bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract B1-16: Attractor analysis of p53 gene network in colon cancer
Authors: Kim Man-Sun , Kim Jeong-Rae 
Published: May 2016

Abstract
Colon cancer is a heterogeneous and complex disease. Mutation of the p53 gene is most commonly observed in many people with colon cancer [1-2]. In this regard, to understand how the p53 network controls cell fate, it is important not only to identify the network components and their molecular interactions but also to study essential network properties at the system level [3]. However, the molecular mechanisms remain largely unexplored. Here, we used Boolean network modeling and attractor landscape analysis to analyze the state transition dynamics of a simplified p53 network.
We first constructed a simplified Boolean network model with multiple feedback loops through p53 for analyzing the p53 network. After building the simplified p53 regulatory network, we modeled the network dynamics using a Boolean network with a set of state transition logics defined on the basis of biological evidence.
Attractor landscape analysis of dynamics and its regulation can identify potential therapeutic strategies for treating cancer.
Citation Format: Man-Sun Kim, Jeong-Rae Kim. Attractor analysis of p53 gene network in colon cancer. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-16.
Abstract B1-30: Receptor-level feedback mechanisms distinguish frequency- and amplitude-modulated ERK activation
Authors: Sparta Breanne , Pargett Michael , Distor Kevin , Minguet Marta , Albeck John G. 
Published: May 2016

Abstract
Receptor tyrosine kinases transduce signals through ERK and drive distinct cellular behaviors, but it is not clear how dynamic patterns of ERK activity are generated. To approach this question quantitatively, we have generated an epithelial cell line expressing a tunable version of the canonical nerve growth factor (NGF) receptor TrkA. Upon NGF stimulation, we visualized the dynamics of ERK activity in live cells using an improved fluorescent reporter for ERK activity, EKAR3. Examination of the NGF receptor pathway in isolation revealed that stimulation with NGF promotes a prolonged ERK activity pulse that is amplitude-modulated by the concentration of the stimulus, in contrast to the frequency modulation observed in the epidermal growth factor (EGF)-RAS-ERK pathway. This result implies that the primary determinants of ERK kinetics are the unique set of signaling proteins recruited to the activating receptor. To dissect the context in which ERK is activated in respect to parallel signaling pathways, we have engineered mutant NGF receptors that cannot be phosphorylated at one or more of the three adaptor binding sites and characterized how each signaling pathway contributes to the pattern of dynamic ERK activity. Furthermore, because our NGF receptor expression system allows the tunable and quantifiable expression of receptor levels, we have evaluated the effect of receptor number on ERK kinetics in single cells. Our findings indicate that the dynamics of ERK activity are in fact decided at the receptor-level and may explain how different receptor tyrosine kinase profiles in cancer cells modulate distinct patterns of ERK activity.
Citation Format: Breanne Sparta, Michael Pargett, Kevin Distor, Marta Minguet, John G. Albeck. Receptor-level feedback mechanisms distinguish frequency- and amplitude-modulated ERK activation. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-30.
Abstract B1-35: Enrichr2: Next generation gene set enrichment analysis web-based tool
Authors: Jones Matthew R. 
Published: May 2016

