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Article ; Online: Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Joel Saltz / Rajarsi Gupta / Le Hou / Tahsin Kurc / Pankaj Singh / Vu Nguyen / Dimitris Samaras / Kenneth R. Shroyer / Tianhao Zhao / Rebecca Batiste / John Van Arnam / Ilya Shmulevich / Arvind U.K. Rao / Alexander J. Lazar / Ashish Sharma / Vésteinn Thorsson / Samantha J. Caesar-Johnson / John A. Demchok / Ina Felau /

Cell Reports, Vol 23, Iss 1, Pp 181-193.e

2018 Volume 7

Abstract: Summary: Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on H&E images ... ...

Abstract	Summary: Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment. : Tumor-infiltrating lymphocytes (TILs) were identified from standard pathology cancer images by a deep-learning-derived “computational stain” developed by Saltz et al. They processed 5,202 digital images from 13 cancer types. Resulting TIL maps were correlated with TCGA molecular data, relating TIL content to survival, tumor subtypes, and immune profiles. Keywords: digital pathology, immuno-oncology, machine learning, lymphocytes, tumor microenvironment, deep learning, tumor-infiltrating lymphocytes, artificial intelligence, bioinformatics, computer vision
Keywords	Biology (General) ; QH301-705.5
Subject code	006
Language	English
Publishing date	2018-04-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas

Gregory P. Way / Francisco Sanchez-Vega / Konnor La / Joshua Armenia / Walid K. Chatila / Augustin Luna / Chris Sander / Andrew D. Cherniack / Marco Mina / Giovanni Ciriello / Nikolaus Schultz / Yolanda Sanchez / Casey S. Greene / Samantha J. Caesar-Johnson / John A. Demchok / Ina Felau / Melpomeni Kasapi / Martin L. Ferguson / Carolyn M. Hutter /

Heidi J. Sofia / Roy Tarnuzzer / Zhining Wang / Liming Yang / Jean C. Zenklusen / Jiashan (Julia) Zhang / Sudha Chudamani / Jia Liu / Laxmi Lolla / Rashi Naresh / Todd Pihl / Qiang Sun / Yunhu Wan / Ye Wu / Juok Cho / Timothy DeFreitas / Scott Frazer / Nils Gehlenborg / Gad Getz / David I. Heiman / Jaegil Kim / Michael S. Lawrence / Pei Lin / Sam Meier / Michael S. Noble / Gordon Saksena / Doug Voet / Hailei Zhang / Brady Bernard / Nyasha Chambwe / Varsha Dhankani

Cell Reports, Vol 23, Iss 1, Pp 172-180.e

2018 Volume 3

Abstract: Summary: Precision oncology uses genomic evidence to match patients with treatment but often fails to identify all patients who may respond. The transcriptome of these “hidden responders” may reveal responsive molecular states. We describe and evaluate a ...

Abstract	Summary: Precision oncology uses genomic evidence to match patients with treatment but often fails to identify all patients who may respond. The transcriptome of these “hidden responders” may reveal responsive molecular states. We describe and evaluate a machine-learning approach to classify aberrant pathway activity in tumors, which may aid in hidden responder identification. The algorithm integrates RNA-seq, copy number, and mutations from 33 different cancer types across The Cancer Genome Atlas (TCGA) PanCanAtlas project to predict aberrant molecular states in tumors. Applied to the Ras pathway, the method detects Ras activation across cancer types and identifies phenocopying variants. The model, trained on human tumors, can predict response to MEK inhibitors in wild-type Ras cell lines. We also present data that suggest that multiple hits in the Ras pathway confer increased Ras activity. The transcriptome is underused in precision oncology and, combined with machine learning, can aid in the identification of hidden responders. : Way et al. develop a machine-learning approach using PanCanAtlas data to detect Ras activation in cancer. Integrating mutation, copy number, and expression data, the authors show that their method detects Ras-activating variants in tumors and sensitivity to MEK inhibitors in cell lines. Keywords: Gene expression, machine learning, Ras, NF1, KRAS, NRAS, HRAS, pan-cancer, TCGA, drug sensitivity
Keywords	Biology (General) ; QH301-705.5
Subject code	006
Language	English
Publishing date	2018-04-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Somatic Mutational Landscape of Splicing Factor Genes and Their Functional Consequences across 33 Cancer Types

