bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract IA17: Developing improved methods to measure human DNA repair capacity
Authors: Samson Leona D. 
Published: August 2014

Abstract
The Samson lab has been developing potentially high-throughput methods for measuring DNA repair capacity (DRC) in human cells. These approaches are modeled on the pioneering work of the late Professor Lawrence Grossman and rely on the transfection of plasmids containing specific types of DNA damage and then monitoring the repair of that damage by the expression of a reporter gene that is poorly expressed in the absence of repair. One of our goals is to simultaneously measure DRC for a variety of DNA repair pathways using a multiplexed approach and a collection of fluorescent proteins of different color. So far we have developed methods for measuring relative levels of nucleotide excision repair, homologous recombination, DNA mismatch repair, and MGMT mediated repair. We are currently extending this approach to encompass other DNA repair pathways. Measuring DNA repair capacity in various human cell types may help predict inter-individual differences in the ability of people to respond upon exposure to environmental agents and thus predict their cancer susceptibility. Moreover, knowing the DNA repair capacity of tumor cells for different types of DNA damage could help in choosing more effective chemotherapy regimens.
Citation Format: Leona D. Samson. Developing improved methods to measure human DNA repair capacity [abstract]. In: Proceedings of the AACR Special Conference on Chemical Systems Biology: Assembling and Interrogating Computational Models of the Cancer Cell by Chemical Perturbations; 2012 Jun 27-30; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2012;72(13 Suppl):Abstract nr IA17.
Abstract IA18: A genomics approach to discover small-molecule perturbagens of cancer cells
Authors: Stegmaier Kimberly 
Published: August 2014

Abstract
Aberrantly activated transcription factors and/or transcriptional programs play a critical role in the development of most cancers. In some cancers, the oncogenic transcription factors have been well characterized and result from either the ectopic activity of native transcription factors or from a chromosomal translocations driving overexpression or producing a mutant transcription factor. In other cases, the direct causative event of the aberrant transcriptional signature is unknown. Given their central role in cancer development, directly targeting transcription factors should be a propitious anti-tumor strategy. However, with the exception of the nuclear hormone receptor family of transcription factors, transcription factor oncoproteins have been largely refractory to conventional drug discovery approaches. While there have been advances in our ability to assess gene expression changes on a global scale, almost all existing approaches (e.g., microarrays, next generation sequencing) cannot be applied to large-scale screening endeavors due to cost and throughput limitations. Alternatively, the use of reporter systems and phenotypic endpoints (e.g., viability) has been challenged by false-positives. Recognizing these shortcomings, we developed an assay approach that allows interrogation of complex gene expression signatures in high-throughput format. Gene Expression-based High-throughput Screening (GE-HTS) allows for the assessment of expression of hundreds of endogenous genes in 384-well format, overcoming some of the challenges that are inherent in conventional target- and phenotype-based screening. We have used GE-HTS in small-molecule library screening endeavors to identify compounds that induce differentiation signatures in different cancer types in the absence of a known target, as well as alter the transcriptional activity of aberrant, oncogenic transcription factors. These compounds have served as leads for clinical trial development as well as tool compounds to further dissect biological mechanisms of disease pathogenesis.
Citation Format: Kimberly Stegmaier. A genomics approach to discover small-molecule perturbagens of cancer cells [abstract]. In: Proceedings of the AACR Special Conference on Chemical Systems Biology: Assembling and Interrogating Computational Models of the Cancer Cell by Chemical Perturbations; 2012 Jun 27-30; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2012;72(13 Suppl):Abstract nr IA18.
Abstract IA2: Accelerating natural product discovery with multivariate image-based screening
Authors: Lokey Scott M. 
Published: August 2014

