bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 4830: The correlation between CXCR7 expression and EGFR mutation in non-small cell lung cancer
Authors: Han Yiping 
Published: August 2015

Abstract
Objective: To explore the correlation between CXCR7 expression and EGFR mutation in NSCLC.
Materials and Methods: Forty-nine paired cases of NSCLC carcinoma and para-carcinoma tissues were analyzed for the expression of CXCR7 mRNA by RT-PCR and CXCR7 protein by IHC. EGFR mutations were detected in carcinoma tissues by ARMS. T-test, one-way ANOVA and correlation analysis were used to analyze the data with SPSS software version 18.0.
Results: In NSCLC, CXCR7 protein expression in carcinoma tissues was higher than in para-carcinoma tissues (14450±2049 vs 5206±1066, p&lt;0.0001), while CXCR7 mRNA was not. In NSCLC, CXCR7 mRNA and CXCR7 protein expression levels were both related to the TNM stage (p = 0.007, p = 0.008) and metastatic status (p&lt;0.0001; p&lt;0.0001), but not to gender, age, smoking index and pathology. EGFR and CXCR7 protein were positively correlated (r = 0.289,p = 0.044) Conclusion: Like EGFR, CXCR7 can be used as a marker to predict the prognosis and guide clinical therapy in NSCLC.
Keywords: CXCR7 RDC1 EGFR NSCLC
Note: This abstract was not presented at the meeting.
Citation Format: Yiping Han. The correlation between CXCR7 expression and EGFR mutation in non-small cell lung cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4830. doi:10.1158/1538-7445.AM2015-4830
Abstract 4845: Microarray analysis of melanoma autologous tumor cell lines used as the source of tumor associated antigens in patient-specific dendritic cell immunotherapy phase II trial in patients with metastatic melanoma
Authors: Cornforth Andrew N. , Fogel Gary , Carbonell Denysha , Dillman Robert O. 
Published: August 2015

Abstract
Melanoma cells that are proliferating and self-renewing in short-term cell culture, have the phenotypic and functional characteristics of tumor stem cells, and they express unique patient-specific neoantigens, as well as numerous common melanoma associated antigens. Patient-specific therapeutic vaccines, produced by Incubating autologous dendritic cells with autologous tumor cells from such cell lines (DC-TC), have yielded promising results in phase II trials in metastatic melanoma. In this study we examined the expression of genes on the tumor cell lines which had served as the sources of tumor associated antigens loaded onto autologous dendritic cells as part of a phase II trial in which there was an observed 5-year survival rate of 50%.[Dillman 2009] There was sufficient melanoma tumor cells to analyze for 50 of the 54 patients enrolled in the phase II trial. Microarray analysis was performed by Response Genetics, Inc. (Los Angeles) for 54,000 genes. The Excel data file was entered into an Affymetrix data processing algorithm using the R programming language. Gene ranking was performed using prediction analysis of microarrays.[Tibshirani 2002] Correlation with overall survival was done for 26 patients who survived 5 years or longer, and 24 patients who survived less than 5 years. Hierarchical clustering for over and under expression yielded 9 genes, VEPH1, ZNF280B, FGF13, ST6GALNAC3, QPCT, ZNF280B, BST, PBRM1 and C21orf91 which identified 11/11 patients who had a survival greater than 5 years, and 17/18 patients who died in less than 2 years. Using the top six genes, overall survival greater than 5 years was correctly predicted for 80.3% of patients, and survival less than 5 years was correctly predicted for 74.2% of patients. Gene array data obtained from self-renewing, proliferating, autologous tumor cells was prognostic for survival. It is unclear whether these genes are predictive of an effective immune response after repeated injections of a therapeutic dendritic cell-tumor cell vaccine.
Citation Format: Andrew N. Cornforth, Gary Fogel, Denysha Carbonell, Robert O. Dillman. Microarray analysis of melanoma autologous tumor cell lines used as the source of tumor associated antigens in patient-specific dendritic cell immunotherapy phase II trial in patients with metastatic melanoma. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4845. doi:10.1158/1538-7445.AM2015-4845
Abstract 4849: GenomeSpace: An environment for frictionless bioinformatics
Authors: Reich Michael M. 
Published: August 2015