Michael Seiler / Shouyong Peng / Anant A. Agrawal / James Palacino / Teng Teng / Ping Zhu / Peter G. Smith / Silvia Buonamici / Lihua Yu / Samantha J. Caesar-Johnson / John A. Demchok / Ina Felau / Melpomeni Kasapi / Martin L. Ferguson / Carolyn M. Hutter / Heidi J. Sofia / Roy Tarnuzzer / Zhining Wang / Liming Yang /

Jean C. Zenklusen / Jiashan (Julia) Zhang / Sudha Chudamani / Jia Liu / Laxmi Lolla / Rashi Naresh / Todd Pihl / Qiang Sun / Yunhu Wan / Ye Wu / Juok Cho / Timothy DeFreitas / Scott Frazer / Nils Gehlenborg / Gad Getz / David I. Heiman / Jaegil Kim / Michael S. Lawrence / Pei Lin / Sam Meier / Michael S. Noble / Gordon Saksena / Doug Voet / Hailei Zhang / Brady Bernard / Nyasha Chambwe / Varsha Dhankani / Theo Knijnenburg / Roger Kramer / Kalle Leinonen / Yuexin Liu

Cell Reports, Vol 23, Iss 1, Pp 282-296.e

2018 Volume 4

Abstract: Summary: Hotspot mutations in splicing factor genes have been recently reported at high frequency in hematological malignancies, suggesting the importance of RNA splicing in cancer. We analyzed whole-exome sequencing data across 33 tumor types in The ... ...

Abstract	Summary: Hotspot mutations in splicing factor genes have been recently reported at high frequency in hematological malignancies, suggesting the importance of RNA splicing in cancer. We analyzed whole-exome sequencing data across 33 tumor types in The Cancer Genome Atlas (TCGA), and we identified 119 splicing factor genes with significant non-silent mutation patterns, including mutation over-representation, recurrent loss of function (tumor suppressor-like), or hotspot mutation profile (oncogene-like). Furthermore, RNA sequencing analysis revealed altered splicing events associated with selected splicing factor mutations. In addition, we were able to identify common gene pathway profiles associated with the presence of these mutations. Our analysis suggests that somatic alteration of genes involved in the RNA-splicing process is common in cancer and may represent an underappreciated hallmark of tumorigenesis. : Seiler et al. report that 119 splicing factor genes carry putative driver mutations over 33 tumor types in TCGA. The most common mutations appear to be mutually exclusive and are associated with lineage-independent altered splicing. Samples with these mutations show deregulation of cell-autonomous pathways and immune infiltration. Keywords: splicing, SF3B1, U2AF1, SRSF2, RBM10, FUBP1, cancer, mutation
Keywords	Biology (General) ; QH301-705.5
Language	English
Publishing date	2018-04-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas

Theo A. Knijnenburg / Linghua Wang / Michael T. Zimmermann / Nyasha Chambwe / Galen F. Gao / Andrew D. Cherniack / Huihui Fan / Hui Shen / Gregory P. Way / Casey S. Greene / Yuexin Liu / Rehan Akbani / Bin Feng / Lawrence A. Donehower / Chase Miller / Yang Shen / Mostafa Karimi / Haoran Chen / Pora Kim /

Peilin Jia / Eve Shinbrot / Shaojun Zhang / Jianfang Liu / Hai Hu / Matthew H. Bailey / Christina Yau / Denise Wolf / Zhongming Zhao / John N. Weinstein / Lei Li / Li Ding / Gordon B. Mills / Peter W. Laird / David A. Wheeler / Ilya Shmulevich / Raymond J. Monnat, Jr. / Yonghong Xiao / Chen Wang / Samantha J. Caesar-Johnson / John A. Demchok / Ina Felau / Melpomeni Kasapi / Martin L. Ferguson / Carolyn M. Hutter / Heidi J. Sofia / Roy Tarnuzzer / Zhining Wang / Liming Yang / Jean C. Zenklusen / Jiashan (Julia) Zhang

Cell Reports, Vol 23, Iss 1, Pp 239-254.e

2018 Volume 6

Abstract: Summary: DNA damage repair (DDR) pathways modulate cancer risk, progression, and therapeutic response. We systematically analyzed somatic alterations to provide a comprehensive view of DDR deficiency across 33 cancer types. Mutations with accompanying ... ...

