bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 2270: Integrating 5hmC and gene expression data infers regulatory mechanisms linked to alternative mRNA splicing in breast cancer
Authors: Mitrea Cristina , Wijesinghe Priyanga , Dyson Greg , Girsch Emily , Bao Bin , Kruger Adele , Bollig-Fischer Aliccia 
Published: August 2018

Abstract
Epigenetic mechanisms, such as 5-methylcytosine (5mC), are known to play a major role in breast cancer. However, the role of 5-hydroxymethylcytosine (5hmC) DNA modification remains understudied. We hypothesize that 5hmC mediates redox regulation of gene expression in the triple negative breast cancer (TNBC) subtype. To address this, our objective was to highlight genes that may be the target of this process by identifying redox-regulated, antioxidant-sensitive, gene-localized 5hmC changes associated with mRNA changes in TNBC cells. We proceeded to develop an approach to integrate data sets generated by a methodology called Pvu-sequencing, which measures 5hmC in the genome, and RNA-sequencing. The result of our approach to merge genome-wide, high-throughput TNBC cell line data to identify significant, concordant 5hmC and mRNA changes in response to antioxidant treatment, that perturbs redox signaling, produced a gene set with relevance to alternative mRNA splicing and cancer stem cell function. Key genes identified included TAR DNA binding protein (TARDBP), vimentin (VIM), and splicing factor proline and glutamine rich (SFPQ). Moreover, we have established a method that will be useful for continued research of 5hmC in TNBC cells and tissue samples.
Citation Format: Cristina Mitrea, Priyanga Wijesinghe, Greg Dyson, Emily Girsch, Bin Bao, Adele Kruger, Aliccia Bollig-Fischer. Integrating 5hmC and gene expression data infers regulatory mechanisms linked to alternative mRNA splicing in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2270.
Abstract 2282: VIP: A system biology platform for cell line centric integrative analysis of molecular and functional genomics data
Authors: Fernandez Julio , Ding Ying , Kan Zhengyan 
Published: August 2018

Abstract
Cancer is a heterogeneous disease that is classified by its tissue of origin or molecular characteristics such as genetic alterations that drive tumorigenesis. Large-scale efforts using loss-of-function genetic screens to systematically identify genes essential for the proliferation and survival of cancer cells provide a powerful approach for studying cancer biology and discovering drug targets. However, cancer cell lines from different lineages or molecular background vary in their dependencies on specific genes or pathways. Functional ablation of a gene may be only lethal in one segment of cancer cell lines harboring certain molecular deficiencies or activation of a specific oncogenic signaling pathway, a phenomenon called synthetic lethality. A key challenge here would be to identify the true synthetic lethal interactions from a combinatorially large number of possible interactions between molecular aberrations and gene-level sensitivity to functional perturbation. With recent advances in large-scale studies such as CCLE and Achilles, genome-wide functional genomics screens using CRISPR-Cas9 or shRNA technologies as well as comprehensive molecular profiles have become available for a large number of cancer cell lines. Here we present the Variant Interpretation Portal (VIP), a system biology tool designed to enable integrative analysis of molecular and functional genomics data in cell lines for identifying synthetic lethal interactions. VIP consists of a command-line pipeline and a web-based user interface. The VIP pipeline rigorously filters out germline events and classifies genomic variants into multiple functional tiers. The VIP web portal provides users with a graphic interface to perform exploratory visual analyses of associations between two variables or multiple variables, where a variable could be a genetic alteration or a gene-level sensitivity profile. VIP also enables systematic search for significant association between one variable, such as the sensitivity profile, vs. a database of variables such as histology or molecular attributes. VIP is optimized to perform real-time analytics and enables the incorporation and analysis of user-provided data. The code can be accessed from https://github.com/juferban/vip.
Citation Format: Julio Fernandez, Ying Ding, Zhengyan Kan. VIP: A system biology platform for cell line centric integrative analysis of molecular and functional genomics data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2282.
Abstract 2296: STAR-SEQR: Accurate fusion detection and support for fusion neoantigen applications
Authors: Jasper Jeff , Powers Jason G. , Weigman Victor J. 
Published: August 2018

