bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 49: Constructing pathway based predictors of cancer clinical outcome
Authors: Ng Sam , Vaske Charlie , Benz Steve , Durbin James , Szeto Chris , Heiser Laura , Wang Nicholas , Korkola Jim , Bayani Nora , Spellman Paul , Gray Joe W. , Haussler David , Stuart Joshua 
Published: June 2012

Abstract
Current efforts to understand the mechanism of cancer involve using various whole-genome -omics measurements over large patient cohorts. Since a patient response to treatments is highly variable, the challenge then is to integrate the data in order to infer patient-specific disease mechanisms. Recent advances in the analysis of cancer (TCGA ovarian serous carcinoma and glioblastoma multiforme) has shown that a pathway interpretation of DNA copy number, DNA methylation, mRNA expression, and mutations offers a powerful framework for interpreting complex data. The hope is that a pathway-level interpretation of -omics data can identify pathway signatures to predict differences in clinical outcome, whereas traditional machine learning algorithms do not take advantage of the pathway structure of biological data.
We are developing a pathway prediction method based on PARADIGM to discriminate patient outcome based on pathway signatures. Utilizing conditional random fields (CRFs) allow for formal search for a graphical model that optimizes the prediction of a particular variable of interest (VOI) defined by the given classification task, as opposed to a generative model that optimizes the model to explain the data. The method first merges pathways to build a core network around the VOI. The model then seeks to extend the pathway to include new genes and interactions which improve the model's predictive ability on the training data.
Application of our method to 50 breast cancer cell-lines treated with 80 different compounds revealed general and subtype-specific signatures of response in breast cancer. We compared our CRF-based method against a compendium of standard machine-learning algorithms and found that our CRF outperformed all methods on a majority of drugs tested. We also tested the method on a cancer benchmark consisting of a dozen prediction challenges all involving the prediction of clinical outcomes on large patient cohorts using gene expression and copy number data. Again, the CRF model outperformed a majority of classifiers and performed comparably to the best classifiers on most challenges. We expect our method to generalize to a wide variety of biological systems for which high-throughput genomics and functional genomics are available.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 49. doi:10.1158/1538-7445.AM2011-49
Abstract 490: Cucurbitacin I inhibits tumorigenic ability and enhances radiochemosensitivity in non-small cell lung cancer-derived CD133+ cells
Authors: Chen Kuan-Hsuan , Chou Yueh-Ching , Huang Pin-I , Chen Yi-Wei , Hsu Han-Shui , Chiou Tzeon-Jye , Chiou Shih-Hwa 
Published: June 2012

Abstract
Background: STAT3 signaling is reported to promote tumor malignancy and recurrence in non-small cell lung cancer (NSCLC). The STAT3 pathway has been shown to maintain tumorigenicity and the therapeutic resistance of malignant tumors as well as cancer stem cells (CSC). The aim of this study was to investigate the effect of the strong STAT3 inhibitor, cucurbitacin I, in lung cancer CD133+ cells (NSCLC-CD133+).
Methods: NSCLC-CD133+ and NSCLC-CD133− cells were isolated from 7 NSCLC patients. NSCLC-CD133+ cells treated with or without cucurbitacin I were evaluated for their expression of phosphorylated STAT3 (p-STAT3), tumorigenicity, stemness properties, and resistance to chemotherapeutic drugs and ionizing radiation.
Results: Compared to the parental or NSCLC-CD133− cells, NSCLC-CD133+ cells showed greater tumorigenicity, radioresistance, and high expression of Oct-4, Nanog, and Sox2 at high p-STAT3 levels. Cucurbitacin I treatment at 100 nM effectively abrogated STAT3 activation, tumorigenic capacity, sphere formation ability, radioresistance, and chemoresistance in NSCLC-CD133+ cells. Microarray data suggested that cucurbitacin I inhibited the stemness gene signature of NSCLC-CD133+ cells and facilitated the differentiation of NSCLC-CD133+ cells into NSCLC-CD133- cells. Notably, 150 nM cucurbitacin I effectively blocked STAT3 signaling and downstream survival targets such as Bcl-2 and Bcl-xL expression, and induced apoptosis in NSCLC-CD133+ cells. Finally, xenotransplant experiments revealed that cucurbitacin I plus radiotherapy or chemotherapeutic drugs significantly suppressed tumorigenesis and improved survival in NSCLC-CD133+-transplanted immunocompromised mice.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 490. doi:10.1158/1538-7445.AM2011-490
Abstract 4903: Identification of damage products derived from C3′-deoxy-3′-thymidinyl radical, a proposed intermediate from DNA-LEEs interactions
Authors: Abdallah Buthina , Trabbic Kevin , Bryant-Friedrich Amanda 
Published: June 2012

