, Mills GB, Wyczalkowski MA, Vincent BG, Hutter CM, Zenklusen JC, Hoadley KA, Wendl MC, Shmulevich L, Lazar AJ, Wheeler DA, Getz G, Cancer Genome Atlas Research Network
(2018) Cell 173: 305-320.e10
MeSH Terms: Carcinogenesis, DNA Repair, Databases, Genetic, Genes, Neoplasm, Genomics, Humans, Metabolic Networks and Pathways, Microsatellite Instability, Mutation, Neoplasms, Transcriptome, Tumor Microenvironment
Show Abstract · Added October 30, 2019
The Cancer Genome Atlas (TCGA) has catalyzed systematic characterization of diverse genomic alterations underlying human cancers. At this historic junction marking the completion of genomic characterization of over 11,000 tumors from 33 cancer types, we present our current understanding of the molecular processes governing oncogenesis. We illustrate our insights into cancer through synthesis of the findings of the TCGA PanCancer Atlas project on three facets of oncogenesis: (1) somatic driver mutations, germline pathogenic variants, and their interactions in the tumor; (2) the influence of the tumor genome and epigenome on transcriptome and proteome; and (3) the relationship between tumor and the microenvironment, including implications for drugs targeting driver events and immunotherapies. These results will anchor future characterization of rare and common tumor types, primary and relapsed tumors, and cancers across ancestry groups and will guide the deployment of clinical genomic sequencing.
Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.
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MeSH Terms
Comparative analysis of the GNAQ, GNA11, SF3B1, and EIF1AX driver mutations in melanoma and across the cancer spectrum.
Johnson DB, Roszik J, Shoushtari AN, Eroglu Z, Balko JM, Higham C, Puzanov I, Patel SP, Sosman JA, Woodman SE
(2016) Pigment Cell Melanoma Res 29: 470-3
MeSH Terms: Eukaryotic Initiation Factor-1, GTP-Binding Protein alpha Subunits, GTP-Binding Protein alpha Subunits, Gq-G11, Genes, Neoplasm, Humans, Immunotherapy, Melanoma, Mutation, Mutation, Missense, Neoplasms, Phosphoproteins, Point Mutation, Prognosis, RNA Splicing Factors
Added April 6, 2017
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14 MeSH Terms
Active medulloblastoma enhancers reveal subgroup-specific cellular origins.
Lin CY, Erkek S, Tong Y, Yin L, Federation AJ, Zapatka M, Haldipur P, Kawauchi D, Risch T, Warnatz HJ, Worst BC, Ju B, Orr BA, Zeid R, Polaski DR, Segura-Wang M, Waszak SM, Jones DT, Kool M, Hovestadt V, Buchhalter I, Sieber L, Johann P, Chavez L, Gröschel S, Ryzhova M, Korshunov A, Chen W, Chizhikov VV, Millen KJ, Amstislavskiy V, Lehrach H, Yaspo ML, Eils R, Lichter P, Korbel JO, Pfister SM, Bradner JE, Northcott PA
(2016) Nature 530: 57-62
MeSH Terms: Animals, Cerebellar Neoplasms, Enhancer Elements, Genetic, Female, Gene Expression Regulation, Neoplastic, Gene Regulatory Networks, Genes, Neoplasm, Genes, Reporter, Humans, Male, Medulloblastoma, Mice, Reproducibility of Results, Transcription Factors, Zebrafish
Show Abstract · Added February 15, 2016
Medulloblastoma is a highly malignant paediatric brain tumour, often inflicting devastating consequences on the developing child. Genomic studies have revealed four distinct molecular subgroups with divergent biology and clinical behaviour. An understanding of the regulatory circuitry governing the transcriptional landscapes of medulloblastoma subgroups, and how this relates to their respective developmental origins, is lacking. Here, using H3K27ac and BRD4 chromatin immunoprecipitation followed by sequencing (ChIP-seq) coupled with tissue-matched DNA methylation and transcriptome data, we describe the active cis-regulatory landscape across 28 primary medulloblastoma specimens. Analysis of differentially regulated enhancers and super-enhancers reinforced inter-subgroup heterogeneity and revealed novel, clinically relevant insights into medulloblastoma biology. Computational reconstruction of core regulatory circuitry identified a master set of transcription factors, validated by ChIP-seq, that is responsible for subgroup divergence, and implicates candidate cells of origin for Group 4. Our integrated analysis of enhancer elements in a large series of primary tumour samples reveals insights into cis-regulatory architecture, unrecognized dependencies, and cellular origins.
