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1. ADAPTS: Automated deconvolution augmentation of profiles for tissue specific cells.

Author: Samuel A Danziger, David L Gibbs, Ilya Shmulevich, Mark McConnell, Matthew W B Trotter, Frank Schmitz, David J Reiss, and Alexander V Ratushny
Subjects: Medicine, Science
Abstract: Immune cell infiltration of tumors and the tumor microenvironment can be an important component for determining patient outcomes. For example, immune and stromal cell presence inferred by deconvolving patient gene expression data may help identify high risk patients or suggest a course of treatment. One particularly powerful family of deconvolution techniques uses signature matrices of genes that uniquely identify each cell type as determined from single cell type purified gene expression data. Many methods from this family have been recently published, often including new signature matrices appropriate for a single purpose, such as investigating a specific type of tumor. The package ADAPTS helps users make the most of this expanding knowledge base by introducing a framework for cell type deconvolution. ADAPTS implements modular tools for customizing signature matrices for new tissue types by adding custom cell types or building new matrices de novo, including from single cell RNAseq data. It includes a common interface to several popular deconvolution algorithms that use a signature matrix to estimate the proportion of cell types present in heterogenous samples. ADAPTS also implements a novel method for clustering cell types into groups that are difficult to distinguish by deconvolution and then re-splitting those clusters using hierarchical deconvolution. We demonstrate that the techniques implemented in ADAPTS improve the ability to reconstruct the cell types present in a single cell RNAseq data set in a blind predictive analysis. ADAPTS is currently available for use in R on CRAN and GitHub.
Published: 2019
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2. Solving the influence maximization problem reveals regulatory organization of the yeast cell cycle.

Author: David L Gibbs and Ilya Shmulevich
Subjects: Biology (General), QH301-705.5
Abstract: The Influence Maximization Problem (IMP) aims to discover the set of nodes with the greatest influence on network dynamics. The problem has previously been applied in epidemiology and social network analysis. Here, we demonstrate the application to cell cycle regulatory network analysis for Saccharomyces cerevisiae. Fundamentally, gene regulation is linked to the flow of information. Therefore, our implementation of the IMP was framed as an information theoretic problem using network diffusion. Utilizing more than 26,000 regulatory edges from YeastMine, gene expression dynamics were encoded as edge weights using time lagged transfer entropy, a method for quantifying information transfer between variables. By picking a set of source nodes, a diffusion process covers a portion of the network. The size of the network cover relates to the influence of the source nodes. The set of nodes that maximizes influence is the solution to the IMP. By solving the IMP over different numbers of source nodes, an influence ranking on genes was produced. The influence ranking was compared to other metrics of network centrality. Although the top genes from each centrality ranking contained well-known cell cycle regulators, there was little agreement and no clear winner. However, it was found that influential genes tend to directly regulate or sit upstream of genes ranked by other centrality measures. The influential nodes act as critical sources of information flow, potentially having a large impact on the state of the network. Biological events that affect influential nodes and thereby affect information flow could have a strong effect on network dynamics, potentially leading to disease. Code and data can be found at: https://github.com/gibbsdavidl/miergolf.
Published: 2017
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3. Using incomplete trios to boost confidence in family based association studies

Author: Varsha eDhankani, David L Gibbs, Theo eKnijnenburg, Roger eKramer, Joseph eVockley, John eNiederhuber, Ilya eShmulevich, and Brady eBernard
Subjects: confidence intervals, population stratification, whole genome analysis, Family based association tests, missing genotypes, randomized imputation, Genetics, QH426-470
Abstract: Most currently available family based association tests are designed to account only for nuclear families with complete genotypes for parents as well as offspring. Due to the availability of increasingly less expensive generation of whole genome sequencing information, genetic studies are able to collect data for more families and from large family cohorts with the goal of improving statistical power. However, due to missing genotypes, many families are not included in the family based association tests, negating the benefits of large scale sequencing data. Here, we present the CIFBAT method to use incomplete families in Family Based Association Test (FBAT) to evaluate robustness against missing data. CIFBAT uses quantile intervals of the FBAT statistic by randomly choosing valid completions of incomplete family genotypes based on Mendelian inheritance rules. By considering all valid completions equally likely and computing quantile intervals over several randomized iterations, CIFBAT avoids assumption of a homogeneous population structure or any particular missingness pattern in the data. Using simulated data, we show that the quantile intervals computed by CIFBAT are useful in validating robustness of the FBAT statistic against missing data and in identifying genomic markers with higher precision. We also propose a novel set of candidate genomic markers for uterine related abnormalities from analysis of familial whole genome sequences, and provide validation for a previously established set of candidate markers for Type 1 diabetes. We have provided a software package that incorporates TDT, robustTDT, FBAT and CIFBAT. The data format proposed for the software uses half the memory space that the standard FBAT format (PED) files use, making it efficient for large scale genome wide association studies.
Published: 2016
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4. Multi-omic network signatures of disease

Author: David L Gibbs, Lisa E Gralinski, Ralph S Baric, and Shannon K McWeeney
Subjects: Proteomics, Transcription, Genetic, Transfection, Virology, omics, biomarkers, Genetics, QH426-470
Abstract: To better understand dynamic disease processes, integrated multi-omic methods are needed, yet comparing different types of omic data remains difficult. Integrative solutions benefit experimenters by eliminating potential biases that come with single omic analysis.We have developed the methods needed to explore whether a relationship exists between co-expression network models built from transcriptomic and proteomic data types, and whether this relationship can be used to improve the disease signature discovery process. A naïve, correlation based method is utilized for comparison. Using publicly available infectious disease time series data, we analyzed the related co-expression structure of the transcriptome and proteome in response to SARS-CoV infection in mice. Transcript and peptide expression data was filtered using quality scores and subset by taking the intersection on mapped Entrez IDs. Using this data set, independent co-expression networks were built. The networks were integrated by constructing a bipartite module graph based on module member overlap, module summary correlation, and correlation to phenotypes of interest. Compared to the module level results, the naïve approach is hindered by a lack of correlation across data types, less significant enrichment results, and little functional overlap across data types. Our module graph approach avoids these problems, resulting in an integrated omic signature of disease progression, which allows prioritization across data types for down-stream experiment planning. Integrated modules exhibited related functional enrichments and could suggest novel interactions in response to infection. These disease and platform-independent methods can be used to realize the full potential of multi-omic network signatures.
Published: 2014
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5. CRI iAtlas: an interactive portal for immuno-oncology research [version 1; peer review: 3 approved]

Author: James A. Eddy, Vésteinn Thorsson, Andrew E. Lamb, David L. Gibbs, Carolina Heimann, Jia Xin Yu, Verena Chung, Yooree Chae, Kristen Dang, Benjamin G. Vincent, Ilya Shmulevich, and Justin Guinney
Subjects: Software Tool Article, Articles, genomics, cancer, immunology, systems biology, R, Shiny
Abstract: The Cancer Research Institute (CRI) iAtlas is an interactive web platform for data exploration and discovery in the context of tumors and their interactions with the immune microenvironment. iAtlas allows researchers to study immune response characterizations and patterns for individual tumor types, tumor subtypes, and immune subtypes. iAtlas supports computation and visualization of correlations and statistics among features related to the tumor microenvironment, cell composition, immune expression signatures, tumor mutation burden, cancer driver mutations, adaptive cell clonality, patient survival, expression of key immunomodulators, and tumor infiltrating lymphocyte (TIL) spatial maps. iAtlas was launched to accompany the release of the TCGA PanCancer Atlas and has since been expanded to include new capabilities such as (1) user-defined loading of sample cohorts, (2) a tool for classifying expression data into immune subtypes, and (3) integration of TIL mapping from digital pathology images. We expect that the CRI iAtlas will accelerate discovery and improve patient outcomes by providing researchers access to standardized immunogenomics data to better understand the tumor immune microenvironment and its impact on patient responses to immunotherapy.
Published: 2020
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6. No more bull: pediatric head injuries as a result of mechanical bull rides

Author: Jordan C. Xu, Anthony J. Vargas, Amy Waunch, David L. Gibbs, James P. Cappon, William G. Loudon, and Suresh N. Magge
Subjects: Pediatrics, Perinatology and Child Health, Neurology (clinical), General Medicine
Published: 2022

7. Table S6 from Integrative Molecular Characterization of Malignant Pleural Mesothelioma

Author: Erik Zmuda, Hongxin Zhang, Hailei Zhang, Jiashan Zhang, Jean C. Zenklusen, Marjorie G. Zauderer, Liming Yang, Shogo Yamamoto, Ye Wu, Tina Wong, Ignacio Wistuba, Lisa Wise, Matthew D. Wilkerson, Daniel J. Weisenberger, John N. Weinstein, Joellen Weaver, Jing Wang, Zhining Wang, Yunhu Wan, Douglas Voet, Luciano S. Viana, Umadevi Veluvolu, Nico van Zandwijk, David J. Van Den Berg, Federico Valdivieso, Tohru Tsujimura, Kane Tse, Anne Tsao, Eric Thompson, Nina Thiessen, Barry S Taylor, Kenji Tatsuno, Roy Tarnuzzer, Donghui Tan, Angela Tam, Qiang Sun, Josh Stuart, Chip Stewart, Paul Spellman, Matthew G. Soloway, Heidi J. Sofia, Tara Skelly, Payal Sipahimalani, Janae V. Simons, Henrique C. S. Silveira, Ilya Shmulevich, Yuichi Shiraishi, Yan Shi, Robert Sheridan, Troy Shelton, Candace Shelton, David T Severson, Tanguy Seiwert, Steven E. Schumacher, Nikolaus Schultz, Jacqueline E. Schein, Cristovam Scapulatempo-Neto, Ayuko Sato, Chris Sander, Gordon Saksena, Sara Sadeghi, Valerie Rusch, Jeffrey Roach, Robert C. Rintoul, William G Richards, David Rice, Sheila M. Reynolds, Rui M. Reis, Glen Reid, Gloria Ravegnini, Doris M. Rassl, Nilsa C. Ramirez, Todd Pihl, Charles M. Perou, Robert Penny, Nathan A. Pennell, Arjun Pennathur, Joseph Paulauskis, Harvey I. Pass, Joel S. Parker, Hatice Osmanbeyoglu, Angelica Ochoa, Yulia Newton, Rashi Naresh, Takashi Nakano, Karen Mungall, Andrew J. Mungall, Lisle E. Mose, Scott Morris, Cesar Moran, Richard A. Moore, Gordon B. Mills, Piotr A. Mieczkowski, Matthew Meyerson, Shaowu Meng, Jonathan Melamed, Sam Meier, Michael Mayo, Marco A. Marra, David Mallory, Dennis T. Maglinte, Yussanne Ma, James Luketich, Yiling Lu, Adhemar Longatto-Filho, Laxmi Lolla, Eric Minwei Liu, Wenbin Liu, Jia Liu, Pei Lin, Tara M. Lichtenberg, Kristen M. Leraas, Darlene Lee, Michael S. Lawrence, Peter W. Laird, Phillip H. Lai, David J. Kwiatkowski, Kozo Kuribayashi, Ritika Kundra, Thomas Krausz, Nobuyuki Kondo, Jaegil Kim, Katayoon Kasaian, Rupa S. Kanchi, Corbin D. Jones, Steven J.M. Jones, Stuart R. Jefferys, Carolyn M. Hutter, Aliya Husain, Alan P. Hoyle, Robert A. Holt, Katherine A. Hoadley, Zachary Heins, David I. Heiman, D. Neil Hayes, David Haussler, Seiki Hasegawa, Guangwu Guo, Benjamin Gross, Chandra Goparaju, Gad Getz, Mark Gerken, Nils Gehlenborg, Julie M. Gastier-Foster, Johanna Gardner, Jianjiong Gao, Françoise Galateau Sallé, Stacey B. Gabriel, Shiro Fukuda, Martin L. Ferguson, Michael Feldman, Rajiv Dhir, Noreen Dhalla, John A. Demchok, Timothy DeFreitas, Assunta De Rienzo, Ludmila Danilova, Erin Curley, Daniel Crain, Leslie Cope, Carrie Cibulskis, Sudha Chudamani, Eric Chuah, Juok Cho, Lucian Chirieac, Dorothy Cheung, Andrew D. Cherniack, Andre L. Carvalho, Rebecca Carlsen, Flávio M. Cárcano, Lauren Averett Byers, Denise Brooks, Moiz S. Bootwalla, Tom Bodenheimer, Craig Bielski, Rameen Beroukhim, Carmen Behrens, Michael Becich, Stephen B. Baylin, Saianand Balu, Miruna Balasundaram, J. Todd Auman, Joshua Armenia, Pavana Anur, Adrian Ally, Marc Ladanyi, Peter Campbell, Bruce W. Robinson, Hiroyuki Aburatani, Ronglai Shen, A. Gordon Robertson, Jonathan A. Fletcher, Michael S. Noble, Lauren A. Byers, Rehan Akbani, Jean Claude Zenklusen, Ina Felau, Jay Bowen, Kristen Leraas, Tara Lichtenberg, Jenette Creaney, Anne S. Tsao, Raphael Bueno, Hedy Kindler, Harvey Pass, Valerie W. Rusch, Henrique C. Silveira, Aliya N. Husain, Junya Fujimoto, Françoise Galateau-Sallé, Lucian R. Chirieac, Sanja Dacic, William D. Travis, Achim A. Jungbluth, Patrice Desmeules, David T. Severson, Havish Kantheti, Lisa Iype, Vesteinn Thorsson, David L. Gibbs, Lixia Diao, Carl M. Gay, Manaswi Gupta, Kiley Graim, Jumpei Takeshita, Yoshitaka Sekido, Hatice U. Osmanbeyoglu, Rupa Kanchi, Reanne Bowlby, Ewan A. Gibb, Esther Drill, Patrick Tarpey, David Heiman, Juliann Shih, Francisco Sanchez-Vega, and Julija Hmeljak
Abstract: Table S6 contains results from the analysis of DNA methylation in SETD2 mutated and BAP1 inactivated samples.
Published: 2023

8. Data from Integrative Molecular Characterization of Malignant Pleural Mesothelioma

Author: Erik Zmuda, Hongxin Zhang, Hailei Zhang, Jiashan Zhang, Jean C. Zenklusen, Marjorie G. Zauderer, Liming Yang, Shogo Yamamoto, Ye Wu, Tina Wong, Ignacio Wistuba, Lisa Wise, Matthew D. Wilkerson, Daniel J. Weisenberger, John N. Weinstein, Joellen Weaver, Jing Wang, Zhining Wang, Yunhu Wan, Douglas Voet, Luciano S. Viana, Umadevi Veluvolu, Nico van Zandwijk, David J. Van Den Berg, Federico Valdivieso, Tohru Tsujimura, Kane Tse, Anne Tsao, Eric Thompson, Nina Thiessen, Barry S Taylor, Kenji Tatsuno, Roy Tarnuzzer, Donghui Tan, Angela Tam, Qiang Sun, Josh Stuart, Chip Stewart, Paul Spellman, Matthew G. Soloway, Heidi J. Sofia, Tara Skelly, Payal Sipahimalani, Janae V. Simons, Henrique C. S. Silveira, Ilya Shmulevich, Yuichi Shiraishi, Yan Shi, Robert Sheridan, Troy Shelton, Candace Shelton, David T Severson, Tanguy Seiwert, Steven E. Schumacher, Nikolaus Schultz, Jacqueline E. Schein, Cristovam Scapulatempo-Neto, Ayuko Sato, Chris Sander, Gordon Saksena, Sara Sadeghi, Valerie Rusch, Jeffrey Roach, Robert C. Rintoul, William G Richards, David Rice, Sheila M. Reynolds, Rui M. Reis, Glen Reid, Gloria Ravegnini, Doris M. Rassl, Nilsa C. Ramirez, Todd Pihl, Charles M. Perou, Robert Penny, Nathan A. Pennell, Arjun Pennathur, Joseph Paulauskis, Harvey I. Pass, Joel S. Parker, Hatice Osmanbeyoglu, Angelica Ochoa, Yulia Newton, Rashi Naresh, Takashi Nakano, Karen Mungall, Andrew J. Mungall, Lisle E. Mose, Scott Morris, Cesar Moran, Richard A. Moore, Gordon B. Mills, Piotr A. Mieczkowski, Matthew Meyerson, Shaowu Meng, Jonathan Melamed, Sam Meier, Michael Mayo, Marco A. Marra, David Mallory, Dennis T. Maglinte, Yussanne Ma, James Luketich, Yiling Lu, Adhemar Longatto-Filho, Laxmi Lolla, Eric Minwei Liu, Wenbin Liu, Jia Liu, Pei Lin, Tara M. Lichtenberg, Kristen M. Leraas, Darlene Lee, Michael S. Lawrence, Peter W. Laird, Phillip H. Lai, David J. Kwiatkowski, Kozo Kuribayashi, Ritika Kundra, Thomas Krausz, Nobuyuki Kondo, Jaegil Kim, Katayoon Kasaian, Rupa S. Kanchi, Corbin D. Jones, Steven J.M. Jones, Stuart R. Jefferys, Carolyn M. Hutter, Aliya Husain, Alan P. Hoyle, Robert A. Holt, Katherine A. Hoadley, Zachary Heins, David I. Heiman, D. Neil Hayes, David Haussler, Seiki Hasegawa, Guangwu Guo, Benjamin Gross, Chandra Goparaju, Gad Getz, Mark Gerken, Nils Gehlenborg, Julie M. Gastier-Foster, Johanna Gardner, Jianjiong Gao, Françoise Galateau Sallé, Stacey B. Gabriel, Shiro Fukuda, Martin L. Ferguson, Michael Feldman, Rajiv Dhir, Noreen Dhalla, John A. Demchok, Timothy DeFreitas, Assunta De Rienzo, Ludmila Danilova, Erin Curley, Daniel Crain, Leslie Cope, Carrie Cibulskis, Sudha Chudamani, Eric Chuah, Juok Cho, Lucian Chirieac, Dorothy Cheung, Andrew D. Cherniack, Andre L. Carvalho, Rebecca Carlsen, Flávio M. Cárcano, Lauren Averett Byers, Denise Brooks, Moiz S. Bootwalla, Tom Bodenheimer, Craig Bielski, Rameen Beroukhim, Carmen Behrens, Michael Becich, Stephen B. Baylin, Saianand Balu, Miruna Balasundaram, J. Todd Auman, Joshua Armenia, Pavana Anur, Adrian Ally, Marc Ladanyi, Peter Campbell, Bruce W. Robinson, Hiroyuki Aburatani, Ronglai Shen, A. Gordon Robertson, Jonathan A. Fletcher, Michael S. Noble, Lauren A. Byers, Rehan Akbani, Jean Claude Zenklusen, Ina Felau, Jay Bowen, Kristen Leraas, Tara Lichtenberg, Jenette Creaney, Anne S. Tsao, Raphael Bueno, Hedy Kindler, Harvey Pass, Valerie W. Rusch, Henrique C. Silveira, Aliya N. Husain, Junya Fujimoto, Françoise Galateau-Sallé, Lucian R. Chirieac, Sanja Dacic, William D. Travis, Achim A. Jungbluth, Patrice Desmeules, David T. Severson, Havish Kantheti, Lisa Iype, Vesteinn Thorsson, David L. Gibbs, Lixia Diao, Carl M. Gay, Manaswi Gupta, Kiley Graim, Jumpei Takeshita, Yoshitaka Sekido, Hatice U. Osmanbeyoglu, Rupa Kanchi, Reanne Bowlby, Ewan A. Gibb, Esther Drill, Patrick Tarpey, David Heiman, Juliann Shih, Francisco Sanchez-Vega, and Julija Hmeljak
Abstract: Malignant pleural mesothelioma (MPM) is a highly lethal cancer of the lining of the chest cavity. To expand our understanding of MPM, we conducted a comprehensive integrated genomic study, including the most detailed analysis of BAP1 alterations to date. We identified histology-independent molecular prognostic subsets, and defined a novel genomic subtype with TP53 and SETDB1 mutations and extensive loss of heterozygosity. We also report strong expression of the immune-checkpoint gene VISTA in epithelioid MPM, strikingly higher than in other solid cancers, with implications for the immune response to MPM and for its immunotherapy. Our findings highlight new avenues for further investigation of MPM biology and novel therapeutic options.Significance:Through a comprehensive integrated genomic study of 74 MPMs, we provide a deeper understanding of histology-independent determinants of aggressive behavior, define a novel genomic subtype with TP53 and SETDB1 mutations and extensive loss of heterozygosity, and discovered strong expression of the immune-checkpoint gene VISTA in epithelioid MPM.See related commentary by Aggarwal and Albelda, p. 1508.This article is highlighted in the In This Issue feature, p. 1494
Published: 2023