Abstract
Multiparameter phenotypic profiling has emerged as a powerful tool for characterizing small molecules and their effects on complex biological systems. In particular, the union of high-throughput screening technologies with automated microscopy and image processing has enabled the development of multiparameter image-based profiling techniques (also called “cytological profiling”), in which cells are imaged in multiwell plate format using automated fluorescence microscopy and the resulting images are quantified in terms of various descriptors, or “features.” Rather than focusing on a specific phenotype or biological endpoint, CP strategies employ multiple cytological probes that are selected to cover a wide range of biological pathways. Although CP has been used successfully to classify diverse bioactive compounds, its application in the area of natural products (NP) discovery has been limited. Here we describe the use of cytological profiling to discover natural products with novel mechanisms of action (MOAs). The goal of this project is to invert the NP discovery process from the traditional approach, where compound identification and function are the final steps in the discovery process, to a new paradigm where identities and functions of active constituents are determined directly from primary screening data and lead compounds are selected based on chemical and biological novelty prior to isolation and full structural characterization. Futhermore, the integration of CP fingerprints with metabolomics data allows us to rapidly associate compounds with phenotypes and prioritize unique chemistries for follow up.
Citation Format: Scott M. Lokey. Accelerating natural product discovery with multivariate image-based screening [abstract]. In: Proceedings of the AACR Special Conference on Chemical Systems Biology: Assembling and Interrogating Computational Models of the Cancer Cell by Chemical Perturbations; 2012 Jun 27-30; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2012;72(13 Suppl):Abstract nr IA2.
Abstract IA4: Designing biological systems to report on disease
Authors: Silver Pamela A. 
Published: August 2014

Abstract
Biology presents us with an array of design principles. From studies of both simple and more complex systems, we understand some of the fundamentals of how nature works and what goes wrong in the disease state. We are interested in using the foundations of biology to engineer cells in a logical and predictable way to perform certain functions with relevance to therapeutics and diagnostics. By necessity, the predictable engineering of biology requires knowledge of quantitative behavior of individual cells and communities and the ability to construct reliable models. By building and analyzing synthetic systems, we learn more about the fundamentals of biological design as well as engineer useful living devices with myriad applications. For example, we are interested in building cells that can perform specific tasks, such as remembering past events and thus acting as a biological computer. In particular, we have built genetic circuits whereby cells can ‘remember’ stimuli for many generations including DNA damage and hypoxia. By characterizing these ‘memory’ cells, we can identify key genes that may act as therapeutic targets and explanations for persistence of a subset of cells in tumors.
Citation Format: Pamela A. Silver. Designing biological systems to report on disease [abstract]. In: Proceedings of the AACR Special Conference on Chemical Systems Biology: Assembling and Interrogating Computational Models of the Cancer Cell by Chemical Perturbations; 2012 Jun 27-30; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2012;72(13 Suppl):Abstract nr IA4.
Abstract IA7: Functional genomics to identify cancer targets and resistance
Authors: Hahn William C. 
Published: August 2014

Abstract
Recent advances in genomics now make it possible to consider enumerating all of the genetic lesions in specific cancers. While these approaches will yield critical information regarding the identify, number, and types of alterations found in human tumors, a complementary approach to decipher the molecular basis of malignant transformation depends upon the application of genome scale tools to annotate the function of genes involved in cancer initiation and progression. Over the past several years, we have developed genome scale RNAi libraries and open reading frame expression libraries that permit a systematic evaluation of genes involved in cancer initiation and maintenance. Using these libraries, we have now performed screens in a panel of human cancer cell lines to systematically identify cancer vulnerabilities. By combining these functional approaches with information derived from mapping the structural abnormalities present in cancer genomes, we have identified several new oncogenes that contribute to cancer development. In pilot studies, we have identified several genes that act as synthetic lethal partners to known oncogenes or mediate resistance to targeted therapies. In recent work, we have expanded the scale of these experiments to genome scale in more than 100 cell lines, which has permitted greatly expanded and deeper investigation into genes that program the malignant state. Together, these studies suggest that combining forward and reverse genetic approaches with information derived from the cancer genome characterization projects will yield a comprehensive list of cancer vulnerabilities and establish a general approach for the rational identification of oncogenic and co-dependent pathways in cancer.
Citation Format: William C. Hahn. Functional genomics to identify cancer targets and resistance [abstract]. In: Proceedings of the AACR Special Conference on Chemical Systems Biology: Assembling and Interrogating Computational Models of the Cancer Cell by Chemical Perturbations; 2012 Jun 27-30; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2012;72(13 Suppl):Abstract nr IA7.
Abstract IA8: Genetic approaches to cancer
Authors: Solimini Nicole L. , Bredemeyer Andrea L. , Li Mamie Z. , Beroukhim Rameen , Meyerson Matthew , Elledge Stephen J. , Pavlova Natalya N. , Xu Qikai , Mermel Craig H. , Liang Anthony C. , Schlabach Michael R. , Luo Ji , Burrows Anna E. , Anselmo Anthony N. 
Published: August 2014