Abstract
Genomic structural variation and associated RNA fusions are a common clinical feature known to be involved in the initiation and pathogenesis of cancer. This complex class of variants also has significant implications on therapeutic decisions and has emerging roles in evidence-based clinical applications. Consequently, fusion detection is an emerging aspect of precision medicine that enables clinical utility of fusions as a therapeutically-relevant target which includes small molecules, biologics, and neoantigen-directed approaches.Here we present STAR-SEQR, an algorithm that uses multiple levels of evidence to detect and quantify fusions from RNA-Seq data. STAR-SEQR is a fast and accurate tool that goes beyond fusion detection and also provides rich annotation and useful reporting features to aid in the adoption of fusions in clinical diagnostics. Notably the software produces fusion expression values, PCR primers, fusion RNA sequence, and predicted peptide sequence to facilitate downstream applications.To address the analytical performance of STAR-SEQR to existing tools, we utilized previously characterized synthetic datasets and real-world sample data with matching DNA sequencing. Of note, in a dataset of 50 synthetic fusions, STAR-SEQR had the highest accuracy of all tools tested and called 49/50 events with no false positives. STAR-SEQR has also been run on several large clinical datasets and the fusion events identified have displayed strong overlap with identified DNA structural variants. STAR-SEQR also performed well in the ongoing ICGC-TCGA DREAM SMC-RNA Challenge and achieved the highest F1 score in the simulated data round with an overall average of 0.96. Analytical lab testing has also been performed using the Seracare Fusion RNA Mix V2 sample containing 15 clinically significant fusions with known concentrations in the low range of detectability. STAR-SEQR accurately called 13/15 events with the other events having no identifiable read alignments. We have also benchmarked methods comparing FFPE material, range of read lengths, and library methods where STAR-SEQR demonstrated excellent tradeoff between sensitivity and precision and generally outperformed other tools.In summary, STAR-SEQR has excellent analytical performance that surpasses existing fusion detection tools. It is broadly applicable to RNA-Seq methods, computationally efficient, and robust across sample characteristics. Lastly, STAR-SEQR is explicitly designed to enable fusion-directed clinical diagnostics and support cancer immunotherapy approaches that utilize fusion neoantigens.
Citation Format: Jeff Jasper, Jason G. Powers, Victor J. Weigman. STAR-SEQR: Accurate fusion detection and support for fusion neoantigen applications [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2296.
Abstract 2319: The role of CDKN1A and CDKN2B in the reprogramming of AML cells toward cell cycle arrest
Authors: Roberts Michael , Crossland Grace , Forrester Jeffrey 
Published: August 2018

Abstract
AML is the most common acute leukemia in adults, showing rapid progression if left untreated. With current treatments, AML patients frequently achieve remission, but often experience relapse leading to the overall poor prognosis and high mortality of this cancer. We have employed human AML cell lines to examine the response of leukemia cells to phorbol esters. Phorbol esters mimic the action of diacyl glycerol (DAG), an activator of protein kinase C, which regulates several signal transduction pathways and cellular metabolic activities. In response to the phorbol ester PMA, AML cells undergo cell cycle arrest, differentiate into “normal” macrophage-like cells, and ultimately undergo programmed cell death. The transcriptome variations that accompany these changes have been explored by DNA microarray analysis and quantitative real-time PCR, revealing ~1250 genes that are differentially expressed as the cells transition from the cancer state to non-dividing, myeloid cells that initiate apoptosis. Two genes of great interest regarding cell cycle arrest are the cyclin-dependent kinase inhibitors CDKN1A and CDKN2B. We have shown that both of these genes are expressed at extremely low levels in AML cells, but are rapidly up-regulated during the PMA response, in keeping with their proposed function as tumor suppressor genes. We present here results from the transfection of expression plasmids containing the CDKN1A or CDKN2B genes into HL-60 cells, showing ~100 and ~1,000-fold overexpression, respectively. In addition, we have engineered a plasmid containing both genes and demonstrate their co-overexpression. RNA samples from cells transfected with CDKN1A, CDKN2B, and CDKN1A-CDKN2B have been subjected to RNA sequencing. RNA-seq analysis reveals changes in CDKN1A and CDKN2B-mediated gene expression, altering the AML transcriptome in ways that facilitate cell cycle arrest. Finally, we present results on the phenotypic and transcriptome changes of a stable HL-60 cell line containing an inducible CDKN2B gene after dox-induced overexpression, implicating CDKN2B as an important regulator of cell cycle progression in AML cells.
Citation Format: Michael Roberts, Grace Crossland, Jeffrey Forrester. The role of CDKN1A and CDKN2B in the reprogramming of AML cells toward cell cycle arrest [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2319.
Abstract 2325: Protein homeostasis adaptation to Hsp70 inhibition in cancer
Authors: Sannino Sara , Guerriero Christopher J. , Sabnis Amit J. , Bivona Trever G. , Brodsky Jeffrey L. 
Published: August 2018

