bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 2454: Cancer RNA:DNA interactome analysis and prediction
Authors: Kuznetsov Vladimir A. 
Published: August 2019

Abstract
Transcriptional R-loops are three-stranded RNA:DNA hybrid structures essential for many normal and pathobiological processes. Previously, we have proposed and validated a quantitative R-loop forming structure (RLFS) model, QmRLFS, and predicted about 660,000 RLFSs in the human genome; most of them are localized in total within 75% of the coding and non-coding genes and gene-flanking regions, transcribed enhancer and pseudogene regions. RLFSs are also commonly co-localized as the singleton or clusters at regulatory sites and disease-associated genomic loci. Recently, it was computationally predicted and experimentally shown that RNA:DNA hybrid/R-loop interactome may play critical roles in mutagenesis, chromosome breaks, genome instability and cancer development. Herein, we conducted a comprehensive comparative analysis between the RLFSs, their clusters, publically available DRIP-seq and DRIPc-seq, and our own experimental datasets. QmRLFS demonstrated high accuracy (82-92%) and unbiased performance with resolution on the nucleotide, gene and genome scales. A preferential co-localization of the RLFSs with promoters, U1 splice sites, gene ends, enhancers and non-B DNA structures, such as G-quadruplexes, provides evidences for the mechanic links between RLFSs and dynamical DNA tertiary structures, transcription initiation and critical regulatory genome signals. We introduced and characterized a class of 25000 paired reverse-forward RLFS clusters highly enriched with non-B DNA structures, which co-localized in the transcribed promoters of sense-antisense gene pairs and enhancer’s flanks. In the cancer genomes, we found the RLFSs that form RNA:DNA hybrids/R-loops and could preferentially link open promoters and transcriptionally active enhancer regions. This suggests new mechanisms for in trans RNA:DNA hybrid-mediated 3D-chromatin loop formation. Finally, we found the RLFS clusters supported by cancer cell RNA:DNA hybrids/R-loop data, which were overlapped with hyper-mutated clusters, kataegis expression signatures. Overall, our study provides a rationale for the prediction, discovery, and characterization of the RLFSs as novel functional structures for an emerging quantitative biology and pathobiology of RNA-DNA interactomes.
Citation Format: Vladimir A. Kuznetsov. Cancer RNA:DNA interactome analysis and prediction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2454.
Abstract 2466: Designing an intuitive visualization of BRCAness scores for clinicians
Authors: Allegakoen Hannah R. , Goldman Mary , Caughey Bennett , Consunji Martin , Benz Christopher C. , Haussler David , Zhu Jingchun , Collisson Eric A. 
Published: August 2019

Abstract
Objective: Genomic interrogation of tumors has changed the way we care for cancer patients. Monogenic, single mutation tests clearly and accurately define patient populations whose tumors will or will not likely respond to specific therapies. Next generation sequencing has enabled diagnostics derived from broader footprints, such as signatures of mutational processes. Currently, no universal guidelines exist on translating complex predictive signatures into the clinic, and their interpretability is a barrier to uptake by clinicians. Visually displaying genomic signatures may help clinicians interpret them. We designed a user-directed approach to developing understandable displays of an individual’s BRCAness signature through testing at clinical cancer conferences.
Materials and Methods: We adopted a two-stage approach to optimizing user-testing methods. First, 44 attendees at the 2018 Gastrointestinal Cancer Symposium participated in a tablet-based test of a visualization of a patient’s BRCAness signature. We refined our testing methods and continued tabled-based testing of 51 attendees at the 2018 American Society of Clinical Oncology. Participants viewed slides explaining the signature and visualization. They were asked to interpret a patient’s BRCAness signature when provided information both as text and as a visualization. Participants received one of three similar versions of the visualization.
Results: Participants interpreted patients’ BRCAness signatures faster when given a visual. When shown a visual indicating BRCAness scores of germline BRCA mutated tumors, participants interpreted the information fastest without sacrificing accuracy or confidence in their answer. Participants’ interpretation of an individual’s BRCAness signature was associated with their self-reported likelihood to recruit the individual to a clinical trial.
Conclusion: Visualizing distributions of BRCAness signatures with germline BRCA mutations displayed helped participants respond faster without sacrificing accuracy or confidence in their answer. Testing attendees at cancer conferences presents a potential platform to include clinicians and researchers in the development of genomics visualization tools.
Citation Format: Hannah R. Allegakoen, Mary Goldman, Bennett Caughey, Martin Consunji, Christopher C. Benz, David Haussler, Jingchun Zhu, Eric A. Collisson. Designing an intuitive visualization of BRCAness scores for clinicians [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2466.
Abstract 2469: The COSMIC Cancer Gene Census - a comprehensive study of all mutated cancer-driving genes
Authors: Sondka Zbyslaw , Speedy Helen E. , Bamford Sally , Cole Charlotte G. , Ward Sari A. , Forbes Simon A. 
Published: August 2019