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15 MeSH Terms
Characterization of HPV and host genome interactions in primary head and neck cancers.
Parfenov M, Pedamallu CS, Gehlenborg N, Freeman SS, Danilova L, Bristow CA, Lee S, Hadjipanayis AG, Ivanova EV, Wilkerson MD, Protopopov A, Yang L, Seth S, Song X, Tang J, Ren X, Zhang J, Pantazi A, Santoso N, Xu AW, Mahadeshwar H, Wheeler DA, Haddad RI, Jung J, Ojesina AI, Issaeva N, Yarbrough WG, Hayes DN, Grandis JR, El-Naggar AK, Meyerson M, Park PJ, Chin L, Seidman JG, Hammerman PS, Kucherlapati R, Cancer Genome Atlas Network
(2014) Proc Natl Acad Sci U S A 111: 15544-9
MeSH Terms: Base Sequence, DNA Methylation, Gene Expression Regulation, Neoplastic, Genes, Neoplasm, Genome, Human, Head and Neck Neoplasms, Host-Pathogen Interactions, Humans, Molecular Sequence Data, Papillomaviridae, Virus Integration
Show Abstract · Added August 8, 2016
Previous studies have established that a subset of head and neck tumors contains human papillomavirus (HPV) sequences and that HPV-driven head and neck cancers display distinct biological and clinical features. HPV is known to drive cancer by the actions of the E6 and E7 oncoproteins, but the molecular architecture of HPV infection and its interaction with the host genome in head and neck cancers have not been comprehensively described. We profiled a cohort of 279 head and neck cancers with next generation RNA and DNA sequencing and show that 35 (12.5%) tumors displayed evidence of high-risk HPV types 16, 33, or 35. Twenty-five cases had integration of the viral genome into one or more locations in the human genome with statistical enrichment for genic regions. Integrations had a marked impact on the human genome and were associated with alterations in DNA copy number, mRNA transcript abundance and splicing, and both inter- and intrachromosomal rearrangements. Many of these events involved genes with documented roles in cancer. Cancers with integrated vs. nonintegrated HPV displayed different patterns of DNA methylation and both human and viral gene expressions. Together, these data provide insight into the mechanisms by which HPV interacts with the human genome beyond expression of viral oncoproteins and suggest that specific integration events are an integral component of viral oncogenesis.
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11 MeSH Terms
Advanced heat map and clustering analysis using heatmap3.
Zhao S, Guo Y, Sheng Q, Shyr Y
(2014) Biomed Res Int 2014: 986048
MeSH Terms: Breast Neoplasms, Cluster Analysis, Databases, Genetic, Female, Genes, Neoplasm, Genomics, Humans, Molecular Sequence Annotation, Software
Show Abstract · Added February 19, 2015
Heat maps and clustering are used frequently in expression analysis studies for data visualization and quality control. Simple clustering and heat maps can be produced from the "heatmap" function in R. However, the "heatmap" function lacks certain functionalities and customizability, preventing it from generating advanced heat maps and dendrograms. To tackle the limitations of the "heatmap" function, we have developed an R package "heatmap3" which significantly improves the original "heatmap" function by adding several more powerful and convenient features. The "heatmap3" package allows users to produce highly customizable state of the art heat maps and dendrograms. The "heatmap3" package is developed based on the "heatmap" function in R, and it is completely compatible with it. The new features of "heatmap3" include highly customizable legends and side annotation, a wider range of color selections, new labeling features which allow users to define multiple layers of phenotype variables, and automatically conducted association tests based on the phenotypes provided. Additional features such as different agglomeration methods for estimating distance between two samples are also added for clustering.