Abstract
Cancer cells experience acute stress conditions due to their increased proliferation rate and synthesis of misfolded proteins. However, by modulating the mechanisms that control protein-folding, cancer cells avoid cell death. Thus, it is perhaps not surprising that molecular chaperones, like Hsp70, are upregulated in cancer cells compared to their normal counterparts. These data suggest that these chaperones are potential targets for cancer therapy. Previous work in our lab demonstrated the dependence of patient-driven rhabdomyosarcoma cell survival on cytoplasmic Hsp70 activity, thanks to the use of a specific Hsp70 inhibitor, MAL3-101. In particular, we discovered that treatment of on RMS13 cell line with an Hsp70 inhibitor, known as MAL3-101, activates the PERK driven unfolded protein response that results in CHOP-dependent cell death (Sabinis et al., 2016). Nevertheless, the mechanism underlying how Hsp70 inhibition triggered apoptosis was poorly understood. By taking advantage of a MAL3-101-resistant cell line (RMS13-R), we have now determined which compensatory mechanism alters MAL3-101-driven cell death. We found that both endoplasmic reticulum-associated degradation (ERAD) and autophagy are upregulated in RMS13-R cells, underlying the increased demand on two protein degradation pathways upon inhibition of Hsp70. Specifically, autophagy related genes were upregulated, and increased conversion of LC3BI to LC3BII and accumulation of LC3BII was detected in RMS13-R cells. Further experiments demonstrated that autophagy was further induced by MAL3-101 treatment in RMS13-R cells, as evidenced by an increase in the messages and proteins corresponding to key autophagy components. Finally, we discovered that only autophagy inhibition—but not inhibition of ERAD—re-sensitized RMS13-R cells to Hsp70 inhibition, which was apparent from an induction of apoptotic markers and cell death. These data highlight a pro-survival role for autophagy induction upon exposure to an Hsp70 inhibitor in cancer, and provide a link between Hsp70, proteasomal degradation, the unfolded protein response, and autophagy in rhabdomyosarcoma.
Citation Format: Sara Sannino, Christopher J. Guerriero, Amit J. Sabnis, Trever G. Bivona, Jeffrey L. Brodsky. Protein homeostasis adaptation to Hsp70 inhibition in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2325.
Abstract 2335: Disulfiram overcomes trastuzumab resistance by targeting cancer stem-like properties and HER2/Akt signaling in HER2-positive breast cancer
Authors: Kim Yoon-Jae , Cho Youngkwan , Sung Daeil , Oh Eunhye , Cho Tae-Min , Jang Seojin , Kim Ji Young , Seo Jae Hong 
Published: August 2018

