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1. Spatially interacting phosphorylation sites and mutations in cancer

Author: Kuan-lin Huang, Adam D. Scott, Daniel Cui Zhou, Liang-Bo Wang, Amila Weerasinghe, Abdulkadir Elmas, Ruiyang Liu, Yige Wu, Michael C. Wendl, Matthew A. Wyczalkowski, Jessika Baral, Sohini Sengupta, Chin-Wen Lai, Kelly Ruggles, Samuel H. Payne, Benjamin Raphael, David Fenyö, Ken Chen, Gordon Mills, and Li Ding
Subjects: Science
Abstract: Dysregulated phosphorylation is well-known in cancers, but it has largely been studied in isolation from mutations. Here the authors introduce HotPho, a tool that can discover spatial interactions between phosphosites and mutations, which are associated with activating mutation and genetic dependencies in cancer.
Published: 2021
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2. Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Author: Matthew H. Bailey, William U. Meyerson, Lewis Jonathan Dursi, Liang-Bo Wang, Guanlan Dong, Wen-Wei Liang, Amila Weerasinghe, Shantao Li, Yize Li, Sean Kelso, MC3 Working Group, PCAWG novel somatic mutation calling methods working group, Gordon Saksena, Kyle Ellrott, Michael C. Wendl, David A. Wheeler, Gad Getz, Jared T. Simpson, Mark B. Gerstein, Li Ding, and PCAWG Consortium
Subjects: Science
Abstract: With the generation of large pan-cancer whole-exome and whole-genome sequencing projects, a question remains about how comparable these datasets are. Here, using The Cancer Genome Atlas samples analysed as part of the Pan-Cancer Analysis of Whole Genomes project, the authors explore the concordance of mutations called by whole exome sequencing and whole genome sequencing techniques.
Published: 2020
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3. Proteogenomic Characterization of Ovarian HGSC Implicates Mitotic Kinases, Replication Stress in Observed Chromosomal Instability

Author: Jason E. McDermott, Osama A. Arshad, Vladislav A. Petyuk, Yi Fu, Marina A. Gritsenko, Therese R. Clauss, Ronald J. Moore, Athena A. Schepmoes, Rui Zhao, Matthew E. Monroe, Michael Schnaubelt, Chia-Feng Tsai, Samuel H. Payne, Chen Huang, Liang-Bo Wang, Steven Foltz, Matthew Wyczalkowski, Yige Wu, Ehwang Song, Molly A. Brewer, Mathangi Thiagarajan, Christopher R. Kinsinger, Ana I. Robles, Emily S. Boja, Henry Rodriguez, Daniel W. Chan, Bing Zhang, Zhen Zhang, Li Ding, Richard D. Smith, Tao Liu, and Karin D. Rodland
Subjects: Medicine (General), R5-920
Published: 2020
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4. Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features

Author: Yan Xu, Zhipeng Jia, Liang-Bo Wang, Yuqing Ai, Fang Zhang, Maode Lai, and Eric I-Chao Chang
Subjects: Deep convolution activation feature, Deep learning, Feature learning, Segmentation, Classification, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract: Abstract Background Histopathology image analysis is a gold standard for cancer recognition and diagnosis. Automatic analysis of histopathology images can help pathologists diagnose tumor and cancer subtypes, alleviating the workload of pathologists. There are two basic types of tasks in digital histopathology image analysis: image classification and image segmentation. Typical problems with histopathology images that hamper automatic analysis include complex clinical representations, limited quantities of training images in a dataset, and the extremely large size of singular images (usually up to gigapixels). The property of extremely large size for a single image also makes a histopathology image dataset be considered large-scale, even if the number of images in the dataset is limited. Results In this paper, we propose leveraging deep convolutional neural network (CNN) activation features to perform classification, segmentation and visualization in large-scale tissue histopathology images. Our framework transfers features extracted from CNNs trained by a large natural image database, ImageNet, to histopathology images. We also explore the characteristics of CNN features by visualizing the response of individual neuron components in the last hidden layer. Some of these characteristics reveal biological insights that have been verified by pathologists. According to our experiments, the framework proposed has shown state-of-the-art performance on a brain tumor dataset from the MICCAI 2014 Brain Tumor Digital Pathology Challenge and a colon cancer histopathology image dataset. Conclusions The framework proposed is a simple, efficient and effective system for histopathology image automatic analysis. We successfully transfer ImageNet knowledge as deep convolutional activation features to the classification and segmentation of histopathology images with little training data. CNN features are significantly more powerful than expert-designed features.
Published: 2017
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5. Author Correction: Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Author: Matthew H. Bailey, William U. Meyerson, Lewis Jonathan Dursi, Liang-Bo Wang, Guanlan Dong, Wen-Wei Liang, Amila Weerasinghe, Shantao Li, Yize Li, Sean Kelso, MC3 Working Group, PCAWG novel somatic mutation calling methods working group, Gordon Saksena, Kyle Ellrott, Michael C. Wendl, David A. Wheeler, Gad Getz, Jared T. Simpson, Mark B. Gerstein, Li Ding, and PCAWG Consortium
Subjects: Science
Abstract: Correction to this paper has been published: https://doi.org/10.1038/s41467-020-20128-w
Published: 2020
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6. 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
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7. Pollock: fishing for cell states

Author: Erik P Storrs, Daniel Cui Zhou, Michael C Wendl, Matthew A Wyczalkowski, Alla Karpova, Liang-Bo Wang, Yize Li, Austin Southard-Smith, Reyka G Jayasinghe, Lijun Yao, Ruiyang Liu, Yige Wu, Nadezhda V Terekhanova, Houxiang Zhu, John M Herndon, Sid Puram, Feng Chen, William E Gillanders, Ryan C Fields, and Li Ding
Subjects: General Medicine
Abstract: MotivationThe use of single-cell methods is expanding at an ever-increasing rate. While there are established algorithms that address cell classification, they are limited in terms of cross platform compatibility, reliance on the availability of a reference dataset and classification interpretability. Here, we introduce Pollock, a suite of algorithms for cell type identification that is compatible with popular single-cell methods and analysis platforms, provides a set of pretrained human cancer reference models, and reports interpretability scores that identify the genes that drive cell type classifications.ResultsPollock performs comparably to existing classification methods, while offering easily deployable pretrained classification models across a wide variety of tissue and data types. Additionally, it demonstrates utility in immune pan-cancer analysis.Availability and implementationSource code and documentation are available at https://github.com/ding-lab/pollock. Pretrained models and datasets are available for download at https://zenodo.org/record/5895221.Supplementary informationSupplementary data are available at Bioinformatics Advances online.
Published: 2021

8. Spatially restricted drivers and transitional cell populations cooperate with the microenvironment in untreated and chemo-resistant pancreatic cancer

Author: Daniel Cui Zhou, Reyka G. Jayasinghe, Siqi Chen, John M. Herndon, Michael D. Iglesia, Pooja Navale, Michael C. Wendl, Wagma Caravan, Kazuhito Sato, Erik Storrs, Chia-Kuei Mo, Jingxian Liu, Austin N. Southard-Smith, Yige Wu, Nataly Naser Al Deen, John M. Baer, Robert S. Fulton, Matthew A. Wyczalkowski, Ruiyang Liu, Catrina C. Fronick, Lucinda A. Fulton, Andrew Shinkle, Lisa Thammavong, Houxiang Zhu, Hua Sun, Liang-Bo Wang, Yize Li, Chong Zuo, Joshua F. McMichael, Sherri R. Davies, Elizabeth L. Appelbaum, Keenan J. Robbins, Sara E. Chasnoff, Xiaolu Yang, Ashley N. Reeb, Clara Oh, Mamatha Serasanambati, Preet Lal, Rajees Varghese, Jay R. Mashl, Jennifer Ponce, Nadezhda V. Terekhanova, Lijun Yao, Fang Wang, Lijun Chen, Michael Schnaubelt, Rita Jui-Hsien Lu, Julie K. Schwarz, Sidharth V. Puram, Albert H. Kim, Sheng-Kwei Song, Kooresh I. Shoghi, Ken S. Lau, Tao Ju, Ken Chen, Deyali Chatterjee, William G. Hawkins, Hui Zhang, Samuel Achilefu, Milan G. Chheda, Stephen T. Oh, William E. Gillanders, Feng Chen, David G. DeNardo, Ryan C. Fields, and Li Ding
Subjects: Pancreatic Neoplasms, Cell Transformation, Neoplastic, Genetics, Tumor Microenvironment, Humans, Pancreas, Carcinoma, Pancreatic Ductal
Abstract: Pancreatic ductal adenocarcinoma is a lethal disease with limited treatment options and poor survival. We studied 83 spatial samples from 31 patients (11 treatment-naïve and 20 treated) using single-cell/nucleus RNA sequencing, bulk-proteogenomics, spatial transcriptomics and cellular imaging. Subpopulations of tumor cells exhibited signatures of proliferation, KRAS signaling, cell stress and epithelial-to-mesenchymal transition. Mapping mutations and copy number events distinguished tumor populations from normal and transitional cells, including acinar-to-ductal metaplasia and pancreatic intraepithelial neoplasia. Pathology-assisted deconvolution of spatial transcriptomic data identified tumor and transitional subpopulations with distinct histological features. We showed coordinated expression of TIGIT in exhausted and regulatory T cells and Nectin in tumor cells. Chemo-resistant samples contain a threefold enrichment of inflammatory cancer-associated fibroblasts that upregulate metallothioneins. Our study reveals a deeper understanding of the intricate substructure of pancreatic ductal adenocarcinoma tumors that could help improve therapy for patients with this disease.
Published: 2021

9. Abstract 4106: Curating protein complexes from multiple resources and validate their co-regulations at RNA/protein level in pan-cancer

Author: Yizhe Song, Daniel Cui Zhou, Yize Li, Liang-Bo Wang, Michael C. Wendl, Gad Getz, and Li Ding
Subjects: Cancer Research, Oncology
Abstract: Protein complexes are sets of proteins that not only physically interact with each other, but also play important roles in progression of diseases like cancer. However, little is known about the co-regulation patterns of complexes and how many of them could be potential therapeutic targets. We collected 16,680 complexes from CORUM, Reactome, Complex Portal, and Havugimana PC. Harmonization of subunits of complexes by UniProt ID removed 4,112 redundant complexes. We further found that 3,018/12,559 complexes have at least one cancer driver gene involved, of which 2,408 complexes were found to have druggable genes, according to CiVic and OncoKB. Co-regulation patterns of 12,559 complexes were annotated utilizing gene expression and protein expression data from 1,101 cases across 10 cancer types (BR, COAD, OV, UCEC, HNSCC, PDAC, LSCC, LUAD, GBM, and ccRCC) from the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Pairwise correlations across subunits of each complex at protein and RNA levels were calculated separately. Correlation >0.8 would suggest strong interaction of the subunits of the complex. Protein-only co-regulation complex was declared for protein pairwise correlation >0.8 and RNA pairwise correlation Citation Format: Yizhe Song, Daniel Cui Zhou, Yize Li, Liang-Bo Wang, Michael C. Wendl, Gad Getz, Li Ding. Curating protein complexes from multiple resources and validate their co-regulations at RNA/protein level in pan-cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4106.
Published: 2022
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10. Abstract 5027: PTMcosmos: A web portal of post-translational modifications and proteogenomic resources in cancer

Author: Liang-Bo Wang, Akshay Govindan, Song Cao, and Li Ding
Subjects: Cancer Research, Oncology
Abstract: PTMcosmos is a comprehensive database with an interactive web portal designed to catalog and visualize post-translational modifications (PTMs) in humans. It contains 469,183 experimentally-validated PTM sites and their supporting evidence from UniProt Knowledge Base, PhosphoSitePlus, and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). PTMcosmos summarizes the entire spectrum of CPTAC proteomics data on human cancer patients, including protein and PTM peptide abundance data from 10 different cancer types. Additionally, PTMcosmos contains cancer somatic mutations from The Cancer Genome Atlas (TCGA), thus allowing for the collective integration and analysis of different data types. In PTMcosmos, we have built an ensemble of interactive visualization tools that allow researchers to investigate altered PTM functions due to genetic alterations in close proximity. The database is live at https://ptmcosmos.wustl.edu. We used PTMcosmos to investigate PTM regulation across cancer types. First, we examined the differential abundance of the PTM sites of cancer driver genes, focusing primarily on phosphorylation of the tumor suppressor retinoblastoma protein (encoded by RB1) and acetylation of the histone acetyltransferase E1A Binding Protein P300 (EP300) across cancer types. We analyzed the association between these PTM events and downstream targets, as well as with tumor subtypes, significantly mutated gene (SMG) mutation status, and clinical features. Second, we investigated the association of the protein abundance of cancer driver genes with ubiquitylsites in lung squamous cell carcinoma (LSCC) to nominate potentially novel modes of regulation of these proteins’ activities. We further analyzed the tumor subtype specificity and tumor-normal abundance changes of these ubiquitylsites and their corresponding substrate proteins, identifying several EGFR ubiquitylsites which may regulate EGFR abundance in LSCC. Finally, we identified the linear and spatial clustering of mutations and PTM sites, identifying multiple mutation-PTM clusters in cancer related genes, including TP53, PIK3CA, CTNNB1, EGFR, and IDH1. We envision that PTMcosmos will serve both the CPTAC consortium and the wider research community to better understand the role of PTMs in cancer. Citation Format: Liang-Bo Wang, Akshay Govindan, Song Cao, Li Ding. PTMcosmos: A web portal of post-translational modifications and proteogenomic resources in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5027.
Published: 2022
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11. Abstract 2832: Identifying tumor specific surface protein markers for glioblastoma immunotherapy by single-nuclei RNA-seq