Abstract
The COSMIC Cancer Gene Census (https://cancer.sanger.ac.uk/census) is a catalogue of genes that drive all forms of human cancer. Since 2004, a consistent curation approach to the scientific literature has grown this resource into a comprehensive description of 723 genes, detailing how each gene contributes to disease causation The entire Cancer Gene Census has been fully re-evaluated, and each gene has been classified as Oncogene, Tumour Suppressor and/or Fusion gene - depending on their somatic mutation profile and functional role in carcinogenesis. Genes included in the CGC are characterised by the presence of somatic or germline mutations, which change the activity or expression of the protein product in a way that promotes one or multiple hallmarks of cancer. The minimum level of evidence required for CGC inclusion are at least 2 publications from independent research groups showing increased mutation frequency in at least one type of cancer; and 2 or more published experimental evidence of functional involvement of a gene in promoting the hallmarks of cancer. The functional description of how each mutated gene causes cancer is now underway, with approximately half the Census now described in both functional and mechanistic terms. These characteristics clearly show that many genes can participate in oncogenesis in multiple ways that are highly dependent on the type of genetic alteration and tissue in which the gene is expressed, as well as disease stage. Alongside this evidence-based characterisation of genes we know drive cancer, a “second tier” of genes is now encompassed in the Census, to describe genes implicated in oncology, but with less robust published evidence. These genes, often revealed by combining whole-tumour-genome sequencing studies are increasing rapidly in number and often underpin exciting new targets in oncology. Inclusion of these in the Census completes its ability to describe the contribution of inherited and acquired genetics to human oncology. With substantial research ongoing, the Census is updated with additional content every three months, included with every new release of the COSMIC database (https://cancer.sanger.ac.uk/).
Citation Format: Zbyslaw Sondka, Helen E. Speedy, Sally Bamford, Charlotte G. Cole, Sari A. Ward, Simon A. Forbes. The COSMIC Cancer Gene Census - a comprehensive study of all mutated cancer-driving genes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2469.
Abstract 2477: CopyCat: estimating genomic copy number profiles from high-throughput single cell RNA seq data
Authors: Gao Ruli , Navin Nicholas 
Published: August 2019

Abstract
CopyCat is a computational approach for estimate genomic-copy number profiles from high-throughput single cell RNAseq data that is generated by technologies, such as Drop-Seq, 10X genomics or Nanowells. CopyCat involves first normalizing and transforming unique molecular index (UMI) count data matrices to calculate single cell copy number states from low-coverage datasets using gene-bin windows across the human genome, and does not require a reference normal cell control. The resulting binned data is normalized and used for segmentation to reduce noise from individual genes that can contribute to gene dosage effects along chromosomes. We show that this methods is highly effective at distinguishing diploid and aneuploid cells in human tumors or cancer cell lines, and can efficiently detect major (&gt;10mb) chromosomal amplifications and deletions compared to copy number data generated by DNA sequencing of match samples. We further show that this method can resolve some clonal substructure within individual tumors, when subpopulations are distinguished by large chromosomal events. Our method outputs segmented copy number profiles and predicted classifications of normal (diploid) and tumor (aneuploid) cell states for individual single cells. It is implemented in R and is available on Github.
Citation Format: Ruli Gao, Nicholas Navin. CopyCat: estimating genomic copy number profiles from high-throughput single cell RNA seq data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2477.
Abstract 2484: An online tool to validate predictive biomarkers of therapy response using transcriptomic data of 3,651 ovarian cancer patients
Authors: Györffy Balazs , Fekete Janos , Osz Agnes , Pete Imre , Szasz Marcell 
Published: August 2019