Abstract	Summary: DNA damage repair (DDR) pathways modulate cancer risk, progression, and therapeutic response. We systematically analyzed somatic alterations to provide a comprehensive view of DDR deficiency across 33 cancer types. Mutations with accompanying loss of heterozygosity were observed in over 1/3 of DDR genes, including TP53 and BRCA1/2. Other prevalent alterations included epigenetic silencing of the direct repair genes EXO5, MGMT, and ALKBH3 in ∼20% of samples. Homologous recombination deficiency (HRD) was present at varying frequency in many cancer types, most notably ovarian cancer. However, in contrast to ovarian cancer, HRD was associated with worse outcomes in several other cancers. Protein structure-based analyses allowed us to predict functional consequences of rare, recurrent DDR mutations. A new machine-learning-based classifier developed from gene expression data allowed us to identify alterations that phenocopy deleterious TP53 mutations. These frequent DDR gene alterations in many human cancers have functional consequences that may determine cancer progression and guide therapy. : Knijnenburg et al. present The Cancer Genome Atlas (TCGA) Pan-Cancer analysis of DNA damage repair (DDR) deficiency in cancer. They use integrative genomic and molecular analyses to identify frequent DDR alterations across 33 cancer types, correlate gene- and pathway-level alterations with genome-wide measures of genome instability and impaired function, and demonstrate the prognostic utility of DDR deficiency scores. Keywords: The Cancer Genome Atlas PanCanAtlas project, DNA damage repair, somatic mutations, somatic copy-number alterations, epigenetic silencing, DNA damage footprints, mutational signatures, integrative statistical analysis, protein structure analysis
Keywords	Biology (General) ; QH301-705.5
Subject code	616
Language	English
Publishing date	2018-04-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Systematic Analysis of Splice-Site-Creating Mutations in Cancer

Reyka G. Jayasinghe / Song Cao / Qingsong Gao / Michael C. Wendl / Nam Sy Vo / Sheila M. Reynolds / Yanyan Zhao / Héctor Climente-González / Shengjie Chai / Fang Wang / Rajees Varghese / Mo Huang / Wen-Wei Liang / Matthew A. Wyczalkowski / Sohini Sengupta / Zhi Li / Samuel H. Payne / David Fenyö / Jeffrey H. Miner /

Matthew J. Walter / Benjamin Vincent / Eduardo Eyras / Ken Chen / Ilya Shmulevich / Feng Chen / Li Ding / Samantha J. Caesar-Johnson / John A. Demchok / Ina Felau / Melpomeni Kasapi / Martin L. Ferguson / Carolyn M. Hutter / Heidi J. Sofia / Roy Tarnuzzer / Zhining Wang / Liming Yang / Jean C. Zenklusen / Jiashan (Julia) Zhang / Sudha Chudamani / Jia Liu / Laxmi Lolla / Rashi Naresh / Todd Pihl / Qiang Sun / Yunhu Wan / Ye Wu / Juok Cho / Timothy DeFreitas / Scott Frazer / Nils Gehlenborg

Cell Reports, Vol 23, Iss 1, Pp 270-281.e

2018 Volume 3

Abstract: Summary: For the past decade, cancer genomic studies have focused on mutations leading to splice-site disruption, overlooking those having splice-creating potential. Here, we applied a bioinformatic tool, MiSplice, for the large-scale discovery of splice- ...