Author: Omar M. Ibrahim, Liang-Bo Wang, Lijun Yao, Wagma Caravan, Nataly Naser Al Deen, Reyka G. Jayasinghe, Milan G. Chheda, and Li Ding
Subjects: Cancer Research, Oncology
Abstract: Glioblastoma (GBM) is one of the deadliest adult brain tumors, in which the typical treatments of surgery, chemotherapy, and/or radiotherapy yield generally poor median survival. GBM is categorized into IDH-mutant subtype and IDH-wildtype subtype, which is subcategorized into proneural, classical, and mesenchymal subtypes, yet few personalized treatment options are available. The use of genetically modified chimeric antigen receptor T cells (CAR-T), such as CD19 CAR-T, has shown significant clinical efficacy in the treatment of hematological malignancies due to specific antitumor response; however, CAR-T response in GBM has been poor. Single nuclei RNA-seq (snRNA-seq) technology offers high-resolution profiling of different cell populations and their specific differential patterns. Accordingly, we developed a robust genomic analysis pipeline that can be applied to external datasets, tumor, and healthy samples. This was followed by unbiased systematic analysis of GBM tumor-specific cell surface markers using 18 single nuclei RNA sequencing (snRNA-seq) samples to identify GBM specific CAR-T targets allowing efficient tumor cells targeting and minimum off-target effects by CAR-T (1). We identified key potential targetable cell surface-specific markers and we also identified subtype-specific targets. Our findings provide insights into tumor-specific cell surface proteins that may be potential targets for engineered immunotherapy. References:(1) Wang, Liang-Bo, Alla Karpova, Marina A. Gritsenko, Jennifer E. Kyle, Song Cao, Yize Li, Dmitry Rykunov, et al. "Proteogenomic and metabolomic characterization of human glioblastoma." Cancer cell 39, no. 4 (2021): 509-528. Citation Format: Omar M. Ibrahim, Liang-Bo Wang, Lijun Yao, Wagma Caravan, Nataly Naser Al Deen, Reyka G. Jayasinghe, Milan G. Chheda, Li Ding. Identifying tumor specific surface protein markers for glioblastoma immunotherapy by single-nuclei RNA-seq [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2832.
Published: 2022
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12. Abstract 1932: Pollock: Fishing for cell states

Author: Erik Storrs, Daniel Cui Zhou, Michael C. Wendl, Matthew A. Wyczalkowski, Alla Karpova, Liang-Bo Wang, Yize Li, Austin Southard-Smith, Reyka G. Jayasinghe, Lijun Yao, Ruiyang Liu, Yige Wu, Nadezhda V. Terekhanova, Houxiang Zhu, John M. Herndon, Feng Chen, William E. Gillanders, Ryan C. Fields, and Li Ding
Subjects: Cancer Research, Oncology
Abstract: The use of single-cell methods is expanding at an ever-increasing rate. While multiple algorithms address the task of cell classification, they are limited in terms of cross platform compatibility, reliance on the availability of a reference dataset, and classification interpretability. Here, we introduce Pollock, a suite of algorithms for cell type identification that is compatible with popular single cell methods and analysis platforms, provides a series of pretrained human cancer reference models, and reports interpretability scores that identify the genes that drive cell type classifications. Our model combines two important approaches, one each from machine learning and deep learning: a variational autoencoder (VAE) and random forest classifier, to make cell type predictions. Pollock is highly versatile, being available as a command line tool, Python library (with scanpy integration), or R library (with Seurat integration), and can be installed as a conda package, or in containerized form via Docker. To allow for easier pan-disease and pan-tissue analyses, Pollock also ships with a library of pretrained cancer type specific and agnostic modules that were trained on expertly-curated single cell data that are ready to “plug and play” with no additional annotation or training required. Conversely, Pollock also allows for the training of custom classification modules, if an annotated reference single cell dataset is available. These pretrained models were fitted on manually curated and annotated single cell data from eight different cancer types spanning three single cell technologies (scRNA-seq, snRNA-seq, and snATAC-seq). Pollock also provides feature importance scores that allow for cell type classifications to be traced back to the genes influencing a particular cell type classification, further promoting biological interpretability. These scores could allow for new, technology-specific biomarker discovery. We also demonstrate the utility of Pollock by applying it in a pan-cancer single cell immune analysis. Citation Format: Erik Storrs, Daniel Cui Zhou, Michael C. Wendl, Matthew A. Wyczalkowski, Alla Karpova, Liang-Bo Wang, Yize Li, Austin Southard-Smith, Reyka G. Jayasinghe, Lijun Yao, Ruiyang Liu, Yige Wu, Nadezhda V. Terekhanova, Houxiang Zhu, John M. Herndon, Feng Chen, William E. Gillanders, Ryan C. Fields, Li Ding. Pollock: Fishing for cell states [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1932.
Published: 2022
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13. Spatially interacting phosphorylation sites and mutations in cancer

Author: Adam D. Scott, Daniel Cui Zhou, Kuan-lin Huang, Liang-Bo Wang, Samuel H. Payne, Li Ding, Chin-Wen Lai, Jessika Baral, Gordon B. Mills, Yige Wu, Michael C. Wendl, Sohini Sengupta, Ruiyang Liu, Abdulkadir Elmas, Benjamin J. Raphael, Amila Weerasinghe, Matthew A. Wyczalkowski, David Fenyö, Ken Chen, and Kelly V. Ruggles
Subjects: Proteomics, 0301 basic medicine, Science, Protein Tyrosine Phosphatase, Non-Receptor Type 12, General Physics and Astronomy, Computational biology, medicine.disease_cause, Article, Mass Spectrometry, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, 0302 clinical medicine, Protein structure, Neoplasms, medicine, Cluster Analysis, Humans, HRAS, Phosphorylation, MAPK1, beta Catenin, Cancer, Mutation, Binding Sites, Multidisciplinary, biology, Kinase, Computational Biology, General Chemistry, medicine.disease, Computational biology and bioinformatics, ErbB Receptors, 030104 developmental biology, Histone, biology.protein, KRAS, 030217 neurology & neurosurgery
Abstract: Advances in mass-spectrometry have generated increasingly large-scale proteomics datasets containing tens of thousands of phosphorylation sites (phosphosites) that require prioritization. We develop a bioinformatics tool called HotPho and systematically discover 3D co-clustering of phosphosites and cancer mutations on protein structures. HotPho identifies 474 such hybrid clusters containing 1255 co-clustering phosphosites, including RET p.S904/Y928, the conserved HRAS/KRAS p.Y96, and IDH1 p.Y139/IDH2 p.Y179 that are adjacent to recurrent mutations on protein structures not found by linear proximity approaches. Hybrid clusters, enriched in histone and kinase domains, frequently include expression-associated mutations experimentally shown as activating and conferring genetic dependency. Approximately 300 co-clustering phosphosites are verified in patient samples of 5 cancer types or previously implicated in cancer, including CTNNB1 p.S29/Y30, EGFR p.S720, MAPK1 p.S142, and PTPN12 p.S275. In summary, systematic 3D clustering analysis highlights nearly 3,000 likely functional mutations and over 1000 cancer phosphosites for downstream investigation and evaluation of potential clinical relevance., Dysregulated phosphorylation is well-known in cancers, but it has largely been studied in isolation from mutations. Here the authors introduce HotPho, a tool that can discover spatial interactions between phosphosites and mutations, which are associated with activating mutation and genetic dependencies in cancer.
Published: 2021
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14. Proteogenomic and metabolomic characterization of human glioblastoma