Abstract
Systemic therapy of ovarian cancer can include chemotherapy and targeted therapy. Prognostic biomarkers are capable to predict survival and predictive biomarkers are capable to predict therapy response. To date, multiple online tools were established to identify prognostic biomarkers, but no platform is yet available for predictive biomarkers. In this study, we describe the first release of an online available tool capable to validate gene expression based predictive biomarkers using transcriptomic data of a large set of ovarian cancer patients.
Published gene expression data of 35 independent datasets was integrated with treatment data into a unified database. The classification is based on either author-reported pathological complete response (n=1,022) or relapse-free survival status at six months (n=1,347) or relapse-free survival status at twelve months (n=1,282). Treatment data includes chemotherapy (platin, taxol, docetaxel, paclitaxel, gemcitabine, topotecan) and targeted therapy (avastin). The transcriptomic database includes 54,675 probe sets corresponding to 20,089 distinctive genes. Finally, we performed a sample collection at the National Institute of Oncology (NIO cohort), and used these patient samples to validate the top six genes in ovarian cancer patients. Gene expression and therapy response were compared using receiver operating characteristics and Mann-Whitney tests.
In the validation of the tool we focused on paclitaxel-resistance associated genes. We selected the top genes after running the analysis across all samples and validated these by PCR in the NIO cohort of patients (n=80). The best performing paclitaxel-resistance biomarker candidates were ARAF (AUC=0.743, p=4.1E-09), PPCS (AUC=0.733, p=4.2E-07), GNL2 (AUC=0.718, p=2.7E-08), TFE3 (AUC=0.718, p=4.8E-06), PDXK (AUC=0.717, p=1.1E-05) and TOP1 (AUC=0.716, p=4.6E-05).
The analysis pipeline enables to validate and rank predictive biomarker nominees. By analyzing the candidate genes in a large set of independent patients, we can select the most reliable candidate and abolish those which are most likely to fail in a clinical setting. The registration-free interface of the online analysis platform is accessible at www.rocplot.org/ovar.
Citation Format: Balazs Györffy, Janos Fekete, Agnes Osz, Imre Pete, Marcell Szasz. An online tool to validate predictive biomarkers of therapy response using transcriptomic data of 3,651 ovarian cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2484.
Abstract 2493: Disulfide bond disproportionation agents activates the death receptor 5 pathway to kill highly resistant breast cancer
Authors: Wang Mengxiong 
Published: August 2019

Abstract
There are significant unmet medical needs among patients who have HER2+ breast cancers that have acquired resistance to targeted agents, and patients with Triple-Negative Breast Cancers (TNBCs) that lack therapeutic targets. Novel treatment strategies are needed to target these cancers. Disulfide bond Disproportionation Agents (DDAs) represent a new chemical class of anticancer drugs and show prominent anti-cancer activities both in vitro and in animal model. Blockade of breast tumor growth by DDAs is associated with downregulation of EGFR/HER2/HER3 in parallel, induction of AKT dephosphorylation, and activation of ER stress. However, it is not known how DDAs trigger cancer cell death without affecting non-transformed cells. This study aims to investigate the death pathways involved in the anti-cancer activities of DDAs, and to develop strategies that target drug resistant breast cancer and cancer metastasis. This study demonstrates that DDAs induce breast cancer cell death by activating the Death Receptor 5 (DR5) pathway. DDAs are the first compounds identified that upregulate DR5 via both transcriptional and post-transcriptional mechanisms and uniquely cause DR5 accumulation and oligomerization by altering DR5 disulfide bond pattern. This study also shows that the combination of DDAs and Tumor Necrosis Family-Related Apoptosis-Inducing Ligand (TRAIL), a natural ligand of DR5, significantly overcomes TRAIL resistance that has been encountered in clinical trials with this ligand. Furthermore, DDAs and TRAIL synergistically kill Lapatinib-resistant breast cancer and cancer metastatic cells, and that this anti-cancer effect is amplified by the overexpression of EGFR/HER2/MYC. Together, this study highlights a promising novel pharmacological approach against drug resistant cancer and cancer metastasis.
Citation Format: Mengxiong Wang. Disulfide bond disproportionation agents activates the death receptor 5 pathway to kill highly resistant breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2493.
Abstract 2498: MCL1 binds and negatively regulates the transcriptional function of tumor suppressor p73
Authors: Widden Hayley , Kaczmarczyk Aneta , Whitaker Robert H. , Placzek William J. 
Published: August 2019

