bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 4375: Inhibition of triple negative breast cancer cell progression using estrogen related receptor alpha endogenous ligands
Authors: Ghanbari Faegheh , Mader Sylvie , Philip Anie 
Published: September 2020

Abstract
Triple-negative breast cancer (TNBC), which lacks estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), is considered to be more metastatic, and has poorer prognosis and higher risk of recurrence than other subtypes of breast cancer. The death rate in patients with TNBC is twice that of ERα positive tumors mainly because there are fewer targeted therapies that treat TNBC patients [1]. Therefore, there is a need to discover new drug-targeted therapy for these patients. Several lines of evidence indicate that estrogen related receptors (ERRs), which belong to the orphan nuclear receptor superfamily, play a crucial role in breast cancer, with ERRα overexpression reportedly leading to adverse clinical outcomes in TNBC patients [2]. Despite intensive efforts, no endogenous ligand has been identified for ERRs so far. The discovery of ligands that bind these receptors may lead to novel strategies for the treatment of TNBC. In this study we show two endogenous ligands of ERRs: 1) estradienolone (ED), a novel endogenous steroid during pregnancy which acts as an inverse agonist of ERRs, 2) cholesterol as an agonist of ERRs. Our recent results show that ED acts as an inverse agonist of ERRα and ERRγ by directly interacting with these receptors, and inhibiting their transcriptional activity. We also demonstrate that ED has strong anti-mitogenic properties. ED inhibits the growth of both estrogen receptor-positive (MCF-7) and estrogen receptor-negative (MDA-MB-231) breast cancer cells in a dose dependent manner, while of displaying a little effect on normal epithelial breast cells. In addition, we show that cholesterol binds directly and specifically to ERRα, it increases the transcriptional activity of ERRα in a peroxisome proliferator-activated receptor coactivator-1α (PGC-1) dependent manner. Our finding suggests that cholesterol enhances the interaction of ERRα and its coactivator PGC-1, and this leads to induce the expression of ERRα itself (an specific auto-induction) and the metabolic target genes of ERRα to fuel TNBC cells proliferation and migration. Moreover, our results demonstrates that the effect of cholesterol or lovastatin (a drug known to inhibit cholesterol synthesis) in triple negative breast cancer (MDA-MB 231) requires ERRα. These data suggest that both ED-ERR and cholesterol-ERR interactions may represent novel druggable signaling pathway in TNBC.
Citation Format: Faegheh Ghanbari, Sylvie Mader, Anie Philip. Inhibition of triple negative breast cancer cell progression using estrogen related receptor alpha endogenous ligands [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4375.
Abstract 4378: Contribution of ERα/PR crosstalk to endocrine therapy resistance in breast cancer
Authors: Huggins Rosemary J. 
Published: September 2020

Abstract
Half of estrogen receptor (ERα)-positive breast cancer patients treated with endocrine therapies display intrinsic or acquired therapy resistance. One-third of patients with acquired resistance present with metastatic tumors containing ERα-Y537S mutations. This mutation results in constitutive activity of ERα and altered ERα-associated gene expression. ERα/PR crosstalk, referring to the complex relationship of ERα and progesterone receptor (PR) interaction, offers therapeutic value in targeting both nuclear receptors simultaneously in ERα/PR-positive breast cancers. Previous research identified a synergistic effect of treating ERα-WT cancer cells with ERα/PR antagonism, where combined treatment results in tumor regression in vivo and increased cell death in vitro. Conversely, preliminary data suggests that treatment of ERα-Y537S-containing tumors with combined ERα and PR antagonists results in significantly increased proliferation in vivo and reduced cell death in vitro. My central hypothesis is that ERα-Y537S alters the transcription factor activity of ERα and PR, causing dysregulation of gene expression in SERM-resistant breast cancer. Using a three-dimensional culture system of patient-derived xenografts grown as organoids (PDxO), I have observed a synergistic effect of certain combinations of ERα/PR antagonists in the context of ERα-WT similar to previous results in two-dimensional cell culture. At the level of transcription, PR-associated transcriptional activity appears to be significantly altered in homozygous ERα-Y537S T47D cells compared to ERα-WT cells. Further understanding the role of ERα-Y537S in altering gene expression and reducing the response to SERM and SPRM treatment will provide understanding as to why these tumors are resistant to standard-of-care treatment and will suggest alternative targets for treatment as well.
Citation Format: Rosemary J. Huggins. Contribution of ERα/PR crosstalk to endocrine therapy resistance in breast cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4378.
Abstract 4382: Bioinformatics analysis for hub genes and pathways in hepatocellular carcinoma
Authors: Zeng Xianzhang , Ao Lu , Lin Xinjian , Lin Xu , Chen Wannan 
Published: September 2020