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9 MeSH Terms
Reproducible combinatorial regulatory networks elucidate novel oncogenic microRNAs in non-small cell lung cancer.
Mitra R, Edmonds MD, Sun J, Zhao M, Yu H, Eischen CM, Zhao Z
(2014) RNA 20: 1356-68
MeSH Terms: Carcinoma, Non-Small-Cell Lung, Computational Biology, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Gene Regulatory Networks, Genes, Neoplasm, Humans, Lung Neoplasms, MicroRNAs, Oncogenes, Reproducibility of Results, Systems Integration, Transcription Factors
Show Abstract · Added January 20, 2015
While previous studies reported aberrant expression of microRNAs (miRNAs) in non-small cell lung cancer (NSCLC), little is known about which miRNAs play central roles in NSCLC's pathogenesis and its regulatory mechanisms. To address this issue, we presented a robust computational framework that integrated matched miRNA and mRNA expression profiles in NSCLC using feed-forward loops. The network consists of miRNAs, transcription factors (TFs), and their common predicted target genes. To discern the biological meaning of their associations, we introduced the direction of regulation. A network edge validation strategy using three independent NSCLC expression profiling data sets pinpointed reproducible biological regulations. Reproducible regulation, which may reflect the true molecular interaction, has not been applied to miRNA-TF co-regulatory network analyses in cancer or other diseases yet. We revealed eight hub miRNAs that connected to a higher proportion of targets validated by independent data sets. Network analyses showed that these miRNAs might have strong oncogenic characteristics. Furthermore, we identified a novel miRNA-TF co-regulatory module that potentially suppresses the tumor suppressor activity of the TGF-β pathway by targeting a core pathway molecule (TGFBR2). Follow-up experiments showed two miRNAs (miR-9-5p and miR-130b-3p) in this module had increased expression while their target gene TGFBR2 had decreased expression in a cohort of human NSCLC. Moreover, we demonstrated these two miRNAs directly bind to the 3' untranslated region of TGFBR2. This study enhanced our understanding of miRNA-TF co-regulatory mechanisms in NSCLC. The combined bioinformatics and validation approach we described can be applied to study other types of diseases.
© 2014 Mitra et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.
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13 MeSH Terms
Large scale comparison of gene expression levels by microarrays and RNAseq using TCGA data.
Guo Y, Sheng Q, Li J, Ye F, Samuels DC, Shyr Y
(2013) PLoS One 8: e71462
MeSH Terms: Exons, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Genes, Neoplasm, Genome, Human, Humans, Neoplasms, Oligonucleotide Array Sequence Analysis, Reference Standards, Sequence Analysis, RNA, Statistics, Nonparametric
Show Abstract · Added December 12, 2013
RNAseq and microarray methods are frequently used to measure gene expression level. While similar in purpose, there are fundamental differences between the two technologies. Here, we present the largest comparative study between microarray and RNAseq methods to date using The Cancer Genome Atlas (TCGA) data. We found high correlations between expression data obtained from the Affymetrix one-channel microarray and RNAseq (Spearman correlations coefficients of ∼0.8). We also observed that the low abundance genes had poorer correlations between microarray and RNAseq data than high abundance genes. As expected, due to measurement and normalization differences, Agilent two-channel microarray and RNAseq data were poorly correlated (Spearman correlations coefficients of only ∼0.2). By examining the differentially expressed genes between tumor and normal samples we observed reasonable concordance in directionality between Agilent two-channel microarray and RNAseq data, although a small group of genes were found to have expression changes reported in opposite directions using these two technologies. Overall, RNAseq produces comparable results to microarray technologies in term of expression profiling. The RNAseq normalization methods RPKM and RSEM produce similar results on the gene level and reasonably concordant results on the exon level. Longer exons tended to have better concordance between the two normalization methods than shorter exons.