9. Supplementary Data from Integrative Molecular Characterization of Malignant Pleural Mesothelioma

Author: Erik Zmuda, Hongxin Zhang, Hailei Zhang, Jiashan Zhang, Jean C. Zenklusen, Marjorie G. Zauderer, Liming Yang, Shogo Yamamoto, Ye Wu, Tina Wong, Ignacio Wistuba, Lisa Wise, Matthew D. Wilkerson, Daniel J. Weisenberger, John N. Weinstein, Joellen Weaver, Jing Wang, Zhining Wang, Yunhu Wan, Douglas Voet, Luciano S. Viana, Umadevi Veluvolu, Nico van Zandwijk, David J. Van Den Berg, Federico Valdivieso, Tohru Tsujimura, Kane Tse, Anne Tsao, Eric Thompson, Nina Thiessen, Barry S Taylor, Kenji Tatsuno, Roy Tarnuzzer, Donghui Tan, Angela Tam, Qiang Sun, Josh Stuart, Chip Stewart, Paul Spellman, Matthew G. Soloway, Heidi J. Sofia, Tara Skelly, Payal Sipahimalani, Janae V. Simons, Henrique C. S. Silveira, Ilya Shmulevich, Yuichi Shiraishi, Yan Shi, Robert Sheridan, Troy Shelton, Candace Shelton, David T Severson, Tanguy Seiwert, Steven E. Schumacher, Nikolaus Schultz, Jacqueline E. Schein, Cristovam Scapulatempo-Neto, Ayuko Sato, Chris Sander, Gordon Saksena, Sara Sadeghi, Valerie Rusch, Jeffrey Roach, Robert C. Rintoul, William G Richards, David Rice, Sheila M. Reynolds, Rui M. Reis, Glen Reid, Gloria Ravegnini, Doris M. Rassl, Nilsa C. Ramirez, Todd Pihl, Charles M. Perou, Robert Penny, Nathan A. Pennell, Arjun Pennathur, Joseph Paulauskis, Harvey I. Pass, Joel S. Parker, Hatice Osmanbeyoglu, Angelica Ochoa, Yulia Newton, Rashi Naresh, Takashi Nakano, Karen Mungall, Andrew J. Mungall, Lisle E. Mose, Scott Morris, Cesar Moran, Richard A. Moore, Gordon B. Mills, Piotr A. Mieczkowski, Matthew Meyerson, Shaowu Meng, Jonathan Melamed, Sam Meier, Michael Mayo, Marco A. Marra, David Mallory, Dennis T. Maglinte, Yussanne Ma, James Luketich, Yiling Lu, Adhemar Longatto-Filho, Laxmi Lolla, Eric Minwei Liu, Wenbin Liu, Jia Liu, Pei Lin, Tara M. Lichtenberg, Kristen M. Leraas, Darlene Lee, Michael S. Lawrence, Peter W. Laird, Phillip H. Lai, David J. Kwiatkowski, Kozo Kuribayashi, Ritika Kundra, Thomas Krausz, Nobuyuki Kondo, Jaegil Kim, Katayoon Kasaian, Rupa S. Kanchi, Corbin D. Jones, Steven J.M. Jones, Stuart R. Jefferys, Carolyn M. Hutter, Aliya Husain, Alan P. Hoyle, Robert A. Holt, Katherine A. Hoadley, Zachary Heins, David I. Heiman, D. Neil Hayes, David Haussler, Seiki Hasegawa, Guangwu Guo, Benjamin Gross, Chandra Goparaju, Gad Getz, Mark Gerken, Nils Gehlenborg, Julie M. Gastier-Foster, Johanna Gardner, Jianjiong Gao, Françoise Galateau Sallé, Stacey B. Gabriel, Shiro Fukuda, Martin L. Ferguson, Michael Feldman, Rajiv Dhir, Noreen Dhalla, John A. Demchok, Timothy DeFreitas, Assunta De Rienzo, Ludmila Danilova, Erin Curley, Daniel Crain, Leslie Cope, Carrie Cibulskis, Sudha Chudamani, Eric Chuah, Juok Cho, Lucian Chirieac, Dorothy Cheung, Andrew D. Cherniack, Andre L. Carvalho, Rebecca Carlsen, Flávio M. Cárcano, Lauren Averett Byers, Denise Brooks, Moiz S. Bootwalla, Tom Bodenheimer, Craig Bielski, Rameen Beroukhim, Carmen Behrens, Michael Becich, Stephen B. Baylin, Saianand Balu, Miruna Balasundaram, J. Todd Auman, Joshua Armenia, Pavana Anur, Adrian Ally, Marc Ladanyi, Peter Campbell, Bruce W. Robinson, Hiroyuki Aburatani, Ronglai Shen, A. Gordon Robertson, Jonathan A. Fletcher, Michael S. Noble, Lauren A. Byers, Rehan Akbani, Jean Claude Zenklusen, Ina Felau, Jay Bowen, Kristen Leraas, Tara Lichtenberg, Jenette Creaney, Anne S. Tsao, Raphael Bueno, Hedy Kindler, Harvey Pass, Valerie W. Rusch, Henrique C. Silveira, Aliya N. Husain, Junya Fujimoto, Françoise Galateau-Sallé, Lucian R. Chirieac, Sanja Dacic, William D. Travis, Achim A. Jungbluth, Patrice Desmeules, David T. Severson, Havish Kantheti, Lisa Iype, Vesteinn Thorsson, David L. Gibbs, Lixia Diao, Carl M. Gay, Manaswi Gupta, Kiley Graim, Jumpei Takeshita, Yoshitaka Sekido, Hatice U. Osmanbeyoglu, Rupa Kanchi, Reanne Bowlby, Ewan A. Gibb, Esther Drill, Patrick Tarpey, David Heiman, Juliann Shih, Francisco Sanchez-Vega, and Julija Hmeljak
Abstract: Supplementary materials and methods, as well as supplementary figures S1-S7.
Published: 2023

10. Gene Set Scoring on the Nearest Neighbor Graph (gssnng) for Single Cell RNA-seq (scRNA-seq)

Author: David L Gibbs, Michael K Strasser, and Sui Huang
Abstract: MotivationGene set scoring is a common task in bioinformatics, but the sparsity of data points in single cell gene expression creates difficulties.ResultsBy applying matrix smoothing to the nearest neighbor graph of cells, high quality gene set scores can be produced on a per-cell level which is useful for visualization and statistical analysis. Importantly, within UMAP visualizations, the method preserves score-gradients across clusters of cells.Contactdavid.gibbs@isbscience.orgSoftwaregssnng is available on the python package index (PyPI). It can be installed using ‘pip install gssnng’.More information and demo notebookSeehttps://github.com/gibbsdavidl/gssnng
Published: 2022

11. 970: DISCRETE FIBROBLAST SUBSETS NURTURE GASTRIC CARCINOGENESIS

Author: Su-Hyung Lee, Ela W. Contreras Panta, David L. Gibbs, Yoonkyung Won, Jimin Min, Changqing Zhang, Lorenzo E. Ferri, Veena Sangwan, Ioannis Ragoussis, Sophie Camilleri-Broet, Joseph Caruso, Michael Strasser, Philippe D. Gascard, Sui Huang, Thea D. Tlsty, Eunyoung Choi, and James R. Goldenring
Subjects: Paediatrics and Reproductive Medicine, Hepatology, Gastroenterology & Hepatology, Clinical Sciences, Gastroenterology, Neurosciences
Published: 2022

12. The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution

Author: Orit Rozenblatt-Rosen, Aviv Regev, Philipp Oberdoerffer, Tal Nawy, Anna Hupalowska, Jennifer E. Rood, Orr Ashenberg, Ethan Cerami, Robert J. Coffey, Emek Demir, Li Ding, Edward D. Esplin, James M. Ford, Jeremy Goecks, Sharmistha Ghosh, Joe W. Gray, Justin Guinney, Sean E. Hanlon, Shannon K. Hughes, E. Shelley Hwang, Christine A. Iacobuzio-Donahue, Judit Jané-Valbuena, Bruce E. Johnson, Ken S. Lau, Tracy Lively, Sarah A. Mazzilli, Dana Pe’er, Sandro Santagata, Alex K. Shalek, Denis Schapiro, Michael P. Snyder, Peter K. Sorger, Avrum E. Spira, Sudhir Srivastava, Kai Tan, Robert B. West, Elizabeth H. Williams, Denise Aberle, Samuel I. Achilefu, Foluso O. Ademuyiwa, Andrew C. Adey, Rebecca L. Aft, Rachana Agarwal, Ruben A. Aguilar, Fatemeh Alikarami, Viola Allaj, Christopher Amos, Robert A. Anders, Michael R. Angelo, Kristen Anton, Jon C. Aster, Ozgun Babur, Amir Bahmani, Akshay Balsubramani, David Barrett, Jennifer Beane, Diane E. Bender, Kathrin Bernt, Lynne Berry, Courtney B. Betts, Julie Bletz, Katie Blise, Adrienne Boire, Genevieve Boland, Alexander Borowsky, Kristopher Bosse, Matthew Bott, Ed Boyden, James Brooks, Raphael Bueno, Erik A. Burlingame, Qiuyin Cai, Joshua Campbell, Wagma Caravan, Hassan Chaib, Joseph M. Chan, Young Hwan Chang, Deyali Chatterjee, Ojasvi Chaudhary, Alyce A. Chen, Bob Chen, Changya Chen, Chia-hui Chen, Feng Chen, Yu-An Chen, Milan G. Chheda, Koei Chin, Roxanne Chiu, Shih-Kai Chu, Rodrigo Chuaqui, Jaeyoung Chun, Luis Cisneros, Graham A. Colditz, Kristina Cole, Natalie Collins, Kevin Contrepois, Lisa M. Coussens, Allison L. Creason, Daniel Crichton, Christina Curtis, Tanja Davidsen, Sherri R. Davies, Ino de Bruijn, Laura Dellostritto, Angelo De Marzo, David G. DeNardo, Dinh Diep, Sharon Diskin, Xengie Doan, Julia Drewes, Stephen Dubinett, Michael Dyer, Jacklynn Egger, Jennifer Eng, Barbara Engelhardt, Graham Erwin, Laura Esserman, Alex Felmeister, Heidi S. Feiler, Ryan C. Fields, Stephen Fisher, Keith Flaherty, Jennifer Flournoy, Angelo Fortunato, Allison Frangieh, Jennifer L. Frye, Robert S. Fulton, Danielle Galipeau, Siting Gan, Jianjiong Gao, Long Gao, Peng Gao, Vianne R. Gao, Tim Geiger, Ajit George, Gad Getz, Marios Giannakis, David L. Gibbs, William E. Gillanders, Simon P. Goedegebuure, Alanna Gould, Kate Gowers, William Greenleaf, Jeremy Gresham, Jennifer L. Guerriero, Tuhin K. Guha, Alexander R. Guimaraes, David Gutman, Nir Hacohen, Sean Hanlon, Casey R. Hansen, Olivier Harismendy, Kathleen A. Harris, Aaron Hata, Akimasa Hayashi, Cody Heiser, Karla Helvie, John M. Herndon, Gilliam Hirst, Frank Hodi, Travis Hollmann, Aaron Horning, James J. Hsieh, Shannon Hughes, Won Jae Huh, Stephen Hunger, Shelley E. Hwang, Heba Ijaz, Benjamin Izar, Connor A. Jacobson, Samuel Janes, Reyka G. Jayasinghe, Lihua Jiang, Brett E. Johnson, Bruce Johnson, Tao Ju, Humam Kadara, Klaus Kaestner, Jacob Kagan, Lukas Kalinke, Robert Keith, Aziz Khan, Warren Kibbe, Albert H. Kim, Erika Kim, Junhyong Kim, Annette Kolodzie, Mateusz Kopytra, Eran Kotler, Robert Krueger, Kostyantyn Krysan, Anshul Kundaje, Uri Ladabaum, Blue B. Lake, Huy Lam, Rozelle Laquindanum, Ashley M. Laughney, Hayan Lee, Marc Lenburg, Carina Leonard, Ignaty Leshchiner, Rochelle Levy, Jerry Li, Christine G. Lian, Kian-Huat Lim, Jia-Ren Lin, Yiyun Lin, Qi Liu, Ruiyang Liu, William J.R. Longabaugh, Teri Longacre, Cynthia X. Ma, Mary Catherine Macedonia, Tyler Madison, Christopher A. Maher, Anirban Maitra, Netta Makinen, Danika Makowski, Carlo Maley, Zoltan Maliga, Diego Mallo, John Maris, Nick Markham, Jeffrey Marks, Daniel Martinez, Robert J. Mashl, Ignas Masilionais, Jennifer Mason, Joan Massagué, Pierre Massion, Marissa Mattar, Richard Mazurchuk, Linas Mazutis, Eliot T. McKinley, Joshua F. McMichael, Daniel Merrick, Matthew Meyerson, Julia R. Miessner, Gordon B. Mills, Meredith Mills, Suman B. Mondal, Motomi Mori, Yuriko Mori, Elizabeth Moses, Yael Mosse, Jeremy L. Muhlich, George F. Murphy, Nicholas E. Navin, Michel Nederlof, Reid Ness, Stephanie Nevins, Milen Nikolov, Ajit Johnson Nirmal, Garry Nolan, Edward Novikov, Brendan O’Connell, Michael Offin, Stephen T. Oh, Anastasiya Olson, Alex Ooms, Miguel Ossandon, Kouros Owzar, Swapnil Parmar, Tasleema Patel, Gary J. Patti, Itsik Pe'er, Tao Peng, Daniel Persson, Marvin Petty, Hanspeter Pfister, Kornelia Polyak, Kamyar Pourfarhangi, Sidharth V. Puram, Qi Qiu, Álvaro Quintanal-Villalonga, Arjun Raj, Marisol Ramirez-Solano, Rumana Rashid, Ashley N. Reeb, Mary Reid, Adam Resnick, Sheila M. Reynolds, Jessica L. Riesterer, Scott Rodig, Joseph T. Roland, Sonia Rosenfield, Asaf Rotem, Sudipta Roy, Charles M. Rudin, Marc D. Ryser, Maria Santi-Vicini, Kazuhito Sato, Deborah Schrag, Nikolaus Schultz, Cynthia L. Sears, Rosalie C. Sears, Subrata Sen, Triparna Sen, Alex Shalek, Jeff Sheng, Quanhu Sheng, Kooresh I. Shoghi, Martha J. Shrubsole, Yu Shyr, Alexander B. Sibley, Kiara Siex, Alan J. Simmons, Dinah S. Singer, Shamilene Sivagnanam, Michal Slyper, Artem Sokolov, Sheng-Kwei Song, Austin Southard-Smith, Avrum Spira, Janet Stein, Phillip Storm, Elizabeth Stover, Siri H. Strand, Timothy Su, Damir Sudar, Ryan Sullivan, Lea Surrey, Mario Suvà, Nadezhda V. Terekhanova, Luke Ternes, Lisa Thammavong, Guillaume Thibault, George V. Thomas, Vésteinn Thorsson, Ellen Todres, Linh Tran, Madison Tyler, Yasin Uzun, Anil Vachani, Eliezer Van Allen, Simon Vandekar, Deborah J. Veis, Sébastien Vigneau, Arastoo Vossough, Angela Waanders, Nikhil Wagle, Liang-Bo Wang, Michael C. Wendl, Robert West, Chi-yun Wu, Hao Wu, Hung-Yi Wu, Matthew A. Wyczalkowski, Yubin Xie, Xiaolu Yang, Clarence Yapp, Wenbao Yu, Yinyin Yuan, Dadong Zhang, Kun Zhang, Mianlei Zhang, Nancy Zhang, Yantian Zhang, Yanyan Zhao, Daniel Cui Zhou, Zilu Zhou, Houxiang Zhu, Qin Zhu, Xiangzhu Zhu, Yuankun Zhu, and Xiaowei Zhuang
Subjects: Cell, Genomics, Computational biology, Tumor initiation, Biology, Article, General Biochemistry, Genetics and Molecular Biology, Metastasis, 03 medical and health sciences, Atlases as Topic, 0302 clinical medicine, Neoplasms, Tumor Microenvironment, medicine, Humans, Precision Medicine, 030304 developmental biology, 0303 health sciences, Atlas (topology), Cancer, medicine.disease, 3. Good health, Human tumor, Cell Transformation, Neoplastic, medicine.anatomical_structure, Single-Cell Analysis, Single point, 030217 neurology & neurosurgery
Abstract: Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.
Published: 2020