Abstract
Hepatocellular carcinoma (HCC) is the most common primary liver cancer with a high recurrence rate and mortality. There is no effective treatment for HCC and the molecular mechanisms of HCC are still not fully understood. To identify the potential biomarkers in the carcinogenesis and progression of HCC, microarray datasets GSE101685, GSE121248, GSE38941, GSE62232 and GSE87630 were downloaded from Gene Expression Omnibus (GEO) database. Screening of differential expressed genes (DEGs), and functional enrichment analyses were performed. The protein-protein interaction network (PPI) were constructed and significant module genes and hub genes were identified using STRING and Cytoscape. 149 DEGs were screened, including 114 down-regulated genes and 35 up-regulated genes. Fifteen hub genes were identified and biological process analysis revealed that these genes were mainly enriched in cell cycle, oocyte meiosis and protein binding. Survival analysis and co-expression network analysis showed that TOP2A, TPX2, RACGAP1 and PTTG1 may be involved in the carcinogenesis, invasion or recurrence of HCC. In conclusion, the present study may help us understand the molecular mechanisms underlying the carcinogenesis and progression of HCC, and provide candidate targets for diagnosis and treatment of HCC.
Key words: Hepatocellular carcinoma; Differentially expressed genes; Protein-protein interaction; Bioinformatics; Microarray
Citation Format: Xianzhang Zeng, Lu Ao, Xinjian Lin, Xu Lin, Wannan Chen. Bioinformatics analysis for hub genes and pathways in hepatocellular carcinoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4382.
Abstract 4388: Weighted gene co-expression network analysis identified cell cycle signaling pathway associated hub genes correlated with progression and prognosis of multiple myeloma
Authors: Adebayo Olayinka O. , Griffen Tiara , Young Corey , Dammer Eric , Lillard James W. 
Published: September 2020

Abstract
There is a gap in understanding the molecular mechanisms behind early relapse / multiple myeloma (MM) progression in the newly diagnosed patient on standard treatment for MM, i.e., lenalidomide, bortezomib, and dexamethasone (RVD). As a result, the relatively high percentage of patients that progress after early treatment with RVD is significant. Therefore, identification of clinically relevant clusters of co-expressed genes or representative biomarkers for MM progression, while on RVD, would help identify new molecular mechanisms and drug targets. The objective of this study is to use weighted gene co-expression network analysis (WGCNA) to identify gene-signaling networks associated with early relapse / MM progression. To this end, we performed a WGCNA to determine module-trait correlations. We next examined the overrepresentation of upstream regulators and signal pathway networks from MM patients in the MMRF CoMMpass dataset (n = 175, gene transcripts= 30,598). WGCNA constructed 48 modules based on the correlations between patients on RVD and death. We are using death as a biomarker for MM progression and two years as cut off for treatment. We identified two modules, Green (p &lt; 4.6 x 10-8), and Pale Turquoise (p &lt; 8.8 x 10-7), that significantly and positively correlated with overall survival of &lt; 2 years following initial treatment with RVD. Further analysis identified HDAC8, PARPBP, HSPG2, MAGE, KIF, and FOXM1 as the top six hub genes having tight variations in positive correlations. The signatures for these cell cycle signaling pathway related hub genes were found to be largely associated with the biological process for proliferation. Future studies will use connectivity mapping to identify drug signatures that can target any of the identified hub genes. The selective targeting of these hub genes is expected to improve response to RVD in MM patients. This transcriptome study is the first using this approach to identify gene signaling networks associated with MM progression.
Citation Format: Olayinka O. Adebayo, Tiara Griffen, Corey Young, Eric Dammer, James W. Lillard. Weighted gene co-expression network analysis identified cell cycle signaling pathway associated hub genes correlated with progression and prognosis of multiple myeloma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4388.
Abstract 4389: Glycogen synthase kinase 3A (GSK3A) gene alterations are negatively correlated with overall and disease-free survival in breast cancer patients: In silico analysis
Authors: Kaur Jasmeet , Kaur Karandeep 
Published: September 2020