Abstract	Summary: For the past decade, cancer genomic studies have focused on mutations leading to splice-site disruption, overlooking those having splice-creating potential. Here, we applied a bioinformatic tool, MiSplice, for the large-scale discovery of splice-site-creating mutations (SCMs) across 8,656 TCGA tumors. We report 1,964 originally mis-annotated mutations having clear evidence of creating alternative splice junctions. TP53 and GATA3 have 26 and 18 SCMs, respectively, and ATRX has 5 from lower-grade gliomas. Mutations in 11 genes, including PARP1, BRCA1, and BAP1, were experimentally validated for splice-site-creating function. Notably, we found that neoantigens induced by SCMs are likely several folds more immunogenic compared to missense mutations, exemplified by the recurrent GATA3 SCM. Further, high expression of PD-1 and PD-L1 was observed in tumors with SCMs, suggesting candidates for immune blockade therapy. Our work highlights the importance of integrating DNA and RNA data for understanding the functional and the clinical implications of mutations in human diseases. : Jayasinghe et al. identify nearly 2,000 splice-site-creating mutations (SCMs) from over 8,000 tumor samples across 33 cancer types. They provide a more accurate interpretation of previously mis-annotated mutations, highlighting the importance of integrating data types to understand the functional and the clinical implications of splicing mutations in human disease. Keywords: splicing, RNA, mutations of clinical relevance
Keywords	Biology (General) ; QH301-705.5
Subject code	616
Language	English
Publishing date	2018-04-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Molecular Characterization and Clinical Relevance of Metabolic Expression Subtypes in Human Cancers

Xinxin Peng / Zhongyuan Chen / Farshad Farshidfar / Xiaoyan Xu / Philip L. Lorenzi / Yumeng Wang / Feixiong Cheng / Lin Tan / Kamalika Mojumdar / Di Du / Zhongqi Ge / Jun Li / George V. Thomas / Kivanc Birsoy / Lingxiang Liu / Huiwen Zhang / Zhongming Zhao / Calena Marchand / John N. Weinstein /

Oliver F. Bathe / Han Liang / Samantha J. Caesar-Johnson / John A. Demchok / Ina Felau / Melpomeni Kasapi / Martin L. Ferguson / Carolyn M. Hutter / Heidi J. Sofia / Roy Tarnuzzer / Zhining Wang / Liming Yang / Jean C. Zenklusen / Jiashan (Julia) Zhang / Sudha Chudamani / Jia Liu / Laxmi Lolla / Rashi Naresh / Todd Pihl / Qiang Sun / Yunhu Wan / Ye Wu / Juok Cho / Timothy DeFreitas / Scott Frazer / Nils Gehlenborg / Gad Getz / David I. Heiman / Jaegil Kim / Michael S. Lawrence / Pei Lin

Cell Reports, Vol 23, Iss 1, Pp 255-269.e

2018 Volume 4

Abstract: Summary: Metabolic reprogramming provides critical information for clinical oncology. Using molecular data of 9,125 patient samples from The Cancer Genome Atlas, we identified tumor subtypes in 33 cancer types based on mRNA expression patterns of seven ... ...

Abstract	Summary: Metabolic reprogramming provides critical information for clinical oncology. Using molecular data of 9,125 patient samples from The Cancer Genome Atlas, we identified tumor subtypes in 33 cancer types based on mRNA expression patterns of seven major metabolic processes and assessed their clinical relevance. Our metabolic expression subtypes correlated extensively with clinical outcome: subtypes with upregulated carbohydrate, nucleotide, and vitamin/cofactor metabolism most consistently correlated with worse prognosis, whereas subtypes with upregulated lipid metabolism showed the opposite. Metabolic subtypes correlated with diverse somatic drivers but exhibited effects convergent on cancer hallmark pathways and were modulated by highly recurrent master regulators across cancer types. As a proof-of-concept example, we demonstrated that knockdown of SNAI1 or RUNX1—master regulators of carbohydrate metabolic subtypes—modulates metabolic activity and drug sensitivity. Our study provides a system-level view of metabolic heterogeneity within and across cancer types and identifies pathway cross-talk, suggesting related prognostic, therapeutic, and predictive utility. : Peng et al. analyze a cohort of 9,125 TCGA samples across 33 cancer types to characterize tumor subtypes based on the expression of seven metabolic pathways. They find metabolic expression subtypes are associated with patient survivals and suggest the therapeutic and predictive relevance of subtype-related master regulators. Keywords: The Cancer Genome Atlas, tumor subtypes, prognostic markers, somatic drivers, master regulator, therapeutic targets, drug sensitivity, carbohydrate metabolism
Keywords	Biology (General) ; QH301-705.5
Language	English
Publishing date	2018-04-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Hua-Sheng Chiu / Sonal Somvanshi / Ektaben Patel / Ting-Wen Chen / Vivek P. Singh / Barry Zorman / Sagar L. Patil / Yinghong Pan / Sujash S. Chatterjee / Anil K. Sood / Preethi H. Gunaratne / Pavel Sumazin / Samantha J. Caesar-Johnson / John A. Demchok / Ina Felau / Melpomeni Kasapi / Martin L. Ferguson / Carolyn M. Hutter / Heidi J. Sofia /