Abstract
Over the past several years, cancer genome characterization initiatives such as The Cancer Genome Atlas and the International Cancer Genome Consortium have produced an explosion of genomic data. These efforts, together with the proliferation of repositories for small-molecule screens and large-scale data production efforts to discover the breadth of genomic variation in humans, offer a new era of potential for the understanding of the basic mechanisms of cancer and identification of novel treatments.
Comprehensive analysis of these datasets requires the coordinated use of Web-based applications and data repositories, desktop analysis tools and visualizers, and single-purpose algorithms. However, the effort required to transfer data between tools, convert between data formats, and manage results often prevents researchers from utilizing the wealth of methods available to them. Many integrative genomics and translational “bench to bedside” discoveries are possible with combinations of existing tools, but the necessary transitions between them puts them out of the reach of most researchers.
GenomeSpace, http://www.genomespace.org, is an environment that brings together diverse computational tools, enabling scientists without programming skills to easily combine their capabilities. It aims to offer a common space to create, manipulate and share an ever-growing range of genomic analysis tools. GenomeSpace features support for cloud-based data storage and analysis, multi-tool analytic workflows, automatic conversion of data formats, and ease of connecting new tools to the environment. A set of six “GenomeSpace-enabled” seed tools developed by collaborating organizations provides a comprehensive platform for the analysis of cancer data: Cytoscape (UCSD), Galaxy (Penn State University), GenePattern (Broad Institute), Genomica (Weitzmann Institute), Integrative Genomics Viewer (Broad Institute), and the UCSC Genome Browser (UCSC). Its open architecture has driven its adoption by a number of additional tools, including the Cancer Cell Line Encyclopedia, Multiple Myeloma Genomics Portal, Project Achilles Portal, ArrayExpress, and Cistrome, with new tools and resources being released regularly.
We show how researchers can use GenomeSpace to effortlessly combine the capabilities of all of these tools in several cancer research scenarios.
Citation Format: Michael M. Reich. GenomeSpace: An environment for frictionless bioinformatics. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4849. doi:10.1158/1538-7445.AM2015-4849
Abstract 4858: Cloud-based variant analysis solution using control-accessed sequencing data
Authors: Xiao Chunlin , Yaschenko Eugene , Sherry Stephen 
Published: August 2015

Abstract
Variation analysis plays an important role in elucidating the causes of various human diseases. The drastically reduced costs of genome sequencing driven by next generation sequence technologies now make it possible to analyze genetic variations with hundreds or thousands of samples simultaneously, but currently with the cost of ever increasing local storage requirements. The tera- and peta-byte scale footprint for sequence data imposes significant technical challenges for data management and analysis, including the tasks of collection, storage, transfer, sharing, and privacy protection. Currently, each analysis group facing these analysis tasks must download all the relevant sequence data into a local file system before variation analysis is initiated. This heavy-weight transaction not only slows down the pace of the analysis, but also creates financial burdens for researchers due to the cost of hardware and time required to transfer the data over typical academic internet connections. To overcome such limitations and explore the feasibility of analyzing control-accessed sequencing data in cloud environment while maintaining data privacy and security, here we introduce a cloud-based analysis framework that facilitates variation analysis using direct access to the NCBI Sequence Read Archive through NCBI sratoolkit, which allows the users to programmatically access data housed within SRA with encryption and decryption capabilities and converts it from the SRA format to the desired format for data analysis. A customized machine image (swift) with preconfigured tools (including NCBI sratoolkit) and resources essential for variant analysis has been created for instantiating an EC2 instance or instance cluster on Amazon cloud. Performance of this framework has been evaluated and compared with that from traditional analysis pipeline, and security handling in cloud environment when dealing with control-accessed sequence data has been addressed. We demonstrate that it is cost effective to make variant calls using control-accessed SRA sequence data without first transferring the entire set of aligned sequence data into a local storage environment, thereby accelerating variation discovery using control-accessed sequencing data.
Citation Format: Chunlin Xiao, Eugene Yaschenko, Stephen Sherry. Cloud-based variant analysis solution using control-accessed sequencing data. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4858. doi:10.1158/1538-7445.AM2015-4858
Abstract 4868: CODEX: a normalization and copy number variation detection method for whole-exome sequencing
Authors: Jiang Yuchao , Oldridge Derek A. , Diskin Sharon J. , Zhang Nancy R. 
Published: August 2015