Author: Cristina E. Tognon, Larisa Polonskaya, Tara Skelly, Shuang Cai, Francesmary Modugno, Larissa Rossell, Nancy Roche, Chen Huang, Jessika Baral, Fulvio D'Angelo, Wen-Wei Liang, Chia-Feng Tsai, Sneha P. Couvillion, Karin D. Rodland, Jun Zhu, Liang-Bo Wang, Paul D. Piehowski, Antonio Colaprico, Anupriya Agarwal, Matthew A. Wyczalkowski, Umut Ozbek, Francesca Petralia, Alexis Demopoulos, William W. Maggio, Lin Chen, Katherine A. Hoadley, Richard D. Smith, Sandra Cottingham, John McGee, Marcin J. Domagalski, Houxiang Zhu, Emek Demir, Rebecca I. Montgomery, Jamie Moon, Rashna Madan, George D. Wilson, Luciano Garofano, Ewa P. Malc, Chelsea J. Newton, Steven A. Carr, Chandan Kumar-Sinha, Donghui Tan, Christopher R. Kinsinger, Oxana Paklina, Weiqing Wan, Stephanie De Young, Sandra Cerda, Shankha Satpathy, Wojciech Kaspera, Linda Hannick, Gad Getz, Runyu Hong, Shuangjia Lu, Ziad Hanhan, Daniel C. Rohrer, Annette Marrero-Oliveras, Wojciech Szopa, Yuxing Liao, Amanda G. Paulovich, Jiayi Ji, Denis A. Golbin, Tara Hiltke, Weiva Sieh, Piotr A. Mieczkowski, Matthew E. Monroe, Gilbert S. Omenn, Jill S. Barnholtz-Sloan, Azra Krek, Bing Zhang, Brittany Henderson, Peter B. McGarvey, Ratna R. Thangudu, Maciej Wiznerowicz, Saravana M. Dhanasekaran, Alex Webster, Kai Li, Karna Robinson, Nan Ji, Karl K. Weitz, Simina M. Boca, Xiaoyu Song, Anna Calinawan, Adam C. Resnick, Brian J. Druker, Dana R. Valley, David J. Clark, Tao Liu, Eric J. Jaehnig, Alicia Francis, Michele Ceccarelli, Rui Zhao, Dmitry Rykunov, Boris Reva, Elizabeth R. Duffy, Antonio Iavarone, Dave Tabor, Joshua F. McMichael, Daniel Cui Zhou, Maureen Dyer, Kimberly Elburn, Scott D. Jewell, Negin Vatanian, Shirley Tsang, Seungyeul Yoo, Alexander R. Pico, Grace Zhao, Kent J. Bloodsworth, Chet Birger, Jena Lilly, Eunkyung An, Jeffrey R. Whiteaker, Albert H. Kim, Yige Wu, Karen A. Ketchum, Felipe D. Leprevost, Alcida Karz, Uma Borate, Nathan Edwards, Uma Velvulou, Melissa Borucki, Vasileios Stathias, Sanford P. Markey, Corbin D. Jones, Ronald J. Moore, MacIntosh Cornwell, Karsten Krug, Michael J. Birrer, James Suh, Tomasz Czernicki, Jason E. McDermott, Emily S. Boja, Pei Wang, Nina Martinez, Wenke Liu, Yan Shi, Lili Blumenberg, Emily Kawaler, Jeffrey W. Tyner, Feng Chen, Jakub Stawicki, Ki Sung Um, Arul M. Chinnaiyan, Robert Zelt, Jacob J. Day, Zhen Zhang, Caleb M. Lindgren, Li Ding, Nikolay Gabrovski, Hongwei Liu, Jonathan T. Lei, Alla Karpova, Ramani B. Kothadia, Sailaja Mareedu, Mitual Amin, Hannah Boekweg, Jennifer E. Kyle, Sara R. Savage, Brian R. Rood, Yuriy Zakhartsev, Matthew L. Anderson, Alyssa Charamut, Wagma Caravan, Shakti Ramkissoon, Junmei Wang, Song Cao, Samuel H. Payne, Rosalie K. Chu, Rajiv Dhir, David W. Andrews, Galen Hostetter, Liqun Qi, Zhiao Shi, Milan G. Chheda, Robert Edwards, Hui Zhang, Weiping Ma, Jennifer M. Eschbacher, Stacey Gabriel, Jan Lubinski, Lijun Yao, Erika M. Zink, Kelly L. Stratton, William Bocik, Mathangi Thiagarajan, Shilpi Singh, Michael A. Gillette, Lisa M. Bramer, Thomas L. Bauer, Michael Vernon, Henry Rodriguez, Dimitris G. Placantonakis, Eric E. Schadt, Alexey I. Nesvizhskii, Vladislav A. Petyuk, Ana I. Robles, Yvonne Shutack, Anna Malovannaya, Stephen E. Stein, Xi Chen, Lyndon Kim, Yize Li, Shannon Richey, Stephan C. Schürer, Barbara Hindenach, Matthew J. Ellis, Yongchao Dou, David Fenyö, Amy M. Perou, Olga Potapova, Shrabanti Chowdhury, Andrew K. Godwin, Marcin Cieślik, Michael C. Wendl, Marina A. Gritsenko, Pietro Pugliese, Elie Traer, Simona Migliozzi, D. R. Mani, Houston Culpepper, Gregory J. Riggins, Xiaolu Yang, Mehdi Mesri, David Chesla, Lindsey K. Olsen, Lori J. Sokoll, Suhas Vasaikar, Liwei Zhang, Meghan C. Burke, Kelly V. Ruggles, Qing Kay Li, Daniel W. Chan, Bo Wen, Nicollette Maunganidze, Darlene Tansil, Joseph H. Rothstein, Barbara Pruetz, Pushpa Hariharan, Wang, L. -B., Karpova, A., Gritsenko, M. A., Kyle, J. E., Cao, S., Li, Y., Rykunov, D., Colaprico, A., Rothstein, J. H., Hong, R., Stathias, V., Cornwell, M., Petralia, F., Wu, Y., Reva, B., Krug, K., Pugliese, P., Kawaler, E., Olsen, L. K., Liang, W. -W., Song, X., Dou, Y., Wendl, M. C., Caravan, W., Liu, W., Cui Zhou, D., Ji, J., Tsai, C. -F., Petyuk, V. A., Moon, J., Ma, W., Chu, R. K., Weitz, K. K., Moore, R. J., Monroe, M. E., Zhao, R., Yang, X., Yoo, S., Krek, A., Demopoulos, A., Zhu, H., Wyczalkowski, M. A., Mcmichael, J. F., Henderson, B. L., Lindgren, C. M., Boekweg, H., Lu, S., Baral, J., Yao, L., Stratton, K. G., Bramer, L. M., Zink, E., Couvillion, S. P., Bloodsworth, K. J., Satpathy, S., Sieh, W., Boca, S. M., Schurer, S., Chen, F., Wiznerowicz, M., Ketchum, K. A., Boja, E. S., Kinsinger, C. R., Robles, A. I., Hiltke, T., Thiagarajan, M., Nesvizhskii, A. I., Zhang, B., Mani, D. R., Ceccarelli, M., Chen, X. S., Cottingham, S. L., Li, Q. K., Kim, A. H., Fenyo, D., Ruggles, K. V., Rodriguez, H., Mesri, M., Payne, S. H., Resnick, A. C., Wang, P., Smith, R. D., Iavarone, A., Chheda, M. G., Barnholtz-Sloan, J. S., Rodland, K. D., Liu, T., Ding, L., Agarwal, A., Amin, M., An, E., Anderson, M. L., Andrews, D. W., Bauer, T., Birger, C., Birrer, M. J., Blumenberg, L., Bocik, W. E., Borate, U., Borucki, M., Burke, M. C., Cai, S., Calinawan, A. P., Carr, S. A., Cerda, S., Chan, D. W., Charamut, A., Chen, L. S., Chesla, D., Chinnaiyan, A. M., Chowdhury, S., Cieslik, M. P., Clark, D. J., Culpepper, H., Czernicki, T., D'Angelo, F., Day, J., De Young, S., Demir, E., Dhanasekaran, S. M., Dhir, R., Domagalski, M. J., Druker, B., Duffy, E., Dyer, M., Edwards, N. J., Edwards, R., Elburn, K., Ellis, M. J., Eschbacher, J., Francis, A., Gabriel, S., Gabrovski, N., Garofano, L., Getz, G., Gillette, M. A., Godwin, A. K., Golbin, D., Hanhan, Z., Hannick, L. I., Hariharan, P., Hindenach, B., Hoadley, K. A., Hostetter, G., Huang, C., Jaehnig, E., Jewell, S. D., Ji, N., Jones, C. D., Karz, A., Kaspera, W., Kim, L., Kothadia, R. B., Kumar-Sinha, C., Lei, J., Leprevost, F. D., Li, K., Liao, Y., Lilly, J., Liu, H., Lubinski, J., Madan, R., Maggio, W., Malc, E., Malovannaya, A., Mareedu, S., Markey, S. P., Marrero-Oliveras, A., Martinez, N., Maunganidze, N., Mcdermott, J. E., Mcgarvey, P. B., Mcgee, J., Mieczkowski, P., Migliozzi, S., Modugno, F., Montgomery, R., Newton, C. J., Omenn, G. S., Ozbek, U., Paklina, O. V., Paulovich, A. G., Perou, A. M., Pico, A. R., Piehowski, P. D., Placantonakis, D. G., Polonskaya, L., Potapova, O., Pruetz, B., Qi, L., Ramkissoon, S., Resnick, A., Richey, S., Riggins, G., Robinson, K., Roche, N., Rohrer, D. C., Rood, B. R., Rossell, L., Savage, S. R., Schadt, E. E., Shi, Y., Shi, Z., Shutack, Y., Singh, S., Skelly, T., Sokoll, L. J., Stawicki, J., Stein, S. E., Suh, J., Szopa, W., Tabor, D., Tan, D., Tansil, D., Thangudu, R. R., Tognon, C., Traer, E., Tsang, S., Tyner, J., Um, K. S., Valley, D. R., Vasaikar, S., Vatanian, N., Velvulou, U., Vernon, M., Wan, W., Wang, J., Webster, A., Wen, B., Whiteaker, J. R., Wilson, G. D., Zakhartsev, Y., Zelt, R., Zhang, H., Zhang, L., Zhang, Z., Zhao, G., and Zhu, J.
Subjects: Proteomics, 0301 basic medicine, Cancer Research, CPTAC, Histone H2B acetylation, Protein Tyrosine Phosphatase, Non-Receptor Type 11, Computational biology, Biology, Article, 03 medical and health sciences, lipidome, 0302 clinical medicine, Metabolomics, proteogenomic, Humans, Phosphorylation, EP300, proteomic, Proteogenomics, acetylome, single nuclei RNA-seq, Brain Neoplasms, Phospholipase C gamma, glioblastoma, Computational Biology, Lipidome, 030104 developmental biology, Histone, Oncology, Acetylation, 030220 oncology & carcinogenesis, Mutation, biology.protein, metabolome, signaling
Abstract: Glioblastoma (GBM) is the most aggressive nervous system cancer. Understanding its molecular pathogenesis is crucial to improving diagnosis and treatment. Integrated analysis of genomic, proteomic, post-translational modification and metabolomic data on 99 treatment-naive GBMs provides insights to GBM biology. We identify key phosphorylation events (e.g., phosphorylated PTPN11 and PLCG1) as potential switches mediating oncogenic pathway activation, as well as potential targets for EGFR-, TP53-, and RB1-altered tumors. Immune subtypes with distinct immune cell types are discovered using bulk omics methodologies, validated by snRNA-seq, and correlated with specific expression and histone acetylation patterns. Histone H2B acetylation in classical-like and immune-low GBM is driven largely by BRDs, CREBBP, and EP300. Integrated metabolomic and proteomic data identify specific lipid distributions across subtypes and distinct global metabolic changes in IDH-mutated tumors. This work highlights biological relationships that could contribute to stratification of GBM patients for more effective treatment. Wang et al. perform integrated proteogenomic analysis of adult glioblastoma (GBM), including metabolomics, lipidomics, and single nuclei RNA-Seq, revealing insights into the immune landscape of GBM, cell-specific nature of EMT signatures, histone acetylation in classical GBM, and the existence of signaling hubs which could provide therapeutic vulnerabilities.
Published: 2021

15. MOMC-4. Proteogenomic and metabolomic characterization of glioblastoma

Author: Alla Karpova, Karin D. Rodland, Marina A. Gritsenko, Song Cao, Jennifer E. Kyle, Li Ding, Yize Li, Liang-Bo Wang, and Tao Liu
Subjects: Therapy naive, Metabolomics, Metabolic disturbance, medicine, Computational biology, Biology, Proteogenomics, medicine.disease, Protein p53, Glioblastoma
Abstract: Glioblastoma (GBM) is the most aggressive nervous system cancer, with median survival under 2 years. Understanding its molecular pathogenesis is crucial for improving diagnosis and treatment. We performed an integrated analysis of genomic, proteomic, post-translational modification and metabolomic data on 99 treatment-naive GBMs. We identified key phosphorylation events (e.g., phosphorylated PTPN11 and PLCG1) as potential switches mediating oncogenic pathway activation as well as potential targets for EGFR-, TP53- and RB1-altered tumors. We detected immune subtypes, driven by the presence of distinct immune cell populations using bulk omics, validated by single nulcei RNA sequencing (snRNA-seq), and they were correlated with specific expression and histone acetylation patterns. Acetylation of histone H2B in classical-like and immune-low GBM was driven largely by BRDs, CREBBP, and EP300. Integrated metabolomic and proteomic data identified specific lipid distributions across subtypes and distinct global metabolic changes in IDH mutated tumors. This work highlights biological relationships which could potentially aid GBM patient stratifications for more effective treatments.
Published: 2021
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16. Abstract 2170: Proteogenomic and metabolomic characterization of human glioblastoma

Author: Runyu Hong, Vasileios Stathias, Joseph H. Rothstein, Jennifer E. Kyle, Antonio Iavarone, Karin D. Rodland, Yize Li, Song Cao, Liang-Bo Wang, Li Ding, Alla Karpova, Francesca Petralia, Dmitry Rykunov, Jill S. Barnholtz-Sloan, Milan G. Chheda, Richard D. Smith, Marina A. Gritsenko, MacIntosh Cornwell, Antonio Colaprico, and Tao Liu
Subjects: Cancer Research, Metabolomics, Oncology, medicine, Computational biology, Biology, medicine.disease, Glioblastoma
Abstract: Glioblastoma (GBM) is the most aggressive nervous system cancer, with median survival under 2 years. Understanding its molecular pathogenesis is crucial for improving diagnosis and treatment. We performed an integrated analysis of genomic, proteomic, post-translational modification and metabolomic data on 99 treatment-naive GBMs. We identified key phosphorylation events (e.g., phosphorylated PTPN11 and PLCG1) as potential switches mediating oncogenic pathway activation as well as potential targets for EGFR-, TP53- and RB1-altered tumors. We detected immune subtypes, driven by the presence of distinct immune cell populations using bulk omics, validated by snRNA-seq, and they were correlated with specific expression and histone acetylation patterns. Acetylation of histone H2B in classical-like and immune-low GBM was driven largely by BRDs, CREBBP, and EP300. Integrated metabolomic and proteomic data identified specific lipid distributions across subtypes and distinct global metabolic changes in IDH mutated tumors. By comparing the adult GBM proteomics to the adolescent and young adult (AYA) GBM cohort from the HOPE study, we found downregulated IDH1 expression and up-regulated expression of genes in the NADH dehydrogenase family, including NDUFB1, and NDUFB3 among others, which may be related to high IDH1 mutation frequency in AYA. This work highlights biological relationships which could potentially aid GBM patient stratifications for more effective treatments. Citation Format: Liang-Bo Wang, Alla Karpova, Marina A. Gritsenko, Jennifer E. Kyle, Song Cao, Yize Li, Dmitry Rykunov, Antonio Colaprico, Joseph Rothstein, Runyu Hong, Vasileios Stathias, MacIntosh Cornwell, Francesca Petralia, Richard D. Smith, Antonio Iavarone, Milan G. Chheda, Jill S. Barnholtz-Sloan, Karin D. Rodland, Tao Liu, Li Ding, Clinical Proteomic Tumor Analysis Consortium. Proteogenomic and metabolomic characterization of human glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2170.
Published: 2021
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17. Database of evidence for precision oncology portal

Author: Sohini Sengupta, R. Jay Mashl, Li Ding, Weihua Wang, Prag Batra, Adam D. Scott, Matthew A. Wyczalkowski, Liang-Bo Wang, and Sam Q. Sun
Subjects: 0301 basic medicine, Statistics and Probability, Databases, Factual, Computer science, Druggability, Medical Oncology, computer.software_genre, Biochemistry, 03 medical and health sciences, Search engine, 0302 clinical medicine, Neoplasms, Humans, Precision Medicine, Molecular Biology, Internet, Database, Genomics, Applications Notes, Computer Science Applications, Search Engine, Computational Mathematics, 030104 developmental biology, Computational Theory and Mathematics, Precision oncology, 030220 oncology & carcinogenesis, computer
Abstract: Summary A database of curated genomic variants with clinically supported drug therapies and other oncological annotations is described. The accompanying web portal provides a search engine with two modes: one that allows users to query gene, cancer type, variant type or position for druggable mutations, and another to search for and to visualize, on three-dimensional protein structures, putative druggable sites that cluster with known druggable mutations. Availability and implementation http://dinglab.wustl.edu/depo
Published: 2018
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18. GENE-19. DEEP PROTEOMIC SURVEY ACROSS SEVEN CHILDHOOD BRAIN TUMORS