Roy Tarnuzzer / Zhining Wang / Liming Yang / Jean C. Zenklusen / Jiashan (Julia) Zhang / Sudha Chudamani / Jia Liu / Laxmi Lolla / Rashi Naresh / Todd Pihl / Qiang Sun / Yunhu Wan / Ye Wu / Juok Cho / Timothy DeFreitas / Scott Frazer / Nils Gehlenborg / Gad Getz / David I. Heiman / Jaegil Kim / Michael S. Lawrence / Pei Lin / Sam Meier / Michael S. Noble / Gordon Saksena / Doug Voet / Hailei Zhang / Brady Bernard / Nyasha Chambwe / Varsha Dhankani / Theo Knijnenburg

Cell Reports, Vol 23, Iss 1, Pp 297-312.e

2018 Volume 12

Abstract: Summary: Long noncoding RNAs (lncRNAs) are commonly dysregulated in tumors, but only a handful are known to play pathophysiological roles in cancer. We inferred lncRNAs that dysregulate cancer pathways, oncogenes, and tumor suppressors (cancer genes) by ... ...

Abstract	Summary: Long noncoding RNAs (lncRNAs) are commonly dysregulated in tumors, but only a handful are known to play pathophysiological roles in cancer. We inferred lncRNAs that dysregulate cancer pathways, oncogenes, and tumor suppressors (cancer genes) by modeling their effects on the activity of transcription factors, RNA-binding proteins, and microRNAs in 5,185 TCGA tumors and 1,019 ENCODE assays. Our predictions included hundreds of candidate onco- and tumor-suppressor lncRNAs (cancer lncRNAs) whose somatic alterations account for the dysregulation of dozens of cancer genes and pathways in each of 14 tumor contexts. To demonstrate proof of concept, we showed that perturbations targeting OIP5-AS1 (an inferred tumor suppressor) and TUG1 and WT1-AS (inferred onco-lncRNAs) dysregulated cancer genes and altered proliferation of breast and gynecologic cancer cells. Our analysis indicates that, although most lncRNAs are dysregulated in a tumor-specific manner, some, including OIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergistically dysregulate cancer pathways in multiple tumor contexts. : Chiu et al. present a pan-cancer analysis of lncRNA regulatory interactions. They suggest that the dysregulation of hundreds of lncRNAs target and alter the expression of cancer genes and pathways in each tumor context. This implies that hundreds of lncRNAs can alter tumor phenotypes in each tumor context. Keywords: lncRNA, regulation, modulation, cancer gene, pan-cancer, noncoding RNA, microRNA, RNA-binding proteins, interactome
Keywords	Biology (General) ; QH301-705.5
Language	English
Publishing date	2018-04-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Zhongqi Ge / Jake S. Leighton / Yumeng Wang / Xinxin Peng / Zhongyuan Chen / Hu Chen / Yutong Sun / Fan Yao / Jun Li / Huiwen Zhang / Jianfang Liu / Craig D. Shriver / Hai Hu / Helen Piwnica-Worms / Li Ma / Han Liang / Samantha J. Caesar-Johnson / John A. Demchok / Ina Felau /