Abstract
Background: High throughput sequencing of DNA coding regions has become a common way of assaying genomic variation in the study of cancer. Copy number aberration (CNA) is an important type of genomic change, but detecting and characterizing CNA from whole-exome sequencing (WES) is challenging due to the high level of biases and artifacts.
Methods: We propose CODEX, a normalization and CNA calling procedure for WES data. The Poisson latent factor model in CODEX includes terms that specifically remove biases due to GC content, exon capture and amplification efficiency, and latent systemic artifacts. CODEX also includes a Poisson likelihood-based recursive segmentation procedure that explicitly models the count-based WES data. CODEX can be used to detect both germline and somatic CNAs in cancer samples with or without matched normal.
Results: Compared to existing approaches, CODEX is shown to be more effective in removing the biases in WES, and attains better sensitivity and specificity in detecting copy number aberrations by in silico spike-in studies. We further evaluate performance on 222 neuroblastoma samples with matched normal from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) Project. We carry out systematic genome-wide analysis and detailed characterization of both germline and somatic copy number events. With a focus on a well-studied rare somatic CNV within the ATRX gene, we show that the cross-sample normalization procedure of CODEX is more effective in removing noise than the standard pipeline of normalizing the tumor against the matched normal, and that the segmentation procedure performs well in detecting CNVs with recurrent complex nested structures. For detecting germline mutations, CODEX is compared to existing methods on a population analysis of HapMap samples from the 1000 Genomes Project, and shown to be perform well on three microarray-based validation data sets.
Conclusions: The cross-sample normalization procedure of CODEX, when applied to the matrix of tumor and normal samples, is more effective in reducing noise than normalizing each tumor to its matched normal. The somatic deletions in the ATRX region have a nested structure, which CODEX was able to recover. Through multiple types of validation, CODEX is shown to be applicable to a wide range of study designs for copy number estimation using WES data.
Citation Format: Yuchao Jiang, Derek A. Oldridge, Sharon J. Diskin, Nancy R. Zhang. CODEX: a normalization and copy number variation detection method for whole-exome sequencing. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4868. doi:10.1158/1538-7445.AM2015-4868
Abstract 4869: Validation of a procedure for simultaneous variant calling and differential expression in cancer study
Authors: Feng Gang , Osman Jamie , Leighton Sean , Carmichael Lynn , Bai Yuchen , Begemann John , Mazzarella Richard 
Published: August 2015

Abstract
We have designed an integrated RNA-seq pipeline that determines both sequence variants and the differential expression of the transcripts from the same alignment in cancer samples. Comparison with other widely used protocols for RNA-seq data analysis showed that our method was more sensitive for determining subtle phenotypes in studies with large sample size, which frequently occur in compound screening approaches for cancer therapy. This whole transcriptome sequencing technology provides a cost-effective approach to gain understanding into the RNA relative biological events by deciphering RNA sequences. Benefiting from the fast developing technology, the cost of deep RNA-seq is no longer a barrier for current applications. Furthermore, an RNA-seq dataset provides more insight into disease processes than DNA-seq, since single nucleotide variant (SNV) detection in addition to gene and exon level expression and new isoform detection can be garnered from the same information. Based on the recent public data for mixed cancer cell lines and pooled normal human samples in MicroArray Quality Control (MAQC) phase III, also referred to as Sequencing Quality Control (SEQC), we evaluated our method with previous published results by means of different alignment strategies, sequencing depths and replicates numbers. Receiver operating characteristic (ROC) analysis showed our gene expression results agreed with the ones from the SEQC teams. In most cases, our solution has similar performance with other widely used strategies, while our STAR 2 pass based strategy has better true positive and true negative rates in the experiments with large sample size and subtle difference between groups. At the same time, the Genome Analysis Toolkit (GATK) 3 in our pipeline gained a reasonable balance between accuracy and performance. In summary, our new RNA-seq analysis pipeline has good performance and balance between precision and speed. Furthermore, this work also implicitly indicates that our gene expression method generates more accurate results in a large sample size experiment among similar groups.
Citation Format: Gang Feng, Jamie Osman, Sean Leighton, Lynn Carmichael, Yuchen Bai, John Begemann, Richard Mazzarella. Validation of a procedure for simultaneous variant calling and differential expression in cancer study. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4869. doi:10.1158/1538-7445.AM2015-4869
Abstract 4872: Improving bioinformatic analysis pipelines for calling somatic mutations in tumors and tumor derived cell lines
Authors: Wubbenhorst Bradley 
Published: August 2015

