bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 5271: Optimization and detection of focal somatic copy number variants in whole genome, whole exome and panel sequencing for tumor/normal matched pairs and tumor only analysis
Authors: Aldrich Jessica , Keats Jonathan J. , Christofferson Austin , Liang Winnie S. , Carpten John D. , Baumbach-Reardon Lisa , Craig David W. 
Published: July 2016

Abstract
Often in the clinical setting, tumor samples may be derived from historical tissue or from fresh frozen tissue and it may not be feasible to obtain normal tissue from the individual. Comparing unmatched tissues from different individuals possess a unique challenge of addressing common copy number variations not faced when comparing matched samples. In addition to the challenges of comparing unmatched samples, DNA extracted from samples preserved as formalin-fixed, paraffin-embedded (FFPE) tissue is often highly degraded and when compared to more intact DNA extracted from different source (e.g. blood) can increase the inherent noise in the system. Here we analytically characterize the accuracy and performance of algorithmic approaches for separating germline inherited copy number variation from somatic copy number changes, focusing on both filtering approaches and use of pooled reference samples sequenced under similar conditions. Example approaches include filtering known common copy number variation within 1000 Genomes Phase 3 and Database of Genomic Variants (DGV) Gold Standard. Additionally, we utilized a tumor/reference pool based analysis where a reference pool was constructed by equimolar pooling of multiple individuals. Determination of fold changes between tumor and reference was calculated by determining physical coverage of read pair fragments in 100 bases increments. Next, to address differences in sequencing performance between tumor and reference, the read depth data for each sample is collapsed/averaged into a lower resolution according to user-selected parameters (e.g. distance between points and read depth). Normalized log2 fold-changes between tumor and reference samples are then calculated and an adjustable smoothing window is applied. In addition, we utilize tumor allele frequencies of known heterozygous germline SNPs identified within the normal to both evaluate potential false positives and correct biases. Lastly, a segmentation algorithm is applied to summarize the individual log2 fold-changes into intervals with a constant copy number state. We will present the advantages and limitations of these approaches both when a germline normal is available and when tumor only analysis is necessary.
Citation Format: Jessica Aldrich, Jonathan J. Keats, Austin Christofferson, Winnie S. Liang, John D. Carpten, Lisa Baumbach-Reardon, David W. Craig. Optimization and detection of focal somatic copy number variants in whole genome, whole exome and panel sequencing for tumor/normal matched pairs and tumor only analysis. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5271.
Abstract 5278: NGS-SWIFT: A cloud-based variant analysis framework using control-accessed sequencing data from dbGaP/SRA
Authors: Xiao Chunlin , Yaschenko Eugene , Sherry Stephen 
Published: July 2016

Abstract
Genetic 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 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 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 SRA Toolkit, 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 (ngs-swift) with preconfigured tools, including NCBI SRA Toolkit and NGS Software Development Kit, 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 using dbGaP study phs000710.v1.p1 (1000Genome Dataset in dbGaP, http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id = phs000710.v1.p1), 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 with this framework, it is cost effective to make variant calls without first transferring the entire set of aligned sequence data into a local storage environment, thereby accelerating variant discovery using control-accessed sequencing data.
Citation Format: Chunlin Xiao, Eugene Yaschenko, Stephen Sherry. NGS-SWIFT: A cloud-based variant analysis framework using control-accessed sequencing data from dbGaP/SRA. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5278.
Abstract 528: Identify and prioritize candidate neoantigens from cancer exome sequencing with unmatched accuracy
Authors: White James , Angiuoli Sam , Sausen Mark , Jones Sian , Kann Lisa , Shukla Manish , Sevdali Maria , Velculescu Victor , Diaz Luis , Zhang Theresa 
Published: July 2016