Abstract
Radiotherapy uses ionizing radiation (IR) to target deoxyribonucleic acids (DNA) among other biological molecules for cancer treatment. As a result genotoxic, mutagenic and recombinogenic lesions can be formed. Oxidative damage to DNA through IR is partially produced by the secondary particles created along the ionization track. Secondary low-energy electrons (LEEs, 1-20 eV) are the most abundant secondary species produced by primary IR. Although it has been established that LEEs can produce substantial yields of DNA single and double strand breaks, the mechanism of their formation has not been established. Elucidation of the mechanism of damage by LEEs is essential for the development of global models of cellular radiolysis and more efficient methods of cancer radiotherapy. It was proposed that LEE can add to DNA constituents resulting in the formation of transient molecular anion intermediates that eventually lead to bond dissociation. It is believed that carbon-centered radicals on the sugar moiety are a major reactive intermediate formed in this process. One of these is the C3′-deoxy-3′-thymidinyl radical. The synthesis of C3′-deoxy-3′-(selenophenyl)thymidine as a precursor of this radical has been completed. Initial photolysis experiments for photochemical generation of the radical of interest were performed and the formation of the radical intermediate of interest has been established. Identification of damage products derived from this intermediate in nucleosides is currently in progress. The modified nucleoside was incorporated into oligonucleotides using reverse DNA synthesis. Photochemical generation of the C3′-deoxy-3′-thymidinyl radical within DNA was achieved using ultraviolet light (≥ 320nm). Identification of the damage products obtained from the generation of the C3’ radical is currently in progress. Oligonucleotides sequences with different base context were synthesized in order to investigate the effect of sequence on the distribution of damage products. The effect of the concentration of GSH on the damage product distribution is also under investigation. Through these efforts a link will be established between the products of LEEs and the deleterious effects of radiation in cancer radiotherapy.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4903. doi:10.1158/1538-7445.AM2011-4903
Abstract 4905: Patient-specific pathway analysis using PARADIGM identifies key activities in multiple cancers
Authors: Benz Stephen C. , Vaske Charles , Ng Sam , Sanborn John Zachary , Zhu Jing , Haussler David , Stuart Joshua 
Published: June 2012