Abstract
Breast cancer is a collection of diseases with distinct clinical behaviors and underlying genetic causes. We have searched genetically for genes that have cancer relevant phenotypes including genes that alter cellular proliferation, cellular transformation, cell survival, cellular senescence, and genes that are essential for the proliferation of cancer cells. We have approached this by the generation of libraries of shRNAs for loss of function experiments and libraries of ORFs for gain of function experiments. We will discuss these technologies and how they can be applied to the functional dissection of genes important for breast cancer.
Citation Format: Nicole L. Solimini, Andrea L. Bredemeyer, Mamie Z. Li, Rameen Beroukhim, Matthew Meyerson, Stephen J. Elledge, Natalya N. Pavlova, Qikai Xu, Craig H. Mermel, Anthony C. Liang, Michael R. Schlabach, Ji Luo, Anna E. Burrows, Anthony N. Anselmo. Genetic approaches to cancer [abstract]. In: Proceedings of the AACR Special Conference on Chemical Systems Biology: Assembling and Interrogating Computational Models of the Cancer Cell by Chemical Perturbations; 2012 Jun 27-30; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2012;72(13 Suppl):Abstract nr IA8.
Abstract PR2: A systems biology approach to elucidating the consequences of complex ternary interactions of heparin, FGF ligands, and FGF receptor on downstream signaling in NSCLC cells
Authors: Chai Diana H. , Kim Jaeyeon , Vollmer Jannik , Radivojevic Andrijana , Kanodia Jitendra , Muda Marco , Schoeberl Birgit 
Published: August 2014

Abstract
Autocrine/paracrine FGF signaling has been implicated in non-small cell lung cancer (NSCLC) development and progression. In multiple NSCLC cell lines, a switch from FGFR2-IIIb expressed in lung epithelial cells to FGFR1-IIIc and FGFR2-IIIc, results in autocrine response to FGF2 and FGF9 ligands also expressed by these cell lines. FGF2 and FGFR1 expression have also been detected in patient samples and shown to correlate with short survival times. The FGF signaling complex involves a ternary interaction of FGF ligand, receptor, and heparin sulfate glycosaminoglycans (HSGAGs). HSGAGs bind directly to both ligand and receptor, and can serve both to stabilize the receptor-ligand complex and to sequester autocrine/paracrine ligands, increasing local concentrations. A systems approach combining experiment and modeling was used to understand the complex interplay of these components and their implications on cell signaling and behavior.
We focused on the signaling behavior of NSCLC cell line NCI-H1703, which predominantly expressed FGFR1. We interrogated pathway dynamics with both FGF2 and FGF9 ligands and found varying Erk1/2 activation kinetics. Using a model encompassing the ternary complex formation in the cellular membrane, ligand-mediated receptor dimerization and phosphorylation, and subsequent kinase cascade to activate Erk1/2, we were able to identify potential mechanisms driving these differences. Additional perturbations of extracellular interactions and intracellular signaling were able to support these hypotheses. These results highlight the role of the extracellular interactions between ligand, receptor, and HSGAGs in controlling downstream behavior.
This proffered talk is also presented as Poster A5.
Citation Format: Diana H. Chai, Jaeyeon Kim, Jannik Vollmer, Andrijana Radivojevic, Jitendra Kanodia, Marco Muda, Birgit Schoeberl. A systems biology approach to elucidating the consequences of complex ternary interactions of heparin, FGF ligands, and FGF receptor on downstream signaling in NSCLC cells [abstract]. In: Proceedings of the AACR Special Conference on Chemical Systems Biology: Assembling and Interrogating Computational Models of the Cancer Cell by Chemical Perturbations; 2012 Jun 27-30; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2012;72(13 Suppl):Abstract nr PR2.
Abstract PR5: The receptor tyrosine kinase layer of breast cancer cell lines is predictive of the response to therapeutic drugs
Authors: Niepel Mario , Hafner Marc , Pace Emily , Schoeberl Birgit , Sorger Peter K. 
Published: August 2014