Abstract
Background: System-wide profiling of genes and proteins in mammalian cells produces lists of differentially expressed genes/proteins needing to be further analyzed for their collective functions in order to extract new knowledge. Once lists of genes or proteins are generated from such experiments, they are compared to existing annotated lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set library databases have been developed, there is still room for improvement. Enrichr2 is a major update of the published popular tool Enrichr.
Methods: Enrichr2's architecture has been redesigned to be highly scalable. The new database centric design allows Enrichr2 to perform enrichment analysis within seconds while accommodating for the rapid growth of our gene-set library database. Recent additions to our gene-set library database include: disease signatures from GEO, drug perturbations from GEO, kinase perturbations from GEO and L1000, as well as tissue protein expression from two recent publications. Principal Angle Enrichment Analysis has been implemented and is one of the available enrichment analysis scoring methods. Enrichr2 can be restricted to only consider a subspace of the genome, and it accepts input formats that are crisp or fuzzy gene sets, probe IDs from microarrays, BED files and SOFT files. Gene synonyms are also recognized and automatically converted. Users can upload their own gene-set libraries as well as access the legacy versions of our libraries.
Results: Enrichr2 is an easy to use intuitive enrichment analysis web-based tool providing various visualization summaries of collective functions of gene lists. Enrichr2 includes many new gene-set libraries, alternative approaches to rank enriched terms, and various new interactive visualization approaches to display enrichment results. We applied Enrichr2 to analyze many of the gene expression datasets available at The Cancer Genome Atlas (TCGA). Global visualization of such enrichment analysis results provides a global view of the biological molecular mechanisms driving tumor behavior across cancers at the individual patient level.
Conclusions: Enrichr2 is emerging as a leading search engine for annotated gene lists, taking gene set enrichment analysis to a new level. Enrichr is an open source web-based tool available online at: http://amp.pharm.mssm.edu/Enrichr
Note: This abstract was not presented at the conference.
Citation Format: Matthew R. Jones. Enrichr2: Next generation gene set enrichment analysis web-based tool. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-35.
Abstract B1-44: G-DOC Plus: A cloud based next-generation systems medicine platform for precision medicine
Authors: Bhuvaneshwar Krithika , Belouali Anas , Singh Varun , Johnson Robert M. , Song Lei , Alaoui Adil , Harris Michael , Gusev Yuriy , Clarke Robert , Madhavan Subha 
Published: May 2016

Abstract
Systems medicine leverages complex computational tools and high dimensional data offering the potential for effective individualized diagnosis, prognosis and treatment options. Our flagship web platform, the Georgetown Database of Cancer (G-DOC), was deployed with the goal of enabling translational research by integrating patient characteristics and clinical outcome data with a variety of high-throughput research data in a unified environment. With the goal of improving health outcomes through genomics research, we present G-DOC Plus, our enhanced web platform offering precision medicine, translational research and population genetics workflows. This enhanced platform takes advantage of cloud computing to handle next generation sequencing (NGS) data so that they can be analyzed in the full context of other omics and clinical information.
G-DOC Plus uses cloud computing and other advanced computational tools to enable analysis of NGS and medical images in the full context of other omics and clinical information. It allows translational science researchers to explore data one sample at a time, as a sub-cohort of samples; or as a population as a whole, providing the user with a comprehensive view of the data. G-DOC Plus tools have been leveraged in cancer to support detection of prognostic markers for relapse in colorectal cancer samples, and to detect key metabolites related to disease severity; hypothesis generation; biomarker detection and multi-omic analysis, in-silico and population genetics analysis; and to explore somatic mutation and breast cancer MRI images. The long-term vision of G-DOC Plus is to extend this systems medicine platform to hospital networks to provide clinical decision support using multi-omics and relevant clinical information to support personalized patient care. G-DOC Plus was released in October 2014, and is available at: https://gdoc.georgetown.edu.
Citation Format: Krithika Bhuvaneshwar, Anas Belouali, Varun Singh, Robert M. Johnson, Lei Song, Adil Alaoui, Michael Harris, Yuriy Gusev, Robert Clarke, Subha Madhavan. G-DOC Plus: A cloud based next-generation systems medicine platform for precision medicine. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-44.
Abstract B1-45: Chr3q26 amplification and associated pathways is the key driver in squamous cell carcinoma
Authors: Wu Song , Aggarwal Amit 
Published: May 2016