Abstract
Somatic, nonsynonymous genetic alterations present in cancer can lead to the formation of novel protein sequences and thus production of immunogenic “non-self” neoantigens. Some of those neoantigens will be processed, presented on MHC molecules, and induce tumor-specific T cell responses. Because neoantigens play central roles in the cancer-immunity cycle, it is critical to identify the most potent immunogenic neoantigens effectively and accurately.
Combining PGDx's highly accurate cancer exome analyses (CancerXome™) with in silico neoantigen prediction, we have launched ImmunoSelect-R™ that identifies and prioritizes the most relevant mutation-derived neoantigens. To ensure detection of true somatic mutations and prevent false positive mutations from confounding neoantigen identification, ImmunoSelect utilizes CancerXome that delivers unparalleled cancer whole exome sequencing accuracy, achieving 95% sensitivity and 97% positive predictive value at 10% mutant allele frequency with 150x coverage. ImmunoSelect also provides accurate HLA typing from whole exome sequencing with &gt;99.9% sensitivity and specificity. Once exome-based mutations and novel open-reading-frames are identified and HLA genotypes defined, ImmunoSelect utilizes state of art bioinformatics pipelines for prediction and prioritization of the most relevant neoantigens. When applied to a set of experimentally validated neoantigens, ImmunoSelect identified 18 out of 19 of them as being strong neoantigen candidates, suggesting a sensitivity of greater than 90%. Moreover, ImmunoSelect consistently ranked experimentally validated neoantigens within top 20% of all neoantigen candidates derived from whole exome sequencing.
In summary, ImmunoSelect is able to identify and prioritize candidate neoantigens from cancer exome sequencing results effectively and accurately, enabling personalized cancer vaccine development, adoptive T-cell transfer, and prediction of response to checkpoint inhibitors
Citation Format: James White, Sam Angiuoli, Mark Sausen, Sian Jones, Lisa Kann, Manish Shukla, Maria Sevdali, Victor Velculescu, Luis Diaz, Theresa Zhang. Identify and prioritize candidate neoantigens from cancer exome sequencing with unmatched accuracy. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 528.
Abstract 5280: A web-based platform for exploring ‘omics and clinical data from healthy breast tissues
Authors: Kuang Xingyan , Marian Catalin , Taslim Cenny , Song Min-Ae , Weng Daniel , Freudenheim Jo L. , Brasky Theodore , Huang Kun , Coombes Kevin , Shields Peter G. 
Published: July 2016

Abstract
Integrating ‘omics, epidemiology and clinical data from healthy patients and their biologic samples is important for identifying the determinants of early carcinogenesis. In a cross-sectional study of 249 women with no history of cancer and who underwent reduction mammoplasty, whole transcriptome (i.e., gene expression) and epigenome (i.e., methylation and microRNA) data were collected from dissected breast tissues. An extensive epidemiologic questionnaire ascertained data on breast cancer risk factors. Subjects with benign lesions considered at risk for future cancer were excluded. In order to manage, analyze, and share these data efficiently, we organized the data into a relational database to support a series of web-based tools for analyzing and visualizing the multidimensional data in an intuitive graphical user interface (GUI). The current platform improves upon existing web resources that integrate ‘omics and clinical data by including novel features that allow for greater flexibility of bioinformatics analyses. It also focuses on the ‘omics as an outcome, rather, e.g., what epidemiology risk factor or particular gene is associated with downstream effects across the ‘omics. Such features include: 1) a plot function that can visualize up to 4 dimension data chosen by a user from the 3 types of genetic profiles and more than 100 clinical and biomedical variables; 2) a differentiation visualization tool based on the ‘omics data of 2 groups of samples; and 3) miRNA target gene network generated by the correlations between gene expression and miRNA expression of our patient samples and validated miRNA target gene database. The GUI facilitates the discovery of research scientists by reducing complex biological and clinical data into easily understandable views. Even without the expertise of computational biology, researchers can integrate and analyze the high dimensional data that they are interested conveniently on our user-friendly designed platform.
Citation Format: Xingyan Kuang, Catalin Marian, Cenny Taslim, Min-Ae Song, Daniel Weng, Jo L. Freudenheim, Theodore Brasky, Kun Huang, Kevin Coombes, Peter G. Shields. A web-based platform for exploring ‘omics and clinical data from healthy breast tissues. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5280.
Abstract 5284: Characterizing the differences of allelic imbalance between tumor and normal tissues by next-generation sequencing
Authors: Jheng Yao-Wei , Chiu Yu-Chiao , Lu Tzu-Pin , Lai Liang-Chuan , Tsai Mong-Hsun , Hsiao Tzu-Hung , Chuang Eric Y 
Published: July 2016