13. Patient-Specific Cell Communication Networks Associate With Disease Progression in Cancer

Author: Alexander V. Ratushny, David L Gibbs, Vesteinn Thorsson, Boris Aguilar, and Ilya Shmulevich
Subjects: Cell type, Tumor microenvironment, Cell signaling, Systems biology, Cancer, systems biology, cell communication, Computational biology, Disease, bioinformatics, QH426-470, Biology, medicine.disease, Phenotype, computational oncology, networks, Gene expression, medicine, Genetics, Molecular Medicine, Gene, immuno-oncology, Genetics (clinical), Original Research
Abstract: The maintenance and function of tissues in health and disease depends on cell-cell communication. This work shows how high-level features, representing cell-cell communication, can be defined and used to associate certain signaling ‘axes’ with clinical outcomes. Using cell-sorted gene expression data, we generated a scaffold of cell-cell interactions and define a probabilistic method for creating per-patient weighted graphs based on gene expression and cell deconvolution results. With this method, we generated over 9,000 graphs for TCGA patient samples, each representing likely channels of intercellular communication in the tumor microenvironment. It was shown that particular edges were strongly associated with disease severity and progression, in terms of survival time and tumor stage. Within individual tumor types, there are predominant cell types and the collection of associated edges were found to be predictive of clinical phenotypes. Additionally, genes associated with differentially weighted edges were enriched in Gene Ontology terms associated with tissue structure and immune response. Code, data, and notebooks are provided to enable the application of this method to any expression dataset (https://github.com/IlyaLab/Pan-Cancer-Cell-Cell-Comm-Net).
Published: 2021

14. PRoBE the cloud toolkit: finding the best biomarkers of drug response within a breast cancer clinical trial

Author: Christina Yau, David L Gibbs, Nicholas O'Grady, Laura J. Esserman, Smita Asare, Denise M. Wolf, Adam Asare, Gillian L. Hirst, Kawther Abdilleh, Amrita Basu, Lamorna Brown-Swigart, Sara J. Venters, and Laura J. van't Veer
Subjects: 0301 basic medicine, SQL, AcademicSubjects/SCI01060, Computer science, data analysis, Bioengineering, Health Informatics, Cloud computing, Context (language use), Research and Applications, computer.software_genre, 03 medical and health sciences, 0302 clinical medicine, Documentation, breast cancer, Genetics, cloud, Cancer, computer.programming_language, Codebase, clinical trials, business.industry, Human Genome, Gateway (computer program), Data structure, Data science, 030104 developmental biology, Networking and Information Technology R&D (NITRD), 030220 oncology & carcinogenesis, Patient Safety, AcademicSubjects/SCI01530, I-SPY 2, AcademicSubjects/MED00010, business, computer, Data integration
Abstract: ObjectivesIn this paper, we discuss leveraging cloud-based platforms to collect, visualize, analyze, and share data in the context of a clinical trial. Our cloud-based infrastructure, Patient Repository of Biomolecular Entities (PRoBE), has given us the opportunity for uniform data structure, more efficient analysis of valuable data, and increased collaboration between researchers.Materials and MethodsWe utilize a multi-cloud platform to manage and analyze data generated from the clinical Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging And moLecular Analysis 2 (I-SPY 2 TRIAL). A collaboration with the Institute for Systems Biology Cancer Gateway in the Cloud has additionally given us access to public genomic databases. Applications to I-SPY 2 data have been built using R Shiny, while leveraging Google's BigQuery tables and SQL commands for data mining.ResultsWe highlight the implementation of PRoBE in several unique case studies including prediction of biomarkers associated with clinical response, access to the Pan-Cancer Atlas, and integrating pathology images within the cloud. Our data integration pipelines, documentation, and all codebase will be placed in a Github repository.Discussion and conclusionWe are hoping to develop risk stratification diagnostics by integrating additional molecular, magnetic resonance imaging, and pathology markers into PRoBE to better predict drug response. A robust cloud infrastructure and tool set can help integrate these large datasets to make valuable predictions of response to multiple agents. For that reason, we are continuously improving PRoBE to advance the way data is stored, accessed, and analyzed in the I-SPY 2 clinical trial.
Published: 2021

15. A functional module states framework reveals transcriptional states for drug and target prediction

Author: Guangrong Qin, Theo A. Knijnenburg, David L. Gibbs, Russell Moser, Raymond J. Monnat, Christopher J. Kemp, and Ilya Shmulevich
Subjects: Machine Learning, Gene Expression Profiling, Humans, Breast Neoplasms, Female, Molecular Targeted Therapy, Transcriptome, General Biochemistry, Genetics and Molecular Biology
Abstract: Cells are complex systems in which many functions are performed by different genetically defined and encoded functional modules. To systematically understand how these modules respond to drug or genetic perturbations, we develop a functional module states framework. Using this framework, we (1) define the drug-induced transcriptional state space for breast cancer cell lines using large public gene expression datasets and reveal that the transcriptional states are associated with drug concentration and drug targets, (2) identify potential targetable vulnerabilities through integrative analysis of transcriptional states after drug treatment and gene knockdown-associated cancer dependency, and (3) use functional module states to predict transcriptional state-dependent drug sensitivity and build prediction models for drug response. This approach demonstrates a similar prediction performance as approaches using high-dimensional gene expression values, with the added advantage of more clearly revealing biologically relevant transcriptional states and key regulators.
Published: 2021

16. A functional module states framework reveals cell states for drug and target prediction

Author: Raymond J. Monnat, Theo A. Knijnenburg, Russell Moser, Guangrong Qin, Christopher J. Kemp, David L Gibbs, and Ilya Shmulevich
Subjects: Drug, Gene knockdown, Drug treatment, Drug concentration, medicine.anatomical_structure, Computer science, media_common.quotation_subject, Functional module, Gene expression, Cell, medicine, Computational biology, media_common
Abstract: SummaryCells are complex systems in which many functions are performed by different genetically-defined and encoded functional modules. To systematically understand how these modules respond to drug or genetic perturbations, we developed a Functional Module States framework. Using this framework, we 1) defined the drug induced transcriptional state space for breast cancer cell lines using large public gene expression datasets, and revealed that the transcriptional states are associated with drug concentration and drug targets; 2) identified potential targetable vulnerabilities through integrative analysis of transcriptional states after drug treatment and gene knockdown associated cancer dependency; and 3) used functional module states to predict transcriptional state-dependent drug sensitivity and built prediction models using the functional module states for drug response. This approach demonstrates a similar prediction performance as do approaches using high dimensional gene expression values, with the added advantage of more clearly revealing biologically relevant transcriptional states and key regulators.
Published: 2020

17. CRI iAtlas: an interactive portal for immuno-oncology research [version 1; peer review: 2 approved]

Author: James A. Eddy, Vésteinn Thorsson, Andrew E. Lamb, David L. Gibbs, Carolina Heimann, Jia Xin Yu, Verena Chung, Yooree Chae, Kristen Dang, Benjamin G. Vincent, Ilya Shmulevich, and Justin Guinney
Subjects: lcsh:R, lcsh:Medicine, chemical and pharmacologic phenomena, lcsh:Q, lcsh:Science
Abstract: The Cancer Research Institute (CRI) iAtlas is an interactive web platform for data exploration and discovery in the context of tumors and their interactions with the immune microenvironment. iAtlas allows researchers to study immune response characterizations and patterns for individual tumor types, tumor subtypes, and immune subtypes. iAtlas supports computation and visualization of correlations and statistics among features related to the tumor microenvironment, cell composition, immune expression signatures, tumor mutation burden, cancer driver mutations, adaptive cell clonality, patient survival, expression of key immunomodulators, and tumor infiltrating lymphocyte (TIL) spatial maps. iAtlas was launched to accompany the release of the TCGA PanCancer Atlas and has since been expanded to include new capabilities such as (1) user-defined loading of sample cohorts, (2) a tool for classifying expression data into immune subtypes, and (3) integration of TIL mapping from digital pathology images. We expect that the CRI iAtlas will accelerate discovery and improve patient outcomes by providing researchers access to standardized immunogenomics data to better understand the tumor immune microenvironment and its impact on patient responses to immunotherapy.
Published: 2020

18. A generalizable data-driven multicellular model of pancreatic ductal adenocarcinoma

Author: Mark McConnell, Samuel A. Danziger, Boris Aguilar, David J. Reiss, Matthew Trotter, Alexander V. Ratushny, Andrew Dervan, Robert M. Hershberg, Ilya Shmulevich, Douglas Bassett, and David L Gibbs
Subjects: data-driven model, Stromal cell, AcademicSubjects/SCI02254, pancreatic ductal adenocarcinoma, Health Informatics, Cell Communication, Biology, Models, Biological, multicellular model, 03 medical and health sciences, 0302 clinical medicine, Pancreatic tumor, Pancreatic cancer, Paracrine Communication, medicine, Humans, Autocrine signalling, 030304 developmental biology, 0303 health sciences, Tumor microenvironment, Research, Cancer, medicine.disease, Computer Science Applications, Gene Expression Regulation, Neoplastic, Autocrine Communication, Phenotype, Organ Specificity, 030220 oncology & carcinogenesis, Cancer cell, Hepatic stellate cell, Cancer research, AcademicSubjects/SCI00960, Cytokines, cancer modeling, Disease Susceptibility, Algorithms, Carcinoma, Pancreatic Ductal
Abstract: Background Mechanistic models, when combined with pertinent data, can improve our knowledge regarding important molecular and cellular mechanisms found in cancer. These models make the prediction of tissue-level response to drug treatment possible, which can lead to new therapies and improved patient outcomes. Here we present a data-driven multiscale modeling framework to study molecular interactions between cancer, stromal, and immune cells found in the tumor microenvironment. We also develop methods to use molecular data available in The Cancer Genome Atlas to generate sample-specific models of cancer. Results By combining published models of different cells relevant to pancreatic ductal adenocarcinoma (PDAC), we built an agent-based model of the multicellular pancreatic tumor microenvironment, formally describing cell type–specific molecular interactions and cytokine-mediated cell-cell communications. We used an ensemble-based modeling approach to systematically explore how variations in the tumor microenvironment affect the viability of cancer cells. The results suggest that the autocrine loop involving EGF signaling is a key interaction modulator between pancreatic cancer and stellate cells. EGF is also found to be associated with previously described subtypes of PDAC. Moreover, the model allows a systematic exploration of the effect of possible therapeutic perturbations; our simulations suggest that reducing bFGF secretion by stellate cells will have, on average, a positive impact on cancer apoptosis. Conclusions The developed framework allows model-driven hypotheses to be generated regarding therapeutically relevant PDAC states with potential molecular and cellular drivers indicating specific intervention strategies.
Published: 2020

19. The ISB Cancer Genomics Cloud: A Flexible Cloud-Based Platform for Cancer Genomics Research

Author: Matthew Bookman, Suzanne M. Paquette, Varsha Dhankani, Michael Miller, Kalle Leinonen, Nicole A. Deflaux, Mark Backus, Todd Pihl, Madelyn Reyes, Sheila Reynolds, Joseph Slagel, Jonathan Bingham, David L Gibbs, David Pot, William J.R. Longabaugh, Abigail Hahn, Zack Rodebaugh, Ilya Shmulevich, and Phyliss Lee
Subjects: 0301 basic medicine, Cancer Research, SQL, Computer science, Datasets as Topic, Cloud computing, Bioinformatics, Article, 03 medical and health sciences, 0302 clinical medicine, Documentation, Neoplasms, Humans, Web application, computer.programming_language, Internet, Genome, Human, business.industry, GENCODE, Research, Computational Biology, Genomics, Cloud Computing, Data science, National Cancer Institute (U.S.), United States, ComputingMethodologies_PATTERNRECOGNITION, 030104 developmental biology, Workflow, Oncology, 030220 oncology & carcinogenesis, Data as a service, business, computer, Software
Abstract: The ISB Cancer Genomics Cloud (ISB-CGC) is one of three pilot projects funded by the National Cancer Institute to explore new approaches to computing on large cancer datasets in a cloud environment. With a focus on Data as a Service, the ISB-CGC offers multiple avenues for accessing and analyzing The Cancer Genome Atlas, TARGET, and other important references such as GENCODE and COSMIC using the Google Cloud Platform. The open approach allows researchers to choose approaches best suited to the task at hand: from analyzing terabytes of data using complex workflows to developing new analysis methods in common languages such as Python, R, and SQL; to using an interactive web application to create synthetic patient cohorts and to explore the wealth of available genomic data. Links to resources and documentation can be found at www.isb-cgc.org. Cancer Res; 77(21); e7–10. ©2017 AACR.
Published: 2017

20. A Pan-Cancer Approach to Predict Responsiveness to Immune Checkpoint Inhibitors by Machine Learning

Author: David L Gibbs, Davide Gentilini, Maurizio Polano, Cesare Furlanello, Lorena Baboci, Michele Dal Bo, Giuseppe Toffoli, Marco Chierici, and Federica Di Cintio
Subjects: 0301 basic medicine, Cancer Research, Immune checkpoint inhibitors, medicine.medical_treatment, immunology-pancancer, immune checkpoint inhibitor, Context (language use), Machine learning, computer.software_genre, lcsh:RC254-282, Article, 03 medical and health sciences, 0302 clinical medicine, medicine, Extreme gradient boosting, Pan cancer, business.industry, Cancer, Immunotherapy, lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Matthews correlation coefficient, medicine.disease, Support vector machine, 030104 developmental biology, machine learning, Oncology, 030220 oncology & carcinogenesis, Artificial intelligence, business, computer
Abstract: Immunotherapy by using immune checkpoint inhibitors (ICI) has dramatically improved the treatment options in various cancers, increasing survival rates for treated patients. Nevertheless, there are heterogeneous response rates to ICI among different cancer types, and even in the context of patients affected by a specific cancer. Thus, it becomes crucial to identify factors that predict the response to immunotherapeutic approaches. A comprehensive investigation of the mutational and immunological aspects of the tumor can be useful to obtain a robust prediction. By performing a pan-cancer analysis on gene expression data from the Cancer Genome Atlas (TCGA, 8055 cases and 29 cancer types), we set up and validated a machine learning approach to predict the potential for positive response to ICI. Support vector machines (SVM) and extreme gradient boosting (XGboost) models were developed with a 10&times, 5-fold cross-validation schema on 80% of TCGA cases to predict ICI responsiveness defined by a score combining tumor mutational burden and TGF- &beta, signaling. On the remaining 20% validation subset, our SVM model scored 0.88 accuracy and 0.27 Matthews Correlation Coefficient. The proposed machine learning approach could be useful to predict the putative response to ICI treatment by expression data of primary tumors.
Published: 2019
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21. Genomic and molecular characterization of preterm birth

Author: Prachi Kothiyal, Joseph Slagel, Crystal Humphries, Rajiv Baveja, Ramaswamy K. Iyer, Ilya Shmulevich, David L Gibbs, Nyasha Chambwe, Jasper Linthorst, Leroy Hood, Gustavo Glusman, Theo A. Knijnenburg, Brady Bernard, Ryan Tasseff, Summer Elasady, Robin Baker, Wendy S.W. Wong, Michael Miller, Jared C. Roach, John E. Niederhuber, George L. Maxwell, Dale L. Bodian, Kathi Huddleston, Denise E. Mauldin, Varsha Dhankani, Roger Kramer, Greg Eley, Kalle Leinonen, Benjamin D. Solomon, Elisabeth Klein, and Joseph G. Vockley
Subjects: Male, family trios, Candidate gene, integrative computational analysis, macromolecular substances, Biology, Quantitative trait locus, environment and public health, Polymorphism, Single Nucleotide, Humans, Genetic Predisposition to Disease, Gene, Whole genome sequencing, Genetics, whole genome sequencing, Multidisciplinary, integumentary system, Systems Biology, Infant, Newborn, preterm birth, Methylation, Genomics, DNA Methylation, Biological Sciences, Phenotype, Biomarker (cell), genomic variants, PNAS Plus, DNA methylation, Premature Birth, Female, Signal Transduction
Abstract: Significance Preterm birth (PTB) complications are the leading cause of long-term morbidity and mortality in children. The genetic and molecular characteristics of PTB and related disorders remain unclear. In this study, a family-based cohort of 791 family trios, including 270 PTB and 521 control families, was investigated by using whole-genome sequencing, RNA sequencing, and DNA methylation data. Integrative analysis identified 160 genomic variants associated with PTB-related phenotypes and led to the discovery of 72 candidate biomarker genes for very early PTB (VEPTB). The genes associated with VEPTB involve growth signaling and inflammation- and immunity-related pathways. With these data, and by stratifying PTB by subphenotype, we have identified PTB genes and pathways that can be used as a starting point in further clinical studies., Preterm birth (PTB) complications are the leading cause of long-term morbidity and mortality in children. By using whole blood samples, we integrated whole-genome sequencing (WGS), RNA sequencing (RNA-seq), and DNA methylation data for 270 PTB and 521 control families. We analyzed this combined dataset to identify genomic variants associated with PTB and secondary analyses to identify variants associated with very early PTB (VEPTB) as well as other subcategories of disease that may contribute to PTB. We identified differentially expressed genes (DEGs) and methylated genomic loci and performed expression and methylation quantitative trait loci analyses to link genomic variants to these expression and methylation changes. We performed enrichment tests to identify overlaps between new and known PTB candidate gene systems. We identified 160 significant genomic variants associated with PTB-related phenotypes. The most significant variants, DEGs, and differentially methylated loci were associated with VEPTB. Integration of all data types identified a set of 72 candidate biomarker genes for VEPTB, encompassing genes and those previously associated with PTB. Notably, PTB-associated genes RAB31 and RBPJ were identified by all three data types (WGS, RNA-seq, and methylation). Pathways associated with VEPTB include EGFR and prolactin signaling pathways, inflammation- and immunity-related pathways, chemokine signaling, IFN-γ signaling, and Notch1 signaling. Progress in identifying molecular components of a complex disease is aided by integrated analyses of multiple molecular data types and clinical data. With these data, and by stratifying PTB by subphenotype, we have identified associations between VEPTB and the underlying biology.
Published: 2019

22. Flexibility of Boolean Network Reservoir Computers in Approximating Arbitrary Recursive and Non-Recursive Binary Filters

Author: Ilya Shmulevich, Max Notarangelo, Moriah Echlin, David L Gibbs, and Boris Aguilar
Subjects: 0301 basic medicine, systems dynamics, Computer science, General Physics and Astronomy, Binary number, lcsh:Astrophysics, Article, Boolean networks, 03 medical and health sciences, biological modeling, lcsh:QB460-466, Sensitivity (control systems), general_theoretical_computer_science, lcsh:Science, Boolean function, signal processing, complex adaptive systems, Signal processing, Reservoir computing, Function (mathematics), dynamical systems, reservoir computing, lcsh:QC1-999, 030104 developmental biology, Recurrent neural network, Boolean network, machine learning, lcsh:Q, Algorithm, lcsh:Physics
Abstract: Reservoir computers (RCs) are biology-inspired computational frameworks for signal processing that are typically implemented using recurrent neural networks. Recent work has shown that Boolean networks (BN) can also be used as reservoirs. We analyze the performance of BN RCs, measuring their flexibility and identifying the factors that determine the effective approximation of Boolean functions applied in a sliding-window fashion over a binary signal, both non-recursively and recursively. We train and test BN RCs of different sizes, signal connectivity, and in-degree to approximate three-bit, five-bit, and three-bit recursive binary functions, respectively. We analyze how BN RC parameters and function average sensitivity, which is a measure of function smoothness, affect approximation accuracy as well as the spread of accuracies for a single reservoir. We found that approximation accuracy and reservoir flexibility are highly dependent on RC parameters. Overall, our results indicate that not all reservoirs are equally flexible, and RC instantiation and training can be more efficient if this is taken into account. The optimum range of RC parameters opens up an angle of exploration for understanding how biological systems might be tuned to balance system restraints with processing capacity.
Published: 2018

23. Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics

Author: Li Ding, Matthew H. Bailey, Eduard Porta-Pardo, Vesteinn Thorsson, Antonio Colaprico, Denis Bertrand, David L. Gibbs, Amila Weerasinghe, Kuan-lin Huang, Collin Tokheim, Isidro Cortés-Ciriano, Reyka Jayasinghe, Feng Chen, Lihua Yu, Sam Sun, Catharina Olsen, Jaegil Kim, Alison M. Taylor, Andrew D. Cherniack, Rehan Akbani, Chayaporn Suphavilai, Niranjan Nagarajan, Joshua M. Stuart, Gordon B. Mills, Matthew A. Wyczalkowski, Benjamin G. Vincent, Carolyn M. Hutter, Jean Claude Zenklusen, Katherine A. Hoadley, Michael C. Wendl, llya Shmulevich, Alexander J. Lazar, David A. Wheeler, Gad Getz, Samantha J. Caesar-Johnson, John A. Demchok, Ina Felau, Melpomeni Kasapi, Martin L. Ferguson, 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, David I. Heiman, 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, Wei Zhang, Bradley M. Broom, Apurva M. Hegde, Zhenlin Ju, Rupa S. Kanchi, Anil Korkut, Jun Li, Han Liang, Shiyun Ling, Wenbin Liu, Yiling Lu, 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, 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, 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, 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, Liu Xi, Fengmei Zhao, Julian Hess, Elizabeth L. Appelbaum, Matthew Bailey, Matthew G. Cordes, 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, Hai Hu, 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. Martignetti, Hani Gabra, Hartmut Juhl, Teniola Akeredolu, Serghei Stepa, Dave Hoon, Keunsoo Ahn, Koo Jeong Kang, Felix Beuschlein, Anne Breggia, Michael Birrer, Debra Bell, Mitesh Borad, Alan H. Bryce, Erik Castle, Vishal Chandan, John Cheville, John A. Copland, Michael Farnell, Thomas Flotte, Nasra Giama, Thai Ho, Michael Kendrick, Jean-Pierre Kocher, Karla Kopp, Catherine Moser, David Nagorney, Daniel O’Brien, Brian Patrick O’Neill, Tushar Patel, Gloria Petersen, Florencia Que, Michael Rivera, Lewis Roberts, Robert Smallridge, Thomas Smyrk, Melissa Stanton, R. Houston Thompson, Michael Torbenson, Ju Dong Yang, Lizhi Zhang, Fadi Brimo, Jaffer A. Ajani, Ana Maria Angulo Gonzalez, Carmen Behrens, Jolanta Bondaruk, Russell Broaddus, Bogdan Czerniak, Bita Esmaeli, Junya Fujimoto, Jeffrey Gershenwald, Charles Guo, Christopher Logothetis, Funda Meric-Bernstam, Cesar Moran, Lois Ramondetta, David Rice, Anil Sood, Pheroze Tamboli, Timothy Thompson, Patricia Troncoso, Anne Tsao, Ignacio Wistuba, Candace Carter, Lauren Haydu, Peter Hersey, Valerie Jakrot, Hojabr Kakavand, Richard Kefford, Kenneth Lee, Georgina Long, Graham Mann, Michael Quinn, Robyn Saw, Richard Scolyer, Kerwin Shannon, Andrew Spillane, Jonathan Stretch, Maria Synott, John Thompson, James Wilmott, Hikmat Al-Ahmadie, Timothy A. Chan, Ronald Ghossein, Anuradha Gopalan, Douglas A. Levine, Victor Reuter, Samuel Singer, Bhuvanesh Singh, Nguyen Viet Tien, Thomas Broudy, Cyrus Mirsaidi, Praveen Nair, Paul Drwiega, Judy Miller, Jennifer Smith, Howard Zaren, Joong-Won Park, Nguyen Phi Hung, Electron Kebebew, W. Marston Linehan, Adam R. Metwalli, Karel Pacak, Peter A. Pinto, Mark Schiffman, Laura S. Schmidt, Cathy D. Vocke, Nicolas Wentzensen, Robert Worrell, Hannah Yang, Marc Moncrieff, Chandra Goparaju, Jonathan Melamed, Harvey Pass, Natalia Botnariuc, Irina Caraman, Mircea Cernat, Inga Chemencedji, Adrian Clipca, Serghei Doruc, Ghenadie Gorincioi, Sergiu Mura, Maria Pirtac, Irina Stancul, Diana Tcaciuc, Monique Albert, Iakovina Alexopoulou, Angel Arnaout, John Bartlett, Jay Engel, Sebastien Gilbert, Jeremy Parfitt, Harman Sekhon, George Thomas, Doris M. Rassl, Robert C. Rintoul, Carlo Bifulco, Raina Tamakawa, Walter Urba, Nicholas Hayward, Henri Timmers, Anna Antenucci, Francesco Facciolo, Gianluca Grazi, Mirella Marino, Roberta Merola, Ronald de Krijger, Anne-Paule Gimenez-Roqueplo, Alain Piché, Simone Chevalier, Ginette McKercher, Kivanc Birsoy, Gene Barnett, Cathy Brewer, Carol Farver, Theresa Naska, Nathan A. Pennell, Daniel Raymond, Cathy Schilero, Kathy Smolenski, Felicia Williams, Carl Morrison, Jeffrey A. Borgia, Michael J. Liptay, Mark Pool, Christopher W. Seder, Kerstin Junker, Larsson Omberg, Mikhail Dinkin, George Manikhas, Domenico Alvaro, Maria Consiglia Bragazzi, Vincenzo Cardinale, Guido Carpino, Eugenio Gaudio, David Chesla, Sandra Cottingham, Michael Dubina, Fedor Moiseenko, Renumathy Dhanasekaran, Karl-Friedrich Becker, Klaus-Peter Janssen, Julia Slotta-Huspenina, Mohamed H. Abdel-Rahman, Dina Aziz, Sue Bell, Colleen M. Cebulla, Amy Davis, Rebecca Duell, J. Bradley Elder, Joe Hilty, Bahavna Kumar, James Lang, Norman L. Lehman, Randy Mandt, Phuong Nguyen, Robert Pilarski, Karan Rai, Lynn Schoenfield, Kelly Senecal, Paul Wakely, Paul Hansen, Ronald Lechan, James Powers, Arthur Tischler, William E. Grizzle, Katherine C. Sexton, Alison Kastl, Joel Henderson, Sima Porten, Jens Waldmann, Martin Fassnacht, Sylvia L. Asa, Dirk Schadendorf, Marta Couce, Markus Graefen, Hartwig Huland, Guido Sauter, Thorsten Schlomm, Ronald Simon, Pierre Tennstedt, Oluwole Olabode, Mark Nelson, Oliver Bathe, Peter R. Carroll, June M. Chan, Philip Disaia, Pat Glenn, Robin K. Kelley, Charles N. Landen, Joanna Phillips, Michael Prados, Jeffry Simko, Karen Smith-McCune, Scott VandenBerg, Kevin Roggin, Ashley Fehrenbach, Ady Kendler, Suzanne Sifri, Ruth Steele, Antonio Jimeno, Francis Carey, Ian Forgie, Massimo Mannelli, Michael Carney, Brenda Hernandez, Benito Campos, Christel Herold-Mende, Christin Jungk, Andreas Unterberg, Andreas von Deimling, Aaron Bossler, Joseph Galbraith, Laura Jacobus, Michael Knudson, Tina Knutson, Deqin Ma, Mohammed Milhem, Rita Sigmund, Andrew K. Godwin, Rashna Madan, Howard G. Rosenthal, Clement Adebamowo, Sally N. 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, 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, Craig D. Shriver, 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, Ding L., Bailey M.H., Porta-Pardo E., Thorsson V., Colaprico A., Bertrand D., Gibbs D.L., Weerasinghe A., Huang K.-L., Tokheim C., Cortes-Ciriano I., Jayasinghe R., Chen F., Yu L., Sun S., Olsen C., Kim J., Taylor A.M., Cherniack A.D., Akbani R., Suphavilai C., Nagarajan N., Stuart J.M., Mills G.B., Wyczalkowski M.A., Vincent B.G., Hutter C.M., Zenklusen J.C., Hoadley K.A., Wendl M.C., Shmulevich L., Lazar A.J., Wheeler D.A., Getz G., Caesar-Johnson S.J., Demchok J.A., Felau I., Kasapi M., Ferguson M.L., Sofia H.J., Tarnuzzer R., Wang Z., Yang L., Zhang J.J., Chudamani S., Liu J., Lolla L., Naresh R., Pihl T., Sun Q., Wan Y., Wu Y., Cho J., DeFreitas T., Frazer S., Gehlenborg N., Heiman D.I., Lawrence M.S., Lin P., Meier S., Noble M.S., Saksena G., Voet D., Zhang H., Bernard B., Chambwe N., Dhankani V., Knijnenburg T., Kramer R., Leinonen K., Liu Y., Miller M., Reynolds S., Shmulevich I., Zhang W., Broom B.M., Hegde A.M., Ju Z., Kanchi R.S., Korkut A., Li J., Liang H., Ling S., Liu W., Lu Y., Ng K.-S., Rao A., Ryan M., Wang J., Weinstein J.N., Zhang J., Abeshouse A., Armenia J., Chakravarty D., Chatila W.K., de Bruijn I., Gao J., Gross B.E., Heins Z.J., Kundra R., La K., Ladanyi M., Luna A., Nissan M.G., Ochoa A., Phillips S.M., Reznik E., Sanchez-Vega F., Sander C., Schultz N., Sheridan R., Sumer S.O., Sun Y., Taylor B.S., Anur P., Peto M., Spellman P., Benz C., Wong C.K., Yau C., Hayes D.N., Parker J.S., Wilkerson M.D., Ally A., Balasundaram M., Bowlby R., Brooks D., Carlsen R., Chuah E., Dhalla N., Holt R., Jones S.J.M., Kasaian K., Lee D., Ma Y., Marra M.A., Mayo M., Moore R.A., Mungall A.J., Mungall K., Robertson A.G., Sadeghi S., Schein J.E., Sipahimalani P., Tam A., Thiessen N., Tse K., Wong T., Berger A.C., Beroukhim R., Cibulskis C., Gabriel S.B., Gao G.F., Ha G., Meyerson M., Schumacher S.E., Shih J., Kucherlapati M.H., Kucherlapati R.S., Baylin S., Cope L., Danilova L., Bootwalla M.S., Lai P.H., Maglinte D.T., Van Den Berg D.J., Weisenberger D.J., Auman J.T., Balu S., Bodenheimer T., Fan C., Hoyle A.P., Jefferys S.R., Jones C.D., Meng S., Mieczkowski P.A., Mose L.E., Perou A.H., Perou C.M., Roach J., Shi Y., Simons J.V., Skelly T., Soloway M.G., Tan D., Veluvolu U., Fan H., Hinoue T., Laird P.W., Shen H., Zhou W., Bellair M., Chang K., Covington K., Creighton C.J., Dinh H., Doddapaneni H., Donehower L.A., Drummond J., Gibbs R.A., Glenn R., Hale W., Han Y., Hu J., Korchina V., Lee S., Lewis L., Li W., Liu X., Morgan M., Morton D., Muzny D., Santibanez J., Sheth M., Shinbrot E., Wang L., Wang M., Xi L., Zhao F., Hess J., Appelbaum E.L., Bailey M., Cordes M.G., Fronick C.C., Fulton L.A., Fulton R.S., Kandoth C., Mardis E.R., McLellan M.D., Miller C.A., Schmidt H.K., Wilson R.K., Crain D., Curley E., Gardner J., Lau K., Mallery D., Morris S., Paulauskis J., Penny R., Shelton C., Shelton T., Sherman M., Thompson E., Yena P., Bowen J., Gastier-Foster J.M., Gerken M., Leraas K.M., Lichtenberg T.M., Ramirez N.C., Wise L., Zmuda E., Corcoran N., Costello T., Hovens C., Carvalho A.L., de Carvalho A.C., Fregnani J.H., Longatto-Filho A., Reis R.M., Scapulatempo-Neto C., Silveira H.C.S., Vidal D.O., Burnette A., Eschbacher J., Hermes B., Noss A., Singh R., Anderson M.L., Castro P.D., Ittmann M., Huntsman D., Kohl B., Le X., Thorp R., Andry C., Duffy E.R., Lyadov V., Paklina O., Setdikova G., Shabunin A., Tavobilov M., McPherson C., Warnick R., Berkowitz R., Cramer D., Feltmate C., Horowitz N., Kibel A., Muto M., Raut C.P., Malykh A., Barnholtz-Sloan J.S., Barrett W., Devine K., Fulop J., Ostrom Q.T., Shimmel K., Wolinsky Y., Sloan A.E., De Rose A., Giuliante F., Goodman M., Karlan B.Y., Hagedorn C.H., Eckman J., Harr J., Myers J., Tucker K., Zach L.A., Deyarmin B., Hu H., Kvecher L., Larson C., Mural R.J., Somiari S., Vicha A., Zelinka T., Bennett J., Iacocca M., Rabeno B., Swanson P., Latour M., Lacombe L., Tetu B., Bergeron A., McGraw M., Staugaitis S.M., Chabot J., Hibshoosh H., Sepulveda A., Su T., Wang T., Potapova O., Voronina O., Desjardins L., Mariani O., Roman-Roman S., Sastre X., Stern M.-H., Cheng F., Signoretti S., Berchuck A., Bigner D., Lipp E., Marks J., McCall S., McLendon R., Secord A., Sharp A., Behera M., Brat D.J., Chen A., Delman K., Force S., Khuri F., Magliocca K., Maithel S., Olson J.J., Owonikoko T., Pickens A., Ramalingam S., Shin D.M., Sica G., Van Meir E.G., Eijckenboom W., Gillis A., Korpershoek E., Looijenga L., Oosterhuis W., Stoop H., van Kessel K.E., Zwarthoff E.C., Calatozzolo C., Cuppini L., Cuzzubbo S., DiMeco F., Finocchiaro G., Mattei L., Perin A., Pollo B., Chen C., Houck J., Lohavanichbutr P., Hartmann A., Stoehr C., Stoehr R., Taubert H., Wach S., Wullich B., Kycler W., Murawa D., Wiznerowicz M., Chung K., Edenfield W.J., Martin J., Baudin E., Bubley G., Bueno R., De Rienzo A., Richards W.G., Kalkanis S., Mikkelsen T., Noushmehr H., Scarpace L., Girard N., Aymerich M., Campo E., Gine E., Guillermo A.L., Van Bang N., Hanh P.T., Phu B.D., Tang Y., Colman H., Evason K., Dottino P.R., Martignetti J.A., Gabra H., Juhl H., Akeredolu T., Stepa S., Hoon D., Ahn K., Kang K.J., Beuschlein F., Breggia A., Birrer M., Bell D., Borad M., Bryce A.H., Castle E., Chandan V., Cheville J., Copland J.A., Farnell M., Flotte T., Giama N., Ho T., Kendrick M., Kocher J.-P., Kopp K., Moser C., Nagorney D., O'Brien D., O'Neill B.P., Patel T., Petersen G., Que F., Rivera M., Roberts L., Smallridge R., Smyrk T., Stanton M., Thompson R.H., Torbenson M., Yang J.D., Zhang L., Brimo F., Ajani J.A., Gonzalez A.M.A., Behrens C., Bondaruk J., Broaddus R., Czerniak B., Esmaeli B., Fujimoto J., Gershenwald J., Guo C., Logothetis C., Meric-Bernstam F., Moran C., Ramondetta L., Rice D., Sood A., Tamboli P., Thompson T., Troncoso P., Tsao A., Wistuba I., Carter C., Haydu L., Hersey P., Jakrot V., Kakavand H., Kefford R., Lee K., Long G., Mann G., Quinn M., Saw R., Scolyer R., Shannon K., Spillane A., Stretch J., Synott M., Thompson J., Wilmott J., Al-Ahmadie H., Chan T.A., Ghossein R., Gopalan A., Levine D.A., Reuter V., Singer S., Singh B., Tien N.V., Broudy T., Mirsaidi C., Nair P., Drwiega P., Miller J., Smith J., Zaren H., Park J.-W., Hung N.P., Kebebew E., Linehan W.M., Metwalli A.R., Pacak K., Pinto P.A., Schiffman M., Schmidt L.S., Vocke C.D., Wentzensen N., Worrell R., Yang H., Moncrieff M., Goparaju C., Melamed J., Pass H., Botnariuc N., Caraman I., Cernat M., Chemencedji I., Clipca A., Doruc S., Gorincioi G., Mura S., Pirtac M., Stancul I., Tcaciuc D., Albert M., Alexopoulou I., Arnaout A., Bartlett J., Engel J., Gilbert S., Parfitt J., Sekhon H., Thomas G., Rassl D.M., Rintoul R.C., Bifulco C., Tamakawa R., Urba W., Hayward N., Timmers H., Antenucci A., Facciolo F., Grazi G., Marino M., Merola R., de Krijger R., Gimenez-Roqueplo A.-P., Piche A., Chevalier S., McKercher G., Birsoy K., Barnett G., Brewer C., Farver C., Naska T., Pennell N.A., Raymond D., Schilero C., Smolenski K., Williams F., Morrison C., Borgia J.A., Liptay M.J., Pool M., Seder C.W., Junker K., Omberg L., Dinkin M., Manikhas G., Alvaro D., Bragazzi M.C., Cardinale V., Carpino G., Gaudio E., Chesla D., Cottingham S., Dubina M., Moiseenko F., Dhanasekaran R., Becker K.-F., Janssen K.-P., Slotta-Huspenina J., Abdel-Rahman M.H., Aziz D., Bell S., Cebulla C.M., Davis A., Duell R., Elder J.B., Hilty J., Kumar B., Lang J., Lehman N.L., Mandt R., Nguyen P., Pilarski R., Rai K., Schoenfield L., Senecal K., Wakely P., Hansen P., Lechan R., Powers J., Tischler A., Grizzle W.E., Sexton K.C., Kastl A., Henderson J., Porten S., Waldmann J., Fassnacht M., Asa S.L., Schadendorf D., Couce M., Graefen M., Huland H., Sauter G., Schlomm T., Simon R., Tennstedt P., Olabode O., Nelson M., Bathe O., Carroll P.R., Chan J.M., Disaia P., Glenn P., Kelley R.K., Landen C.N., Phillips J., Prados M., Simko J., Smith-McCune K., VandenBerg S., Roggin K., Fehrenbach A., Kendler A., Sifri S., Steele R., Jimeno A., Carey F., Forgie I., Mannelli M., Carney M., Hernandez B., Campos B., Herold-Mende C., Jungk C., Unterberg A., von Deimling A., Bossler A., Galbraith J., Jacobus L., Knudson M., Knutson T., Ma D., Milhem M., Sigmund R., Godwin A.K., Madan R., Rosenthal H.G., Adebamowo C., Adebamowo S.N., Boussioutas A., Beer D., Giordano T., Mes-Masson A.-M., Saad F., Bocklage T., Landrum L., Mannel R., Moore K., Moxley K., Postier R., Walker J., Zuna R., Feldman M., Valdivieso F., Dhir R., Luketich J., Pinero E.M.M., Quintero-Aguilo M., Carlotti C.G., Dos Santos J.S., Kemp R., Sankarankuty A., Tirapelli D., Catto J., Agnew K., Swisher E., Creaney J., Robinson B., Shelley C.S., Godwin E.M., Kendall S., Shipman C., Bradford C., Carey T., Haddad A., Moyer J., Peterson L., Prince M., Rozek L., Wolf G., Bowman R., Fong K.M., Yang I., Korst R., Rathmell W.K., Fantacone-Campbell J.L., Hooke J.A., Kovatich A.J., Shriver C.D., DiPersio J., Drake B., Govindan R., Heath S., Ley T., Van Tine B., Westervelt P., Rubin M.A., Lee J.I., Aredes N.D., Mariamidze A., Clinical sciences, Medical Genetics, Medical Sociology, SAIC-F-Frederick, Inc, and Leidos Biomedical Research, Inc.
Subjects: 0301 basic medicine, oncogenic process, INFORMATION, DNA Repair, Tumor Microenvironment/genetics, Carcinogenesis, Cancer Genome Atlas Research Network, medicine.disease_cause, Genome, Biochemistry, Germline, Transcriptome, oncogenic proce, Neoplasms, Databases, Genetic, LS2_1, Tumor Microenvironment, LS4_6, SOMATIC POINT MUTATIONS, 610 Medicine & health, 11 Medical and Health Sciences, Carcinogenesi, Metabolic Networks and Pathways/genetics, cancer genomic, CARCINOGÊNESE, Genomics, TUMORS, Neoplasms/genetics, omics, Proteome, Physique des particules élémentaires, Microsatellite Instability, Life Sciences & Biomedicine, Metabolic Networks and Pathways, Human, EXPRESSION, Biochemistry & Molecular Biology, cancer, cancer genomics, TCGA, omic, Computational biology, Biology, BREAST, CLASSIFICATION, General Biochemistry, Genetics and Molecular Biology, Article, NO, 03 medical and health sciences, DNA Repair/genetics, medicine, Humans, Tumor microenvironment, Science & Technology, LANDSCAPE, IDENTIFICATION, Biochemistry, Genetics and Molecular Biology(all), Metabolic Networks and Pathway, Cell Biology, Epigenome, 06 Biological Sciences, 030104 developmental biology, DISCOVERY, Mutation, DRIVER, Neoplasm, Carcinogenesis/genetics, Developmental Biology, Genetics and Molecular Biology(all), Genes, Neoplasm
Abstract: 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. A synthesized view on oncogenic processes based on PanCancer Atlas analyses highlights the complex impact of genome alterations on the signaling and multi-omic profiles of human cancers as well as their influence on tumor microenvironment., 0, info:eu-repo/semantics/published
Published: 2018