Melpomeni Kasapi / Martin L. Ferguson / Carolyn M. Hutter / Heidi J. Sofia / Roy Tarnuzzer / Zhining Wang / Liming Yang / Jean C. Zenklusen / Jiashan (Julia) Zhang / Sudha Chudamani / Jia Liu / Laxmi Lolla / Rashi Naresh / Todd Pihl / Qiang Sun / Yunhu Wan / Ye Wu / Juok Cho / Timothy DeFreitas / Scott Frazer / Nils Gehlenborg / Gad Getz / David I. Heiman / Jaegil Kim / Michael S. Lawrence / Pei Lin / Sam Meier / Michael S. Noble / Gordon Saksena / Doug Voet / Hailei Zhang / Brady Bernard / Nyasha Chambwe / Varsha Dhankani / Theo Knijnenburg / Roger Kramer / Kalle Leinonen / Yuexin Liu / Michael Miller / Sheila Reynolds / Ilya Shmulevich / Vesteinn Thorsson / Wei Zhang / Rehan Akbani / Bradley M. Broom / Apurva M. Hegde / Zhenlin Ju / Rupa S. Kanchi / Anil Korkut / Shiyun Ling / Wenbin Liu / Yiling Lu / Gordon B. Mills / Kwok-Shing Ng / Arvind Rao / Michael Ryan / Jing Wang / John N. Weinstein / Jiexin Zhang / Adam Abeshouse / Joshua Armenia / Debyani Chakravarty / Walid K. Chatila / Ino de Bruijn / Jianjiong Gao / Benjamin E. Gross / Zachary J. Heins / Ritika Kundra / Konnor La / Marc Ladanyi / Augustin Luna / Moriah G. Nissan / Angelica Ochoa / Sarah M. Phillips / Ed Reznik / Francisco Sanchez-Vega / Chris Sander / Nikolaus Schultz / Robert Sheridan / S. Onur Sumer / Yichao Sun / Barry S. Taylor / Jioajiao Wang / Hongxin Zhang / Pavana Anur / Myron Peto / Paul Spellman / Christopher Benz / Joshua M. Stuart / Christopher K. Wong / Christina Yau / D. Neil Hayes / Joel S. Parker / Matthew D. Wilkerson / Adrian Ally / Miruna Balasundaram / Reanne Bowlby / Denise Brooks / Rebecca Carlsen / Eric Chuah / Noreen Dhalla / Robert Holt / Steven J.M. Jones / Katayoon Kasaian / Darlene Lee / Yussanne Ma / Marco A. Marra / Michael Mayo / Richard A. Moore / Andrew J. Mungall / Karen Mungall / A. Gordon Robertson / Sara Sadeghi / Jacqueline E. Schein / Payal Sipahimalani / Angela Tam / Nina Thiessen / Kane Tse / Tina Wong / Ashton C. Berger / Rameen Beroukhim / Andrew D. Cherniack / Carrie Cibulskis / Stacey B. Gabriel / Galen F. Gao / Gavin Ha / Matthew Meyerson / Steven E. Schumacher / Juliann Shih / Melanie H. Kucherlapati / Raju S. Kucherlapati / Stephen Baylin / Leslie Cope / Ludmila Danilova / Moiz S. Bootwalla / Phillip H. Lai / Dennis T. Maglinte / David J. Van Den Berg / Daniel J. Weisenberger / J. Todd Auman / Saianand Balu / Tom Bodenheimer / Cheng Fan / Katherine A. Hoadley / Alan P. Hoyle / Stuart R. Jefferys / Corbin D. Jones / Shaowu Meng / Piotr A. Mieczkowski / Lisle E. Mose / Amy H. Perou / Charles M. Perou / Jeffrey Roach / Yan Shi / Janae V. Simons / Tara Skelly / Matthew G. Soloway / Donghui Tan / Umadevi Veluvolu / Huihui Fan / Toshinori Hinoue / Peter W. Laird / Hui Shen / Wanding Zhou / Michelle Bellair / Kyle Chang / Kyle Covington / Chad J. Creighton / Huyen Dinh / HarshaVardhan Doddapaneni / Lawrence A. Donehower / Jennifer Drummond / Richard A. Gibbs / Robert Glenn / Walker Hale / Yi Han / Jianhong Hu / Viktoriya Korchina / Sandra Lee / Lora Lewis / Wei Li / Xiuping Liu / Margaret Morgan / Donna Morton / Donna Muzny / Jireh Santibanez / Margi Sheth / Eve Shinbrot / Linghua Wang / Min Wang / David A. Wheeler / Liu Xi / Fengmei Zhao / Julian Hess / Elizabeth L. Appelbaum / Matthew Bailey / Matthew G. Cordes / Li Ding / Catrina C. Fronick / Lucinda A. Fulton / Robert S. Fulton / Cyriac Kandoth / Elaine R. Mardis / Michael D. McLellan / Christopher A. Miller / Heather K. Schmidt / Richard K. Wilson / Daniel Crain / Erin Curley / Johanna Gardner / Kevin Lau / David Mallery / Scott Morris / Joseph Paulauskis / Robert Penny / Candace Shelton / Troy Shelton / Mark Sherman / Eric Thompson / Peggy Yena / Jay Bowen / Julie M. Gastier-Foster / Mark Gerken / Kristen M. Leraas / Tara M. Lichtenberg / Nilsa C. Ramirez / Lisa Wise / Erik Zmuda / Niall Corcoran / Tony Costello / Christopher Hovens / Andre L. Carvalho / Ana C. de Carvalho / José H. Fregnani / Adhemar Longatto-Filho / Rui M. Reis / Cristovam Scapulatempo-Neto / Henrique C.S. Silveira / Daniel O. Vidal / Andrew Burnette / Jennifer Eschbacher / Beth Hermes / Ardene Noss / Rosy Singh / Matthew L. Anderson / Patricia D. Castro / Michael Ittmann / David Huntsman / Bernard Kohl / Xuan Le / Richard Thorp / Chris Andry / Elizabeth R. Duffy / Vladimir Lyadov / Oxana Paklina / Galiya Setdikova / Alexey Shabunin / Mikhail Tavobilov / Christopher McPherson / Ronald Warnick / Ross Berkowitz / Daniel Cramer / Colleen Feltmate / Neil Horowitz / Adam Kibel / Michael Muto / Chandrajit P. Raut / Andrei Malykh / Jill S. Barnholtz-Sloan / Wendi Barrett / Karen Devine / Jordonna Fulop / Quinn T. Ostrom / Kristen Shimmel / Yingli Wolinsky / Andrew E. Sloan / Agostino De Rose / Felice Giuliante / Marc Goodman / Beth Y. Karlan / Curt H. Hagedorn / John Eckman / Jodi Harr / Jerome Myers / Kelinda Tucker / Leigh Anne Zach / Brenda Deyarmin / Leonid Kvecher / Caroline Larson / Richard J. Mural / Stella Somiari / Ales Vicha / Tomas Zelinka / Joseph Bennett / Mary Iacocca / Brenda Rabeno / Patricia Swanson / Mathieu Latour / Louis Lacombe / Bernard Têtu / Alain Bergeron / Mary McGraw / Susan M. Staugaitis / John Chabot / Hanina Hibshoosh / Antonia Sepulveda / Tao Su / Timothy Wang / Olga Potapova / Olga Voronina / Laurence Desjardins / Odette Mariani / Sergio Roman-Roman / Xavier Sastre / Marc-Henri Stern / Feixiong Cheng / Sabina Signoretti / Andrew Berchuck / Darell Bigner / Eric Lipp / Jeffrey Marks / Shannon McCall / Roger McLendon / Angeles Secord / Alexis Sharp / Madhusmita Behera / Daniel J. Brat / Amy Chen / Keith Delman / Seth Force / Fadlo Khuri / Kelly Magliocca / Shishir Maithel / Jeffrey J. Olson / Taofeek Owonikoko / Alan Pickens / Suresh Ramalingam / Dong M. Shin / Gabriel Sica / Erwin G. Van Meir / Hongzheng Zhang / Wil Eijckenboom / Ad Gillis / Esther Korpershoek / Leendert Looijenga / Wolter Oosterhuis / Hans Stoop / Kim E. van Kessel / Ellen C. Zwarthoff / Chiara Calatozzolo / Lucia Cuppini / Stefania Cuzzubbo / Francesco DiMeco / Gaetano Finocchiaro / Luca Mattei / Alessandro Perin / Bianca Pollo / Chu Chen / John Houck / Pawadee Lohavanichbutr / Arndt Hartmann / Christine Stoehr / Robert Stoehr / Helge Taubert / Sven Wach / Bernd Wullich / Witold Kycler / Dawid Murawa / Maciej Wiznerowicz / Ki Chung / W. Jeffrey Edenfield / Julie Martin / Eric Baudin / Glenn Bubley / Raphael Bueno / Assunta De Rienzo / William G. Richards / Steven Kalkanis / Tom Mikkelsen / Houtan Noushmehr / Lisa Scarpace / Nicolas Girard / Marta Aymerich / Elias Campo / Eva Giné / Armando López Guillermo / Nguyen Van Bang / Phan Thi Hanh / Bui Duc Phu / Yufang Tang / Howard Colman / Kimberley Evason / Peter R. Dottino / John A. 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Adebamowo / Alex Boussioutas / David Beer / Thomas Giordano / Anne-Marie Mes-Masson / Fred Saad / Therese Bocklage / Lisa Landrum / Robert Mannel / Kathleen Moore / Katherine Moxley / Russel Postier / Joan Walker / Rosemary Zuna / Michael Feldman / Federico Valdivieso / Rajiv Dhir / James Luketich / Edna M. Mora Pinero / Mario Quintero-Aguilo / Carlos Gilberto Carlotti, Jr. / Jose Sebastião Dos Santos / Rafael Kemp / Ajith Sankarankuty / Daniela Tirapelli / James Catto / Kathy Agnew / Elizabeth Swisher / Jenette Creaney / Bruce Robinson / Carl Simon Shelley / Eryn M. Godwin / Sara Kendall / Cassaundra Shipman / Carol Bradford / Thomas Carey / Andrea Haddad / Jeffey Moyer / Lisa Peterson / Mark Prince / Laura Rozek / Gregory Wolf / Rayleen Bowman / Kwun M. Fong / Ian Yang / Robert Korst / W. Kimryn Rathmell / J. Leigh Fantacone-Campbell / Jeffrey A. Hooke / Albert J. Kovatich / John DiPersio / Bettina Drake / Ramaswamy Govindan / Sharon Heath / Timothy Ley / Brian Van Tine / Peter Westervelt / Mark A. Rubin / Jung Il Lee / Natália D. Aredes / Armaz Mariamidze

