bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract B22: Focal brain radiation induces systemic lymphopenia in an animal model
Authors: Piotrowski Anna F. , Velarde Esteban , Grossman Stuart 
Published: May 2016

Abstract
Background: Approximately half of patients with solid tumors develop severe and prolonged lymphopenia after beginning standard radiation and this lymphopenia is associated with shortened survival. An animal model to understand the mechanisms behind radiation-induced lymphopenia has not previously been reported.
Methods: Using the small animal radiation research platform (SARRP) C57BL/6 and BALB/c mice approximately 7-12 weeks old received focal brain irradiation with prolonged and abbreviated schemes: 2 Gy x 30 fractions and 4 Gy x 10 fractions. Blood was collected weekly via peripheral tail vein. Lymphocytes were labelled with antibodies to CD3, CD4, CD8 and CD45 and analyzed by Hemavet, Flow Cytometry and TruCount.
Results: In all four groups (n=12) - C57BL/6 and BALB/c mice receiving prolonged and abbreviated schemes: 2 Gy x 30 fractions and 4 Gy x 10 fractions - lymphopenia was achieved but not sustained. C57BL/6 and BALB/c mice total lymphocyte counts decreased by 72% and 63% of baseline respectively, after 6 weeks of radiation. C57BL/6 and BALB/c mice total lymphocyte counts decreased by 85% and 87% of baseline respectively, after 2 weeks of radiation. Within 3 weeks of the completion of radiation, counts returned to 60-80% of baseline. All animals had a similar magnitude of change in total lymphocyte counts.
Conclusion: This animal model is a practical way to study the reduction in lymphocytes related to radiation. CD4 counts in BALB/c mice receiving prolonged radiation show a linear decline. Future immunology studies should assess T cell subsets and cytokines. The clinically observed phenomenon persistent radiation-induced lymphopenia is challenging to model in mice.
Citation Format: Anna F. Piotrowski, Esteban Velarde, Stuart Grossman. Focal brain radiation induces systemic lymphopenia in an animal model. [abstract]. In: Proceedings of the AACR Special Conference: Advances in Brain Cancer Research; May 27-30, 2015; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2015;75(23 Suppl):Abstract nr B22.
Abstract IA05: Evolution of glioblastoma subtypes
Authors: Michor Franziska 
Published: May 2016

Abstract
To understand the relationships between the non-GCIMP glioblastoma (GBM) subgroups, we performed mathematical modeling to predict the temporal sequence of driver events during tumorigenesis. The most common order of evolutionary events is 1) chromosome (chr) 7 gain and chr10 loss, followed by 2) CDKN2A loss and/or TP53 mutation, and 3) alterations canonical for specific subtypes. We then developed a computational methodology to identify drivers of broad copy number changes, identifying PDGFA (chr7) and PTEN (chr10) as driving initial nondisjunction events. These predictions were validated using mouse modeling, showing that PDGFA is sufficient to induce proneural-like gliomas and that additional NF1 loss converts proneural to the mesenchymal subtype. We further performed integrated genomic and epigenetic profiling of patient-derived cell lines to identify further genes driving subtype transformation as well as alterations in the three-dimensional architecture of the GBM genome during subtype changes. Our computational methodologies can be applied to study the evolutionary dynamics of other cancer types.
Abstract IA19: Modeling blood-CNS barriers in zebrafish
Authors: Umans Robyn A. , Henson Hannah E. , Parupalli Chaithanyarani , Ju Bensheng , Taylor Michael R. 
Published: May 2016

Abstract
The blood-brain barrier (BBB) plays an important role in the treatment of brain tumors. Yet, a comprehensive understanding of brain tumor vasculaturization has been hampered by difficulties in observing this process in vivo. Recently, we have generated several models of brain tumors in zebrafish. To visualize the vascularization of these tumors, we generated transgenic zebrafish lines to serve as in vivo reporters of brain tumor vasculature. We show that our BBB transgenic line drives expression specifically in brain endothelial cells and not in the vasculature of peripheral tissues or circumventricular organs. Using time-lapse confocal microscopy, we find that barriergenesis (i.e. the initiation of BBB development) occurs immediately as new vessels sprout into the brain parenchyma. Using a combination of genetic and chemical tools, we demonstrate that canonical Wnt signaling, but not VEGF signaling, is essential for barriergenesis. These results indicate that CNS angiogenesis and barriergenesis occur simultaneously, but require distinct signals for proper formation. Furthermore, we created a complementary transgenic line that drives expression in fenestrated endothelium, a hallmark of brain tumor vasculature. The combination of these transgenic lines along with our newly developed tumor models will allow us to visualize the complexities of brain tumor vascularization in a live animal model.
Citation Format: Robyn A. Umans, Hannah E. Henson, Chaithanyarani Parupalli, Bensheng Ju, Michael R. Taylor. Modeling blood-CNS barriers in zebrafish. [abstract]. In: Proceedings of the AACR Special Conference: Advances in Brain Cancer Research; May 27-30, 2015; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2015;75(23 Suppl):Abstract nr IA19.
Abstract IA24: Understanding and tackling differing vascular niches in glioma
Authors: Bergers Gabriele 
Published: May 2016