24. The Immune Landscape of Cancer

Author: Vésteinn Thorsson, David L. Gibbs, Scott D. Brown, Denise Wolf, Dante S. Bortone, Tai-Hsien Ou Yang, Eduard Porta-Pardo, Galen F. Gao, Christopher L. Plaisier, James A. Eddy, Elad Ziv, Aedin C. Culhane, Evan O. Paull, I.K. Ashok Sivakumar, Andrew J. Gentles, Raunaq Malhotra, Farshad Farshidfar, Antonio Colaprico, Joel S. Parker, Lisle E. Mose, Nam Sy Vo, Jianfang Liu, Yuexin Liu, Janet Rader, Varsha Dhankani, Sheila M. Reynolds, Reanne Bowlby, Andrea Califano, Andrew D. Cherniack, Dimitris Anastassiou, Davide Bedognetti, Younes Mokrab, Aaron M. Newman, Arvind Rao, Ken Chen, Alexander Krasnitz, Hai Hu, Tathiane M. Malta, Houtan Noushmehr, Chandra Sekhar Pedamallu, Susan Bullman, Akinyemi I. Ojesina, Andrew Lamb, Wanding Zhou, Hui Shen, Toni K. Choueiri, John N. Weinstein, Justin Guinney, Joel Saltz, Robert A. Holt, Charles S. Rabkin, Alexander J. Lazar, Jonathan S. Serody, Elizabeth G. Demicco, Mary L. Disis, Benjamin G. Vincent, Ilya Shmulevich, 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, Theo Knijnenburg, Roger Kramer, Kalle Leinonen, Michael Miller, Sheila Reynolds, Vesteinn Thorsson, Wei Zhang, Rehan Akbani, Bradley M. Broom, Apurva M. Hegde, Zhenlin Ju, Rupa S. Kanchi, Anil Korkut, Jun Li, Han Liang, Shiyun Ling, Wenbin Liu, Yiling Lu, Gordon B. Mills, Kwok-Shing Ng, Michael Ryan, Jing Wang, 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, Matthew D. Wilkerson, Adrian Ally, Miruna Balasundaram, 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, Carrie Cibulskis, Stacey B. Gabriel, 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, 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, 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, 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. Martignetti, Hani Gabra, Hartmut Juhl, Teniola Akeredolu, Serghei Stepa, Dave Hoon, Keunsoo Ahn, Koo Jeong Kang, Felix Beuschlein, Anne Breggia, Michael Birrer, Debra Bell, Mitesh Borad, Alan H. Bryce, Erik Castle, Vishal Chandan, John Cheville, John A. Copland, Michael Farnell, Thomas Flotte, Nasra Giama, Thai Ho, Michael Kendrick, Jean-Pierre Kocher, Karla Kopp, Catherine Moser, David Nagorney, Daniel O’Brien, Brian Patrick O’Neill, Tushar Patel, Gloria Petersen, Florencia Que, Michael Rivera, Lewis Roberts, Robert Smallridge, Thomas Smyrk, Melissa Stanton, R. Houston Thompson, Michael Torbenson, Ju Dong Yang, Lizhi Zhang, Fadi Brimo, Jaffer A. Ajani, Ana Maria Angulo Gonzalez, Carmen Behrens, Jolanta Bondaruk, Russell Broaddus, Bogdan Czerniak, Bita Esmaeli, Junya Fujimoto, Jeffrey Gershenwald, Charles Guo, Christopher Logothetis, Funda Meric-Bernstam, Cesar Moran, Lois Ramondetta, David Rice, Anil Sood, Pheroze Tamboli, Timothy Thompson, Patricia Troncoso, Anne Tsao, Ignacio Wistuba, Candace Carter, Lauren Haydu, Peter Hersey, Valerie Jakrot, Hojabr Kakavand, Richard Kefford, Kenneth Lee, Georgina Long, Graham Mann, Michael Quinn, Robyn Saw, Richard Scolyer, Kerwin Shannon, Andrew Spillane, onathan Stretch, Maria Synott, John Thompson, James Wilmott, Hikmat Al-Ahmadie, Timothy A. Chan, Ronald Ghossein, Anuradha Gopalan, Douglas A. Levine, Victor Reuter, Samuel Singer, Bhuvanesh Singh, Nguyen Viet Tien, Thomas Broudy, Cyrus Mirsaidi, Praveen Nair, Paul Drwiega, Judy Miller, Jennifer Smith, Howard Zaren, Joong-Won Park, Nguyen Phi Hung, Electron Kebebew, W. Marston Linehan, Adam R. Metwalli, Karel Pacak, Peter A. Pinto, Mark Schiffman, Laura S. Schmidt, Cathy D. Vocke, Nicolas Wentzensen, Robert Worrell, Hannah Yang, Marc Moncrieff, Chandra Goparaju, Jonathan Melamed, Harvey Pass, Natalia Botnariuc, Irina Caraman, Mircea Cernat, Inga Chemencedji, Adrian Clipca, Serghei Doruc, Ghenadie Gorincioi, Sergiu Mura, Maria Pirtac, Irina Stancul, Diana Tcaciuc, Monique Albert, Iakovina Alexopoulou, Angel Arnaout, John Bartlett, Jay Engel, Sebastien Gilbert, Jeremy Parfitt, Harman Sekhon, George Thomas, Doris M. Rassl, Robert C. Rintoul, Carlo Bifulco, Raina Tamakawa, Walter Urba, Nicholas Hayward, Henri Timmers, Anna Antenucci, Francesco Facciolo, Gianluca Grazi, Mirella Marino, Roberta Merola, Ronald de Krijger, Anne-Paule Gimenez-Roqueplo, Alain Piché, Simone Chevalier, Ginette McKercher, Kivanc Birsoy, Gene Barnett, Cathy Brewer, Carol Farver, Theresa Naska, Nathan A. Pennell, Daniel Raymond, Cathy Schilero, Kathy Smolenski, Felicia Williams, Carl Morrison, Jeffrey A. Borgia, Michael J. Liptay, Mark Pool, Christopher W. Seder, Kerstin Junker, Larsson Omberg, Mikhail Dinkin, George Manikhas, Domenico Alvaro, Maria Consiglia Bragazzi, Vincenzo Cardinale, Guido Carpino, Eugenio Gaudio, David Chesla, Sandra Cottingham, Michael Dubina, Fedor Moiseenko, Renumathy Dhanasekaran, Karl-Friedrich Becker, Klaus-Peter Janssen, Julia Slotta-Huspenina, Mohamed H. Abdel-Rahman, Dina Aziz, Sue Bell, Colleen M. Cebulla, Amy Davis, Rebecca Duell, J. Bradley Elder, Joe Hilty, Bahavna Kumar, James Lang, Norman L. Lehman, Randy Mandt, Phuong Nguyen, Robert Pilarski, Karan Rai, Lynn Schoenfield, Kelly Senecal, Paul Wakely, Paul Hansen, Ronald Lechan, James Powers, Arthur Tischler, William E. Grizzle, Katherine C. Sexton, Alison Kastl, Joel Henderson, Sima Porten, Jens Waldmann, Martin Fassnacht, Sylvia L. Asa, Dirk Schadendorf, Marta Couce, Markus Graefen, Hartwig Huland, Guido Sauter, Thorsten Schlomm, Ronald Simon, Pierre Tennstedt, Oluwole Olabode, Mark Nelson, Oliver Bathe, Peter R. Carroll, June M. Chan, Philip Disaia, Pat Glenn, Robin K. Kelley, Charles N. Landen, Joanna Phillips, Michael Prados, Jeffry Simko, Karen Smith-McCune, Scott VandenBerg, Kevin Roggin, Ashley Fehrenbach, Ady Kendler, Suzanne Sifri, Ruth Steele, Antonio Jimeno, Francis Carey, Ian Forgie, Massimo Mannelli, Michael Carney, Brenda Hernandez, Benito Campos, Christel Herold-Mende, Christin Jungk, Andreas Unterberg, Andreas von Deimling, Aaron Bossler, Joseph Galbraith, Laura Jacobus, Michael Knudson, Tina Knutson, Deqin Ma, Mohammed Milhem, Rita Sigmund, Andrew K. Godwin, Rashna Madan, Howard G. Rosenthal, Clement Adebamowo, Sally N. 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, 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, Craig D. Shriver, 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, Thorsson V., Gibbs D.L., Brown S.D., Wolf D., Bortone D.S., Ou Yang T.-H., Porta-Pardo E., Gao G.F., Plaisier C.L., Eddy J.A., Ziv E., Culhane A.C., Paull E.O., Sivakumar I.K.A., Gentles A.J., Malhotra R., Farshidfar F., Colaprico A., Parker J.S., Mose L.E., Vo N.S., Liu J., Liu Y., Rader J., Dhankani V., Reynolds S.M., Bowlby R., Califano A., Cherniack A.D., Anastassiou D., Bedognetti D., Rao A., Chen K., Krasnitz A., Hu H., Malta T.M., Noushmehr H., Pedamallu C.S., Bullman S., Ojesina A.I., Lamb A., Zhou W., Shen H., Choueiri T.K., Weinstein J.N., Guinney J., Saltz J., Holt R.A., Rabkin C.E., Caesar-Johnson S.J., Demchok J.A., Felau I., Kasapi M., Ferguson M.L., Hutter C.M., Sofia H.J., Tarnuzzer R., Wang Z., Yang L., Zenklusen J.C., Zhang J.J., Chudamani S., Lolla L., Naresh R., Pihl T., Sun Q., Wan Y., Wu Y., Cho J., DeFreitas T., Frazer S., Gehlenborg N., Getz G., Heiman D.I., Kim J., Lawrence M.S., Lin P., Meier S., Noble M.S., Saksena G., Voet D., Zhang H., Bernard B., Chambwe N., Knijnenburg T., Kramer R., Leinonen K., Miller M., Reynolds S., Shmulevich I., Zhang W., Akbani R., Broom B.M., Hegde A.M., Ju Z., Kanchi R.S., Korkut A., Li J., Liang H., Ling S., Liu W., Lu Y., Mills G.B., Ng K.-S., Ryan M., Wang J., Zhang J., Abeshouse A., Armenia J., Chakravarty D., Chatila W.K., de Bruijn I., Gao J., Gross B.E., Heins Z.J., Kundra R., La K., Ladanyi M., Luna A., Nissan M.G., Ochoa A., Phillips S.M., Reznik E., Sanchez-Vega F., Sander C., Schultz N., Sheridan R., Sumer S.O., Sun Y., Taylor B.S., Anur P., Peto M., Spellman P., Benz C., Stuart J.M., Wong C.K., Yau C., Hayes D.N., Wilkerson M.D., Ally A., Balasundaram M., Brooks D., Carlsen R., Chuah E., Dhalla N., Jones S.J.M., Kasaian K., Lee D., Ma Y., Marra M.A., Mayo M., Moore R.A., Mungall A.J., Mungall K., Robertson A.G., Sadeghi S., Schein J.E., Sipahimalani P., Tam A., Thiessen N., Tse K., Wong T., Berger A.C., Beroukhim R., Cibulskis C., Gabriel S.B., Ha G., Meyerson M., Schumacher S.E., Shih J., Kucherlapati M.H., Kucherlapati R.S., Baylin S., Cope L., Danilova L., Bootwalla M.S., Lai P.H., Maglinte D.T., Van Den Berg D.J., Weisenberger D.J., Auman J.T., Balu S., Bodenheimer T., Fan C., Hoadley K.A., Hoyle A.P., Jefferys S.R., Jones C.D., Meng S., Mieczkowski P.A., Perou A.H., Perou C.M., Roach J., Shi Y., Simons J.V., Skelly T., Soloway M.G., Tan D., Veluvolu U., Fan H., Hinoue T., Laird P.W., Bellair M., Chang K., Covington K., Creighton C.J., Dinh H., Doddapaneni H., Donehower L.A., Drummond J., Gibbs R.A., Glenn R., Hale W., Han Y., Hu J., Korchina V., Lee S., Lewis L., Li W., Liu X., Morgan M., Morton D., Muzny D., Santibanez J., Sheth M., Shinbrot E., Wang L., Wang M., Wheeler D.A., Xi L., Zhao F., Hess J., Appelbaum E.L., Bailey M., Cordes M.G., Ding L., Fronick C.C., Fulton L.A., Fulton R.S., Kandoth C., Mardis E.R., McLellan M.D., Miller C.A., Schmidt H.K., Wilson R.K., Crain D., Curley E., Gardner J., Lau K., Mallery D., Morris S., Paulauskis J., Penny R., Shelton C., Shelton T., Sherman M., Thompson E., Yena P., Bowen J., Gastier-Foster J.M., Gerken M., Leraas K.M., Lichtenberg T.M., Ramirez N.C., Wise L., Zmuda E., Corcoran N., Costello T., Hovens C., Carvalho A.L., de Carvalho A.C., Fregnani J.H., Longatto-Filho A., Reis R.M., Scapulatempo-Neto C., Silveira H.C.S., Vidal D.O., Burnette A., Eschbacher J., Hermes B., Noss A., Singh R., Anderson M.L., Castro P.D., Ittmann M., Huntsman D., Kohl B., Le X., Thorp R., Andry C., Duffy E.R., Lyadov V., Paklina O., Setdikova G., Shabunin A., Tavobilov M., McPherson C., Warnick R., Berkowitz R., Cramer D., Feltmate C., Horowitz N., Kibel A., Muto M., Raut C.P., Malykh A., Barnholtz-Sloan J.S., Barrett W., Devine K., Fulop J., Ostrom Q.T., Shimmel K., Wolinsky Y., Sloan A.E., De Rose A., Giuliante F., Goodman M., Karlan B.Y., Hagedorn C.H., Eckman J., Harr J., Myers J., Tucker K., Zach L.A., Deyarmin B., Kvecher L., Larson C., Mural R.J., Somiari S., Vicha A., Zelinka T., Bennett J., Iacocca M., Rabeno B., Swanson P., Latour M., Lacombe L., Tetu B., Bergeron A., McGraw M., Staugaitis S.M., Chabot J., Hibshoosh H., Sepulveda A., Su T., Wang T., Potapova O., Voronina O., Desjardins L., Mariani O., Roman-Roman S., Sastre X., Stern M.-H., Cheng F., Signoretti S., Berchuck A., Bigner D., Lipp E., Marks J., McCall S., McLendon R., Secord A., Sharp A., Behera M., Brat D.J., Chen A., Delman K., Force S., Khuri F., Magliocca K., Maithel S., Olson J.J., Owonikoko T., Pickens A., Ramalingam S., Shin D.M., Sica G., Van Meir E.G., Eijckenboom W., Gillis A., Korpershoek E., Looijenga L., Oosterhuis W., Stoop H., van Kessel K.E., Zwarthoff E.C., Calatozzolo C., Cuppini L., Cuzzubbo S., DiMeco F., Finocchiaro G., Mattei L., Perin A., Pollo B., Chen C., Houck J., Lohavanichbutr P., Hartmann A., Stoehr C., Stoehr R., Taubert H., Wach S., Wullich B., Kycler W., Murawa D., Wiznerowicz M., Chung K., Edenfield W.J., Martin J., Baudin E., Bubley G., Bueno R., De Rienzo A., Richards W.G., Kalkanis S., Mikkelsen T., Scarpace L., Girard N., Aymerich M., Campo E., Gine E., Guillermo A.L., Van Bang N., Hanh P.T., Phu B.D., Tang Y., Colman H., Evason K., Dottino P.R., Martignetti J.A., Gabra H., Juhl H., Akeredolu T., Stepa S., Hoon D., Ahn K., Kang K.J., Beuschlein F., Breggia A., Birrer M., Bell D., Borad M., Bryce A.H., Castle E., Chandan V., Cheville J., Copland J.A., Farnell M., Flotte T., Giama N., Ho T., Kendrick M., Kocher J.-P., Kopp K., Moser C., Nagorney D., O'Brien D., O'Neill B.P., Patel T., Petersen G., Que F., Rivera M., Roberts L., Smallridge R., Smyrk T., Stanton M., Thompson R.H., Torbenson M., Yang J.D., Zhang L., Brimo F., Ajani J.A., Gonzalez A.M.A., Behrens C., Bondaruk J., Broaddus R., Czerniak B., Esmaeli B., Fujimoto J., Gershenwald J., Guo C., Lazar A.J., Logothetis C., Meric-Bernstam F., Moran C., Ramondetta L., Rice D., Sood A., Tamboli P., Thompson T., Troncoso P., Tsao A., Wistuba I., Carter C., Haydu L., Hersey P., Jakrot V., Kakavand H., Kefford R., Lee K., Long G., Mann G., Quinn M., Saw R., Scolyer R., Shannon K., Spillane A., Stretch O., Synott M., Thompson J., Wilmott J., Al-Ahmadie H., Chan T.A., Ghossein R., Gopalan A., Levine D.A., Reuter V., Singer S., Singh B., Tien N.V., Broudy T., Mirsaidi C., Nair P., Drwiega P., Miller J., Smith J., Zaren H., Park J.-W., Hung N.P., Kebebew E., Linehan W.M., Metwalli A.R., Pacak K., Pinto P.A., Schiffman M., Schmidt L.S., Vocke C.D., Wentzensen N., Worrell R., Yang H., Moncrieff M., Goparaju C., Melamed J., Pass H., Botnariuc N., Caraman I., Cernat M., Chemencedji I., Clipca A., Doruc S., Gorincioi G., Mura S., Pirtac M., Stancul I., Tcaciuc D., Albert M., Alexopoulou I., Arnaout A., Bartlett J., Engel J., Gilbert S., Parfitt J., Sekhon H., Thomas G., Rassl D.M., Rintoul R.C., Bifulco C., Tamakawa R., Urba W., Hayward N., Timmers H., Antenucci A., Facciolo F., Grazi G., Marino M., Merola R., de Krijger R., Gimenez-Roqueplo A.-P., Piche A., Chevalier S., McKercher G., Birsoy K., Barnett G., Brewer C., Farver C., Naska T., Pennell N.A., Raymond D., Schilero C., Smolenski K., Williams F., Morrison C., Borgia J.A., Liptay M.J., Pool M., Seder C.W., Junker K., Omberg L., Dinkin M., Manikhas G., Alvaro D., Bragazzi M.C., Cardinale V., Carpino G., Gaudio E., Chesla D., Cottingham S., Dubina M., Moiseenko F., Dhanasekaran R., Becker K.-F., Janssen K.-P., Slotta-Huspenina J., Abdel-Rahman M.H., Aziz D., Bell S., Cebulla C.M., Davis A., Duell R., Elder J.B., Hilty J., Kumar B., Lang J., Lehman N.L., Mandt R., Nguyen P., Pilarski R., Rai K., Schoenfield L., Senecal K., Wakely P., Hansen P., Lechan R., Powers J., Tischler A., Grizzle W.E., Sexton K.C., Kastl A., Henderson J., Porten S., Waldmann J., Fassnacht M., Asa S.L., Schadendorf D., Couce M., Graefen M., Huland H., Sauter G., Schlomm T., Simon R., Tennstedt P., Olabode O., Nelson M., Bathe O., Carroll P.R., Chan J.M., Disaia P., Glenn P., Kelley R.K., Landen C.N., Phillips J., Prados M., Simko J., Smith-McCune K., VandenBerg S., Roggin K., Fehrenbach A., Kendler A., Sifri S., Steele R., Jimeno A., Carey F., Forgie I., Mannelli M., Carney M., Hernandez B., Campos B., Herold-Mende C., Jungk C., Unterberg A., von Deimling A., Bossler A., Galbraith J., Jacobus L., Knudson M., Knutson T., Ma D., Milhem M., Sigmund R., Godwin A.K., Madan R., Rosenthal H.G., Adebamowo C., Adebamowo S.N., Boussioutas A., Beer D., Giordano T., Mes-Masson A.-M., Saad F., Bocklage T., Landrum L., Mannel R., Moore K., Moxley K., Postier R., Walker J., Zuna R., Feldman M., Valdivieso F., Dhir R., Luketich J., Pinero E.M.M., Quintero-Aguilo M., Carlotti C.G., Dos Santos J.S., Kemp R., Sankarankuty A., Tirapelli D., Catto J., Agnew K., Swisher E., Creaney J., Robinson B., Shelley C.S., Godwin E.M., Kendall S., Shipman C., Bradford C., Carey T., Haddad A., Moyer J., Peterson L., Prince M., Rozek L., Wolf G., Bowman R., Fong K.M., Yang I., Korst R., Rathmell W.K., Fantacone-Campbell J.L., Hooke J.A., Kovatich A.J., Shriver C.D., DiPersio J., Drake B., Govindan R., Heath S., Ley T., Van Tine B., Westervelt P., Rubin M.A., Lee J.I., Aredes N.D., Mariamidze A., Serody J.S., Demicco E.G., Disis M.L., Vincent B.G., Shmulevich L., SAIC-F-Frederick, Inc, and Leidos Biomedical Research, Inc.
Subjects: Neuroblastoma RAS viral oncogene homolog, Male, 0301 basic medicine, INFORMATION, PREDICTION, Macrophage, medicine.medical_treatment, Lymphocyte, Cell, Cancer Genome Atlas Research Network, COMPREHENSIVE ANALYSIS, computer.software_genre, GENOMIC ANALYSES, 0302 clinical medicine, Transforming Growth Factor beta, Neoplasms, Immunologie, LS2_1, immune subtypes, LS4_6, Immunology and Allergy, tumor immunology, 610 Medicine & health, Child, Th1-Th2 Balance, immunomodulatory, Aged, 80 and over, T-CELL-RECEPTOR, integrative network analysi, cancer genomic, Genomics, Middle Aged, Prognosis, medicine.anatomical_structure, cancer genomics, immuno-oncology, immunotherapy, integrative network analysis, tumor microenvironment, Infectious Diseases, 1107 Immunology, 030220 oncology & carcinogenesis, immune subtype, Female, GENÔMICA, Life Sciences & Biomedicine, Natural language processing, Sentence, Human, Adult, SEQUENCING DATA, Adolescent, Prognosi, Immunology, Cancer immunity, Biology, CLASSIFICATION, Article, NO, Set (abstract data type), Interferon-gamma, Young Adult, 03 medical and health sciences, Immune system, microRNA, REVEALS, medicine, Humans, Epigenetics, Aged, Tumor microenvironment, Wound Healing, Science & Technology, business.industry, REGULATORY NETWORK, Macrophages, Gene sets, Scoring methods, Immunotherapy, SOMATIC MUTATIONS, R package, 030104 developmental biology, Cancer research, Genomic, Artificial intelligence, business, computer
Abstract: We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes—wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-β dominant—characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field. Thorsson et al. present immunogenomics analyses of more than 10,000 tumors, identifying six immune subtypes that encompass multiple cancer types and are hypothesized to define immune response patterns impacting prognosis. This work provides a resource for understanding tumor-immune interactions, with implications for identifying ways to advance research on immunotherapy., 0, info:eu-repo/semantics/published
Published: 2019