Author: Steven P. Gygi, Liang Bo Wang, Li Ding, Nicole Tignor, Amanda G. Paulovich, Jeffery R. Whiteaker, Sanjukta Guha Thakurta, Adam C. Resnick, Alexey Nescizhskii, Tara Hiltke, Pichai Raman, Pei Wang, Uliana J. Voytovich, Brian R. Rood, Yuankun Zhu, Richard G. Ivey, and Jacob J. Kennedy
Subjects: Medulloblastoma, Cancer Research, Pathology, medicine.medical_specialty, Astrocytoma, Genetics/Epigentics, Biology, Brain tumor childhood, medicine.disease, Oncology, Glioma, medicine, Low-Grade Glioma, Neurology (clinical), Transluminal attenuation gradient, Gene, Childhood brain tumor
Abstract: Genomic characterization has allowed for the differentiation of different tumor types based upon the abundance of gene transcripts. However, owing to the many layers of regulation between transcript and the post-translationally modified protein, the functional moiety of the cell, it has been challenging to extrapolate biology from these transcriptional differences. We hypothesized that a comparative analysis of the proteome and phosphoproteome across 7 childhood brain tumors would yield a deeper understanding of the differences in their functional biology. We performed tandem mass tag labeling and triple mass spectrometry of 226 fresh frozen tumor samples collected in a single institution representing the histologic diagnoses of: high grade astrocytoma (27), low grade astrocytoma (97), ganglioglioma (20), ependymoma (32), medulloblastoma (22), atypical teratoid rhabdoid tumor (12), and craniopharyngioma (16). Among these samples were 22 pairs from pre/post recurrence. Across this sample set, we quantified 9155 proteins and 13632 phospho sites. After data preprocessing including normalization, batch correction and missing value imputation, we performed consensus clustering and showed that protein profiles can effectively distinguish major histology types. Additional regression based differential analyses revealed groups of proteins and pathways showing distinct activity patterns across different histologic types. Further leveraging the WGS data, we characterized the functional impact of mutation and fusion features of the glial tumors. Specifically, focusing on the largest cohort, low grade glioma, we analyzed the proteomic ramifications of BRAF status: V600E, BRAF-fusion and wild type. Moreover, from the primary and recurrent tumor pairs, we discovered the upregulation of proteins associated with immune evasion in more advanced LGG tumors. The incorporation of the proteomic dimension into large scale efforts at tumor characterization adds functional insight that can help drive translational efforts.
Published: 2019

19. Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer

Author: Matthew E. Monroe, Saravana M. Dhanasekaran, Brian R. Rood, Zeynep H. Gümüş, Jena Lilly, Samuel G. Winebrake, Richard G. Ivey, William Bocik, Mahdi Sarmady, Alicia Francis, Lamiya Tauhid, Nathan Edwards, Lizabeth Katsnelson, Rui Zhao, Matilda Broberg, Jo Lynne Rokita, Mateusz Koptyra, Henry Rodriguez, Cassie Kline, Shrabanti Chowdhury, Nicole Tignor, Ying Wang, Christopher R. Kinsinger, Antonio Colaprico, Amanda G. Paulovich, Weiping Ma, Emily S. Boja, Tara Hiltke, Sabine Mueller, Liang-Bo Wang, Javad Nazarian, Marcin J. Domagalski, Karl K. Weitz, Jessica B. Foster, Robert Lober, Carina A. Leonard, Bo Zhang, Gerald A. Grant, Anna Calinawan, Gonzalo Lopez, Shuang Cai, Joanna J. Phillips, Guo Ci Teo, July E. Palma, Felipe da Veiga Leprevost, Yiran Guo, Angela Waanders, Xiaoyu Song, Li Ding, Allison Heath, Steven P. Gygi, Rosalie K. Chu, Vasileios Stathias, Bailey Farrow, Oren J. Becher, Dmitry Rykunov, Nithin D. Adappa, Ron Firestein, Adam C. Resnick, Marcin Cieślik, Jennifer Mason, D. R. Mani, Selim Kalayci, Boris Reva, Antonio Iavarone, MacIntosh Cornwell, Uliana J. Voytovich, Gabrielle S. Stone, Miguel A. Brown, Jacob J. Kennedy, Tao Liu, Ronald J. Moore, Emily Kawaler, Eric H. Raabe, Marina A. Gritsenko, Valerie Baubet, Francesca Petralia, Maciej Wiznerowicz, Olena Morozova Vaske, Eric E. Schadt, Ian F. Pollack, Arul M. Chinnaiyan, Meghan Connors, Jason E. Cain, Lei Zhao, Matthew A. Wyczalkowski, Nalin Gupta, Bing Zhang, Jiayi Ji, Marilyn M. Li, Samuel Rivero-Hinojosa, Mariarita Santi, Wenke Liu, John Szpyt, Brian Ennis, Alexey I. Nesvizhskii, Joshua M. Wang, Jeffrey P. Greenfield, Sanjukta Guha Thakurta, Hui Yin Chang, Peter B. McGarvey, Xi Chen, Karen A. Ketchum, Stephan C. Schürer, Sarah Leary, Lili Blumenberg, Matthew J. Ellis, Pei Wang, Anna Maria Buccoliero, Karsten Krug, Chiara Caporalini, Gad Getz, David E. Kram, Pichai Raman, Eric M. Jackson, James N. Palmer, Mehdi Mesri, Kelly V. Ruggles, Chunde Li, Jun Zhu, Sonia Partap, Jeffrey R. Whiteaker, Mirko Scagnet, Krutika S. Gaonkar, Azra Krek, Allison M. Morgan, Tatiana Omelchenko, Richard D. Smith, Elizabeth Appert, Karin D. Rodland, Derek Hanson, Phillip B. Storm, Jamie Moon, Vladislav A. Petyuk, Nathan Young, Travis D. Lorentzen, David Fenyö, Angela N. Viaene, Seungyeul Yoo, Yuankun Zhu, Nicholas A Vitanza, Toan Le, Tatiana Patton, and Ana I. Robles
Subjects: DNA Copy Number Variations, Computational biology, Biology, Proteomics, Article, General Biochemistry, Genetics and Molecular Biology, Ganglioglioma, 03 medical and health sciences, Lymphocytes, Tumor-Infiltrating, 0302 clinical medicine, Glioma, medicine, Humans, Gene Regulatory Networks, RNA, Messenger, Copy-number variation, Phosphorylation, Child, Proteogenomics, 030304 developmental biology, Medulloblastoma, 0303 health sciences, Brain Neoplasms, Genome, Human, Phosphoproteomics, Phosphoproteins, medicine.disease, Gene Expression Regulation, Neoplastic, Mutation, Atypical teratoid rhabdoid tumor, Neoplasm Grading, Neoplasm Recurrence, Local, Transcriptome, 030217 neurology & neurosurgery
Abstract: We report a comprehensive proteogenomics analysis, including whole-genome sequencing, RNA sequencing, and proteomics and phosphoproteomics profiling, of 218 tumors across 7 histological types of childhood brain cancer: low-grade glioma (n = 93), ependymoma (32), high-grade glioma (25), medulloblastoma (22), ganglioglioma (18), craniopharyngioma (16), and atypical teratoid rhabdoid tumor (12). Proteomics data identify common biological themes that span histological boundaries, suggesting that treatments used for one histological type may be applied effectively to other tumors sharing similar proteomics features. Immune landscape characterization reveals diverse tumor microenvironments across and within diagnoses. Proteomics data further reveal functional effects of somatic mutations and copy number variations (CNVs) not evident in transcriptomics data. Kinase-substrate association and co-expression network analysis identify important biological mechanisms of tumorigenesis. This is the first large-scale proteogenomics analysis across traditional histological boundaries to uncover foundational pediatric brain tumor biology and inform rational treatment selection.
Published: 2020
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20. Proteogenomic Characterization of Ovarian HGSC Implicates Mitotic Kinases, Replication Stress in Observed Chromosomal Instability

Author: Zhen Zhang, Tao Liu, Karin D. Rodland, Christopher R. Kinsinger, Ehwang Song, Molly Brewer, Osama A. Arshad, Steven M. Foltz, Emily S. Boja, Chen Huang, Liang-Bo Wang, Mathangi Thiagarajan, Ronald J. Moore, Marina A. Gritsenko, Michael Schnaubelt, Vladislav A. Petyuk, Samuel H. Payne, Yige Wu, Rui Zhao, Therese R. W. Clauss, Jason E. McDermott, Matthew E. Monroe, Athena A. Schepmoes, Henry Rodriguez, Richard D. Smith, Chia-Feng Tsai, Daniel W. Chan, Bing Zhang, Yi Fu, Matthew A. Wyczalkowski, Li Ding, and Ana I. Robles
Subjects: Adult, DNA Replication, Mitosis, Context (language use), Biology, medicine.disease_cause, Proteomics, Article, General Biochemistry, Genetics and Molecular Biology, Cohort Studies, proteomics, Chromosomal Instability, Chromosome instability, medicine, Fallopian Tube Neoplasms, Humans, Aged, Aged, 80 and over, Ovarian Neoplasms, Mutation, fallopian tube, lcsh:R5-920, Phosphotransferases, Phosphoproteomics, Cancer, phosphoproteomics, Cell Cycle Checkpoints, Middle Aged, medicine.disease, Cystadenocarcinoma, Serous, Gene Expression Regulation, Neoplastic, homologous repair deficiency, Histone, ovarian cancer, proteogenomics, Cancer research, biology.protein, Female, Tumor Suppressor Protein p53, Transcriptome, Ovarian cancer, lcsh:Medicine (General), DNA Damage
Abstract: SUMMARY In the absence of a dominant driving mutation other than uniformly present TP53 mutations, deeper understanding of the biology driving ovarian high-grade serous cancer (HGSC) requires analysis at a functional level, including post-translational modifications. Comprehensive proteogenomic and phosphoproteomic characterization of 83 prospectively collected ovarian HGSC and appropriate normal precursor tissue samples (fallopian tube) under strict control of ischemia time reveals pathways that significantly differentiate between HGSC and relevant normal tissues in the context of homologous repair deficiency (HRD) status. In addition to confirming key features of HGSC from previous studies, including a potential survival-associated signature and histone acetylation as a marker of HRD, deep phosphoproteomics provides insights regarding the potential role of proliferation-induced replication stress in promoting the characteristic chromosomal instability of HGSC and suggests potential therapeutic targets for use in precision medicine trials., Graphical Abstract, In Brief McDermott et al. present the proteogenomic analysis of prospectively collected ovarian high-grade serous cancer samples and appropriate normal precursor samples under tight ischemic control. They identify tumor-associated signaling pathways and mitotic and cyclin-dependent kinases as key oncogenic drivers potentially related to chromosomal instability.
Published: 2020
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21. Proteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma

Author: Yuxing Liao, Meghan C. Burke, Donghui Tan, Xiaoyu Song, Lauren C. Tang, Elizabeth R. Duffy, Shayan C. Avanessian, Daniel Cui Zhou, Maureen Dyer, Sara R. Savage, Jennifer M. Eschbacher, Shaleigh Smith, Alex Webster, Alicia Francis, Kelly V. Ruggles, Christopher R. Kinsinger, Shirley Tsang, Melissa Borucki, Nancy Roche, Pei Wang, Qing Kay Li, David Chesla, Ronald Matteotti, Zeynep H. Gümüş, Kai Li, Kei Suzuki, Thomas L. Bauer, Lori J. Sokoll, John McGee, Marcin J. Domagalski, Ki Sung Um, Tara Hiltke, Hai-Quan Chen, Hongwei Liu, Eric E. Schadt, Antonio Colaprico, Alyssa Charamut, Lijun Chen, Emily Kawaler, Ramani B. Kothadia, Mehdi Mesri, Jiayi Ji, Simina M. Boca, Myvizhi Esai Selvan, Emily S. Boja, Xi Chen, Shuang Cai, Kim Elburn, Samuel H. Payne, George D. Wilson, Peter B. McGarvey, Chelsea J. Newton, Felipe da Veiga Leprevost, Uma Velvulou, Tara Skelly, Corbin D. Jones, Michael J. Birrer, Steven A. Carr, Erik J. Bergstrom, Jeffrey R. Whiteaker, Michael H.A. Roehrl, Gad Getz, Tanya Krubit, Zhen Zhang, Yan Shi, Lili Blumenberg, Melanie A. MacMullan, Song Cao, Zhiao Shi, Weiping Ma, Chet Birger, Karl R. Clauser, Runyu Hong, Shankha Satpathy, Andrii Karnuta, Boris Reva, Barbara Hindenach, Matthew J. Ellis, Amanda G. Paulovich, Michael C. Wendl, Bing Zhang, Marina A. Gritsenko, Matthew A. Wyczalkowski, Stacey Gabriel, Michael A. Gillette, Jacob J. Day, Maciej Wiznerowicz, Stephen E. Stein, Ewa P. Malc, Robert J. Welsh, Sunita Shankar, Brian J. Druker, Li Ding, Lijun Yao, David J. Clark, Małgorzata Wojtyś, Dmitry Rykunov, Eugene S. Fedorov, Linda Hannick, Andrew K. Godwin, Sailaja Mareedu, Pushpa Hariharan, Mary Beth Beasley, Stephanie De Young, Arul M. Chinnaiyan, Robert Zelt, Mathangi Thiagarajan, Munziba Khan, Suhas Vasaikar, Tao Liu, Karin D. Rodland, Katherine A. Hoadley, Wen-Wei Liang, Ana I. Robles, Dana R. Valley, Sanford P. Markey, Mikhail Krotevich, David I. Heiman, Piotr A. Mieczkowski, Galen Hostetter, Liqun Qi, Yige Wu, Hui Zhang, Liang-Bo Wang, Nathan Edwards, Ramaswamy Govindan, Yifat Geffen, Seema Chugh, Alexey I. Nesvizhskii, Karen A. Ketchum, Pankaj Vats, Matthew E. Monroe, Marcin Cieslik, Yingwei Hu, Karsten Krug, Yosef E. Maruvka, Yize Li, Ratna R. Thangudu, William W. Maggio, Sandra Cottingham, Saravana M. Dhanasekaran, Wenke Liu, Hua Sun, Sonya Carter, Volodymyr Sovenko, M. Harry Kane, Annette Marrero-Oliveras, Barbara Pruetz, Amy M. Perou, Gilbert S. Omenn, Azra Krek, Olga Potapova, Michael S. Noble, Daniel W. Chan, Seungyeul Yoo, Eric J. Burks, Bo Wen, William Bocik, Michael Vernon, Henry Rodriguez, James Suh, Scott D. Jewell, MacIntosh Cornwell, Richard D. Smith, Chandan Kumar-Sinha, Halina M. Krzystek, Daniel C. Rohrer, Tatiana Omelchenko, D. R. Mani, Houston Culpepper, Vladislav A. Petyuk, Meng-Hong Sun, Michelle Chaikin, David Fenyö, Rahul Mannan, Bartosz Kubisa, Rohit Mehra, Rajwanth R. Veluswamy, Umut Ozbek, Michael Schnaubelt, Francesca Petralia, Elena V. Ponomareva, Rashna Madan, Pamela Grady, Karna Robinson, and Negin Vatanian
Subjects: 0303 health sciences, Phosphoproteomics, Genomics, Environmental exposure, Computational biology, Biology, Proteomics, Proteogenomics, medicine.disease_cause, Article, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, 0302 clinical medicine, Neutrophil degranulation, medicine, KRAS, 030217 neurology & neurosurgery, 030304 developmental biology, Epigenomics
Abstract: To explore the biology of lung adenocarcinoma (LUAD) and identify new therapeutic opportunities, we performed comprehensive proteogenomic characterization of 110 tumors and 101 matched normal adjacent tissues (NATs) incorporating genomics, epigenomics, deep-scale proteomics, phosphoproteomics, and acetylproteomics. Multi-omics clustering revealed four subgroups defined by key driver mutations, country, and gender. Proteomic and phosphoproteomic data illuminated biology downstream of copy number aberrations, somatic mutations, and fusions and identified therapeutic vulnerabilities associated with driver events involving KRAS, EGFR, and ALK. Immune subtyping revealed a complex landscape, reinforced the association of STK11 with immune-cold behavior, and underscored a potential immunosuppressive role of neutrophil degranulation. Smoking-associated LUADs showed correlation with other environmental exposure signatures and a field effect in NATs. Matched NATs allowed identification of differentially expressed proteins with potential diagnostic and therapeutic utility. This proteogenomics dataset represents a unique public resource for researchers and clinicians seeking to better understand and treat lung adenocarcinomas.
Published: 2020
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22. Proteogenomic Characterization of Endometrial Carcinoma