Abstract
The large scale characterization of molecular aberrations in cancer through efforts such as TCGA is resulting in better disease understanding as well as rational development of cancer therapies. Recently, in the pan-cancer analysis of TCGA data, Chr3q26 amplification was identified as the most frequent copy number amplification in over 5,000 samples across 12 cancer types (Ciriello et al. 2013). This aberration is more commonly seen in solid tumors of the squamous cells. In this study, we analyze three types of squamous cell carcinomas, Lung (N=494), Head&Neck (N=306) and Cervical (N=192), which have high prevalence of Chr3q26 amplification. We provide insights into core genetic aberration signals relating to Chr3q26 amplicon by integrating gene mutation, mRNA expression and DNA copy number data. These include concordant co-activation of multiple pathways relating to development biology, receptor tyrosine kinases and cell cycle. The co-activation of multiple key oncogenic signals in tumors with Chr3q26 may suggest that the need of combinational therapies. We also show lack of pre-clinical models that reflect similar molecular characteristics, there by suggesting challenge in translating such findings. Importantly, we then developed a Chr3q26 amplification gene expression signature, which may help to stratify patients into subgroups with different response to adjuvant chemotherapy
Citation Format: Song Wu, Amit Aggarwal. Chr3q26 amplification and associated pathways is the key driver in squamous cell carcinoma. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-45.
Abstract B2-27: Systems biological identification of optimal drug targets for combination therapy of non-small cell lung cancer
Authors: Lee Jonghoon , Cho Kwang-Hyun 
Published: May 2016

Abstract
Non-small cell lung cancer (NSCLC) is the major subtype of lung cancer, accounting for about 85% of all lung patients. Recently, molecularly targeted inhibitors of receptor tyrosine kinase (RTK) such as epidermal growth factor receptor (EGFR) and fibroblast growth factor receptor (FGFR) signalling pathways have been highlighted as promising anti-tumor drugs because of the prevalence of genetic alterations within EGFR and FGFR signalling pathways in NSCLC patients. However, their functional effects have been reported as varying over a wide range depending on the patient-specific mutational conditions. To understand the underlying mechanism of the heterogeneous responses to EGFR and FGFR inhibitors and to find the optimal strategy for combination therapy, we have developed an integrative Boolean network model of EGFR, FGFR and DNA damage signalling pathways by integrating previous experimental evidences from the literature. Systematic perturbation analysis revealed that the multiple feedback regulations embedded in the signalling pathways play important roles in determining the responses to EGFR and FGFR inhibitors. We further analyzed the mechanism for the heterogeneous responses of cells to the EGFR and FGFR inhibitors under various mutational conditions and identified key genetic alterations that can lead to undesirable responses. Finally, extensive combinatorial perturbation analysis and subsequent comparison of the attractor profiles in the presence and absence of targeted inhibitors identified novel candidate drug targets for combinatorial therapy. Our analysis provides a new insight into the underlying mechanism of the heterogeneous cellular responses to EGFR and FGFR inhibitors and the optimal combinatorial treatment strategies.
Acknowledgements: This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea Government, the Ministry of Science, ICT & Future Planning (2014R1A2A1A10052404, 2013M3A9A7046303, and 2010-0017662) and by a grant of the Korean Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea. (HI13C2162).
Citation Format: Jonghoon Lee, Kwang-Hyun Cho. Systems biological identification of optimal drug targets for combination therapy of non-small cell lung cancer. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B2-27.
Abstract B2-43: Bioinformatics analysis of TCGA tumors to identify cancer associated epigenetic regulators
Authors: Gnad Florian , Zhang Zemin 
Published: May 2016