Abstract
Although trastuzumab has shown a significant clinical benefit for HER2-positive breast cancers, responses are limited by the frequent emergence of resistance. Trastuzumab resistance is a multi-factorial phenomenon thought to arise from the presence of cancer stem cells and interactions between truncated p95HER2 and HER family members. Disulfiram (DSF), an anti-alcoholism drug, is known to elicit cytotoxicity in numerous cancer cell types in the presence of copper (Cu). We sought to investigate the effect of DSF on apoptosis, HER2/Akt signaling, cancer stem-like properties and trastuzumab resistance in HER2-positive breast cancer cells in vitro and in vivo. DSF/Cu treatment induced apoptosis, associated with a significant downregulation of HER2, truncated p95HER2, phospho-HER2, HER3, phospho-HER3 and phospho-Akt levels in both trastuzumab-sensitive and -resistant cell lines. This was accompanied by the eradication of cancer stem-like populations, concomitant with the suppression of aldehyde dehydrogenase 1 (ALDH1) activity and mammosphere-forming ability. DSF administration also caused significant tumor suppression in trastuzumab-resistant xenografts, coinciding with the downregulation of BCSC-related markers and intracellular HER2 in vivo. These findings highlight the mechanisms of action of DSF in overcoming trastuzumab resistance in HER2-positive breast cancer.
Citation Format: Yoon-Jae Kim, Youngkwan Cho, Daeil Sung, Eunhye Oh, Tae-Min Cho, Seojin Jang, Ji Young Kim, Jae Hong Seo. Disulfiram overcomes trastuzumab resistance by targeting cancer stem-like properties and HER2/Akt signaling in HER2-positive breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2335.
Abstract 2342: Digital gene expression of up to 96 targets in 96 samples for cell line screening with nCounter® PlexSet™
Authors: Ong Giang T. , Merritt Chris , Kharkia Anisha , Webster Philippa 
Published: August 2018

Abstract
Cell line screening studies require highly efficient protocols for studying many samples in parallel. We have developed a lyse-and-go protocol for digital gene expression profiling of 96-samples by 96-genes in parallel. This protocol is based on nCounter® PlexSet™ Reagents, which enable multiplexing of samples and gene targets. The NanoString® nCounter Analysis platform uses a novel molecular barcoding technology to measure multiplexed gene expression. The assay counts fluorescent barcodes hybridized to targets to provide precise digital data. This platform is used in a wide variety of research applications. The standard nCounter gene expression assay can be used to process 12 samples per run. We recognize an additional need for a higher sample-throughput assay that would enable researchers to quickly evaluate up to 96 multiplexed targets in samples processed in a 96-well plate-based format. The PlexSet Reagent protocol is simple and fast and can be divided into 3 steps: 1) lyse cells, 2) set-up hybridization with crude lysate, 3) initiate automated post-hybridization processing and digital counting. The data presented are crude cells lysates from various PBMC cell types, two suspension cell lines, and two adherent cell lines using the IO Lymphocyte Activity Panel to measure the gene expression of 5 housekeeper genes and 91 lymphocyte activity genes (96 genes in total). The data demonstrate that up to 96 high-quality, digital data points for gene expression in each of 96 samples can be generated from crude cell lysates in an efficient, high-throughput protocol and that the cell lysate data correlate well with purified total RNA.
Citation Format: Giang T. Ong, Chris Merritt, Anisha Kharkia, Philippa Webster. Digital gene expression of up to 96 targets in 96 samples for cell line screening with nCounter® PlexSet™ [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2342.
Abstract 2346: ARDE: Detecting selectively expressed cancer driver mutations through integration of exome and transcriptome sequencing data
Authors: Wang Fang , Zhang Shaojun , Chen Yiwen , Hart Traver , Shaw Kenna , Meric-Bernstam Funda , Mills Gordon , Chen Ken 
Published: August 2018