Author: David J. Clark, Jiayi Ji, Beom-Jun Kim, Doug W. Chan, Zhen Zhang, Kim Elburn, Munziba Khan, Katherine Fuh, Katherine A. Hoadley, Jeffrey R. Whiteaker, Peter B. McGarvey, Dana R. Valley, Douglas A. Levine, Sanford P. Markey, Hui Zhang, Tanya Krubit, Christopher R. Kinsinger, Hui Yin Chang, Yuxing Liao, Karen A. Ketchum, Piotr A. Mieczkowski, Pankaj Vats, Matthew E. Monroe, Donghui Tan, Alex Webster, Chet Birger, Kai Li, Ramani Kothadia, Saravana M. Dhanasekaran, Sara R. Savage, Karsten Krug, Marcin Jędryka, Matthew L. Anderson, Alyssa Charamut, Alexander R. Pico, Amanda G. Paulovich, Karna Robinson, Hua Zhou, John McGee, Sean J.I. Beecroft, Amanda E. Oliphant, Annette Marrero-Oliveras, Dmitry Rykunov, Shuang Cai, Francesmary Modugno, Marcin J. Domagalski, Emily Kawaler, Mehdi Mesri, Dmitry M. Avtonomov, Milan G. Chheda, Sue Hilsenbeck, Gilbert S. Omenn, Emek Demir, Rebecca I. Montgomery, Qingsong Gao, Azra Krek, Eric E. Schadt, Li Ding, George D. Wilson, Stephen E. Stein, David Chesla, Gad Getz, Negin Vatanian, Thomas F. Westbrook, Deborah DeLair, David I. Heiman, Karl R. Clauser, Rafal Matkowski, Lori J. Sokoll, Uma Borate, Antonio Colaprico, Jin Chen, Eric J. Jaehnig, Karin D. Rodland, Richard D. Smith, Linda Hannick, Uma Velvulou, Shannon Richey, Andrzej Czekański, Bing Zhang, Arul M. Chinnaiyan, Robert Zelt, Daniel J. Geiszler, Emily L. Hoskins, Ronald J. Moore, Pushpa Hariharan, Suhas Vasaikar, Jason E. McDermott, Yige Wu, Anna Malovannaya, Brian J. Druker, Jeffrey W. Tyner, Yan Shi, Lili Blumenberg, Jamie Moon, Cristina E. Tognon, Chandan Kumar-Sinha, Nathan Edwards, Yifat Geffen, Barbara Hindenach, Matthew J. Ellis, Zhi Li, Michael Schnaubelt, David C. Wheeler, Tara Skelly, Ewa P. Malc, Shrabanti Chowdhury, Andrew K. Godwin, Zhiao Shi, Francesca Petralia, Lin Chen, Scott D. Jewell, Daniel C. Rohrer, Elie Traer, Michael Ittmann, Shankha Satpathy, Marcin Cieslik, Weiping Ma, Daniel Cui Zhou, Maureen Dyer, Boris Reva, Rashna Madan, William Bocik, Stacey Gabriel, Stephanie De Young, Yosef E. Maruvka, Sandra Cottingham, Pamela Grady, D. R. Mani, Houston Culpepper, Meenakshi Anurag, Michael T. Lewis, Anupriya Agarwal, Felipe D. Leprevost, Jonathan C. Jarman, Michael Vernon, Henry Rodriguez, Matthew A. Wyczalkowski, Rui Zhao, Vladislav A. Petyuk, Michelle Chaikin, James Suh, Daniel W. Chan, Bo Wen, Patricia Castro, Alexey I. Nesvizhskii, Chia-Feng Tsai, Grace Zhao, Alicia Francis, Feng Chen, Mathangi Thiagarajan, Pei Wang, Marina A. Gritsenko, Anna Calinawan, David G. Mutch, Melissa Borucki, Xi Steven Chen, Guo Ci Teo, Peter Dottino, Corbin D. Jones, Michael J. Birrer, Ying Wang, Meghan C. Burke, MacIntosh Cornwell, Song Cao, Rosalie K. Chu, Larisa Polonskaya, Samuel H. Payne, Darlene Tansil, Yongchao Dou, David Fenyö, Kelly V. Ruggles, Qing Kay Li, Yuping Zhang, James J. Hsieh, Andy T. Kong, Ana I. Robles, Emily S. Boja, Chelsea J. Newton, Steven A. Carr, Sandra Cerda, Runyu Hong, Jacob J. Day, Sailaja Mareedu, Jan Lubinski, Galen Hostetter, Liqun Qi, Yize Li, Chen Huang, Liang-Bo Wang, Tao Liu, Renee Karabon, Paul D. Piehowski, Samuel L. Pugh, Maciej Wiznerowicz, Ratna R. Thangudu, Wenke Liu, Jayson B. Field, Sonya Carter, Ki Sung Um, Hongwei Liu, Alla Karpova, Yuriy Zakhartsev, Amy M. Perou, Michael S. Noble, Rajiv Dhir, Nancy Roche, Sunantha Sethuraman, Tara Hiltke, Lijun Chen, Xiaoyu Song, Elizabeth R. Duffy, Robert Edwards, Yingwei Hu, John A. Martignetti, Simina M. Boca, Michael A. Gillette, and David W Adams
Subjects: Epithelial-Mesenchymal Transition, Proteome, Druggability, Biology, Proteomics, Genomic Instability, Article, General Biochemistry, Genetics and Molecular Biology, Mice, 03 medical and health sciences, 0302 clinical medicine, Antigens, Neoplasm, medicine, Animals, Humans, Phosphorylation, 030304 developmental biology, Feedback, Physiological, 0303 health sciences, Endometrial cancer, Carcinoma, Wnt signaling pathway, Cancer, Acetylation, medicine.disease, Proteogenomics, Endometrial Neoplasms, Gene Expression Regulation, Neoplastic, MicroRNAs, Serous fluid, Histone, Cancer research, biology.protein, Female, Transcriptome, Protein Processing, Post-Translational, 030217 neurology & neurosurgery, Microsatellite Repeats, Signal Transduction
Abstract: We undertook a comprehensive proteogenomic characterization of 95 prospectively collected endometrial carcinomas, comprising 83 endometrioid and 12 serous tumors. This analysis revealed possible new consequences of perturbations to the p53 and Wnt/β-catenin pathways, identified a potential role for circRNAs in the epithelial-mesenchymal transition, and provided new information about proteomic markers of clinical and genomic tumor subgroups, including relationships to known druggable pathways. An extensive genome-wide acetylation survey yielded insights into regulatory mechanisms linking Wnt signaling and histone acetylation. We also characterized aspects of the tumor immune landscape, including immunogenic alterations, neoantigens, common cancer/testis antigens, and the immune microenvironment, all of which can inform immunotherapy decisions. Collectively, our multi-omic analyses provide a valuable resource for researchers and clinicians, identify new molecular associations of potential mechanistic significance in the development of endometrial cancers, and suggest novel approaches for identifying potential therapeutic targets.
Published: 2020
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23. Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma

Author: David J. Clark, Jianbo Pan, Gerald W. Hart, Katherine A. Hoadley, Negin Vatanian, Shuang Cai, Yige Wu, Felipe da Veiga Leprevost, A. Ari Hakimi, Sanford P. Markey, Thomas F. Westbrook, Maciej Wiznerowicz, Nathan Edwards, Alla Y. Karpova, Sohini Sengupta, Marcin Cieslik, Samuel H. Payne, Xi Steven Chen, Guo Ci Teo, Jin Chen, Boris Reva, Corbin D. Jones, Michael J. Birrer, Ying Wang, Kelly V. Ruggles, Doug W. Chan, John McGee, Marcin J. Domagalski, Song Cao, Linda Hannick, Christopher R. Kinsinger, David I. Heiman, Jennifer M. Eschbacher, Munziba Khan, Jason E. McDermott, Dmitry M. Avtonomov, Sue Hilsenbeck, Qing Kay Li, Jiayi Ji, Emek Demir, Rebecca I. Montgomery, Qingsong Gao, Beom-Jun Kim, Xiaoyu Song, Karl R. Clauser, Christian P. Pavlovich, Richard D. Smith, Maureen Dyer, Jeffrey W. Tyner, Amy M. Perou, Yuping Zhang, Dana R. Valley, George D. Wilson, Shiyong Ma, Minghui Ao, Jiang Qian, Umut Ozbek, Melissa Borucki, Zhi Li, Michael Schnaubelt, Chen Huang, Piotr A. Mieczkowski, Francesca Petralia, Abdul Samad Hashimi, Hui Yin Chang, Liang-Bo Wang, Matthew E. Monroe, Peter B. McGarvey, Tao Liu, Karen A. Ketchum, Hui Zhang, Bing Zhang, D. R. Mani, Houston Culpepper, Hua Zhou, Saravana M. Dhanasekaran, Paul D. Piehowski, Zhidong Tu, Brian J. Druker, Ki Sung Um, Zhiao Shi, Uma Borate, Uma Velvulou, Michael Ittmann, Weiping Ma, Steven M. Foltz, Heng Zhu, Stacey Gabriel, Hongwei Liu, Ramani B. Kothadia, Lin Chen, Ewa P. Malc, Marina A. Gritsenko, Jun Zhu, David Chesla, Lori J. Sokoll, Stephen E. Stein, Andrzej Antczak, Matthew L. Anderson, Alyssa Charamut, Pamela Grady, Michael T. Lewis, Shannon Richey, Tanya Krubit, Alexander R. Pico, Kyung-Cho Cho, Daniel C. Rohrer, Francesmary Modugno, Stephanie De Young, Li Ding, Michael Smith, Mathangi Thiagarajan, Alexey I. Nesvizhskii, Shrabanti Chowdhury, Noam D. Beckmann, Kimberly R. Holloway, Ratna R. Thangudu, Sherri R. Davies, Tung-Shing M. Lih, Nicole Tignor, Anna Calinawan, Meghan C. Burke, Karna Robinson, Chet Birger, Shalin Patel, Antonio Colaprico, Sarah Keegan, Daniel J. Geiszler, Scott D. Jewell, William Bocik, Snehal Patil, Pei Wang, MacIntosh Cornwell, Emily Kawaler, Seungyeul Yoo, Jasmine Huang, Vladislav A. Petyuk, Ross Bremner, Donghui Tan, Stefani N. Thomas, Emily S. Boja, Anna Malovannaya, Xi Chen, Wenke Liu, Eric E. Schadt, Shankha Satpathy, Nancy Roche, Rajiv Dhir, Cristina E. Tognon, Michelle Chaikin, Gabriel Bromiński, Daniel C. Zhou, Yifat Geffen, Tara Skelly, Jacob J. Day, Sunantha Sethuraman, Sonya Carter, Zhen Zhang, Selim Kalayci, Michael Vernon, Zeynep H. Gümüş, Kai Li, Barbara Hindenach, Matthew J. Ellis, Meenakshi Anurag, David C. Wheeler, Sailaja Mareedu, Andy T. Kong, Arul M. Chinnaiyan, Robert Zelt, Annette Marrero-Oliveras, Henry Rodriguez, James Suh, Anupriya Agarwal, David Fenyö, Galen Hostetter, Liqun Qi, Matthew A. Wyczalkowski, W. Marston Linehan, Tara Hiltke, Feng Chen, Lijun Chen, Jan Lubinski, Chelsea J. Newton, Steven A. Carr, Tatiana Omelchenko, Gilbert S. Omenn, Karsten Krug, Ana I. Robles, Azra Krek, Runyu Hong, Milan G. Chheda, Yize Li, Yan Shi, Lili Blumenberg, Ruiyang Liu, Karin D. Rodland, Hua Sun, Kim Elburn, Jeffrey R. Whiteaker, Christopher J. Ricketts, Gaddy Getz, Daniel W. Chan, Bo Wen, Robert Edwards, Patricia Castro, Yingwei Hu, Pushpa Hariharan, Simina M. Boca, Darlene Tansil, Phillip M. Pierorazio, Yosef E. Maruvka, Sandra Cottingham, James J. Hsieh, Amanda G. Paulovich, Barbara Pruetz, Michael A. Gillette, Yihao Lu, Dmitry Rykunov, Mehdi Mesri, Marc M. Loriaux, Reyka G Jayasinghe, and Suhas Vasaikar
Subjects: Adult, Male, Cell, Computational biology, Biology, Proteomics, Disease-Free Survival, Oxidative Phosphorylation, Article, General Biochemistry, Genetics and Molecular Biology, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, Exome Sequencing, medicine, Biomarkers, Tumor, Tumor Microenvironment, Humans, Exome, Phosphorylation, Carcinoma, Renal Cell, 030304 developmental biology, Epigenomics, Aged, Proteogenomics, Aged, 80 and over, 0303 health sciences, Tumor microenvironment, Genome, Human, Phosphoproteomics, Middle Aged, medicine.disease, Neoplasm Proteins, Gene Expression Regulation, Neoplastic, Clear cell renal cell carcinoma, medicine.anatomical_structure, Female, 030217 neurology & neurosurgery, Signal Transduction
Abstract: SUMMARY To elucidate the deregulated functional modules that drive clear cell renal cell carcinoma (ccRCC), we performed comprehensive genomic, epigenomic, transcriptomic, proteomic, and phosphoproteomic characterization of treatment-naive ccRCC and paired normal adjacent tissue samples. Genomic analyses identified a distinct molecular subgroup associated with genomic instability. Integration of proteogenomic measurements uniquely identified protein dysregulation of cellular mechanisms impacted by genomic alterations, including oxidative phosphorylation-related metabolism, protein translation processes, and phospho-signaling modules. To assess the degree of immune infiltration in individual tumors, we identified microenvironment cell signatures that delineated four immune-based ccRCC subtypes characterized by distinct cellular pathways. This study reports a large-scale proteogenomic analysis of ccRCC to discern the functional impact of genomic alterations and provides evidence for rational treatment selection stemming from ccRCC pathobiology., Graphical Abstract, In Brief Comprehensive proteogenomic characterization in 103 treatment-naive clear cell renal cell carcinoma patient samples highlights tumor-specific alterations at the proteomic level that are unrevealed by transcriptomic profiling and proposes a revised subtyping scheme based on integrated omics analysis.
Published: 2020
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24. Pathogenic Germline Variants in 10,389 Adult Cancers