Abstract
MCL1 is an anti-apoptotic member of the Bcl-2 family, which has become a popular therapeutic target in cancer research over the past decade due to its propensity to promote cancer cell survival and facilitate chemoresistance. Through a phage display screen, we identified a novel MCL1 binding motif that is the reverse of the canonical BH3-binding helix that mediates interactions amongst Bcl-2 family members. Upon a BLAST sequence analysis of native proteins, we identified a putative reverse BH3 (rBH3) motif in the tumor suppressor protein, p73. Here, we show that MCL1 and p73 bind through a direct protein-protein interaction. Using fluorescence polarization assay, we characterize the strength of this interaction and reduce binding down to the rBH3-containing alpha helix in the tetramerization domain of p73. To elucidate biological function, we employ electrophoretic mobility shift assay to show that excess MCL1 negatively impacts p73 binding to DNA. Finally, using two p53-/- cancer cell lines, we demonstrate that MCL1 inhibits the transcriptional function of p73 through analysis of p73 target gene expression. In summary, we characterize a novel protein-protein interaction between MCL1 and p73 and show that MCL1 negatively impacts p73 target gene activation. This work establishes a novel function of MCL1 outside of apoptotic regulation at the mitochondria and provides new evidence of cross talk between the Bcl-2 family and the DNA damage response pathway.
Citation Format: Hayley Widden, Aneta Kaczmarczyk, Robert H. Whitaker, William J. Placzek. MCL1 binds and negatively regulates the transcriptional function of tumor suppressor p73 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2498.
Abstract 2499: In vitro and in vivo efficacy of MCL1 inhibitor S63845 in small cell lung cancer
Authors: Yasuda Yuto , Ozasa Hiroaki , Tsuji Takahiro , Nomizo Takashi , Yamamoto Tomoko , Ajimizu Hitomi , Yoshida Hironori , Sakamori Yuichi , Hirai Toyohiro , Kim Young Hak 
Published: August 2019

Abstract
MCL1 is a member of the BCL-2 family, which regulates apoptosis. Targeting MCL1 represents a potential breakthrough of cancer treatment. We tested S63845, a MCL1 inhibitor, in four SCLC cell lines (DMS114, DMS53, SW1271, and NCI-H69) and, in addition, one patient derived SCLC cell line (KTOR201). S63845 had greater efficacy in two of five SCLC cell lines (DMS114 and KTOR201). These two SCLC cell lines had higher expression of MCL1 and lower expression of BCL-XL, which is another member of the BCL-2 family. The other three SCLC cell lines (DMS53, SW1271 and NCI-H69) were resistant to S63845 and had a higher expression of BCL-XL or lower expression of MCL1. In vivo activity was evaluated in xenograft models. DMS114 was subcutaneously implanted into immunocompromised mice. When the tumor volume was reached 150-200 mm3, mice were administered with S63845 (25 mg/kg) or vehicle intravenously twice a week. S63845 showed significant antitumor efficacy in vivo. Finally, immunohistochemical evaluation of MCL1 and BCL-XL was assessed in SCLC obtained from 33 patients at Kyoto University Hospital. High MCL1 expression with low BCL-XL expression was observed in 16 specimens (48.5%).These data suggested that S63845 might be a powerful treatment of SCLC as a new therapeutic strategy. It is possible that the expressions of MCL1 and other members of the BCL-2 family predict the sensitivity of S63845.
Citation Format: Yuto Yasuda, Hiroaki Ozasa, Takahiro Tsuji, Takashi Nomizo, Tomoko Yamamoto, Hitomi Ajimizu, Hironori Yoshida, Yuichi Sakamori, Toyohiro Hirai, Young Hak Kim. In vitro and in vivo efficacy of MCL1 inhibitor S63845 in small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2499.
Abstract 2511: Analysis of circulating tumor DNA by targeted ultra-deep sequencing across various non-Hodgkin lymphoma subtypes
Authors: Kim Yeon Jeong , Shin Seung-Ho , Lee Danbi , Park Woong-Yang , Kim Seok Jin , Kim Won Seog , Park Donghyun 
Published: August 2019

Abstract
Although targeted deep sequencing of cell-free DNA (cfDNA) was recently used to investigate tumor somatic mutations in particular subtypes of non-Hodgkin lymphomas (NHLs), the immense genetic heterogeneity across subtypes poses a hurdle to design a universal gene panel applicable for diverse subtypes of NHLs. We designed a panel targeting 66 genes associated with NHLs and performed targeted deep sequencing to analyze plasma cfDNA from patients with various subtypes of NHLs. Genetic profiling in plasma cfDNA using the method resulted in 88.0% sensitivity and &gt;99% specificity in detecting mutations present at a frequency greater than 20% in the tumor biopsies. Furthermore, the level of ctDNA significantly decreased and increased depending on designated clinical responses to therapy and disease progression. These results demonstrated that ctDNA sensitively indicated the presence of cancer and reliably correlated with tumor burden, suggesting potential utility of the method for patients with various subtypes of NHLs.
Citation Format: Yeon Jeong Kim, Seung-Ho Shin, Danbi Lee, Woong-Yang Park, Seok Jin Kim, Won Seog Kim, Donghyun Park. Analysis of circulating tumor DNA by targeted ultra-deep sequencing across various non-Hodgkin lymphoma subtypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2511.