Abstract
Genetic variation has proved to contribute to tumor formation. One of the phenomenon caused by genetic variation, allelic imbalance, means different expression abundance between two alleles, recently has become a special landmark on cancer biology, with high prevalence in recent cancer studies. However, the present of allelic imbalance has not been fully understood yet. Taking the advantages of next generation sequencing, allelic imbalance can be identified and quantified through bioinformatics algorithms. Here, we developed a mathematical model to identify with allelic imbalance by concurrently analyzing RNA-Seq and DNA-Seq. First we called heterozygous SNP variants from both RNA-Seq and Exome-Seq data. The allele ratio is counted by the ratio of read depth between two alleles after integrating SNPs into gene level. Next, we identify allelic imbalance genes by differences of RNA and DNA allele ratios between tumor and normal samples. As results, a total of 91143 SNPs were identified in Exome-Seq, and about 3% (2586 out of 91143) SNPs were heterozygous in both tumor and normal tissues. We found that about 118 allelic imbalance genes in tumor tissue are significantly different from those in normal tissue. Furthermore, we investigate the enriched function of these allelic imbalance genes. These result showed that these genes are significantly enriched in DNA repair pathway. We speculated that allelic imbalance in tumor tissue may be involved in the dysregulation of DNA repair. Also other enriched functions were identified, such as z-finger protein pathway.
Our goal is to investigate the landscape of allelic imbalance between tumor sample and normal sample. Our results demonstrate the role of allelic imbalance of dysregulation of tumor. Moreover, our method can be applied to uncover more basic mechanisms on allelic imbalance and tumorigenesis.
Citation Format: Yao-Wei Jheng, Yu-Chiao Chiu, Tzu-Pin Lu, Liang-Chuan Lai, Mong-Hsun Tsai, Tzu-Hung Hsiao, Eric Y Chuang. Characterizing the differences of allelic imbalance between tumor and normal tissues by next-generation sequencing. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5284.
Abstract 5291: OncoMD: A powerful genomics data analysis and interpretation platform for cancer discovery research
Authors: Gupta Ravi , Paul Kiran V. , Kumaramangalam Kartik , Santhosh Sam , Chaudhuri Amitabha 
Published: July 2016

Abstract
Cancer cells gain fitness advantage over normal cells by accumulating mutations over time. Next Generation DNA sequencing technologies have made the detection of genetic alterations in tumor genomes affordable. Powerful data analysis and interpretation tools enable extraction of disease-relevant information from diverse genomics data to guide diagnosis and treatment decisions. We have built a robust cancer analytics platform OncoMD that combines tumor mutation profiles, expression signatures, copy number variations, epigenetic alterations and drug sensitivity to create a holistic view of human cancer enabling discovery of new targets for therapy and prognosis. We have analyzed the rich mutational data captured in OncoMD to identify potential T-cell neo-epitopes in different cancers and examined their prevalence in the context of the tumor microenvironment. The presence of T-cell neo-epitopes alone is not sufficient to sensitize tumors to immunotherapy drugs and requires cooperation from the tumor microenvironment. Although mutational burden was a strong predictor of T-cell neo-epitopes in our analysis, other genetic alterations such as overexpression, splicing, gene-fusions and insertions/deletions can produce altered peptides exhibiting high affinity binding to HLA class I and II molecules in cancers with low burden of missense mutations. We determined the number of potential T-cell neo-epitopes after classifying tumors based on their epithelial, stromal and immune cell content using gene expression signatures. We observed that the normalized T-cell neo-epitope burden was strongly anti-correlated with the T-cell content of the tumors, compared to their epithelial or stromal content. Together, our findings suggest that tumors enriched in T-cells may be subjected to immunoediting processes and carry relatively fewer T-cell neo-epitopes to avoid elimination. These tumors are likely to respond to immunotherapy treatments.
Citation Format: Ravi Gupta, Kiran V. Paul, Kartik Kumaramangalam, Sam Santhosh, Amitabha Chaudhuri. OncoMD: A powerful genomics data analysis and interpretation platform for cancer discovery research. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5291.
Abstract 536: Excessive HCK kinase activity in the tumor stroma promotes colorectal cancer progression
Authors: Ernst Matthias , Poh Ashleigh , Masson Frederic , Putoczki Tracy , O’Donoghue Robert 
Published: July 2016