Author: Kuan-lin Huang, R. Jay Mashl, Yige Wu, Deborah I. Ritter, Jiayin Wang, Clara Oh, Marta Paczkowska, Sheila Reynolds, Matthew A. Wyczalkowski, Ninad Oak, Adam D. Scott, Michal Krassowski, Andrew D. Cherniack, Kathleen E. Houlahan, Reyka Jayasinghe, Liang-Bo Wang, Daniel Cui Zhou, Di Liu, Song Cao, Young Won Kim, Amanda Koire, Joshua F. McMichael, Vishwanathan Hucthagowder, Tae-Beom Kim, Abigail Hahn, Chen Wang, Michael D. McLellan, Fahd Al-Mulla, Kimberly J. Johnson, Olivier Lichtarge, Paul C. Boutros, Benjamin Raphael, Alexander J. Lazar, Wei Zhang, Michael C. Wendl, Ramaswamy Govindan, Sanjay Jain, David Wheeler, Shashikant Kulkarni, John F. Dipersio, Jüri Reimand, Funda Meric-Bernstam, Ken Chen, Ilya Shmulevich, Sharon E. Plon, Feng Chen, Li Ding, Samantha J. Caesar-Johnson, John A. Demchok, Ina Felau, Melpomeni Kasapi, Martin L. Ferguson, Carolyn M. Hutter, Heidi J. Sofia, Roy Tarnuzzer, Zhining Wang, Liming Yang, Jean C. Zenklusen, Jiashan (Julia) Zhang, Sudha Chudamani, Jia Liu, Laxmi Lolla, Rashi Naresh, Todd Pihl, Qiang Sun, Yunhu Wan, Ye Wu, Juok Cho, Timothy DeFreitas, Scott Frazer, Nils Gehlenborg, Gad Getz, David I. Heiman, Jaegil Kim, Michael S. Lawrence, Pei Lin, Sam Meier, Michael S. Noble, Gordon Saksena, Doug Voet, Hailei Zhang, Brady Bernard, Nyasha Chambwe, Varsha Dhankani, Theo Knijnenburg, Roger Kramer, Kalle Leinonen, Yuexin Liu, Michael Miller, Vesteinn Thorsson, 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, Arvind Rao, Michael Ryan, Jing Wang, John N. Weinstein, Jiexin Zhang, Adam Abeshouse, Joshua Armenia, Debyani Chakravarty, Walid K. Chatila, Ino de Bruijn, Jianjiong Gao, Benjamin E. Gross, Zachary J. Heins, Ritika Kundra, Konnor La, Marc Ladanyi, Augustin Luna, Moriah G. Nissan, Angelica Ochoa, Sarah M. Phillips, Ed Reznik, Francisco Sanchez-Vega, Chris Sander, Nikolaus Schultz, Robert Sheridan, S. Onur Sumer, Yichao Sun, Barry S. Taylor, Jioajiao Wang, Hongxin Zhang, Pavana Anur, Myron Peto, Paul Spellman, Christopher Benz, Joshua M. Stuart, Christopher K. Wong, Christina Yau, D. Neil Hayes, Joel S. Parker, Matthew D. Wilkerson, Adrian Ally, Miruna Balasundaram, Reanne Bowlby, Denise Brooks, Rebecca Carlsen, Eric Chuah, Noreen Dhalla, Robert Holt, Steven J.M. Jones, Katayoon Kasaian, Darlene Lee, Yussanne Ma, Marco A. Marra, Michael Mayo, Richard A. Moore, Andrew J. Mungall, Karen Mungall, A. Gordon Robertson, Sara Sadeghi, Jacqueline E. Schein, Payal Sipahimalani, Angela Tam, Nina Thiessen, Kane Tse, Tina Wong, Ashton C. Berger, Rameen Beroukhim, Carrie Cibulskis, Stacey B. Gabriel, Galen F. Gao, Gavin Ha, Matthew Meyerson, Steven E. Schumacher, Juliann Shih, Melanie H. Kucherlapati, Raju S. Kucherlapati, Stephen Baylin, Leslie Cope, Ludmila Danilova, Moiz S. Bootwalla, Phillip H. Lai, Dennis T. Maglinte, David J. Van Den Berg, Daniel J. Weisenberger, J. Todd Auman, Saianand Balu, Tom Bodenheimer, Cheng Fan, Katherine A. Hoadley, Alan P. Hoyle, Stuart R. Jefferys, Corbin D. Jones, Shaowu Meng, Piotr A. Mieczkowski, Lisle E. Mose, Amy H. Perou, Charles M. Perou, Jeffrey Roach, Yan Shi, Janae V. Simons, Tara Skelly, Matthew G. Soloway, Donghui Tan, Umadevi Veluvolu, Huihui Fan, Toshinori Hinoue, Peter W. Laird, Hui Shen, Wanding Zhou, Michelle Bellair, Kyle Chang, Kyle Covington, Chad J. Creighton, Huyen Dinh, HarshaVardhan Doddapaneni, Lawrence A. Donehower, Jennifer Drummond, Richard A. Gibbs, Robert Glenn, Walker Hale, Yi Han, Jianhong Hu, Viktoriya Korchina, Sandra Lee, Lora Lewis, Wei Li, Xiuping Liu, Margaret Morgan, Donna Morton, Donna Muzny, Jireh Santibanez, Margi Sheth, Eve Shinbrot, Linghua Wang, Min Wang, David A. Wheeler, Liu Xi, Fengmei Zhao, Julian Hess, Elizabeth L. Appelbaum, Matthew Bailey, Matthew G. Cordes, Catrina C. Fronick, Lucinda A. Fulton, Robert S. Fulton, Cyriac Kandoth, Elaine R. Mardis, 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, 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, Sharon Heath, Timothy Ley, Brian Van Tine, Peter Westervelt, Mark A. Rubin, Jung Il Lee, Natália D. Aredes, Armaz Mariamidze, SAIC-F-Frederick, Inc, Leidos Biomedical Research, Inc., Huang K.-L., Mashl R.J., Wu Y., Ritter D.I., Wang J., Oh C., Paczkowska M., Reynolds S., Wyczalkowski M.A., Oak N., Scott A.D., Krassowski M., Cherniack A.D., Houlahan K.E., Jayasinghe R., Wang L.-B., Zhou D.C., Liu D., Cao S., Kim Y.W., Koire A., McMichael J.F., Hucthagowder V., Kim T.-B., Hahn A., Wang C., McLellan M.D., Al-Mulla F., Johnson K.J., 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., Liu J., Lolla L., Naresh R., Pihl T., Sun Q., Wan 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., Dhankani V., Knijnenburg T., Kramer R., Leinonen K., Liu Y., Miller M., Shmulevich I., Thorsson V., 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., Rao A., Ryan M., 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., Stuart J.M., 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., Hoadley K.A., 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., 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., 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., 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 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., Lichtarge O., Boutros P.C., Raphael B., Wendl M.C., Jain S., Wheeler D., Kulkarni S., Dipersio J.F., Reimand J., Chen K., Plon S.E., and Chen F.
Subjects: 0301 basic medicine, SDHA, Loss of Heterozygosity, Cancer Genome Atlas Research Network, GUIDELINES, Germline, Loss of heterozygosity, Gene Frequency, Neoplasms, Genotype, Databases, Genetic, LS2_1, LS4_6, LOH, 610 Medicine & health, 11 Medical and Health Sciences, DNA Copy Number Variation, RISK, Genetics, SITES, medicine.diagnostic_test, Proto-Oncogene Proteins c-met, germline and somatic genomes, Life Sciences & Biomedicine, Human, Biochemistry & Molecular Biology, DNA Copy Number Variations, PALB2, Mutation, Missense, cancer predisposition, Biology, GENOMAS, Polymorphism, Single Nucleotide, Germ Cell, General Biochemistry, Genetics and Molecular Biology, Article, NO, 03 medical and health sciences, Germline mutation, variant pathogenicity, KINASE, medicine, Humans, Genetic Predisposition to Disease, germline and somatic genome, Allele frequency, Germ-Line Mutation, Genetic testing, Science & Technology, MUTATIONS, Tumor Suppressor Proteins, Proto-Oncogene Proteins c-ret, Cell Biology, 06 Biological Sciences, BRCA1, 030104 developmental biology, Germ Cells, DISCOVERY, Neoplasm, GENOMICS, Gene Deletion, Developmental Biology
Abstract: We conducted the largest investigation of predisposition variants in cancer to date, discovering 853 pathogenic or likely pathogenic variants in 8% of 10,389 cases from 33 cancer types. Twenty-one genes showed single or cross-cancer associations, including novel associations of SDHA in melanoma and PALB2 in stomach adenocarcinoma. The 659 predisposition variants and 18 additional large deletions in tumor suppressors, including ATM, BRCA1, and NF1, showed low gene expression and frequent (43%) loss of heterozygosity or biallelic two-hit events. We also discovered 33 such variants in oncogenes, including missenses in MET, RET, and PTPN11 associated with high gene expression. We nominated 47 additional predisposition variants from prioritized VUSs supported by multiple evidences involving case-control frequency, loss of heterozygosity, expression effect, and co-localization with mutations and modified residues. Our integrative approach links rare predisposition variants to functional consequences, informing future guidelines of variant classification and germline genetic testing in cancer. A pan-cancer analysis identifies hundreds of predisposing germline variants.
Published: 2018
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25. Bioconda: A sustainable and comprehensive software distribution for the life sciences