Abstract
Integrating multi-omics data from cancer patient samples can potentially advance the discovery of cancer driver mutations (CDM) towards establishing a knowledgebase for cancer precision medicine. However, it remains unclear whether CDMs are selectively expressed at RNA levels unexpected from DNA copy numbers and whether identifying such selectivity will enable more accurate discovery of CDMs. To answer this question, we developed a novel statistical algorithm, ARDE (Allelic RNA DNA expression), which estimates tumor purity, allele-specific copy number and intratumor heterogeneity (ITH) from matched whole-exome and whole-transcriptome sequencing data, deconvolutes tumor allele-specific expression levels (ASEL) and uses a two-component mixture probability model to identify selectively expressed alleles (SEAs) in the tumor compartment of heterogeneous tumor tissues. We applied ARDE to analyze melanoma and breast samples from The Cancer Genome Atlas (TCGA). Tumor purities estimated by ARDE were highly correlated with estimation from pathology review, DNA methylation array, gene expression microarray and SNP array data. ITH scores can significantly stratify breast cancer subtypes and survival time of melanoma patients. The deconvoluted tumor ASEL showed consistently higher correlation with the averaged gene expression levels in tissue-matched single tumor cells than in normal cells. Higher proportions of somatic mutations are selectively expressed than germline. Top SEAs are highly enriched in deleterious and gain-of-function (GOF) mutations. Stratifying melanoma patients by the expression selectivity of BRAF V600E mutation resulted in a 10-fold improvement of significance in survival analysis. These results indicate that ARDE will likely be an effective, widely applicable approach for accurate discovery of CDMs from tumor tissue sequencing data. (See Table 1.)
Table 1. Top 10 significant positive-selectively expressed somatic mutations
MelanomaBreast cancerGenepSNVFunctionSIFTGenepSNVFunctionSIFTBRAFp. V600EGOFDAKT1p.E17KGOFDNRASp. Q61RGOFDPIK3CAp.H1047RGOFTCDKN2Ap. R114LGOFDERBB2p.L755SGOFDIDH1p. R132CSwitch of functionDTP53p.R273HGOFDEEF1A1p. H349YDGATA3p.M293KDRAC1p. P29SGOFDTP53p.P278TDNRASp. Q61KGOFDFOXA1p.S250FDSF3B1p. R625HGOFDTP53p. R175HGOFDNRASp. Q61LGOFDPIK3CAp.H1047LGOFTMAP2K1p. P124SGOFDERBB2p.V777LGOFT
Citation Format: Fang Wang, Shaojun Zhang, Yiwen Chen, Traver Hart, Kenna Shaw, Funda Meric-Bernstam, Gordon Mills, Ken Chen. ARDE: Detecting selectively expressed cancer driver mutations through integration of exome and transcriptome sequencing data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2346.
Abstract 2348: Low-cost and accurate human leukocyte antigen (HLA) class I typing of The Cancer Genome Atlas on the Seven Bridges Cancer Genomics Cloud
Authors: Malhotra Raunaq , Krasnitz Alexandar , Sethi Anurag , Lehnert Erik , Williams Elizabeth H. , Brandi Davis-Dusenbery N. 
Published: August 2018

Abstract
The Cancer Genome Atlas (TCGA)network has generated more than2.5petabytes of genomic dataover the last decade, with
petabyte-scale additions of dataexpected inthe coming years. Access and analysis of this informationonalocal compute
environment is challenging due tothe volume of dataand lack of sufficient computing resources at many research organizations.
The Cancer Genomics Cloud Pilot project from the National Cancer Institute (NCI)has helped indemocratizing access toTCGA
by co-localizing datawith computational resources onthe cloud. Funded as part of this project, the SevenBridges Cancer
Genomics Cloud (CGC)hosts nearly 5petabytes of public datafrom TCGA, the Simons Genome Diversity Project, the
Therapeutically Applicable Research toGenerate Effective Treatments (TARGET)initiative, The Cancer Imaging Archive (TCIA),
and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). The CGCprovides academic researchers with asecure,
scalable, cloud-based cancer research platform that includes collaborative tools for accessing, uploading, analyzing, and
visualizing data. The platform uses resource descriptionframeworks, dataharmonization, and metadatacurationtofacilitate
effective querying, and bioinformatics tools are implemented onthe CGCusing the CommonWorkflow Language (CWL), an
emerging standard for describing computational workflows, tosupport computational reproducibility. Since its launch in2016, the
CGChas enabled researchers from around the world tounderstand humangenetics and cancer biology through the analysis of
large public datasets and private datainacloud computing environment. Inthis poster, we present anexample of analysis of
TCGAdataonthe CGC. The OptiType tool for HumanLeukocyte Antigenclass I typing was used toprofile 8,872RNA-Seq
samples present inthe TCGAdataset. All samples were accurately processed within2days using arobust, fault-tolerant, and
cost-efficient CWL descriptionof OptiType that enabled analysis for less than50cents per sample onaverage. This case study
demonstrates how cloud computing resources canfacilitate the successful analysis of large cohorts of datausing custom
pipelines in a robust, scalable, and reproducible manner.
Citation Format: Raunaq Malhotra, Alexandar Krasnitz, Anurag Sethi, Erik Lehnert, Elizabeth H. Williams, Davis-Dusenbery N. Brandi. Low-cost and accurate human leukocyte antigen (HLA) class I typing of The Cancer Genome Atlas on the Seven Bridges Cancer Genomics Cloud [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2348.