Abstract
Breast cancer is a heterogeneous disease and most widespread malignancy in women. It is one of three leading causes of deaths worldwide, together with lung and colon cancer. Glycogen synthase kinase 3 (GSK3) is a serine/threonine protein kinase involved in glucose regulation, inflammation, immune responses, proliferation, migration and apoptosis. GSK3 is expressed in all mammalian tissues and has a role in diseases including mood disorders, Alzheimer disease, diabetes and cancer. GSK3A and GSK3B are two isoforms, encoded by distinct genes and are 98% identical within their kinase domain. Gene knockout studies demonstrate that the two isoforms are not completely redundant, but perform similar functions in several settings. So far, very little is known about GSK3A role in cancer. We analyzed correlation of GSK3A gene mutations and copy number changes with overall and disease-free survival in breast cancer patients using cBioPortal for cancer genomics. Fourteen datasets for breast cancer studies were analyzed and GSK3A alterations were found in seven datasets. These alterations included mutations (missense and truncating), amplifications and deep deletions; with amplifications being the most abundant alteration. Out of the seven datasets, three datasets showed significant negative correlation of GSK3A alterations with overall and disease-free survival in breast cancer patients. Kaplan-Meier curve analysis demonstrated that amplification of GSK3A gene is significantly correlated with poor overall and disease-free survival of breast cancer patients, specifically in estrogen receptor positive (ER+) tumors. Further, missense and truncating mutations leads to poor disease-free survival in patients with ER+ tumors and truncating mutation showed poor overall survival in patients with Her2+ tumors. Collectively, analysis of GSK3A genomic profile in breast cancer patients provides a novel insight that GSK3A is crucial in the biological behavior and/or pathogenesis of breast cancer and hence is an important therapeutic target for breast cancer treatment.
Citation Format: Jasmeet Kaur, Karandeep Kaur. Glycogen synthase kinase 3A (GSK3A) gene alterations are negatively correlated with overall and disease-free survival in breast cancer patients: In silico analysis [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4389.
Abstract 4396: A bioinformatics approach for cancer immunotherapeutic target identification by evaluating surfaceome gene and protein expression (CITIESGAPE) in tumor and pan-normal tissues
Authors: Tian Xiangjun , Wang Jing , Wang Yifei , Zhang Zhongting , Roth Michael , Gill Jonathan B. , Gorlick Richard 
Published: September 2020

Abstract
Targeted therapeutic strategies and some immunotherapies for cancer including but not limited to therapeutic antibodies, antibody-drug conjugates (ADCs) and CAR-T cells therapy demand precisely targeting cell-surface proteins that are distinctively expressed by cancer cells. We developed a robust analytical tool for cancer immunotherapeutic target identification by evaluating surfaceome gene and protein expression (CITIESGAPE) that are uniquely overrepresented by the tumor in contrast to pan-normal tissues. The first step was to run differential expression testing on RNA-seq transcriptome of cancer (for example, data from TARGET and TCGA) against each normal tissue (31 tissues from GTEx_v8), from which candidate genes were defined as those are higher expressed in tumor than all normal tissues (minimum LogFC &gt; 1 and maximum q &lt; 0.05). We then validated the protein expression level of candidate genes through the Human Protein Atlas (HPA) and ProteomicsDB (The latter includes mass spectrometry-based label-free proteomic data of some tumor cells and 39 normal tissues). The final and crucial step was to run surfaceome protein predicting using the COMPARTMENTS database, the HPA database and the Membranome database (v2.0), depending on an optimized confidence score. We have applied this analytical pipeline to search surfaceome proteins that are unique to Osteosarcoma, a common primary malignant bone tumor in children and young adults, and obtained 57 candidates that are potential immunotherapeutic targets. To date, eight of the top confident candidates have been further validated in clinical samples, patient-derived cell lines and xenograft models. We believe this robust analytical tool could be adaptable to other types/subtypes of cancer in searching of candidate surfaceome proteins for targeted therapy and immunotherapy, as well as precision medicine and personalized therapy.
Citation Format: Xiangjun Tian, Jing Wang, Yifei Wang, Zhongting Zhang, Michael Roth, Jonathan B. Gill, Richard Gorlick. A bioinformatics approach for cancer immunotherapeutic target identification by evaluating surfaceome gene and protein expression (CITIESGAPE) in tumor and pan-normal tissues [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4396.
Abstract 4400: Translational drug interaction database (TDID): A knowledgebase for drug interactions
Authors: Zhang Shijun , Wu Heng-Yi , Vanam Rohith , chien-WeiChiang  , Wang Lei , Li Lang 
Published: September 2020