Abstract
Cancer is a disease of genomic perturbations that lead to dysregulation of multiple pathways within the cellular system. While common pathways are believed to be shared within specific cancer types, the mechanisms of why particular patients respond differently to treatment is not fully understood. Current -omics studies such as The Cancer Genome Atlas (TCGA) and Stand Up To Cancer (SU2C) have attempted to address this issue by using large-scale whole-genome measurements of mRNA expression, DNA copy number, and epigenetic features. Typical analysis of these measurements relies on integrating data from multiple samples to distinguish signal from noise. However, few analytical methods allow for sample-specific differences to identify features and pathways that are significant for prognosis and clinical treatment classifications.
We developed a pathway inference method called PARADIGM (PAthway Recognition Algorithm using Data Integration on Genomic Models) (Bioinformatics (2010) vol. 26 (12) pp. i237) to identify patient- and sample-specific pathway activities. Previously we have shown that PARADIGM is capable of stratifying patients into clinically relevant subgroups in both TCGA ovarian serous carcinoma and glioblastoma multiforme using gene expression and copy number alteration values. We have since enhanced PARADIGM in multiple ways allowing more in-depth analysis and discovery across multiple cancer types.
We have expanded the underlying pathway database previously consisting of NCI's Pathway Interaction Database to include pathways from BioCarta and Reactome, increasing the number of pathways by ten-fold and increasing the total number of features to approximately 22,000. We have refined the central dogma framework underlying all features in each pathway to support a more accurate regulatory model, allowing us to efficiently learn the strength of each interaction. Finally, PARADIGM now supports additional input data sources in the form of methylation and mutational interventions. By adding gene-level mutational information on the new regulatory model we show it is possible to distinguish between gain-of-function and loss-of-function mutations in each patient. In addition, through these enhancements we show increased ability to identify key pathway activities that effectively stratify patient cohorts.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4905. doi:10.1158/1538-7445.AM2011-4905
Abstract 4913: Systematic analysis of LY30-induced sensitization of cancer cells to TRAIL
Authors: Shi Yuan , Tucker-Kellogg Lisa , White Jakob , Pervaiz Shazib 
Published: June 2012

Abstract
TRAIL (Tumor necrosis factor-Related Apoptosis Inducing Ligand) can selectively induce apoptosis in cancer cells expressing the TRAIL receptors. However, the problem of TRAIL resistance poses a significant therapeutic challenge. We recently demonstrated that the small molecule compound, LY30, sensitizes human cancer cells to TRAIL induced apoptosis via mechanisms that involve upregulation and/or oligomerization of the death receptors. However, the underlying biology of this phenomenon remains less well defined. Here we describe a mathematical modeling approach to tease out the molecular circuitry in the synergistic response of human cervical cancer cells (HeLa) to LY30 and TRAIL mediated apoptosis. We made use of ODE (Ordinary Differential Equation) to build two distinct pathway models for Hela cells to represent TRAIL-induced apoptosis (TRAIL Model) or LY30-induced cellular modification (LY30 Model) using the existing models and previous data sets. Moreover, in accordance with the experimental settings we integrated the LY30 Model and TRAIL Model as the CTM (Combination Treatment Model). The CTM showed that the level of simulated death correlated well with experimental observations, including the synergistic level of death from the combination treatment. However, simulation and experimental data exhibited numerous mismatches for some molecules in the pathway, such as differences in the slope and onset of the synergistic activation of caspase-8 and caspase-3. Such mismatch implies some flaw in the model and thus led us to additional measurement on the early dynamics of the species involved upstream of the apoptotic pathway. In specific, assessment of cFLIP, an inhibitor of caspase-8 activation, showed slight up-regulation very early after LY30 treatment (1 hour), followed by a significant down-regulation over prolonged exposure. By including such biological phenomenon into the CTM, we are able to improve the agreement between data and simulation while maintaining the consistency in synergistic cell death. Further model analysis revealed the fluctuation of FLIP is regulated by LY30 through a complicated ratio change between two main ROS (Reactive Oxygen Species), superoxide and hydrogen peroxide. These data provide a novel means for assessment of mechanisms for small molecule-induced sensitization of human cancer cells to TRAIL mediated apoptosis, with broader clinical implications.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4913. doi:10.1158/1538-7445.AM2011-4913
Abstract 4916: Systems biology analysis of subpopulation-based heterogeneity for cellular radiosensitivity modeling
Authors: Kam Yoonseok , Rejniak Katarzyna A. , Zhao Haiyan , Torres-Roca Javier F. 
Published: June 2012