Abstract
Currently, clinicians use the levels of the hormone receptors for estrogen (ER) and progesterone (PR) and amplifications of the ErbB2 receptor tyrosine kinase (RTK) to distinguish three groups of breast cancers and guide the choice of therapeutic treatments. In this work we show that a limited number of measurements characterizing breast cancer cell lines at the level of RTKs can be used to predict cell line sensitivity to therapeutic inhibitors.
We chose a panel of approximately 40 breast cancer cell lines, treated them with 22 different growth factors and cytokines at a saturating and subsaturating dose for 10, 30, and 90 min and measured the phosphorylation of key kinases downstream of the receptors. In addition, we measured the expression and phosphorylation levels of approximately 20 RTKs under serum starvation conditions. We used the measurements to predict the GI50 values of the cell lines in response to over 70 therapeutic inhibitors by partial least square regression. We found that our measurements are sufficient to predict the response to about half of the drugs studied. The majority of drugs we are able to predict target RTKs directly or kinases immediately downstream of the receptors. In some cases we are able to predict the response to drugs that target processes seemingly unrelated to our measurements. We are currently exploring the data to uncover what drives sensitivity and resistance to drugs, gain mechanistic insight in the action of cancer therapeutics, and find predictors of drug response that may be useful in a clinical setting.
Our results suggest that a carefully selected set of measurements might be sufficient to classify responsiveness of breast cancers to a number of cancer drugs currently used in the clinic. This is particularly relevant in the case of cancers like triple-negative breast cancers for which choosing effective treatment has proven difficult.
This proffered talk is also presented as Poster A17.This proffered talk is also presented as Poster A17.
Citation Format: Mario Niepel, Marc Hafner, Emily Pace, Birgit Schoeberl, Peter K. Sorger. The receptor tyrosine kinase layer of breast cancer cell lines is predictive of the response to therapeutic drugs [abstract]. In: Proceedings of the AACR Special Conference on Chemical Systems Biology: Assembling and Interrogating Computational Models of the Cancer Cell by Chemical Perturbations; 2012 Jun 27-30; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2012;72(13 Suppl):Abstract nr PR5.
Abstract PR6: A cooperative molecular response to combined mTOR/HDAC inhibition revealed by transcriptional co-expression analysis
Authors: Simmons John Kyle , Mock Beverly A. , Michalowski Aleksandra , Patel Jyoti , Wei Bih-Rong , Simpson R. Mark , Kuehl W. Michael , Zhang Shuling , Zhang Ke , Landgren Ola 
Published: August 2014