Abstract
Many cancer cells show distorted epigenetic landscapes. The Cancer Genome Atlas (TCGA) project profiles thousands of tumors, allowing the discovery of somatic alterations in the epigenetic machinery and the identification of potential cancer drivers among members of epigenetic protein families. We integrated mutation, expression, and copy number data from thousands of tumors from major cancer types to train a classification model that predicts the likelihood of being an oncogene (OG), tumor suppressor (TSG) or neutral gene (NG). Using classical driver genes to train an OG/TSG predictor, we determined mutation parameters as the most predictive features. We applied this predictor to epigenetic regulator genes (ERGs), and used differential expression and correlation network analysis to identify dysregulated ERGs along with co-expressed cancer genes. Mutation-based classifiers uncovered the TSG-like profiles for several ERGs, but did not detect any OG-like profile. Differential gene expression and correlation network analyses revealed that EZH2 is the most significantly over-expressed ERG in cancer and is co-regulated with a cell cycle network. Furthermore we quantified global proteomic changes by mass spectrometry after EZH2 inhibition. Proteomic analysis showed that EZH2 inhibition induced down-regulation of cell cycle regulators in lymphoma cells. We have now expanded our analysis to other types of proteins including kinases and secreted proteins.
Note: This abstract was not presented at the conference.
Citation Format: Florian Gnad, Zemin Zhang. Bioinformatics analysis of TCGA tumors to identify cancer associated epigenetic regulators. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B2-43.
Abstract PR07: Functional analysis of diverse oncogenic driver mutations using an isogenic cell line library identifies novel drug responses and alterations in metabolism
Authors: Martins Maria M. , Zhou Alicia Y. , Corella Alexandra , Horiuchi Dai , Yau Christina , Rakshandehroo Taha , Gordan John D. , Levin Rebecca S. , Johnson Jeff , Jascur John , Shales Mike , Sorrentino Antonio , Cheah Jaime , Clemons Paul A. , Shamji Alykhan , Schreiber Stuart , Schreiber Stuart , Krogan Nevan J. , Shokat Kevan M. , Shokat Kevan M. , McCormick Frank , Nomura Daniel , Bandyopadhyay Sourav , Goga Andrei 
Published: May 2016

Abstract
There is an urgent need in oncology to link molecular aberrations in tumors with altered cellular behaviors, such as metabolic derangements, and to identify novel therapeutics for cancer treatment. We have sought to identify synthetic-lethal genetic interactions that cancer cells acquire in the presence of specific mutations. Using engineered isogenic cells, we generated an unbiased and quantitative chemical-genetic interaction map that measures the influence of 51 aberrant cancer genes on 90 drug responses. The dataset strongly predicts drug responses found in cancer cell line collections, indicating that isogenic cells can model more complex cellular contexts. Applied to triple-negative breast cancer, we report clinically actionable interactions with the MYC oncogene including resistance to PI3K/AKT pathway inhibitors and an unexpected sensitivity to dasatinib through LYN inhibition in a synthetic-lethal manner. These studies provide new drug and biomarker pairs for clinical investigation. We have also performed global metabolomics analysis in a subset of the isogenic cell lines demonstrating alterations in metabolic pathways that are shared across multiple oncogenes, as well as those that are distinct to specific oncogenic drivers. This scalable approach enables the prediction of drug responses from patient data and can be used to accelerate the development of new genotype-directed therapies.
This abstract is also presented as a poster at the Translation of the Cancer Genome conference.
Citation Format: Maria M. Martins, Alicia Y. Zhou, Alexandra Corella, Dai Horiuchi, Christina Yau, Taha Rakshandehroo, John D. Gordan, Rebecca S. Levin, Jeff Johnson, John Jascur, Mike Shales, Antonio Sorrentino, Jaime Cheah, Paul A. Clemons, Alykhan Shamji, Stuart Schreiber, Stuart Schreiber, Nevan J. Krogan, Kevan M. Shokat, Kevan M. Shokat, Frank McCormick, Daniel Nomura, Sourav Bandyopadhyay, Andrei Goga. Functional analysis of diverse oncogenic driver mutations using an isogenic cell line library identifies novel drug responses and alterations in metabolism. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr PR07.
Abstract PR15: Predicting the essentialities of protein-protein interactions in cancer
Authors: Cooper Lee AD , Moran Josue D. , Li Zenggang , Du Yuhong , Harati Sahar , Ivanov Andrey A. , Webber Phillip , Havel Jonathan J. , Johns Margaret A. , Fu Haian , Moreno Carlos S. 
Published: May 2016