25. P4-103: CELL-TYPE SPECIFIC MECHANISTIC AND DIRECTIONAL TRANSCRIPTIONAL REGULATORY NETWORKS IN ALZHEIMER'S DISEASE

Author: Paul Shannon, Noa Rappaport, Cory C. Funk, Nathan D. Price, Nilufer Ertekin-Taner, Leroy Hood, Ilya Shmulevich, Mariet Allen, David L Gibbs, Minerva M. Carrasquillo, and Todd E. Golde
Subjects: Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Epidemiology, Health Policy, Cell type specific, Neurology (clinical), Disease, Computational biology, Geriatrics and Gerontology, Biology
Published: 2019

26. ADAPTS: Automated deconvolution augmentation of profiles for tissue specific cells

Author: Frank Schmitz, Samuel A. Danziger, Mark McConnell, David J. Reiss, Ilya Shmulevich, David L Gibbs, Matthew Trotter, and Alexander V. Ratushny
Subjects: 0301 basic medicine, Support Vector Machine, Computer science, Interface (Java), Datasets as Topic, Gene Expression, Hematologic Cancers and Related Disorders, White Blood Cells, 0302 clinical medicine, Animal Cells, Neoplasms, Tumor Microenvironment, Medicine and Health Sciences, Adipocytes, Cluster Analysis, RNA-Seq, Connective Tissue Cells, Multidisciplinary, Signature matrix, Applied Mathematics, Simulation and Modeling, Cell Differentiation, Hematology, Signature (logic), Gene Expression Regulation, Neoplastic, Myelomas, Oncology, Connective Tissue, 030220 oncology & carcinogenesis, Physical Sciences, Medicine, Deconvolution, Single-Cell Analysis, Anatomy, Cellular Types, Algorithms, Research Article, Immune Cells, Science, Plasma Cells, Immunology, Endocrine System, Computational biology, Research and Analysis Methods, 03 medical and health sciences, Exocrine Glands, Component (UML), Genetics, Humans, Myelomas and Lymphoproliferative Diseases, Cluster analysis, Pancreas, Blood Cells, business.industry, Computational Biology, Biology and Life Sciences, Cancers and Neoplasms, Pattern recognition, Cell Biology, Support vector machine, Data set, Biological Tissue, 030104 developmental biology, Artificial intelligence, business, Software, Mathematics, Developmental Biology
Abstract: Immune cell infiltration of tumors can be an important component for determining patient outcomes, e.g. by inferring immune cell presence by deconvolving gene expression data drawn from a heterogenous mix of cell types. One particularly powerful family of deconvolution techniques uses signature matrices of genes that uniquely identify each cell type as determined from cell type purified gene expression data. Many methods of this type have been recently published, often including new signature matrices appropriate for a single purpose, such as investigating a specific type of tumor. The package ADAPTS helps users make the most of this expanding knowledge base by introducing a framework for cell type deconvolution. ADAPTS implements modular tools for customizing signature matrices for new tissue types by adding custom cell types or building new matrices de novo, including from single cell RNAseq data. It includes a common interface to several popular deconvolution algorithms that use a signature matrix to estimate the proportion of cell types present in heterogenous samples. ADAPTS also implements a novel method for clustering cell types into groups that are hard to distinguish by deconvolution and then re-splitting those clusters using hierarchical deconvolution. We demonstrate that the techniques implemented in ADAPTS improve the ability to reconstruct the cell types present in a single cell RNAseq data set in a blind predictive analysis. ADAPTS is currently available for use in R on CRAN and GitHub.
Published: 2019

27. Abstract 1184: Comprehensive analysis with interactive exploration of immune response signatures in 10,000 tumor samples

Author: Alexander J. Lazar, Vesteinn Thorsson, Elizabeth G. Demicco, Benjamin G. Vincent, Ilya Shmulevich, Jonathan S. Serody, David L Gibbs, Justin Guinney, Mary L. Disis, and James A. Eddy
Subjects: Cancer Research, Cell type, Tumor microenvironment, Lymphocyte, Cancer, Biology, medicine.disease, Immune system, medicine.anatomical_structure, Oncology, microRNA, Cancer cell, Cancer research, medicine, Epigenetics
Abstract: In recent years, analysis of cancer genomics data using methods of immunogenomics has yielded valuable insight into how cancer cells interact with immune cells in the tumor microenvironment. A recent analysis of the multiple molecular platforms by The Cancer Genome Atlas (TCGA) of over 10,000 tumors comprising 33 cancer types identified six immune subtypes, spanning multiple tumor types, that are characterized by differences in: macrophage vs. lymphocyte signatures; Th1:Th2 cell ratio; extent of intratumoral heterogeneity; aneuploidy; extent of neoantigen load; signatures of cell proliferation; expression of immunomodulatory genes; and disease outcome [1]. Particular driver mutations correlate with variation in leukocyte levels across all cancers or with the fraction of individual immune cell types. Intracellular and extracellular networks (involving transcription, microRNAs, copy number and epigenetic processes) are predicted to play a role in establishing the observed tumor-immune cell interactions, both across and within immune subtypes. Additionally, machine learning methods have been applied to H&E images to extract information on which tissue regions contain tumor infiltrating lymphocytes (TILs), yielding TIL maps of whole slide images from digital pathology[2]. Spatial patterns of TILs are associated with a variety of genomic alterations, including cancer subtypes. The CRI iAtlas (www.cri-iatlas.org) is a cloud-based platform for data exploration and discovery, allowing researchers to study TCGA immune response characterizations, and the relationships among them in individual tumor types, tumor subtypes, and immune subtypes. iAtlas supports the adaptive exploration of correlations within the cellularity of the tumor microenvironment, immune expression signatures, tumor mutation burden, cancer driver mutations, adaptive cell clonality, patient survival, and expression of key immunomodulators. iAtlas was launched in April 2018, and has since been expanded to include new capabilities such as (1) user-defined loading of cohorts, (2) a tool for classifying expression data into immune subtypes, (3) integration of TIL mapping from digital pathology images, and (4) addition of annotated genomics datasets from immunotherapy clinical trials as comparative data sources. As the resource evolves, we expect that the CRI iAtlas will help to accelerate discovery and improve patient outcomes by providing researchers greater access to immunogenomics data to better understand the immunological characteristics of the tumor microenvironment and its potential impact on patient responses to immunotherapy. [1] Thorsson, V, et al., The Immune Landscape of Cancer; Immunity 48, p812 - 830.e14 (2018) [2] Saltz, J et al. Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images; Cell Reports 23 pp.181-193.e7 (2018) Citation Format: Vesteinn Thorsson, David L. Gibbs, Mary L. Disis, Elizabeth G. Demicco, Alexander J. Lazar, Jonathan S. Serody, James A. Eddy, Ilya Shmulevich, Justin Guinney, Benjamin G. Vincent. Comprehensive analysis with interactive exploration of immune response signatures in 10,000 tumor samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1184.
Published: 2019

28. Abstract B051: Facilitating translational research with interactive tools for immuno-oncology data

Author: Vesteinn Thorsson, Justin Guinney, James A. Eddy, David L Gibbs, Ilya Shmulevich, and Andrew Lamb
Subjects: Cancer Research, Tumor microenvironment, Computer science, Systems biology, medicine.medical_treatment, Repertoire, Immunology, Translational research, Immunotherapy, Computational biology, Immune system, Workflow, Cancer immunotherapy, medicine
Abstract: With the explosive growth in data and results from immuno-oncology (IO) studies, improved ways to easily share, integrate and explore available data and results are needed. The Cancer Research Institute (CRI) iAtlas (www.cri-iatlas.org) is an interactive web-based platform and set of analytic tools for studying interactions between tumors and the immune microenvironment. These tools allow researchers to explore associations among a variety of immune characterizations as well as with genomic and clinical phenotypes. The initial version of CRI iAtlas is based on an analysis performed by The Cancer Genome Atlas (TCGA) Research Network on the TCGA data set comprising over 10,000 tumor samples and 33 tumor types (Thorsson et al., 2018). The platform will be expanded to include other immunogenomic data sets and workflows. In the TCGA analysis, each tumor sample was scored for a variety of computationally estimated immune-based readouts, including immune cell composition, adaptive cell receptor repertoire, neoantigen load, and expression of genes coding for immunomodulatory proteins. Immune-based subtypes, spanning multiple tumor types, were identified. The web tool allows researchers to explore the data readouts as well as the relation between them in individual TCGA tumor types and across the global immune subtypes identified in the study. CRI iAtlas is made possible through a collaboration between CRI, Sage Bionetworks, and the Institute for Systems Biology. The main feature of the iAtlas web tool is the iAtlas Explorer, which provides several Analysis Modules to explore and visualize results. Each module presents information organized by theme, with multiple views and interactive controls to enhance and extend the information included in the original manuscript figures. Sample Group Overview: summaries of selected groups (including six immune subtypes that span cancer tissue types and molecular subtypes); Tumor Microenvironment: overall immune infiltrate and relative immune cell proportions in selected sample groups; Immune Feature Trends: distributions of immune readouts across selected groups, and associations between readouts within groups; Clinical Outcomes: trends of and associations with survival outcomes across sample groups based on immune characteristics; Immunomodulators: expression trends of genes that code for immunomodulating proteins, including checkpoint proteins. In response to community feedback, we are extending the iAtlas portal with two modules, one allowing researchers to classify their own RNAseq samples into immune subtypes, and the other allowing researchers to upload their own sample categories for analysis with the tool. As the resource evolves, we expect that the CRI iAtlas will help to accelerate discovery and improve patient outcomes by providing researchers greater access to immunogenomics data to better understand the immunologic characteristics of the tumor microenvironment and its potential impact on patient responses to immunotherapy. Reference: Thorsson et al. The immune landscape of cancer. Immunity 2018;48(4):812-30.e14. doi: 10.1016/j.immuni.2018.03.023. Citation Format: Vesteinn Thorsson, James A. Eddy, Andrew Lamb, David L. Gibbs, Ilya Shmulevich, Justin Guinney. Facilitating translational research with interactive tools for immuno-oncology data [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr B051.
Published: 2019