Abstract
Identification of biologically relevant molecular targets responding cooperatively to drug combinations is challenging, yet potentially useful in predicting clinical outcomes. We found that combining sirolimus (rapamycin), an inhibitor of mechanistic target of rapamycin (mTOR), with entinostat (MS-275), a selective class I histone deacytlase (HDAC) inhibitor, was synergistic in controlling 90% of tested cell lines derived from B cell malignancies, including multiple myeloma, mantle cell lymphoma, and murine plasmacytoma, in vitro, and effective in limiting xenograft growth in vivo. To understand the transcriptional underpinnings of the drug synergy, weighted gene co-expression analysis (WGCNA) was adapted to analyze gene expression profile data from cells treated with sirolimus and entinostat individually and in combination. WGCNA delineated the contribution of each inhibitor to the overall gene expression change of the combination by considering not only measures of fold-change and significance testing, but also the degree of gene expression interconnectedness. A co-expression network consisting of five gene modules was defined, where each module represents a unique transcriptional effect of the drug combination. In the next phase of analysis, each gene module was individually tested for functional and clinical enrichment. The module containing genes cooperatively affected by both compounds was highly enriched (p&lt;0.001) for genes involved in cell cycle, immune recognition, and DNA damage/repair, which we have validated. Gene set enrichment analysis of each drug-induced gene expression module demonstrated that genes of all five modules were significantly enriched when healthy donors were compared to MM patients. Finally, interrogation of the cooperative drug signature in patient GEP datasets with survival annotation found it predictive of increased survival (p&lt;0.01), thus linking the drug combination-induced transcriptional changes to predictions for enhanced survival. Thus, systems-level genomic approaches identified markers and a gene signature associated with drug combination activity, disease specificity, and clinical potential.
This proffered talk is also presented as Poster A16.
Citation Format: John Kyle Simmons, Beverly A. Mock, Aleksandra Michalowski, Jyoti Patel, Bih-Rong Wei, R. Mark Simpson, W. Michael Kuehl, Shuling Zhang, Ke Zhang, Ola Landgren. Sorger. A cooperative molecular response to combined mTOR/HDAC inhibition revealed by transcriptional co-expression analysis [abstract]. In: Proceedings of the AACR Special Conference on Chemical Systems Biology: Assembling and Interrogating Computational Models of the Cancer Cell by Chemical Perturbations; 2012 Jun 27-30; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2012;72(13 Suppl):Abstract nr PR6.
Abstract PR7: Visualizing the dynamic and heterogeneous responses of single cells to inhibitors of the growth factor signaling network
Authors: Albeck John , Hung Yin P. , Leya Gregory , Yellen Gary , Mills Gordon , Brugge Joan 
Published: August 2014

Abstract
The growth factor signaling network, including the Akt, ERK, mTOR and other pathways, is deregulated in many diseases and is a prominent target for therapeutic inhibitors. The effectiveness of these inhibitors is limited by the existence of homeostatic feedback loops that act to restore signaling and attenuate the effects of inhibition. To obtain the single-cell data necessary to fully characterize these rapid and heterogeneous responses, we developed a library of mammary epithelial cells stably expressing over 90 fluorescent protein-based reporters. This library comprises reporters for: cellular processes including cell death, cell cycle, protein translation, and metabolism; kinases including ERK, Akt and AMPK; and microRNAs involved in the regulation of cellular differentiation and stress response. Using high-throughput live-cell microscopy and computational image analysis, we monitored the responses of these pathways to inhibitors of EGFR, ErbB2, PI3K, mTOR, MEK, Raf, and Bcl-2. This analysis revealed unexpectedly dynamic fluctuations in key pathways including ERK, Akt, and glycolysis. In one example, inhibition of EGFR transformed constitutive ERK activity into intermittent bursts of activity ranging in duration from 20-90 minutes with high cell-to-cell variability. Characterization of ERK activity pulses revealed that their frequency depended on the level of EGF receptor activity and that their duration was essential for controlling the commitment to cell proliferation. In sharp contrast, inhibition of MEK altered the amplitude, rather than the frequency, of ERK activity. Using these live-cell measurements in combination with high content immunofluorescence, we constructed a computational model of feedback loops controlling ERK activity pulses. Quantitative mapping of ERK output to proliferation rate revealed a distinct and exquisitely sensitive threshold of ERK output required for proliferation. Potential strategies for reducing ERK output below this threshold and effectively blocking proliferation include dual inhibition of upstream regulators or intermittent high-dose inhibition. Single-cell quantitative analysis of inhibitor responses is now being extended to other pathways to identify signaling dynamics and information transfer properties that enable effective therapeutic targeting of the network.
This proffered talk is also presented as Poster A19.
Citation Format: John Albeck, Yin P. Hung, Gregory Leya, Gary Yellen, Gordon Mills, Joan Brugge. Visualizing the dynamic and heterogeneous responses of single cells to inhibitors of the growth factor signaling network [abstract]. In: Proceedings of the AACR Special Conference on Chemical Systems Biology: Assembling and Interrogating Computational Models of the Cancer Cell by Chemical Perturbations; 2012 Jun 27-30; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2012;72(13 Suppl):Abstract nr PR7.