Abstract
The use of massively parallel sequencing data (MPS) is a common technique for identifying genetic alterations in disease. A number of tools and resources have been developed to assist in variant identification, but most are not specifically designed for somatic cancer studies. Tumor samples often suffer from admixture and heterogeneity, and tumor DNA often is derived from formalin fixed paraffin embedded samples rather than fresh samples, which introduces artificial bias. These confounders require adjustments to variant quality filtering to ensure a confident call set, but no universal guidelines exist for bioinformatics analyses. In addition, no single tool characterizes the spectrum of possible DNA-based alterations in tumors, specifically single nucleotide variants, small to medium sized indels, large genomic rearrangements and copy number alterations. To address these issues, we compared several methods of bioinformatic analysis using whole exome and targeted high-throughput sequencing data from various tumor and tumor-derived cell line studies. Using a data sets with known somatic alterations, we developed modifications to best practices guidelines for the publically available Genome Analysis Toolkit (GATK) for use in tumor and cell line variant calling. The haplotype score annotation was removed from all filtering consideration due to its expectance of no more than two segregating haplotypes at a given site, which, for example, caused all known BRAF V600 mutations to be removed. Scores from the read position rank sum test required a reduced filter threshold due to presence of high alternate allele fractions, which may contribute an artificial bias for this annotation value. Where applicable, we combined GATK results with calls from MuTect to increase specificity of the somatic changes identified as well as further increase sensitivity to singleton mutations. Finally, we have analyzed three methods to detect large genomic rearrangements and copy number changes (Pindel, ngCGH, and Sequenza) from MPS data, and found inconsistent results which require further scrutiny. In conclusion, we have developed a somatic variant calling pipeline which includes several modifications to the best practices in standard variant calling algorithms to enhance the sensitivity and specificity for somatic variant calling of the entire range of DNA-based alterations in tumors.
Citation Format: Bradley Wubbenhorst. Improving bioinformatic analysis pipelines for calling somatic mutations in tumors and tumor derived cell lines. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4872. doi:10.1158/1538-7445.AM2015-4872
Abstract 4874: OASIS: A centralized portal for cancer omics data analysis
Authors: Fernandez-Banet Julio , Esposito Anthony , Coffin Scott , Schefzick Sabine , Ding Ying , Ching Keith , Horvath Istvan , Roberts Peter , Rejto Paul , Kan Zhengyan 
Published: August 2015

Abstract
Advances in cancer research and sequencing technologies have contributed to the proliferation of large-scale cancer omics data sets from both public consortia and privately funded collaborations. There are increasing demands for broadly accessible tools that enable scientists to perform ad hoc analyses of cancer omics data. Here we present OASIS (http://oasis.pfizer.com), an open-access web portal that enables complex analytical queries across somatic mutations, copy number changes (CNV) and gene expression data from public domain and Pfizer funded omics studies. OASIS was designed to perform multidimensional data integration, allowing users to analyze correlations among multiple data types, as well as visualize and compare alterations across different cancers. Users can browse alteration summary reports at the gene and sample level and perform analyses using interactive visualizations. These include the Pan-Cancer Report, a unique tool that provides a high-level overview of genetic variation across multiple cancer types. Through the use of the interactive visualizations researchers can explore differentially expressed genes, identify tumor samples for over expression, investigate the correlation between CNV and expression or survey alteration patterns for a list of genes. Users can explore molecular profiles from the Cancer Cell Line Encyclopedia (CCLE) and the Catalog of Somatic Mutations in Cancer (COSMIC), to identify cell line models of interest. Researchers can also construct complex queries through a user-friendly web interface supported by the Biomart query engine. Programmatic access is also available through web services. OASIS is a powerful tool that enables cancer researchers to perform integrative analyses of large-scale cancer omics data sets, thereby facilitating key steps in oncology drug discovery ranging from target identification to model selection.
Citation Format: Julio Fernandez-Banet, Anthony Esposito, Scott Coffin, Sabine Schefzick, Ying Ding, Keith Ching, Istvan Horvath, Peter Roberts, Paul Rejto, Zhengyan Kan. OASIS: A centralized portal for cancer omics data analysis. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4874. doi:10.1158/1538-7445.AM2015-4874