Abstract
Many protein-protein interactions (PPIs) are essential to cellular proliferation and survival, and thus represent potential targets for novel compounds with high specificity and therapeutic potential in the treatment of cancer. Despite their therapeutic significance, there are currently no high-throughput experimental platforms to interrogate the essentiality of individual protein-protein interactions. The ability to computationally predict or infer PPI essentiality would help prioritize the development of therapeutic targets and advance understanding of cancer biology, and remains a significant barrier. We have developed a computational method to predict PPI essentiality by combining shRNA studies with network models of protein interaction pathways in an analytic framework. High-throughput single-gene shRNA silencing is a well-established experimental approach to study protein essentiality in genome-wide screens. The silencing of a single gene in an shRNA screen effectively disrupts multiple PPIs, masking their individual contributions to the observed protein essentiality. Our method uses a network model to infer cliques of PPIs that are disrupted as each gene is silenced, then uses these sets to formulate an additive model of how the unknown PPI essentialities combine to form the observed protein essentialities. This process is then deconvoluted to recover the unknown essentialities of each PPI using a regularized solver. We performed this analysis in 108 cell lines characterized for protein essentiality in Project Achilles [1, 2]. A superpathway of 2186 proteins and 11488 protein interactions was constructed from prior knowledge databases [3] and protein interaction screenings. We demonstrate the validity of our approach via prediction of essential PPIs such as TP53-MDM4 in cell lines sensitive to nutlin and CDK4-RB1 interactions in cell lines sensitive to CDK4 inhibitors.
[1] Cowley, Weir & Vazquez, et al. Nature Scientific Data 1, Article number: 140035. September 30, 2014
[2] Cheung HW, Cowley GS, Weir BA, et al. Proc Natl Acad Sci. 2011 Jul 26; 108(30):12372-7.
[3] Schaefer CF, Anthony K, Krupa S, et al. Nucleic Acids Research. Jan 2009. 37:674-9
Citation Format: Lee AD Cooper, Josue D. Moran, Zenggang Li, Yuhong Du, Sahar Harati, Andrey A. Ivanov, Phillip Webber, Jonathan J. Havel, Margaret A. Johns, Haian Fu, Carlos S. Moreno. Predicting the essentialities of protein-protein interactions in cancer. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr PR15.
Abstract A17: A lincRNA orchestrates glioma stem cell-mediated angiogenesis in glioblastoma
Authors: Wang Jenny J. , Wu Megan , Li Christopher , Shahzad Uswa , Karamchandani Jason , Marsden Phil , Das Sunit 
Published: May 2016

Abstract
Angiogenesis is a hallmark of glioblastoma. Recent data suggest that glioma stem cells (GSCs) contribute to pathologic angiogenesis through transdifferentiation into endothelial cells or vascular pericytes. Using an unbiased screen, we identified a lincRNA near the VEGFR1 gene, which we named LIVE (lincRNA-VEGFR1), that directs physiologic vascular network formation through global effects on gene expression. LIVE is highly expressed in glioblastoma and enriched in GSCs. Over-expression of LIVE results in specification of GSCs along a vascular lineage. LIVE associates with PARP1 in GSCs and RNA helicase A in endothelial cells to drive pericyte-specific and endothelial-associated gene expression, respectively. Depletion of LIVE in a xenograft model of glioblastoma results in decreased microvascular density, vascular perfusion and pericyte coverage, and depletion of GSCs within the vascular niche. Our findings demonstrate a novel role for lincRNAs in the orchestration of tumour angiogenesis and reveal a therapeutic potential for targeting LIVE in glioblastoma.
Citation Format: Jenny J. Wang, Megan Wu, Christopher Li, Uswa Shahzad, Jason Karamchandani, Phil Marsden, Sunit Das. A lincRNA orchestrates glioma stem cell-mediated angiogenesis in glioblastoma. [abstract]. In: Proceedings of the AACR Special Conference on Noncoding RNAs and Cancer: Mechanisms to Medicines ; 2015 Dec 4-7; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2016;76(6 Suppl):Abstract nr A17.