Abstract
Glioblastoma multiforme (GBM) is the most belligerent adult brain tumor, which is defined by its aggressive growth due to uncontrolled cellular proliferation, resistance to apoptosis, diffuse infiltration and invasion, propensity for necrosis and hypoxia, and vigorous angiogenesis. Recurrence of these invasive tumors is inevitable despite advanced multimodal therapy with surgery followed by radiation and temozolomide treatment resulting in a dismal median survival of beyond one year. A recent milestone in glioma research has been the revelation that therapies targeting other cell types besides tumor cells; i.e., the cell constituents of the vascular niche can convey substantial improvements in radiographic response, progression-free survival, and quality of life. The changing perspective on the environment as a functional contributor represents a profound shift in the approach to biology and therapeutic decisions in GBM. Here, we describe paracrine signaling pathways of the differing vascular niches that regulate angiogenesis or tumor invasiveness and present therapeutic modalities to target those to more efficiently block diffuse and angiogenic GBM growth.
Citation Format: Gabriele Bergers. Understanding and tackling differing vascular niches in glioma. [abstract]. In: Proceedings of the AACR Special Conference: Advances in Brain Cancer Research; May 27-30, 2015; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2015;75(23 Suppl):Abstract nr IA24.
Abstract B1-12: RNA Architect: High performance tools to exhaustively detect cancer-specific structural events in RNA sequencing data
Authors: Arnold Roland Chrisitian , Rosic Andrej , Hayes Reid JP , Shlien Adam 
Published: May 2016

Abstract
The transcriptomes of cancer cells differ structurally and numerically from normal tissue and between different types of cancer. However, a comprehensive large-scale analysis of structural transcriptomic events has been intractable so far due to computational limitations and the lack of an integrative software tool to detect a complete list of events. We developed an optimized computation pipeline, named RNA Architect, which enables the detection of such events from RNA sequencing data for thousands of cancer samples. RNA Architect detects events by discordant read pair and split-read analysis down to the resolution of individual breakpoints. Beside different kinds of fusion events (as in-frame, out of frame, fusions to intergenic regions), it reports a comprehensive overview of other transcriptional events including specific and novel splice-forms, inversions, cryptic and alternative splice sites, exon skips, exon re-usages, and early polyadenylation sites.
In this poster, we describe the individual steps of the algorithm as well as our latest benchmarking results on a compilation of literature curated gene fusions: the approach shows a high sensitivity and recovers 92% of known fusion events. We also describe the post-processing of the data, which enables us to delineate relevant events from non-cancer specific ones and noise. We especially discuss optimizations implemented to run the software efficiently on a state of the art compute cluster. These optimizations enable the computation of a large amount of samples in a reasonable time frame (~ 8h on a 100 CPU system for the core split-read algorithm and ~3 hours for the discordant pair analysis on a 50 CPU system per sample).
The high performance of the pipeline allows generating a comprehensive catalogue of all transcriptional events from large sets of cancer samples. RNA Architect is also fast enough to be used in time critical clinical settings as a potential diagnostic tool.
Citation Format: Roland Chrisitian Arnold, Andrej Rosic, Reid JP Hayes, Adam Shlien. RNA Architect: High performance tools to exhaustively detect cancer-specific structural events in RNA sequencing data. [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 B1-12.
Abstract B1-27: Optimal combined anticancer therapy that can overcome cell-type specific drug resistance of breast cancer
Authors: Choi Minsoo , Cho Kwang-Hyun 
Published: May 2016