Abstract
Intrinsic cellular radiosensitivity is a key parameter used in radiotherapy modeling. Our previous studies showed that a cellular model based on the in vitro clonogenic assay measuring survival fraction at 2Gy (SF2) accurately predicts tumor response in patients treated with radiotherapy. However, SF2 as determined from a heterogeneous cell population represents its averaged sensitivity, whilst we have shown, using live cell imaging, that cultured tumor cell populations include colonies of varying sizes, cell numbers and growth rates. In order to link this subpopulation-based heterogeneity to the parental cell line radiosensitivity, and to build an integrative predicting model, we designed a novel systems biology approach that combines SF2 clonogenic assay experimentation, high-content screening and image analysis together with a quantitative bio-mathematical study. We integrated a 96-well plate-based SF2 assay with a semi-automated image analysis using CellProfilerTM, and measured morphological parameters including SF2, colony tightness index (colony density), and colony growth rate. We have also developed new algorithms to complement those implemented in CellProfilerTM to identify some highly dispersed colonies. As a preliminary trial, we analyzed five non-small cell lung cancer (NSCLC) cell lines characterized by different levels of radiosensitivity in vitro. We observed several common phenotypic shifts after irradiation, such as reduction in growth rate and cell density. However, the response patterns for each subpopulation was highly heterogenous across cell lines. Interestingly, the variability in morphological response was better correlated to radiosensitivity than the average colony density. In order to further assess subpopulation heterogeneity, we isolated 15 random sub-clones from H460 cells and found significant intrinsic SF2 heterogeneity. In addition to morphology, we examined the intrinsic variability in the expression level of several molecular biomarkers including STAT1, a molecular maker in our prediction model, and CD44, a cancer stem cell marker proposed to be related with radioresistance. Both markers showed significant variability, which was negatively correlated with the colony density. In conclusion, we propose a novel systems-based approach that integrates high content phenotypic information at a single cell level to study tumor-intrinsic heterogeneity and its contribution to clinically-relevant radioresistance.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4916. doi:10.1158/1538-7445.AM2011-4916
Abstract 4918: Predicting the effect of drug combination schedules on xenograft growth using the Virtual Tumor
Authors: Orrell David , Fernandez Eric , Cronier Damien , Gelbert Lawrence M. , Fell David A. , De Alwis Dinesh , Chassagnole Christophe 
Published: June 2012

Abstract
Since the early 1960s, drug combination therapy has been used to treat cancer, because of the limited number of malignancies that could respond to single-agent chemotherapy. Combination chemotherapy regimens have been designed on the basis of mechanism of action of the drugs, tumor cell specificity, balance between effectiveness and toxicity, and synergy between drugs. But when multiple drugs, combination schedules, sequences and doses are considered, the number of possibilities increases combinatorically, and can not be realistically tested either clinically or in animal models.
We have developed a “Virtual Tumor” model to aid with the design of optimal drug schedules. The model combines disparate data, at the cell and tumor level, into a consistent picture, and leverages them to make testable predictions about tumor response. Thousands of simulations can be performed if necessary to find the best treatment regime.
We present here a validation study of our Virtual Tumor. We predicted xenograft growth of two anti-cancer drug combinations using experimental data collected from single drug exposure uniquely. We accurately predicted the xenograft course for two different regimens – one simultaneous and one sequential – of the two drugs, which were compared with experimental results in a single-blind test. We show how a computational approach helps explain how multiple drug exposure and correct sequence leads to synergy, and how it can be used to subsequently design optimal schedule and combination treatments.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4918. doi:10.1158/1538-7445.AM2011-4918
Abstract 4919: Multiplexed EGFR signaling pathway analysis in FFPE tissue using quantitative mass spectrometry
Authors: Hembrough Todd , Thyparambil Sheeno , Krizman David , Darfler Marlene , Wei Yuhong , Taylor Paul , Tong Jiefei , Pham Nhu-An , Jasani Bharat , Adams Richard , Tsao Ming-Sound , Moran Michael , Burrows Jon 
Published: June 2012