Abstract
Excessive activation of the myeloid-specific Src-family kinase Hematopoietic Cell Kinase (HCK) acts as a tumor intrinsic oncogene by promoting proliferation and survival of immune cells, and confers a poor prognosis for leukemia. However, the role of HCK in the tumor stroma of solid malignancies remains unexplored.
We analyzed the expression level of HCK in matched biopsies from sporadic colorectal cancer patients and observed elevated HCK phosphorylation in tumors compared to unaffected colons. Analysis of corresponding RNAseq data revealed a striking correlation between tumors with high HCK gene expression and a gene signature indicative of a tumor-promoting alternatively-activated macrophage (AAM) endotype. To functionally assess this observation, we exploited HckCA mice [Ernst et al., J Exp Med 2002] that express a constitutively active form of Hck as a knockin mutation, and subjected these mice to a chemically-induced model of sporadic colorectal cancer. HckCA mice developed more and larger tumors compared to wild-type (WT) animals, and this was associated with a significant bias towards CD206+ AAMs in tumors of HckCA mice without affecting the total number of tumor-associated leukocyte and lymphocytes. Likewise, adoptive bone-marrow transfer experiments revealed enhanced tumor formation and AAM differentiation in WT mice reconstituted with HckCA bone marrow, with a reciprocal decrease of these parameters in HckCA mice reconstituted with WT marrow. Finally, pharmacologic targeting of the catalytic activity of Hck with a small molecule inhibitor significantly reduced tumor progression and impaired AAM polarisation.
Collectively, our findings suggest that excessive Hck activity in the tumor stroma promotes the progression of solid cancers by modulating the endotype of tumor-associated myeloid cells. Therefore, Hck represents a rational therapeutic target for macrophage re-education in solid cancers by limiting polarization of tumor-promoting AAM.
Citation Format: Matthias Ernst, Ashleigh Poh, Frederic Masson, Tracy Putoczki, Robert O’Donoghue. Excessive HCK kinase activity in the tumor stroma promotes colorectal cancer progression. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 536.
Abstract 542: Assessing and augmenting the immune response to glioblastoma using repurposed pharmaceuticals
Authors: Brenneman Breanna R. , Floyd Desiree H. , Harris Tajie , Purow Benjamin 
Published: July 2016

Abstract
In order to improve the therapeutic responses to immunotherapies in glioblastoma (GBM) patients, a known pharmaceutical, ritanserin, was repurposed to augment cytotoxic T cell responses and suppress tumor growth. To determine the kinetics of the immune response to GBM, the transplantable syngeneic glioma cell line GL261 was used in C57BL/6 mice to assess the tumor immune response in the brain, spleen, and cervical lymph node via flow cytometry weekly for one month. After identifying the optimal T cell response temporally, mice with established tumors were treated with 100mg/kg of Ritanserin for one week and harvested post treatment. Ritanserin is able to inhibit the activity of diacylglycerol kinase alpha (DGKA), an enzyme that converts diacylglycerol to phosphatidic acid, and can inhibit tumor growth in mice. In addition, DGKA is expressed in T cells, and is more highly expressed in those that have entered an unresponsive (anergic) state, characteristic of dysfunctional T cell responses to tumors. To determine if ritanserin can counteract T cell anergy, T cell responses were determined by assessment of T cell number by subset (CD8+, CD4+, CD4+FoxP3+), by activation (CD62L-CD44+), and by tumor-specific response to ovalbumin-expressing GL261 tumor cells in the brain, spleen, and cervical lymph nodes. In comparison to mock-injected mice, tumor-bearing mice showed a robust T cell response to tumors in the brain, peaking at days 21 and 28 post injection. Treatment with ritanserin resulted in a trend toward increased number and activation of T cells in the brain compared to vehicle-treated mice. In addition, there was a significant increase in OVA-specific T cells in the brain with treatment. In conclusion, T cells respond robustly to brain tumors in mice, and ritanserin treatment appears to increase this response by increased activation and priming of tumor-specific T cells. This indicates that ritanserin may have combinatorial effects on T cells when combined with checkpoint inhibitors such as ipilimumab (anti-CTLA-4) and nivolumab (anti-PD-1). Future experiments will focus on combining ritanserin and other repurposed drugs with these checkpoint inhibitors and further characterizing the effect of ritanserin on T cells.
Citation Format: Breanna R. Brenneman, Desiree H. Floyd, Tajie Harris, Benjamin Purow. Assessing and augmenting the immune response to glioblastoma using repurposed pharmaceuticals. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 542.