Abstract
Breast cancer is a complex disease associated with various genomic alterations that influence the dynamical change of signaling network. The inherent heterogeneous characteristics of breast cancer cells often result in variable anti-cancer drug resistance. Despite the recent progress of targeted cancer therapy, the underlying mechanism of such variable drug resistance still remains unclear and needs to be investigated. To tackle this problem with respect to a breast cancer signaling network, we have reconstructed differential breast cancer singling networks by integrating various genomic data of breast tumors and analyzed their state transition dynamics through perturbation simulations that mimick various anti-cancer drug treatments. We then categorized heterogeneous breast cancer cell types into three groups: sensitive, moderate, or resistant to drugs. For each group, we identified a distinct core-signaling mechanism underlying the drug resistance on the basis of attractor landscape analysis. Finally, we have identified an optimal drug combination that can maximize cell death regardless of heterogeneous breast cancer cell types. Our study shows that the attractor landscape analysis of a breast cancer signaling network can unravel the hidden mechanism underlying individual variation in drug responses and provide a new therapeutic strategy that can overcome variable drug resistance.
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).
Citation Format: Minsoo Choi, Kwang-Hyun Cho. Optimal combined anticancer therapy that can overcome cell-type specific drug resistance of breast 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 B1-27.
Abstract B1-58: Computational modeling provides new insights into KRAS mutant-specific responses to targeted therapy
Authors: Stites Edward C. 
Published: May 2016

Abstract
Emerging data suggest different activating Ras mutants may have different biological behaviors. The most striking example may be in colon cancer, where activating KRAS mutations generally indicate a lack of benefit to treatment with cetuximab, with the activating KRAS G13D mutation an apparent exception. It is unclear why patients with KRAS G13D would respond to cetuximab; KRAS G13D shares the same general biochemical defects as the other oncogenic KRAS mutants. Here, a previously developed mathematical model of the biochemical reactions that regulate Ras signaling is used to investigate this problem. The purpose of the analysis is to determine whether known mechanisms of Ras signaling allow similar, but different, Ras mutants to respond differently to the same inhibitor, and to determine if the known biochemical properties of the G13D Ras mutant are consistent with increased sensitivity to upstream inhibition. Computational analysis of the mathematical model finds that differential response to upstream inhibition between different Ras mutants is indeed consistent with known mechanisms of Ras biology. Moreover, modeling demonstrates that the subtle biochemical differences between the responsive G13D mutant and the non-responsive G12D and G12V mutants are sufficient to explain the different responses to cetuximab. Simulations further identify differences in wild-type Ras signaling as the key difference between the mutants. The analysis suggests the elevated Km for the Ras GAP reaction on the G13D mutant is the biochemical property that most contributes to differences in wild-type Ras signaling. The identification of a single parameter that influences sensitivity to a targeted therapy is significant in that it suggests an approach to evaluate other, less common, Ras mutations for their anticipated response to cetuximab.
Note: This abstract was not presented at the conference.
Citation Format: Edward C. Stites. Computational modeling provides new insights into KRAS mutant-specific responses to targeted therapy. [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 B1-58.
Abstract B1-60: Predicting drug response by profiling the epigenome: Super-enhancers as biomarkers
Authors: Orlando David A. , McKeown Michael R. , Chen Mei Wei , Collins Cindy , Guenther Matthew G. , Fritz Christian C. 
Published: May 2016

Abstract
Identifying biomarkers that predict clinical responses to anticancer therapies is an important challenge in oncology. A desirable biomarker is a cell-identifying feature that can distinguish between drug responsive and unresponsive cell types. Most of the best studied biomarkers are gene mutations or copy-number changes, where the genomic alteration is the key defining feature. However, not all differences in clinical responses can be attributed to genetic differences. Therefore, it is critical that we identify other cellular features that can predict response. A recently described epigenomic feature, termed super-enhancer (SE), defines key cell identity and disease genes and SEs are effectors of initiating and maintaining cell type-specific gene expression programs. In this work we generate super-enhancer maps across multiple cancer cell lines and connect those maps to response to different drug treatments. We show that super-enhancers can be used as biomarkers to predict response to drugs across multiple cell-line models of cancer. This work suggests that interrogating epigenomic features, and in particular super-enhancers, can be a powerful biomarker discovery platform and can enable rational patient selection and therapeutic strategies.
Citation Format: David A. Orlando, Michael R. McKeown, Mei Wei Chen, Cindy Collins, Matthew G. Guenther, Christian C. Fritz. Predicting drug response by profiling the epigenome: Super-enhancers as biomarkers. [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 B1-60.
Abstract B2-25: DNA methylation-dependent transcription regulatory networks elucidate dynamics of transcription regulatory circuitry in cancers
Authors: Liu Yu , Liu Yang , Xiao Zhengtao , Yang Xuerui 
Published: May 2016