29. Solving the influence maximization problem reveals regulatory organization of the yeast cell cycle

Author: Ilya Shmulevich and David L Gibbs
Subjects: 0301 basic medicine, Information transfer, Saccharomyces cerevisiae Proteins, Computer science, Cell Cycle Proteins, Saccharomyces cerevisiae, computer.software_genre, Models, Biological, 03 medical and health sciences, Cellular and Molecular Neuroscience, 0302 clinical medicine, Gene Expression Regulation, Fungal, Genetics, Computer Simulation, Information flow (information theory), Social network analysis, lcsh:QH301-705.5, Molecular Biology, Ecology, Evolution, Behavior and Systematics, Markov chain, Ecology, Node (networking), Cell Cycle, Correction, Maximization, Network dynamics, 030104 developmental biology, lcsh:Biology (General), Computational Theory and Mathematics, Ranking, Modeling and Simulation, Transfer entropy, Data mining, Centrality, computer, 030217 neurology & neurosurgery, Algorithms, Network analysis, Signal Transduction
Abstract: The Influence Maximization Problem (IMP) aims to discover the set of nodes with the greatest influence on network dynamics. The problem has previously been applied in epidemiology and social network analysis. Here, we demonstrate the application to cell cycle regulatory network analysis of Saccharomyces cerevisiae.Fundamentally, gene regulation is linked to the flow of information. Therefore, our implementation of the IMP was framed as an information theoretic problem on a diffusion network. Utilizing all regulatory edges from YeastMine, gene expression dynamics were encoded as edge weights using a variant of time lagged transfer entropy, a method for quantifying information transfer between variables. Influence, for a particular number of sources, was measured using a diffusion model based on Markov chains with absorbing states. By maximizing over different numbers of sources, an influence ranking on genes was produced.The influence ranking was compared to other metrics of network centrality. Although ‘top genes’ from each centrality ranking contained well-known cell cycle regulators, there was little agreement and no clear winner. However, it was found that influential genes tend to directly regulate or sit upstream of genes ranked by other centrality measures. This is quantified by computing node reachability between gene sets; on average, 59% of central genes can be reached when starting from the influential set, compared to 7% of influential genes when starting at another centrality measure.The influential nodes act as critical sources of information flow, potentially having a large impact on the state of the network. Biological events that affect influential nodes and thereby affect information flow could have a strong effect on network dynamics, potentially leading to disease.Code and example data can be found at: https://github.com/Gibbsdavidl/miergolfAuthor SummaryThe Influence Maximization Problem (IMP) is general and is applied in fields such as epidemiology, social network analysis, and as shown here, biological network analysis. The aim is to discover the set of regulatory genes with the greatest influence in the network dynamics. As gene regulation, fundamentally, is about the flow of information, the IMP was framed as an information theoretic problem. Dynamics were encoded as edge weights using time lagged transfer entropy, a quantity that defines information transfer across variables. The information flow was accomplished using a diffusion model based on Markov chains with absorbing states. Ant optimization was applied to solve the subset selection problem, recovering the most influential nodes.The influential nodes act as critical sources of information flow, potentially affecting the network state. Biological events that impact the influential nodes and thereby affecting normal information flow, could have a strong effect on the network, potentially leading to disease.
Published: 2016

30. Using Incomplete Trios to Boost Confidence in Family Based Association Studies

Author: Varsha eDhankani, David L Gibbs, Theo eKnijnenburg, Roger eKramer, Joseph eVockley, John eNiederhuber, Ilya eShmulevich, and Brady eBernard
Subjects: 0301 basic medicine, lcsh:QH426-470, population stratification, Computer science, randomized imputation, Genome-wide association study, Population stratification, computer.software_genre, whole genome analysis, Statistical power, 03 medical and health sciences, quantile intervals, Genetics, Methods, Nuclear family, Statistic, confidence intervals, Genetics (clinical), Missing data, Confidence interval, lcsh:Genetics, 030104 developmental biology, family based association tests, missing genotypes, Molecular Medicine, Data mining, computer, memory efficient data format, Quantile
Abstract: Most currently available family based association tests are designed to account only for nuclear families with complete genotypes for parents as well as offspring. Due to the availability of increasingly less expensive generation of whole genome sequencing information, genetic studies are able to collect data for more families and from large family cohorts with the goal of improving statistical power. However, due to missing genotypes, many families are not included in the family based association tests, negating the benefits of large scale sequencing data. Here, we present the CIFBAT method to use incomplete families in Family Based Association Test (FBAT) to evaluate robustness against missing data. CIFBAT uses quantile intervals of the FBAT statistic by randomly choosing valid completions of incomplete family genotypes based on Mendelian inheritance rules. By considering all valid completions equally likely and computing quantile intervals over several randomized iterations, CIFBAT avoids assumption of a homogeneous population structure or any particular missingness pattern in the data. Using simulated data, we show that the quantile intervals computed by CIFBAT are useful in validating robustness of the FBAT statistic against missing data and in identifying genomic markers with higher precision. We also propose a novel set of candidate genomic markers for uterine related abnormalities from analysis of familial whole genome sequences, and provide validation for a previously established set of candidate markers for Type 1 diabetes. We have provided a software package that incorporates TDT, robustTDT, FBAT and CIFBAT. The data format proposed for the software uses half the memory space that the standard FBAT format (PED) files use, making it efficient for large scale genome wide association studies.
Published: 2015

31. Combining Dependent P-values with an Empirical Adaptation of Browns Method

Author: David L Gibbs, Brady Bernard, William Poole, Ilya Shmulevich, and Theo A. Knijnenburg
Subjects: 0301 basic medicine, Statistics and Probability, Computer science, computer.software_genre, Biochemistry, Bioconductor, 03 medical and health sciences, Cancer genome, Neoplasms, Gene expression, Statistics, Null distribution, Humans, MATLAB, Molecular Biology, ECCB 2016: The 15th European Conference on Computational Biology, Mathematics, Statistical hypothesis testing, computer.programming_language, Python (programming language), Computer Science Applications, High-Throughput Screening Assays, Computational Mathematics, 030104 developmental biology, Computational Theory and Mathematics, Simulated data, Data Interpretation, Statistical, Data mining, computer, Algorithms, Software
Abstract: Motivation Combining P-values from multiple statistical tests is a common exercise in bioinformatics. However, this procedure is non-trivial for dependent P-values. Here, we discuss an empirical adaptation of Brown’s method (an extension of Fisher’s method) for combining dependent P-values which is appropriate for the large and correlated datasets found in high-throughput biology. Results We show that the Empirical Brown’s method (EBM) outperforms Fisher’s method as well as alternative approaches for combining dependent P-values using both noisy simulated data and gene expression data from The Cancer Genome Atlas. Availability and Implementation The Empirical Brown’s method is available in Python, R, and MATLAB and can be obtained from https://github.com/IlyaLab/CombiningDependentPvalues UsingEBM. The R code is also available as a Bioconductor package from https://www.bioconductor.org/packages/devel/bioc/html/EmpiricalBrownsMethod.html. Contact Theo.Knijnenburg@systemsbiology.org Supplementary information Supplementary data are available at Bioinformatics online.
Published: 2015
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32. Multi-omic network signatures of disease

Author: Shannon K. McWeeney, Lisa E. Gralinski, Ralph S. Baric, and David L Gibbs
Subjects: Transcription, Genetic, lcsh:QH426-470, Physiology, Biology, Transfection, computer.software_genre, Data type, Business process discovery, Correlation, transcriptomics, proteomics, Genetics, Genetics(clinical), Original Research Article, Time series, data integration, Genetics (clinical), Network model, SARS, biomarkers, 3. Good health, omics, virology, Entrez, lcsh:Genetics, networks, Bipartite graph, Molecular Medicine, Data mining, computer, Data integration
Abstract: To better understand dynamic disease processes, integrated multi-omic methods are needed, yet comparing different types of omic data remains difficult. Integrative solutions benefit experimenters by eliminating potential biases that come with single omic analysis. We have developed the methods needed to explore whether a relationship exists between co-expression network models built from transcriptomic and proteomic data types, and whether this relationship can be used to improve the disease signature discovery process. A naïve, correlation based method is utilized for comparison. Using publicly available infectious disease time series data, we analyzed the related co-expression structure of the transcriptome and proteome in response to SARS-CoV infection in mice. Transcript and peptide expression data was filtered using quality scores and subset by taking the intersection on mapped Entrez IDs. Using this data set, independent co-expression networks were built. The networks were integrated by constructing a bipartite module graph based on module member overlap, module summary correlation, and correlation to phenotypes of interest. Compared to the module level results, the naïve approach is hindered by a lack of correlation across data types, less significant enrichment results, and little functional overlap across data types. Our module graph approach avoids these problems, resulting in an integrated omic signature of disease progression, which allows prioritization across data types for down-stream experiment planning. Integrated modules exhibited related functional enrichments and could suggest novel interactions in response to infection. These disease and platform-independent methods can be used to realize the full potential of multi-omic network signatures. The data (experiment SM001) are publically available through the NIAID Systems Virology (https://www.systemsvirology.org) and PNNL (http://omics.pnl.gov) web portals. Phenotype data is found in the supplementary information. The ProCoNA package is available as part of Bioconductor 2.13.
Published: 2014
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33. Congenital diaphragmatic defects and associated syndromes, malformations, and chromosome anomalies: A retrospective study of 60 patients and literature review

Author: Gregory M. Enns, Victoria A. Cox, Ruth B. Goldstein, Mahin Golabi, David L Gibbs, and Michael R. Harrison
Subjects: Genetics, Pediatrics, medicine.medical_specialty, Pregnancy, business.industry, Diaphragmatic breathing, Prenatal diagnosis, Retrospective cohort study, Karyotype, medicine.disease, Lethal Multiple Pterygium Syndrome, Fryns syndrome, Agenesis, medicine, business, Genetics (clinical)
Abstract: Congenital diaphragmatic defects (CDDs) may occur in malformation syndromes of varied causes. Syndromic cases of CDDs due to chromosomal defects, autosomal recessive, autosomal dominant, or X-linked inheritance have been described. In order to determine the frequency and nature of syndromes, malformations, and chromosome abnormalities associated with CDDs, we reviewed the records of all patients with CDDs evaluated over a 4-year period. During the 4-year interval, a total of 60 patients was evaluated. Of these, 29 had a therapeutic or spontaneous abortion, and 31 received postnatal care. On prenatal ultrasonography, 20 of 60 (33%) of patients with CDDs had additional anomalies. Additional anomalies, besides CDDs, were present in 15 of 31 (48%) of liveborn patients on newborn evaluation. In total, 16 patients with multiple anomalies were evaluated. Of these, 12 of 16 (75%) had additional abnormalities detected by prenatal ultrasonography. The 4 of 16 (25%) without additional anomalies on prenatal sonography had multiple anomalies found neonatally, lethal multiple pterygium syndrome being diagnosed in one case. Prenatal chromosome analysis was performed in 7 of 16 patients, and all had postnatal karyotypes. All initial karyotypes were normal. Tetrasomy 12p was documented on postnatal fibroblast analysis in one case who had percutaneous umbilical blood sampling (PUBS). Syndromes diagnosed postnatally in 7 of 16 patients (44%) include: Fryns syndrome (2), Simpson-Golabi-Behmel syndrome (2), tetrasomy 12p (1), Brachmann-de Lange syndrome (1), and lethal multiple pterygium syndrome (1). We were unable to make a specific diagnosis in 9 of 16 patients (56%) with multiple malformations. In patients with CDDs, a normal prenatal karyotype, especially if obtained by PUBS, and absence of other detected abnormalities by fetal ultrasonography, do not exclude the presence of other major anomalies, including chromosome abnormalities and severe multiple malformation syndromes.
Published: 1998

34. A comparison of fluconazole with ketoconazole, itraconazole, and clotrimazole in the treatment of patients with pityriasis versicolor

Author: Jorge Arguedas, David L. Gibbs, and Honorio Silva
Subjects: Pharmacology, medicine.medical_specialty, Itraconazole, business.industry, Pityriasis, medicine.disease, Tinea versicolor, Surgery, Regimen, Internal medicine, medicine, Pharmacology (medical), Ketoconazole, Adverse effect, business, Fluconazole, Mycosis, medicine.drug
Abstract: Pityriasis versicolor is a superficial recurring mycotic infection frequently encountered in tropical countries. The challenge for clinicians is selecting an antifungal therapy that produces long-lasting cure, good patient compliance, and a low incidence of adverse effects. Three open-label, randomized, comparative multicenter studies were conducted in South America to assess the efficacy and safety of fluconazole (two 300-mg doses given 1 week apart, and, if necessary, a third dose given 1 week later) with those of ketoconazole (200 mg daily for 10 or 14 days), itraconazole (200 mg daily for 7 days), or clotrimazole 1% cream (two applications daily for 21 days) in the treatment of patients with mycologically proven pityriasis versicolor. Clinical signs and symptoms of skin infection and microscopic examination and cultures of lesion scrapings were assessed at baseline and on days 14, 30 (or 37 for patients who received the third fluconazole dose), and 60. Safety was assessed on days 7, 14, 30 (or 37 for patients who received the third fluconazole dose), and 60. A total of 388 patients received study drug: 194 in the three fluconazole groups and 194 in the ketoconazole, itraconazole, and clotrimazole comparator groups. The combined clinical response rate (cure plus improvement) at early follow-up (day 14) in the fluconazole treatment groups was 91.2%, a response that was maintained at the day 60 evaluation. Similar clinical response rates were found among patients treated with the comparator drugs. Although the eradication rate at day 14 was significantly lower in the fluconazole group (39.1%) than in the itraconazole group (56.5%), no significant differences in eradication rate between fluconazole and each of the comparator drugs were found at later follow-up assessments (days 30 and 60). The highest rate of reinfection (15.3%) was found in itraconazole-treated patients at day 60. The incidence of adverse events was low in all treatment groups, and no clinically significant laboratory abnormalities were reported. The results of these studies indicate that fluconazole administered as two or three weekly 300-mg doses to patients with pityriasis versicolor was well tolerated and at least as effective as accepted regimens employing ketoconazole, itraconazole, and topical clotrimazole. This fluconazole regimen represents an attractive alternative to prolonged daily oral or topical therapy.
Published: 1998

35. Fluconazole in the treatment of systemic candidiasis: an open-label, noncomparative, multinational study

Author: David L. Gibbs
Subjects: Pharmacology, medicine.medical_specialty, biology, Opportunistic infection, business.industry, medicine.disease, biology.organism_classification, Internal medicine, Amphotericin B, medicine, Vomiting, Pharmacology (medical), Systemic candidiasis, medicine.symptom, Intensive care medicine, Adverse effect, Candida albicans, business, Mycosis, Fluconazole, medicine.drug
Abstract: The incidence of opportunistic fungal infections has been increasing in recent years. Fluconazole has demonstrated significant clinical potential and is an attractive alternative to amphotericin B for the treatment of these infections. The present study investigated the efficacy and safety of fluconazole (100 to 400 mg/d) in severely ill patients with microbiologically documented systemic candidiasis. Of the 51 patients enrolled, 39 were assessable for efficacy. The predominant pathogen isolated was Candida albicans. At the end of treatment, clinical and mycologic response was 100%. At follow-up, one patient experienced a relapse caused by C albicans. The most common drug-related adverse events involved the gastrointestinal system (eg, diarrhea and vomiting); no patients withdrew from the study because of adverse events. Thus fluconazole appeared to be effective and safe in the treatment of systemic candidiasis.
Published: 1998

36. 13th International Congress The Fetus as a Patient

Author: Reuben Amster, Georgios Daskalakis, Elizabeth J.T. Winsor, Liria L.T. Miyague, P L Giacalone, Esmie Rose, John F. Bealer, R. D. Wilson, Ariel J. Jaffa, Annaleena Heikkilä, Susan M. Irtenkauf, Scott Adzick, Jean-Michel Faure, Rubén A. Quintero, Jerome Dansereau, D. Aravantinos, D. Kalousek, Pertti Kirkinen, Michelle Bonnett, Yuval Yaron, Joy L Graf, Alessandro Ghidini, John W. Hotra, Jo-Ann Johnson, Sherman Elias, Françoise Deschamps, R. Windrim, Pierre Boulot, Cyril Legum, S. Michalas, Nelson I. Miyague, Markku Ryynänen, William J. Crossland, Kim Waller, Carlos A. Carreno, J. Singer, Joseph Har-Toov, Aristides Antsaklis, Janice L. B. Byrne, Michael R. Harrison, Seppo Saarikoski, William J. Kupsky, David L Gibbs, Mordechai Hallak, and Joseph B. Lessing
Subjects: Embryology, Fetus, medicine.medical_specialty, business.industry, International congress, Pediatrics, Perinatology and Child Health, Physical therapy, Obstetrics and Gynecology, Medicine, Radiology, Nuclear Medicine and imaging, General Medicine, business, Intensive care medicine
Published: 1997

37. Protein co-expression network analysis (ProCoNA)

Author: Shannon K. McWeeney, David L Gibbs, Richard D. Smith, Ralph S. Baric, Michael G. Katze, Eric S. Orwoll, Arie Baratt, and Yoshihiro Kawaoka
Subjects: Proteomics, Sarcopenia, Systems biology, Health Informatics, Computational biology, Biology, Bioinformatics, 03 medical and health sciences, 0302 clinical medicine, Virology, 030304 developmental biology, Data source, 0303 health sciences, Network construction, Biological networks, Methodology, Disease etiology, LC-MS, Expression (architecture), Networks, 030217 neurology & neurosurgery, Biological network, Biomarkers, Network analysis
Abstract: Background Biological networks are important for elucidating disease etiology due to their ability to model complex high dimensional data and biological systems. Proteomics provides a critical data source for such models, but currently lacks robust de novo methods for network construction, which could bring important insights in systems biology. Results We have evaluated the construction of network models using methods derived from weighted gene co-expression network analysis (WGCNA). We show that approximately scale-free peptide networks, composed of statistically significant modules, are feasible and biologically meaningful using two mouse lung experiments and one human plasma experiment. Within each network, peptides derived from the same protein are shown to have a statistically higher topological overlap and concordance in abundance, which is potentially important for inferring protein abundance. The module representatives, called eigenpeptides, correlate significantly with biological phenotypes. Furthermore, within modules, we find significant enrichment for biological function and known interactions (gene ontology and protein-protein interactions). Conclusions Biological networks are important tools in the analysis of complex systems. In this paper we evaluate the application of weighted co-expression network analysis to quantitative proteomics data. Protein co-expression networks allow novel approaches for biological interpretation, quality control, inference of protein abundance, a framework for potentially resolving degenerate peptide-protein mappings, and a biomarker signature discovery.
Published: 2013
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38. Copy Number Variation Analysis in 98 Individuals with PHACE Syndrome