Author: Björn Grüning, Ryan Dale, Andreas Sjödin, Brad A. Chapman, Jillian Rowe, Christopher H. Tomkins-Tinch, Renan Valieris, Adam Caprez, Bérénice Batut, Mathias Haudgaard, Thomas Cokelaer, Kyle A. Beauchamp, Brent S Pedersen, Youri Hoogstrate, Anthony Bretaudeau, Devon Ryan, Gildas Le Corguillé, Dilmurat Yusuf, Sebastian Luna-Valero, Rory Kirchner, Karel Brinda, Thomas Wollmann, Martin Raden, Simon J. van Heeringen, Nicola Soranzo, Lorena Pantano, Zachary Charlop-Powers, Per Unneberg, Matthias De Smet, Marcel Martin, Greg Von Kuster, Tiago Antao, Milad Miladi, Kevin Thornton, Christian Brueffer, Marius van den Beek, Daniel Maticzka, Clemens Blank, Sebastian Will, K´evin Gravouil, Joachim Wolff, Manuel Holtgrewe, Jörg Fallmann, Vitor C. Piro, Ilya Shlyakhter, Ayman Yousif, Philip Mabon, Xiao-Ou Zhang, Wei Shen, Jennifer Cabral, Cristel Thomas, Eric Enns, Joseph Brown, Jorrit Boekel, Mattias de Hollander, Jerome Kelleher, Nitesh Turaga, Julian R. de Ruiter, Dave Bouvier, Simon Gladman, Saket Choudhary, Nicholas Harding, Florian Eggenhofer, Arne Kratz, Zhuoqing Fang, Robert Kleinkauf, Henning Timm, Peter J. A. Cock, Enrico Seiler, Colin Brislawn, Hai Nguyen, Endre Bakken Stovner, Philip Ewels, Matt Chambers, James E. Johnson, Emil Hägglund, Simon Ye, Roman Valls Guimera, Elmar Pruesse, W. Augustine Dunn, Lance Parsons, Rob Patro, David Koppstein, Elena Grassi, Inken Wohlers, Alex Reynolds, MacIntosh Cornwell, Nicholas Stoler, Daniel Blankenberg, Guowei He, Marcel Bargull, Alexander Junge, Rick Farouni, Mallory Freeberg, Sourav Singh, Daniel R. Bogema, Fabio Cumbo, Liang-Bo Wang, David E Larson, Matthew L. Workentine, Upendra Kumar Devisetty, Sacha Laurent, Pierrick Roger, Xavier Garnier, Rasmus Agren, Aziz Khan, John M Eppley, Wei Li, Bianca Katharina Stöcker, Tobias Rausch, James Taylor, Patrick R. Wright, Adam P. Taranto, Davide Chicco, Bengt Sennblad, Jasmijn A. Baaijens, Matthew Gopez, Nezar Abdennur, Iain Milne, Jens Preussner, Luca Pinello, Avi Srivastava, Aroon T. Chande, Philip Reiner Kensche, Yuri Pirola, Michael Knudsen, Ino de Bruijn, Kai Blin, Giorgio Gonnella, Oana M. Enache, Vivek Rai, Nicholas R. Waters, Saskia Hiltemann, Matthew L. Bendall, Christoph Stahl, Alistair Miles, Yannick Boursin, Yasset Perez-Riverol, Sebastian Schmeier, Erik Clarke, Kevin Arvai, Matthieu Jung, Tom´as Di Domenico, Julien Seiler, Eric Rasche, Etienne Kornobis, Daniela Beisser, Sven Rahmann, Alexander S Mikheyev, Camy Tran, Jordi Capellades, Christopher Schröder, Adrian Emanuel Salatino, Simon Dirmeier, Timothy H. Webster, Oleksandr Moskalenko, Gordon Stephen, and Johannes Köster
Subjects: 0303 health sciences, 03 medical and health sciences, 0302 clinical medicine, Software, Computer science, business.industry, Software distribution, Software engineering, business, 030217 neurology & neurosurgery, Software versioning, 030304 developmental biology
Abstract: We present Bioconda (https://bioconda.github.io), a distribution of bioinformatics software for the lightweight, multiplatform and language-agnostic package manager Conda. Currently, Bioconda offers a collection of over 3000 software packages, which is continuously maintained, updated, and extended by a growing global community of more than 200 contributors. Bioconda improves analysis reproducibility by allowing users to define isolated environments with defined software versions, all of which are easily installed and managed without administrative privileges.
Published: 2017
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26. Additional file 2: of iGCâ an integrated analysis package of gene expression and copy number alteration

Author: Yi-Pin Lai, Liang-Bo Wang, Wang, Wei-An, Liang-Chuan Lai, Mong-Hsun Tsai, Tzu-Pin Lu, and Chuang, Eric
Subjects: GeneralLiterature_INTRODUCTORYANDSURVEY, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING
Abstract: The tutorial of the package iGC. (PDF 129 kb)
Published: 2017
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27. Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features

Author: Maode Lai, Eric Chang, Zhipeng Jia, Fang Zhang, Yuqing Ai, Yan Xu, and Liang-Bo Wang
Subjects: 0301 basic medicine, Support Vector Machine, Computer science, Colorectal cancer, 02 engineering and technology, Biochemistry, Convolutional neural network, Deep convolution activation feature, Segmentation, Structural Biology, 0202 electrical engineering, electronic engineering, information engineering, Image Processing, Computer-Assisted, Computer vision, lcsh:QH301-705.5, Contextual image classification, Brain Neoplasms, Applied Mathematics, Methodology Article, Classification, Computer Science Applications, Colonic Neoplasms, lcsh:R858-859.7, 020201 artificial intelligence & image processing, Algorithms, medicine.medical_specialty, Brain tumor, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, lcsh:Computer applications to medicine. Medical informatics, 03 medical and health sciences, Carcinoma, medicine, Humans, Molecular Biology, business.industry, Deep learning, Feature learning, Digital pathology, Cancer, Image segmentation, medicine.disease, Visualization, 030104 developmental biology, ComputingMethodologies_PATTERNRECOGNITION, lcsh:Biology (General), Histopathology, Artificial intelligence, Neural Networks, Computer, business
Abstract: Background Histopathology image analysis is a gold standard for cancer recognition and diagnosis. Automatic analysis of histopathology images can help pathologists diagnose tumor and cancer subtypes, alleviating the workload of pathologists. There are two basic types of tasks in digital histopathology image analysis: image classification and image segmentation. Typical problems with histopathology images that hamper automatic analysis include complex clinical representations, limited quantities of training images in a dataset, and the extremely large size of singular images (usually up to gigapixels). The property of extremely large size for a single image also makes a histopathology image dataset be considered large-scale, even if the number of images in the dataset is limited. Results In this paper, we propose leveraging deep convolutional neural network (CNN) activation features to perform classification, segmentation and visualization in large-scale tissue histopathology images. Our framework transfers features extracted from CNNs trained by a large natural image database, ImageNet, to histopathology images. We also explore the characteristics of CNN features by visualizing the response of individual neuron components in the last hidden layer. Some of these characteristics reveal biological insights that have been verified by pathologists. According to our experiments, the framework proposed has shown state-of-the-art performance on a brain tumor dataset from the MICCAI 2014 Brain Tumor Digital Pathology Challenge and a colon cancer histopathology image dataset. Conclusions The framework proposed is a simple, efficient and effective system for histopathology image automatic analysis. We successfully transfer ImageNet knowledge as deep convolutional activation features to the classification and segmentation of histopathology images with little training data. CNN features are significantly more powerful than expert-designed features.
Published: 2016

28. Before and After: Comparison of Legacy and Harmonized TCGA Genomic Data Commons’ Data

Author: Peter W. Laird, Katherine A. Hoadley, A. Gordon Robertson, Denise Brooks, Hu Chen, John A. Demchok, Matthew H. Bailey, Jean C. Zenklusen, Hui Shen, Roy Tarnuzzer, Daniela S. Gerhard, Ina Felau, Kyle M. Hernandez, Michael S. Noble, Tiago C. Silva, Michael K A Mensah, Zhining Wang, Theo A. Knijnenburg, Matthew A. Wyczalkowski, Reyka G Jayasinghe, Liming Yang, Han Liang, David I. Heiman, Liang-Bo Wang, Andrew D. Cherniack, Li Ding, Wanding Zhou, Sharon Gaheen, Galen F. Gao, Saianand Balu, Andrew J. Mungall, Rehan Akbani, Joel S. Parker, Sheila Reynolds, Z. Zhang, Anab Kemal, and Benjamin P. Berman
Subjects: 0303 health sciences, Histology, Computer science, Genomic data, Cell Biology, Computational biology, Pathology and Forensic Medicine, Omics data, 03 medical and health sciences, 0302 clinical medicine, Mirna expression, Cancer genome, DNA methylation, Degree of similarity, Commons, 030217 neurology & neurosurgery, 030304 developmental biology, Reference genome
Abstract: We present a systematic analysis of the effects of synchronizing a large-scale, deeply characterized, multi-omic dataset to the current human reference genome, using updated software, pipelines, and annotations. For each of 5 molecular data platforms in The Cancer Genome Atlas (TCGA)—mRNA and miRNA expression, single nucleotide variants, DNA methylation and copy number alterations—comprehensive sample, gene, and probe-level studies were performed, towards quantifying the degree of similarity between the ‘legacy’ GRCh37 (hg19) TCGA data and its GRCh38 (hg38) version as ‘harmonized’ by the Genomic Data Commons. We offer gene lists to elucidate differences that remained after controlling for confounders, and strategies to mitigate their impact on biological interpretation. Our results demonstrate that the hg19 and hg38 TCGA datasets are very highly concordant, promote informed use of either legacy or harmonized omics data, and provide a rubric that encourages similar comparisons as new data emerge and reference data evolve. Gao et al. performed a systematic analysis of the effects of synchronizing the large-scale, widely used, multi-omic dataset of The Cancer Genome Atlas to the current human reference genome. For each of the five molecular data platforms assessed, they demonstrated a very high concordance between the ‘legacy’ GRCh37 (hg19) TCGA data and its GRCh38 (hg38) version as ‘harmonized’ by the Genomic Data Commons.
Published: 2019
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29. Abstract 419: Reproducibility assessment of mutations calls in exome- and whole-genome sequencing using consensus calling from TCGA and ICGC

Author: Li Ding, Jared T. Simpson, Michael C. Wendl, Mark Gerstein, Angela C. Hirbe, Michael D. McLellan, Wen-Wei Liang, Steven M. Foltz, Guanlan Dong, Liang-Bo Wang, and Matthew H. Bailey
Subjects: Untranslated region, Whole genome sequencing, Cancer Research, Computational biology, Biology, medicine.disease_cause, Genome, Germline, Exon, Oncology, medicine, Carcinogenesis, Exome, Exome sequencing
Abstract: Two large cancer genomic consortia recently published the largest and highest-quality consensus mutations calls for both whole-exome sequencing (WES) and whole-genome sequencing (WGS) in cancer: The Cancer Genome Atlas (TCGA), and the International Cancer Genetics Consortia (ICGC), respectively. Together these datasets encompass more than 60M mutations from ~13,000 samples (~10,000 WES and ~3,000 WGS). An intersecting set of 742 samples, from 22 cancer types, was sequenced using both platforms and mutations were identified using a combined 13 variant calling tools (7 WES and 5 WGS). These samples represent an ideal dataset to compare and contrast WES with WGS performance, reliability, and reproducibility of mutation calling in exons, and provide the community with key regions flanking exons that play a role in carcinogenesis. MAF files were collected using strict filtering criteria for initial file release, including the elimination of germline contaminants, 8-oxo-guanine artifacts, depth filtering and repeat masking. Additional filtering included minimum coverage requirements and restriction of both WES and WGS to variants detected within targeted exons. Finally, we restricted our data to known cancer genes. This final step suggests that these 742 samples have anywhere between 11.5K to 12.3K mutations from covered exons in potential cancer driver genes—WES and WGS, respectively. Preliminary results found that ~70% of samples had had >80% congruent mutations between both platforms; ~25% of samples had had >80% congruent mutations calls in one or the other platform; and the remaining samples had poor performance in replicating identical mutations. We observed that a majority of the variants unique to a sequencing platform were primarily from mutations with low VAF. We also sought to explore regions of the genome that are captured by both technologies despite the knowledge that WES did not target these regions. This is made possible by obtaining access to the primary data resources, and relaxing filtering criteria to include other regions such as 3' and 5' UTR, exon flanking regions, and intronic regions. We identified many recurrent mutations from non-exonic regions that were corroborated using both platforms that have not been previously reported in pan-cancer efforts. At this historic junction in time, as preliminary results from whole-genome sequencing efforts emerge and large exome sequencing efforts taper, 742 samples spanning both efforts can provide insights into the lessons learned from exome sequencing, and provide a solid foundation stepping forward into whole-genome analysis. We will continue to glean insights into the etiology of human disease by using both technologies; however, these mutation calls highlight the challenges that still exist in somatic variant calling, and provide grounds for more critical evaluation of genomic findings in cancer. Citation Format: Matthew H. Bailey, Liang-Bo Wang, Wen-Wei Liang, Steven Foltz, Guanlan Dong, Michael C. Wendl, Michael McLellan, Angela C. Hirbe, Jared Simpson, Mark Gerstein, Li Ding. Reproducibility assessment of mutations calls in exome- and whole-genome sequencing using consensus calling from TCGA and ICGC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 419.
Published: 2018
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30. Abstract 2706: Effects of germline and somatic mutations on protein expression in tumor and adjacent normal tissues in breast, ovarian, and colorectal tumors