Abstract
Context-dependent DNA methylation plays a critical role in regulating gene transcription, thereby serving as an important epigenetic marker or regulator in many biological activities such as pluripotency, development, and complex diseases such as cancer. However, previously in most of the de novo reconstructions of cancer type-specific transcription regulatory networks (TRN), DNA methylation has rarely been taken into account as a significant factor in regulating transcription factor activity and controlling gene expression. The present study was set to systematically assess the involvement of DNA methylation in transcription regulatory circuitry in cancer. We took advantages of the multi-dimensional profiling data of DNA methylations and gene expressions in tumor samples of different cancers in The Cancer Genome Atlas (TCGA) consortium, and developed an integrative analysis pipeline based on conditional mutual information theory, to quantify the cooperative regulatory effects of CpG site methylations and transcription factors on gene expressions, in a genome-wide scale. Our analysis showed that DNA methylation and transcription factors indeed control gene expressions often in duet. To map the interplay between these two major defining factors of gene expression, DNA Methylation-dependent Transcription Regulatory Network (MeTRN), the first of its kind, was assembled for each of 19 major cancer types in TCGA. MeTRN provides a comprehensive view specifically on DNA methylation-dependent transcription events in each cancer type, and has been largely validated by public ChIP-seq and DNaseI-seq data. Comparison of these networks across cancer types showed that context-specificity of transcriptional circuits can be largely attributed to the context-dependent nature of DNA methylation patterns. Therefore, by assembling the cooperative effects of transcription factors and DNA methylations on gene transcription, MeTRN recapitulates an epigenetic scheme that implements dynamics of transcription regulatory circuitry across cancers via context-dependent DNA methylation marks. Supported by extensive cross-validations and experimental evidence, this epigenetically marked and dynamic transcription regulatory circuitry serves as a new basis for further mechanistic studies of gene expression dysregulations in cancers.
Citation Format: Yu Liu, Yang Liu, Zhengtao Xiao, Xuerui Yang. DNA methylation-dependent transcription regulatory networks elucidate dynamics of transcription regulatory circuitry in cancers. [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 B2-25.
Abstract B2-28: The development of an individualized Boolean network model for targeted anticancer therapy of glioblastoma
Authors: Lee Hwang-Yeol , Cho Kwang-Hyun 
Published: May 2016

Abstract
Understanding the complexity of a cancer signaling network contributed to the discovery of potential targets for therapeutic intervention. Mathematical and computational models have been developed and investigated to predict the molecular dynamics during cancer development and drug treatment. Recent studies analyzing large-scale patient data, however, showed that genetic alterations are dramatically different among patients even for the same type of cancer, and that different therapeutic strategies should be employed for the patients. To resolve this problem using Boolean network modeling and analysis, we have integrated genome-wide data of glioblastoma patients from TCGA database and constructed an individualized network model for each patient. We defined three attractor states that represent different cell fates: proliferation, cell cycle arrest, and apoptosis. We then applied the information of patient-specific genetic alterations to the Boolean model by adjusting the interaction weights of the mutated molecules, and investigated the change of attractors for each patient. Finally, we have investigated the targeted therapy for each patient by performing perturbation analysis of the individualized network model to find the optimal drug combination. Together, our study provides a new insight into the impact of genetic alterations on the drug responses and the patient-specific therapeutic 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 the KUSTAR-KAIST Institute, Korea, under the R&D program supervised by the KAIST.
Citation Format: Hwang-Yeol Lee, Kwang-Hyun Cho. The development of an individualized Boolean network model for targeted anticancer therapy of glioblastoma. [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 B2-28.
Abstract B2-44: Structure-based design of inhibitory peptides for LRP6
Authors: Rismani Elham , Rahimi Hamzeh , Azadmanesh Kayhan , Arab Shahriar , Karimipoor Morteza , Teimoori-Toolabi Ladan 
Published: May 2016

Abstract
Wnt signaling pathway plays a major role in regulation of cell proliferation, migration, tissue homeostasis, tumor progression and cancer. This pathway can be antagonized by DKK proteins, which disrupt the intiationary complex (Frizzled-LRP5/6 complex). Nowadays, use of peptides in order to target tumor cells is attracting much attention. In this study, a computational protocol is presented to structural design of inhibitory peptides aginst Low-density lipoprotein receptor-related protein 6 (LRP6) as receptor. Firstly, natural ligand of LRP6 divided to 12 fragments as peptide derivatives. Then, molecular dynamics simulations used for structural energy minimization by means of Gromacs 4.5.4. The binding affinities of designed peptides were investigated via molecular docking using ClusPro webTool. Finally, stability and binding free energy of peptides were calculated by FoldX software. The results showed that four of designed peptides had the highest affinity to interact with the receptor and can be considered as candidates for inhibition of wnt signaling pathway through LRP5/6 receptor.
Citation Format: Elham Rismani, Hamzeh Rahimi, Kayhan Azadmanesh, Shahriar Arab, Morteza Karimipoor, Ladan Teimoori-Toolabi. Structure-based design of inhibitory peptides for LRP6. [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 B2-44.