Cell Reports, Vol 23, Iss 1, Pp 213-226.e

2018 Volume 3

Abstract: Summary: Protein ubiquitination is a dynamic and reversible process of adding single ubiquitin molecules or various ubiquitin chains to target proteins. Here, using multidimensional omic data of 9,125 tumor samples across 33 cancer types from The Cancer ... ...

Abstract	Summary: Protein ubiquitination is a dynamic and reversible process of adding single ubiquitin molecules or various ubiquitin chains to target proteins. Here, using multidimensional omic data of 9,125 tumor samples across 33 cancer types from The Cancer Genome Atlas, we perform comprehensive molecular characterization of 929 ubiquitin-related genes and 95 deubiquitinase genes. Among them, we systematically identify top somatic driver candidates, including mutated FBXW7 with cancer-type-specific patterns and amplified MDM2 showing a mutually exclusive pattern with BRAF mutations. Ubiquitin pathway genes tend to be upregulated in cancer mediated by diverse mechanisms. By integrating pan-cancer multiomic data, we identify a group of tumor samples that exhibit worse prognosis. These samples are consistently associated with the upregulation of cell-cycle and DNA repair pathways, characterized by mutated TP53, MYC/TERT amplification, and APC/PTEN deletion. Our analysis highlights the importance of the ubiquitin pathway in cancer development and lays a foundation for developing relevant therapeutic strategies. : Ge et al. analyze a cohort of 9,125 TCGA samples across 33 cancer types to provide a comprehensive characterization of the ubiquitin pathway. They detect somatic driver candidates in the ubiquitin pathway and identify a cluster of patients with poor survival, highlighting the importance of this pathway in cancer development. Keywords: ubiquitin pathway, pan-cancer analysis, The Cancer Genome Atlas, tumor subtype, cancer prognosis, therapeutic targets, biomarker, FBXW7
Keywords	Biology (General) ; QH301-705.5
Language	English
Publishing date	2018-04-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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