Abstract
The epidermal growth factor receptor (EGFR) is a drug target for both small molecule and antibody therapeutics and has been approved in non small-cell lung carcinoma (NSCLC) and colorectal carcinoma (CRC) among other indications. These drugs block receptor signaling though blockade of the tyrosine kinase domain, or through inhibition of ligand binding. Current genomic tests measure receptor amplification, RNA levels, the mutation status of receptor or pathway molecules (EGFR or kRAS mutations) but no current assay can directly assess the activation state of the EGFR or its downstream signaling pathway components. Indeed, the EGFR mutation positive NSCLC tumors (thought to be constitutively active) show a high response rate to TKI therapy, but the many non responders (50% or more) demonstrate the limitation of genomic analysis.
Since activation of EGFR is necessary for the response to these targeted agents, it is critical to measure what levels of receptor activation and downstream signaling determines tumor responsiveness to EGFR targeted therapies in these patients. For this reason, we have developed a panel of new diagnostic assays which measure the activation of the EGFR and key downstream signaling proteins through quantitation of the phosphorylation state of these proteins. These assays are based on the Liquid Tissue®-SRM technology platform. This approach enables relative and absolute quantification of proteins and their phosphorylation status directly in formalin fixed paraffin embedded (FFPE) tissue.
We preclinically validated the multiplexed Liquid Tissue® phospho-SRM assay on formalin fixed EGF stimulated A431 cells. We followed up these in vitro studies with phospho-SRM analysis of FFPE NSCLC xenograft explants where extensive independent histopathologic and molecular characterization had been performed, allowing us to benchmark our phospho-SRM analysis with standard diagnostic analyses.
We have now extended these quantitation studies by measuring the expression of EGFR and phospho-EGFR in FFPE tissues obtained from relevant human clinical trial cohorts – Gefitinib treated NSCLC and Cetuximab treated CRC. It is hoped that we will be able to correlate EGFR expression, activation and signaling in these tumors with responsiveness to EGFR targeted therapy, and to validate this assay for use as a companion diagnostic to guide therapy in both NSCLC and CRC.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4919. doi:10.1158/1538-7445.AM2011-4919
Abstract 4935: microRNA profiling of esophageal squamous cell carcinoma patients from formalin-fixed paraffin-embedded (FFPE) specimens
Authors: Shimada Yutaka , Okumura Tomoyuki , Sekine Shin-ichi , Osawa Soshi , Fukuoka Junya , Tsukada Kazuhiro 
Published: June 2012

Abstract
Introduction: microRNA (miRNA) are small non-coding RNAs which target specific mRNA. Recent progression of RNA extraction technology from formalin-fixed paraffin-embedded (FFPE) tissues enables us to access miRNA profiling using world wide stored specimens. Here, we present miRNA profiling of esophageal squamous cell carcinoma (ESCC) patients from FFPE specimens.
Method: First, we examined and contrasted the levels of cross-linking caused by formalin and degradation of mRNA and miRNA from daily to yearly time frames. Then, we established the criteria of evaluable RNA from FFPE specimens. The miRNA profiling was obtained from 44 ESCC patients who received operations from 1991 to 2009 using 3D-Gene human miRNA chip (Toray) which contains 904 has-miRNAs. The approval number from Toyama University institutional review board was #19-67.
Results: Out of 44 paired samples, 24 pairs (54.5%) were determined evaluable with our criteria. We found that 39 miRNAs (including miR-21, and miR-25) were significantly up-regulated and that 24 miRNAs (including miR-145) were down-regulated in ESCC in comparison to normal esophageal tissue. Of these, miR-15b, miR-200c and miR761 were identified as prognostic factors of ESCC patients. Furthermore, we found 22 miRNAs that have association with the hematogenic recurrence and 9 miRNAs (including miR-103) have association with lymph node metastasis.
Conclusion: We have discovered miRNAs that are associated with prognosis, hematogenic recurrence and lymph node metastasis in ESCC patients. miRNA profiling using FFPE specimens is a useful and promising method of evaluation of world wide stored specimens that accompany many valuable clinical data.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4935. doi:10.1158/1538-7445.AM2011-4935