Author: Susan J. Hayflick, Pui-Yan Kwok, William B. Dobyns, Pinar Bayrak-Toydemir, Sheri Mitchell, Beth A. Drolet, Beth Wilmot, Elizabeth A. Worthey, Elena Pope, Rachel Lorier, Johannes Fredrik Grimmer, Shannon K. McWeeney, Maria R. Cordisco, Denise W. Metry, Ulrich Broeckel, Eun Kyung M. Kwon, Jennifer L. Santoro, Francine Blei, David A. Stevenson, Ilona J. Frieden, Kelly J. Duffy, Joseph T. Shieh, Dawn H. Siegel, Alfons Krol, Andrea Matter, David L Gibbs, Maria C. Garzon, and Eulalia Baselga
Subjects: Adult, Male, congenital, hereditary, and neonatal diseases and abnormalities, Adolescent, DNA Copy Number Variations, Genotyping Techniques, Coarctation of the aorta, Dermatology, Biology, Biochemistry, Article, Aortic Coarctation, 03 medical and health sciences, Young Adult, 0302 clinical medicine, Posterior fossa malformations, Dna genetics, medicine, Humans, Copy-number variation, Eye Abnormalities, Child, Molecular Biology, 030304 developmental biology, Genetics, 0303 health sciences, PHACE syndrome, Neurocutaneous Syndromes, Extramural, copy number variation, Infant, Reproducibility of Results, Cell Biology, DNA, PHACES syndrome, medicine.disease, Pascual-Castroviejo II syndrome, Xq28, Eye abnormality, hemangioma, aortic arch anomaly, Case-Control Studies, Child, Preschool, Female, 030217 neurology & neurosurgery, Signal Transduction
Abstract: PHACE syndrome is the association of large segmental facial hemangiomas and congenital anomalies, such as posterior fossa malformations, cerebral arterial anomalies, coarctation of the aorta, eye anomalies, and sternal defects. To date, the reported cases of PHACE syndrome have been sporadic, suggesting that PHACE may have a complex pathogenesis. We report here genomic copy number variation (CNV) analysis of 98 individuals with PHACE syndrome as a first step in deciphering a potential genetic basis of PHACE syndrome. A total of 3,772 CNVs (2,507 duplications and 1,265 deletions) were detected in 98 individuals with PHACE syndrome. CNVs were then eliminated if they failed to meet established criteria for quality, spanned centromeres, or did not contain genes. CNVs were defined as "rare" if not documented in the database of genomic variants. Ten rare CNVs were discovered (size range: 134-406 kb), located at 1q32.1, 1q43, 3q26.32-3q26.33, 3p11.1, 7q33, 10q24.32, 12q24.13, 17q11.2, 18p11.31, and Xq28. There were no rare CNV events that occurred in more than one subject. Therefore, further study is needed to determine the significance of these CNVs in the pathogenesis of PHACE syndrome. Journal of Investigative Dermatology (2013) 133, 677-684; doi:10.1038/jid.2012.367; published online 25 October 2012
Published: 2013

39. Neurodevelopmental outcome after open fetal surgery

Author: Jody A. Farrell, Carol H. Leonard, Robert E. Piecuch, Michael R. Harrison, Joy L Graf, and David L Gibbs
Subjects: Male, Postnatal Care, Pediatrics, medicine.medical_specialty, medicine.medical_treatment, Neurological disorder, Central Nervous System Diseases, Pregnancy, Cystic Adenomatoid Malformation of Lung, Congenital, medicine, Humans, Hernia, Diaphragmatic, Neurologic Examination, Psychomotor learning, Fetus, Fetal surgery, business.industry, Infant, Newborn, Pregnancy Outcome, Infant, Congenital diaphragmatic hernia, General Medicine, Prognosis, medicine.disease, Respiratory support, Survival Rate, Fetal Diseases, Treatment Outcome, In utero, Surgical Procedures, Operative, Anesthesia, Pediatrics, Perinatology and Child Health, Female, Surgery, Hernias, Diaphragmatic, Congenital, business
Abstract: Purpose: This study examined the neurological and ageappropriate developmental outcomes of 11 children who underwent open fetal surgery. Results: Nine children have normal neurological outcomes, and nine have normal development. Conclusion: Intensive postnatal care, intracranial hemorrhage, and requirement for prolonged respiratory support were associated with a worse neurological and developmental prognosis.
Published: 1998

40. Fluconazole orally dispersible tablets for the treatment of patients with oropharyngeal candidiasis

Author: Bernard Vandercam, H Jäger, David L. Gibbs, O Armignacco, and M Valtonen
Subjects: Adult, Male, medicine.medical_specialty, Antifungal Agents, medicine.medical_treatment, Administration, Oral, 030204 cardiovascular system & hematology, Biochemistry, Gastroenterology, Oropharyngeal Candidiasis, 03 medical and health sciences, 0302 clinical medicine, Candidiasis, Oral, Internal medicine, Candida albicans, medicine, Humans, Adverse effect, Sida, Fluconazole, Mycosis, Chemotherapy, biology, business.industry, Biochemistry (medical), Cell Biology, General Medicine, Middle Aged, biology.organism_classification, medicine.disease, Surgery, Tolerability, 030220 oncology & carcinogenesis, Female, business, medicine.drug
Abstract: The efficacy and tolerability of fluconazole orally dispersible tablets (ODT) in the treatment of oropharyngeal candidiasis was evaluated in this multicentre non-comparative study. A total of 89 adults with signs and symptoms of oropharyngeal candidiasis were enrolled; 70 of whom completed therapy with fluconazole ODT 100 mg once daily for 7–14 days. Acquired immunodeficiency syndrome (AIDS)/ AIDS-related complex was an underlying illness in 69% of patients (61). An antimicrobial and corticosteroid therapy was given in 52% (46) and 20% (18) of patients, respectively. Of the 60 patients who had baseline signs and symptoms of infection and a culture positive for Candida albicans, 90% (54) were cured or had improved at the end of therapy, and the fungal pathogen was eradicated in 19/57 (33%) patients. At the 4-week post-treatment follow-up, signs and symptoms of oropharyngeal candidiasis were absent in 73% (27/37) patients. The adverse events and laboratory abnormalities recorded during the study period were attributable to underlying illnesses rather than to fluconazole therapy. These results indicate that this novel dosage form of fluconazole is effective and well tolerated in the treatment of oropharyngeal candidiasis.
Published: 1998

41. Chorioamniotic membrane separation: a potentially lethal finding

Author: John F. Bealer, Joy L Graf, N S Adzick, Michael R. Harrison, and David L Gibbs
Subjects: Adult, Embryology, medicine.medical_specialty, Cord, medicine.medical_treatment, Amniotic Band, Extraembryonic Membranes, Umbilical cord, Ultrasonography, Prenatal, Umbilical Cord, Postoperative Complications, Pregnancy, medicine, Humans, Radiology, Nuclear Medicine and imaging, Hysterotomy, Amnion, Fetal Death, Fetus, business.industry, Obstetrics, Fetal surgery, Uterus, Infant, Newborn, Pregnancy Outcome, Obstetrics and Gynecology, General Medicine, Chorion, Surgery, Pregnancy Complications, medicine.anatomical_structure, Pediatrics, Perinatology and Child Health, Gestation, Female, Amniotic Band Syndrome, business, Complication
Abstract: Sonographic detection of chorioamniotic membrane separation (CMS) has been considered a benign incidental finding. We now report 6 cases of CMS identified by prenatal ultrasound; 1 in an otherwise normal pregnancy and 5 following fetal surgery. Following membrane separation, amniotic bands formed and compromised the umbilical cord in 4 cases leading to 2 fetal deaths.In the first case, a CMS was detected by ultrasound at 22 weeks’ gestation in an otherwise uncomplicated pregnancy. Because CMS was considered benign and umbilical cord blood flow was ample, the mother was followed by intermittent sonographic examinations. Fetal demise occurred 2 weeks later, clearly due to umbilical cord strangulation by an amniotic band. Surprised by this unexpected outcome, we reviewed our experience with CMS after hysterotomy for fetal surgery. Out of more than 40 fetal surgical cases, we have 5 cases in which CMS was recognized after hysterotomy. Three of these fetuses had umbilical cord compromise by a band of amniotic membrane leading to 1 fetal death.This experience demonstrates that membrane separation may be associated with amniotic band formation which can lead to cord strangulation and fetal compromise. Following fetal surgery, serial ultrasound evaluation and close fetal monitoring are indicated. In otherwise unremarkable pregnancies, clinician awareness of the possibility of amniotic band formation following CMS should be heightened. In either situation, knowledge of this potentially life-threatening complication may identify cases in which cord compromise requires emergent delivery or fetoscopic release of the strangulating amniotic band.
Published: 1997

42. Familial diaphragmatic agenesis: an autosomal-recessive syndrome with a poor prognosis

Author: N. Scott Adzick, Michael R. Harrison, Jody A. Farrell, Henry E. Rice, and David L Gibbs
Subjects: Adult, Male, Poor prognosis, medicine.medical_specialty, Diaphragm, Diaphragmatic breathing, Genes, Recessive, medicine, Respiratory muscle, Humans, Hernia, Hernia, Diaphragmatic, business.industry, Infant, Newborn, Congenital diaphragmatic hernia, General Medicine, medicine.disease, Prognosis, Surgery, Pedigree, Posterolateral diaphragmatic hernia, Agenesis, Pediatrics, Perinatology and Child Health, Female, Autosomal recessive form, business, Hernias, Diaphragmatic, Congenital
Abstract: Diaphragmatic agenesis is a severe form of congenital diaphragmatic hernia for which an autosomal recessive form of inheritance has been proposed. The authors report six families with 13 pregnancies with diaphragmatic agenesis in which inheritance followed an autosomal recessive pattern, including the first reported case of bilateral diaphragmatic agenesis in twins. None of the thirteen affected fetuses survived. Familial diaphragmatic agenesis appears to be a distinct clinical entity with a worse prognosis than posterolateral diaphragmatic hernia.
Published: 1997

43. Fetal hydrops after in utero tracheal occlusion

Author: N. Scott Adzick, David L Gibbs, Michael R. Harrison, and Joy L Graf
Subjects: Adult, medicine.medical_specialty, medicine.medical_treatment, Hydrops Fetalis, Fetus, Pregnancy, Hydrops fetalis, medicine, Respiratory muscle, Humans, Diaphragmatic hernia, Hernia, Hernia, Diaphragmatic, Lung, business.industry, Fetal surgery, Infant, Newborn, Congenital diaphragmatic hernia, General Medicine, respiratory system, medicine.disease, Constriction, Surgery, Trachea, Fetal Diseases, medicine.anatomical_structure, In utero, Pediatrics, Perinatology and Child Health, Female, business, Hernias, Diaphragmatic, Congenital
Abstract: In utero tracheal occlusion accelerates fetal lung growth in experimental animals. Following tracheal occlusion, animals with an intact diaphragm can develop hydrops; presumably the enlarged lungs increase intrathoracic pressure, compress the fetal heart, and occlude venous return. In contrast, in animals with a diaphragmatic hernia, even excessive lung expansion has not led to hydrops because the lung can expand through the diaphragmatic defect into the abdomen. The authors had assumed that the diaphragmatic defect in human fetuses with congenital diaphragmatic hernia would provide a similar "release valve" if excessive lung growth occurred. A recent case proved this assumption wrong. At 26 weeks' gestation, an in utero fetal tracheal occlusion was performed on a human fetus with a diaphragmatic hernia. Over the next 9 days there was rapid lung expansion and overdistention, compression of the fetal heart, and hydrops. These findings mimic those seen in fetuses with congenital high airway obstruction syndrome and has implications for in utero treatment of congenital diaphragmatic hernia.
Published: 1997

44. Treatment of kala-azar with oral fluconazole

Author: N. Agrawal, Vijay P. Singh, Shyam Sundar, Henry W. Murray, and David L Gibbs
Subjects: Adult, Male, medicine.medical_specialty, Adolescent, business.industry, Administration, Oral, General Medicine, Dermatology, Drug Administration Schedule, Child, Preschool, Medicine, Humans, Leishmaniasis, Visceral, Female, business, Child, Fluconazole, medicine.drug
Published: 1996

45. Uroepithelium in a colonic diverticulum

Author: Thomas K. Lee, Samuel Z. Soffer, Nelson G. Rosen, Elsa Valderrama, and David L. Gibbs
Subjects: Adult, Male, medicine.medical_specialty, Pathology, medicine.medical_treatment, Umbilicus (mollusc), Diverticulum, Colon, Ultrasonography, Prenatal, Pregnancy, Colon surgery, Colostomy, medicine, Humans, Urothelium, business.industry, Infant, Newborn, General Medicine, Diverticulitis, medicine.disease, digestive system diseases, Diverticulosis, Bowel obstruction, Pediatrics, Perinatology and Child Health, Female, Surgery, Histopathology, business, Intestinal Obstruction
Abstract: Giant colonic diverticula are rare entities and often present in adulthood as acute diverticulitis. The authors present a case of giant colonic pseudodiverticulum lined with uroepithelium causing bowel obstruction in a neonate. The presence of uroepithelium in a colonic pseudodiverticuium remains unexplained and to the authors' knowledge unreported. This heterotopic tissue may be a result of an embryonic rest or could be urachal in origin adhering initially to the colon and eventually detaching from the umbilicus. These lesions should be resected because of the risk of infection, perforation, or obstruction.
Published: 2000

46. Projecting chromatic aberrations

Author: Phil Ryan, David L. Gibbs, and Jay S. Huebner
Subjects: Physics, Optics, law, business.industry, Overhead projector, Chromatic aberration, General Physics and Astronomy, Optoelectronics, Fresnel lens, Color filter array, business, law.invention
Published: 2000

47. Atovaquone Alone or with Fluconazole as Oral Therapy for Indian Kala‐Azar

Author: Manoj K. Makharia, Shyam Sundar, Prasanna C. K. Kumar, A. K. Goyal, Henry W. Murray, Michael S. Rogers, and David L Gibbs
Subjects: Adult, Male, Microbiology (medical), Antifungal Agents, Adolescent, medicine.medical_treatment, Antiprotozoal Agents, Pilot Projects, Pharmacology, Oral administration, Animals, Humans, Medicine, Child, Protozoal disease, Fluconazole, Oral therapy, Atovaquone, Chemotherapy, business.industry, Leishmaniasis, Middle Aged, medicine.disease, Infectious Diseases, Child, Preschool, Triazole derivatives, Leishmaniasis, Visceral, Drug Therapy, Combination, Female, business, Naphthoquinones, medicine.drug
Published: 1998

48. A Comparative Study of Once versus Twice Daily Treatment of Superficial Dermatophyte and Yeast Infections with Tioconazole (1%) Cream

Author: Monique C Phyfferoen, David L. Gibbs, and Philip Kashin
Subjects: Adult, Male, medicine.medical_specialty, Antifungal Agents, Adolescent, medicine.medical_treatment, 030204 cardiovascular system & hematology, medicine.disease_cause, Biochemistry, Random Allocation, 03 medical and health sciences, 0302 clinical medicine, Tinea, Dermatomycoses, Humans, Medicine, Mycosis, Aged, Clinical Trials as Topic, Microscopy, Chemotherapy, business.industry, Biochemistry (medical), Candidiasis, Imidazoles, Drug Tolerance, Cell Biology, General Medicine, Middle Aged, medicine.disease, Dermatology, Once daily treatment, Clinical trial, 030220 oncology & carcinogenesis, Tioconazole, Dermatophyte, Female, Once daily, business, After treatment, medicine.drug
Abstract: Two open, multicentre studies were performed to evaluate the efficacy of 1% tioconazole cream (Trosyd®, Pfizer) as a once daily treatment for patients with common superficial dermatophyte and yeast infections (t. Pedis, t. versicolor, t. corporis, t. cruris, candidiasis). In Study I, forty-eight patients were treated once daily and forty-nine were treated twice daily. The clinical response rate at the last treatment visit was 96% and 98%, respectively, in the two treatment groups. In Study II, all 107 patients were treated once daily, and the clinical response rate was 95% at the last treatment visit. The mycological cure rates were 85% and 91% in the Study I groups, and 94% in Study II. At the long-term follow-up evaluation (approximately 4 weeks after treatment) favourable clinical responses were seen in 88% of patients in both the Study I groups, and in 94% of patients in Study II. Long-term mycological cures were seen in 80% of patients in both groups of Study I, and in 94% of patients in Study II. Side effects of treatment were generally transient, mild and coincident with disease symptoms, and occurred in six patients (6%) in Study I and in four patients (4%) in Study II. The results of both studies have shown no clinically or statistically significant differences in toleration or efficacy between once daily and twice daily treatment regimens with 1% tioconazole cream for the treatment of superficial dermatophyte infections.
Published: 1985

49. Comparative and Non-Comparative Studies of the Efficacy and Tolerance of Tioconazole Cream 1% versus Another Imidazole and/or Placebo in Neonates and Infants with Candidal Diaper Rash and/or Impetigo

Author: S. Jevons, P. Kashin, and David L. Gibbs
Subjects: Male, medicine.medical_specialty, Antifungal Agents, Impetigo, Econazole, Adolescent, Erythema, Candidiasis, Cutaneous, 030204 cardiovascular system & hematology, Placebo, Biochemistry, 03 medical and health sciences, 0302 clinical medicine, Diaper rash, medicine, Humans, Child, skin and connective tissue diseases, Mycosis, business.industry, Biochemistry (medical), Imidazoles, Infant, Newborn, Infant, Cell Biology, General Medicine, medicine.disease, Dermatology, Surgery, Diaper Rash, Child, Preschool, 030220 oncology & carcinogenesis, Tioconazole, Female, Miconazole, medicine.symptom, business, medicine.drug
Abstract: Eleven open multicentre studies were conducted to evaluate the efficacy of tioconazole cream 1% as a treatment for diaper rash with or without fungal ( Candida) involvement, or impetigo in neonates and infants. In the dermal candidiasis/diaper rash group, 320 patients had either tioconazole ( n = 220), a comparative imidazole ( n = 43), or vehicle cream ( n = 57) applied to the affected area twice daily. Twenty-one impetigo patients had only tioconazole cream 1% applied three times daily to lesions. The overall cure rate (patients with both clinical and mycological cure) at the end of treatment for tioconazole treated patients was 78%, for the comparative imidazole group it was 76% and for vehicle cream it was 39%. At the long-term follow-up evaluation approximately 6 weeks after treatment for patients with diaper rash, the overall cure rate was about the same in both tioconazole- and comparative imidazole-treated patients (87% and 90%, respectively), and 14% in patients using vehicle cream. Side-effects were coincident with disease symptoms and consisted primarily of erythema localized to the treatment area; they occurred in 5.4% (13/241) of the patients who received tioconazole and in 21% (9/43) of the patients who received comparative imidazole (econazole or miconazole). No side-effects were reported in this open study for the 57 patients who used vehicle cream. The results of these studies show that tioconazole cream 1% is safe and effective for the treatment of neonates and infants with dermal candidiasis, diaper rash and impetigo.
Published: 1987

50. Susceptibility of Gram-negative bacteria to β-lactam antibiotics and rapid characterization of β-lactamase activity

Author: David L. Gibbs and Clyde Thornsberry
Subjects: Bacilli, Gram-negative bacteria, biology, Visual test, medicine.drug_class, Cephalosporin, Antibiotics, General Medicine, biology.organism_classification, Applied Microbiology and Biotechnology, Microbiology, Proteus mirabilis, Biochemistry, medicine, Cefoxitin, Bacteria, medicine.drug
Abstract: Various species of Gram-negative bacteria were tested for susceptibility to β-lactam antibiotics and for production of β-lactamase. The rapid cephalosporin 87/312 visual test was more sensitive than either the acidometric or the iodometric test; the iodometric test was least sensitive. Characteristic β-lactamase hydrolysis product patterns were obtained by scanning mixtures of β-lactamase-producing bacteria and cephalosporin substrates. β-Lactamase could not be detected on bacterial cells by fluorescent antibody techniques. The presence of β-lactamase can be correlated with minimal inhibitory concentrations of β-lactam antibiotics only in certain Gram-negative bacilli.
Published: 1979

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