Author: Li Ding, Fernanda Martins Rodrigues, Matthew H. Bailey, Matthew A. Wyczalkowski, Gordon B. Mills, Liang-Bo Wang, Daniel C. Zhou, David Fenyö, Samuel H. Payne, and Yige Wu
Subjects: Cancer Research, Somatic cell, Cancer, AZGP1, Biology, medicine.disease, Germline, Germline mutation, Oncology, MSH2, medicine, Cancer research, Ovarian cancer, ATRX
Abstract: The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) provides proteomic data that accurately quantify protein expression in tumors. This work improves upon previous estimates of expression, i.e., RNA-seq and or microarray, because it directly measures protein quantity using mass spectrometry. These data are critical for establishing more accurate estimates of a mutation's impact on tumor cell microenvironment and molecular processes. Here we address the association of both germline and somatic mutations with protein expression in three cancer types: breast cancer (BRCA), ovarian cancer (OV) and colorectal cancer (CO). Additionally, we analyze both primary tumors and adjacent normal pairs that provide insight into tumor etiology. CPTAC2 prospective proteome and phosphoproteome established a dataset of ~450 samples. For analysis we require samples to have reliable protein and phosphoprotein measures. This filtering strategy resulted in a dataset with measurements for 8,000-12,000 proteins and over 30,000 phosphosites for these three cancer types. We first performed analysis on tumors with likely predisposition germline mutations. We defined germline-predisposed samples as tumor samples with germline mutations in one or more of the following DNA repair genes: BRAC1/2, MSH2/6, PMS2. When analyzing samples with and without germline predisposition, an altered protein expression profile was found, albeit less extreme. We observed overexpressed genes in predisposed samples that include C9orf16, PRDX5, SERPINB8, and CMPK1, and found genes to have lower expression that include TULP1, MAEL, KMT2B, and HIST1H1D. Additionally, we performed differential gene expression analysis using samples with adjacent normal tissue biopsies. We examined both the proteome and phosphoproteome levels, comparing tumor vs. adjacent normal samples, with and without germline predisposition mutations. Preliminary analysis for BRCA between tumor and normal samples showed altered protein expression profile in tumors, with about 60% of the genes showing higher or lower protein expression in the tumor. This pattern is recapitulated when restricting our analysis to known cancer driver genes. Genes found to be overexpressed in tumor samples include GNB1, SERPINA1, CDKN2C, IGF1, ERG, AZGP1, and H3F3A, while genes found to have lower expression include PRKDC, DDX5, NUP93, CTCF, ATRX, MYD88, SMARCA4, KDM6A, SF3B1, and MED12. Proteomic/phosphoproteome data deliver reliable results on both cis and transmutational effects on protein expression at both the germline and somatic level. Furthermore, these data provide a glimpse into the tissue microenvironment of adjacent normal tissue and indicate biologic stresses of germline mutations on tissues. Citation Format: Matthew H. Bailey, Daniel C. Zhou, Yige Wu, Matthew A. Wyczalkowski, Liang-Bo Wang, Fernanda Martins Rodrigues, Gordon Mills, Samuel Payne, David Fenyo, Li Ding. Effects of germline and somatic mutations on protein expression in tumor and adjacent normal tissues in breast, ovarian, and colorectal tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2706.
Published: 2018
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31. A linear-response CMOS-MEMS capacitive tactile sensor

Author: Yun-Bin Lin, Wei-Cheng Tian, Jun-Jih Liou, Liang-Bo Wang, C.-J. Hsieh, and Chia-Hui Sun
Subjects: Microelectromechanical systems, Materials science, CMOS, business.industry, Dynamic range, Capacitive sensing, Electrode, Electrical engineering, Optoelectronics, Linearity, business, Tactile sensor, Microfabrication
Abstract: A linear-response capacitive tactile sensor is presented in this work. The sensor was fabricated by the TSMC 0.35 μ m CMOS process and our self-developed MEMS post-process. The structure of the sensor consisted of one pair of parallel electrodes, with the central part of the membrane electrode hollowed out. A pillar with four beams at each side was set at the center to support the membrane electrode. This structure enhanced the uniformity of the deflection, and thus improved the linearity of the response. The wider dynamic range was also obtained because of the stiffer structure. In addition, the buckling of the membrane was lessened. The performance of this design was compared with the conventional parallel plate one. The measured linearity was 0.9728, and the dynamic range was 400mmHg, with the relieved buckling of 0.22 μm.
Published: 2012
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32. iGC--an integrated analysis package of gene expression and copy number alteration.

Author: Yi-Pin Lai, Liang-Bo Wang, Wei-An Wang, Liang-Chuan Lai, Mong-Hsun Tsai, Tzu-Pin Lu, and Chuang, Eric Y.
Subjects: *DNA copy number variations, *GENE expression, *MICROARRAY technology, *PEARSON correlation (Statistics), *TRANSCRIPTION factors
Abstract: Background: With the advancement in high-throughput technologies, researchers can simultaneously investigate gene expression and copy number alteration (CNA) data from individual patients at a lower cost. Traditional analysis methods analyze each type of data individually and integrate their results using Venn diagrams. Challenges arise, however, when the results are irreproducible and inconsistent across multiple platforms. To address these issues, one possible approach is to concurrently analyze both gene expression profiling and CNAs in the same individual. Results: We have developed an open-source R/Bioconductor package (iGC). Multiple input formats are supported and users can define their own criteria for identifying differentially expressed genes driven by CNAs. The analysis of two real microarray datasets demonstrated that the CNA-driven genes identified by the iGC package showed significantly higher Pearson correlation coefficients with their gene expression levels and copy numbers than those genes located in a genomic region with CNA. Compared with the Venn diagram approach, the iGC package showed better performance. Conclusion: The iGC package is effective and useful for identifying CNA-driven genes. By simultaneously considering both comparative genomic and transcriptomic data, it can provide better understanding of biological and medical questions. The iGC package's source code and manual are freely available at https://www.bioconductor.org/packages/ release/bioc/html/iGC.html. [ABSTRACT FROM AUTHOR]
Published: 2017
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33. Abstract 2903: Identification of novel miRNAs in breast data of the next generation sequencing using miRDeep2 and Galaxy

Author: Liang-Chuan Lai, Mong-Hsun Tsai, Eric Y. Chuang, Chien-Yueh Lee, and Liang-Bo Wang
Subjects: Genetics, Cancer Research, Small RNA, Massive parallel sequencing, Statistical model, Computational biology, Biology, computer.software_genre, DNA sequencing, MicroRNA biogenesis, Oncology, microRNA, Tissue type, computer, Data integration
Abstract: A growing body of evidence has indicated that microRNAs play important roles in many cellular processes and dysregulation of microRNAs results in several diseases including cancers. With advances in massive parallel sequencing technology, Next-Generation Sequencing (NGS), detection of low-quantity sequences improves and hence achieves better ability to find microRNAs expressed in low numbers. We construct a small RNA pipeline to identify novel microRNAs by miRDeep2 and other tools, which can be fully performed on Galaxy with friendly graphical user interface. After the construction of pipeline and tools, several proper small RNA-seq datasets of breast cancer containing enough samples statistically are derived for novel microRNA identification. MiRDeep2 is an integrated tool which uses a probabilistic model of microRNA biogenesis to score pattern and frequency of sequenced RNA with the secondary structure of the microRNA precursor in order to discover novel microRNAs. The method of finding microRNA using miRDeep2 has been widely used and has been validated with remarkable accuracy. Due to the long time and large space-consuming computing and multiple tools involved when processing NGS data, a bio-info framework made by USCS, Galaxy, is introduced into the pipeline, which serves as a great data integration and analysis platform. As the results, few hundreds of candidates are scored over 0 by miRDeep2. However, judging from the number and the probability of the true positives of being a novel microRNA, we set a score lower bound of 5 and use the 50 predicted candidates that pass the threshold in further analysis, which means we have 39±3 true positives out of 50 with probability of 78±7%. Then, candidates will be discarded when mapped to other functional RNAs by BLAST. Finally, 30 candidates of novel microRNAs are found out. A further investigation shows that 25 of 30 candidates are successfully mapped to another RNA-seq dataset including over a hundred samples of breast cancer and covering all tumor, tumor-adjacent, and normal type of tissue samples. Profiles of these candidates generated by miRDeep2 potentially show the trend of correlation with the tissue type of sample. More RNA-seq datasets can be performed following the pipeline set up in this study for microRNA characterization and in vitro experiments can be designed for further verification and profiling in the future. Citation Format: Chien-Yueh Lee, Liang-Bo Wang, Mong-Hsun Tsai, Liang-Chuan Lai, Eric Y. Chuang. Identification of novel miRNAs in breast data of the next generation sequencing using miRDeep2 and Galaxy. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2903. doi:10.1158/1538-7445.AM2013-2903 Note: This abstract was not presented at the AACR Annual Meeting 2013 because the presenter was unable to attend.
Published: 2013
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34. iGC—an integrated analysis package of gene expression and copy number alteration

Author: Liang-Chuan Lai, Eric Y. Chuang, Wei-An Wang, Mong-Hsun Tsai, Liang-Bo Wang, Tzu-Pin Lu, and Yi-Pin Lai
Subjects: 0301 basic medicine, R/Bioconductor, Source code, DNA Copy Number Variations, Microarray, Computer science, media_common.quotation_subject, Gene Expression, computer.software_genre, Biochemistry, Bioconductor, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, Structural Biology, Gene expression, Humans, Gene, Molecular Biology, media_common, Genome, Gene Expression Profiling, Applied Mathematics, Copy number alteration, Computer Science Applications, Gene expression profiling, 030104 developmental biology, 030220 oncology & carcinogenesis, Data mining, DNA microarray, computer, Software
Abstract: Background With the advancement in high-throughput technologies, researchers can simultaneously investigate gene expression and copy number alteration (CNA) data from individual patients at a lower cost. Traditional analysis methods analyze each type of data individually and integrate their results using Venn diagrams. Challenges arise, however, when the results are irreproducible and inconsistent across multiple platforms. To address these issues, one possible approach is to concurrently analyze both gene expression profiling and CNAs in the same individual. Results We have developed an open-source R/Bioconductor package (iGC). Multiple input formats are supported and users can define their own criteria for identifying differentially expressed genes driven by CNAs. The analysis of two real microarray datasets demonstrated that the CNA-driven genes identified by the iGC package showed significantly higher Pearson correlation coefficients with their gene expression levels and copy numbers than those genes located in a genomic region with CNA. Compared with the Venn diagram approach, the iGC package showed better performance. Conclusion The iGC package is effective and useful for identifying CNA-driven genes. By simultaneously considering both comparative genomic and transcriptomic data, it can provide better understanding of biological and medical questions. The iGC package’s source code and manual are freely available at https://www.bioconductor.org/packages/release/bioc/html/iGC.html. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1438-2) contains supplementary material, which is available to authorized users.
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34 results on '"Liang-Bo Wang"'

1. Spatially interacting phosphorylation sites and mutations in cancer

2. Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

3. Proteogenomic Characterization of Ovarian HGSC Implicates Mitotic Kinases, Replication Stress in Observed Chromosomal Instability

4. Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features

5. Author Correction: Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

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

7. Pollock: fishing for cell states

8. Spatially restricted drivers and transitional cell populations cooperate with the microenvironment in untreated and chemo-resistant pancreatic cancer

9. Abstract 4106: Curating protein complexes from multiple resources and validate their co-regulations at RNA/protein level in pan-cancer

10. Abstract 5027: PTMcosmos: A web portal of post-translational modifications and proteogenomic resources in cancer

11. Abstract 2832: Identifying tumor specific surface protein markers for glioblastoma immunotherapy by single-nuclei RNA-seq

12. Abstract 1932: Pollock: Fishing for cell states

13. Spatially interacting phosphorylation sites and mutations in cancer

14. Proteogenomic and metabolomic characterization of human glioblastoma

15. MOMC-4. Proteogenomic and metabolomic characterization of glioblastoma

16. Abstract 2170: Proteogenomic and metabolomic characterization of human glioblastoma

17. Database of evidence for precision oncology portal

18. GENE-19. DEEP PROTEOMIC SURVEY ACROSS SEVEN CHILDHOOD BRAIN TUMORS

19. Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer

20. Proteogenomic Characterization of Ovarian HGSC Implicates Mitotic Kinases, Replication Stress in Observed Chromosomal Instability

21. Proteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma

22. Proteogenomic Characterization of Endometrial Carcinoma

23. Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma

24. Pathogenic Germline Variants in 10,389 Adult Cancers

25. Bioconda: A sustainable and comprehensive software distribution for the life sciences

26. Additional file 2: of iGCâ an integrated analysis package of gene expression and copy number alteration

27. Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features

28. Before and After: Comparison of Legacy and Harmonized TCGA Genomic Data Commons’ Data

29. Abstract 419: Reproducibility assessment of mutations calls in exome- and whole-genome sequencing using consensus calling from TCGA and ICGC

30. Abstract 2706: Effects of germline and somatic mutations on protein expression in tumor and adjacent normal tissues in breast, ovarian, and colorectal tumors

31. A linear-response CMOS-MEMS capacitive tactile sensor

32. iGC--an integrated analysis package of gene expression and copy number alteration.

33. Abstract 2903: Identification of novel miRNAs in breast data of the next generation sequencing using miRDeep2 and Galaxy

34. iGC—an integrated analysis package of gene expression and copy number alteration

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34 results on '"Liang-Bo Wang"'

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