Abstract
To help physicians and basic scientists in identifying drug-drug interaction mechanisms (DDI) and associated toxicity, we developed ‘The Translational Drug Interaction Database (TDID)', which is a fully searchable web-based resource that has a large collection of drug interaction data. The current TDID is seeded with pharmacokinetics (PK) or pharmacodynamics (PD) based drug interaction evidences that are extracted and classified from PubMed (from 1975 to 2015) via text mining. The DDI evidences are further classified into the following categories: PK in vitro, PD in vitro, PK in vivo, PD in vivo, clinical safety, and case report. Most importantly, cancer drug interaction clinical safety data are mined and manually curated from full-text articles. TDID also includes PK or PD DDI statements from DrugBank, and DDI-ADE associations from FAERS. Altogether, TDID integrates 88,500 pieces of DDI evidences from PubMed, 1,048,000 DDI statements from DrugBank, and over 100,000,000 DDI-ADE pairs from FAERS. Specifically, TDID has 1,480 pieces of cancer DDI toxicity evidences. These cancer DDI toxicity evidences are annotated with patient characteristics, treatment plan including dosages, and toxicity level. The database can be searched by drug name (i.e. single drug, drug pair), and the DDI query results can be sorted by study type. The Translational Drug Interaction Database (TDID) offers user-friendly browsing, searching, and filtering functions. The Translational Drug Interaction Database is available at https://tdid.bmi.osumc.edu.
Citation Format: Shijun Zhang, Heng-Yi Wu, Rohith Vanam, chien-WeiChiang, Lei Wang, Lang Li. Translational drug interaction database (TDID): A knowledgebase for drug interactions [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4400.
Abstract 4410: Automatic detection of consensus gene clusters across multiple single-cell datasets
Authors: Song Qianqian , Su Jing , Miller Lance D. , Zhang Wei 
Published: September 2020

Abstract
Identifying co-expressed gene clusters can provide evidence for cellular activities. Thus, co-expression clustering will be a routine step in single-cell RNA-seq data analysis. We show that commonly used clustering methods for bulk-seq data are not suitable for single-cell data, and produce results that substantially disagree in the biological expectations of co-expressed genes. Herein, we present scLM, a tailored method for single-cell data, to extract co-expressed gene clusters matching the biological expectations and outperform widely used methods. Additionally, scLM can simultaneously cluster multiple single-cell datasets, i.e. consensus clustering, enabling users to leverage the large quantity of public data for novel comparative analysis. ScLM takes raw count data as input and preserves biological variations without being influenced by batch effects from multiple datasets. Results from both simulation data and real experimental data demonstrate that scLM outperforms the existing methods with considerably improved accuracy. To illustrate the biological insights of scLM, we apply it to our in-house and public experimental scRNA-Seq datasets. ScLM identifies novel functional gene modules and refines cell states, which facilitates mechanism discovery and understanding of complex biosystems such as cancers.
Citation Format: Qianqian Song, Jing Su, Lance D. Miller, Wei Zhang. Automatic detection of consensus gene clusters across multiple single-cell datasets [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4410.
Abstract 4411: A gene set enrichment analysis approach in single-cells along pseudotime trajectories reveals the dynamic activity of oncogenic pathways
Authors: Wenzel Alexander T. , Champa Devora , Howell Stephen B. , Mesirov Jill P. , Harismendy Olivier 
Published: September 2020

Abstract
Single cell RNA-sequencing (scRNA-seq) has advanced studies of heterogeneous tissues and cell populations in both healthy and disease states. Dimensionality reduction methods leverage this highly granular data to identify cellular states not captured by “bulk” gene expression or to organize cells along ‘pseudotime' trajectories related to biological processes such as cell cycle or cellular differentiation. Cellular states and trajectories are often annotated using individual marker genes and not gene sets or pathways, despite their more reliable and interpretable association to biological processes. Gene Set Enrichment Analysis (GSEA) was designed to test the enrichment of gene sets in bulk transcriptomic data, but due to the sparsity of the expression data, has not been adapted to single cell analysis. We propose a method to compute gene set enrichment for scRNA-seq data, which calculates an enrichment score for each cell in the dataset. We apply this method to a set of matched ovarian cancer cell lines with acquired resistance to carboplatin. After aligning the cells to a cell-cycle derived pseudotime trajectory, the activity of pathways which were dysregulated in bulk expression profiles appeared to change with the cell cycle progression. The analysis further identified interferon alpha signaling induction in resistant cells, independent of differences in cell cycle progression. Hence, by quantifying and analyzing pathway-level transcriptional activity in the context of a single-cell pseudotime trajectory, the proposed method provides more interpretable annotation of biological processes altered in cancer progression and treatment.
Citation Format: Alexander T. Wenzel, Devora Champa, Stephen B. Howell, Jill P. Mesirov, Olivier Harismendy. A gene set enrichment analysis approach in single-cells along pseudotime trajectories reveals the dynamic activity of oncogenic pathways [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4411.