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11 MeSH Terms
RAMP1 is a direct NKX3.1 target gene up-regulated in prostate cancer that promotes tumorigenesis.
Logan M, Anderson PD, Saab ST, Hameed O, Abdulkadir SA
(2013) Am J Pathol 183: 951-63
MeSH Terms: Animals, Carcinogenesis, Cell Line, Tumor, Cell Proliferation, Down-Regulation, Gene Expression Regulation, Neoplastic, Gene Knockdown Techniques, Genes, Neoplasm, Homeodomain Proteins, Humans, MAP Kinase Signaling System, Male, Mice, Mice, Nude, Prostate, Prostatic Neoplasms, Protein Binding, Receptor Activity-Modifying Protein 1, Transcription Factors, Up-Regulation
Show Abstract · Added March 7, 2014
The homeodomain-containing transcription factor, NKX3.1, plays an important role in the suppression of prostate tumorigenesis. Herein, we identify the receptor activity-modifying protein 1 (RAMP1) as a direct NKX3.1 target gene through analysis of chromatin immunoprecipitation coupled to massively parallel sequencing and gene expression data. RAMP1 is a coreceptor for certain G-protein-coupled receptors, such as the calcitonin gene-related peptide receptor, to the plasma membrane. We found that RAMP1 expression is specifically elevated in human prostate cancer relative to other tumor types. Furthermore, RAMP1 mRNA and protein levels are significantly higher in human prostate cancer compared with benign glands. We identified multiple NKX3.1 binding sites in the RAMP1 locus in human prostate cancer cells and in the normal mouse prostate. Analyses of Nkx3.1 knockout mice and human prostate cancer cell lines indicate that NKX3.1 represses RAMP1 expression. Knockdown of RAMP1 by shRNA decreased prostate cancer cell proliferation and tumorigenicity in vitro and in vivo. By using gene expression profiling and pathway analyses, we identified several cancer-related pathways that are significantly altered in RAMP1 knockdown cells, including the mitogen-activated protein kinase signaling pathway. Further experiments confirmed a reduction in MAP2KI (MEK1) expression and phosphorylated-extracellular signal-regulated kinase 1/2 levels in RAMP1 knockdown cells. These data provide novel insights into the role of RAMP1 in promoting prostate tumorigenesis and support the potential of RAMP1 as a novel biomarker and possible therapeutic target in prostate cancer.
Copyright © 2013 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.
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20 MeSH Terms
Translating genomic information into clinical medicine: lung cancer as a paradigm.
Levy MA, Lovly CM, Pao W
(2012) Genome Res 22: 2101-8
MeSH Terms: Antineoplastic Agents, Genes, Neoplasm, Genetic Testing, Genomics, Humans, Lung Neoplasms, Translational Medical Research
Show Abstract · Added September 3, 2013
We are currently in an era of rapidly expanding knowledge about the genetic landscape and architectural blueprints of various cancers. These discoveries have led to a new taxonomy of malignant diseases based upon clinically relevant molecular alterations in addition to histology or tissue of origin. The new molecularly based classification holds the promise of rational rather than empiric approaches for the treatment of cancer patients. However, the accelerated pace of discovery and the expanding number of targeted anti-cancer therapies present a significant challenge for healthcare practitioners to remain informed and up-to-date on how to apply cutting-edge discoveries into daily clinical practice. In this Perspective, we use lung cancer as a paradigm to discuss challenges related to translating genomic information into the clinic, and we present one approach we took at Vanderbilt-Ingram Cancer Center to address these challenges.
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7 MeSH Terms