bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 5070: Sulforaphane is a novel inhibitor of breast cancer-induced osteolytic bone resorption
Authors: Pore Subrata K. , Latoche Joseph D. , Anderson Carolyn J. , Adamik Juraj , Galson Deborah L. , Weiss Kurt R. , Lee Boeun , Watters Rebecca J. , Kumta Prashant N. , Singh Shivendra V. 
Published: August 2019

Abstract
Bone is the preferred site for metastatic spread in women with advanced breast cancer, and skeletal complications is associated with high morbidity and mortality. Nearly 70% of luminal-type breast cancer patients with metastases to bone experience skeletal complications due to enhanced osteoclastogenesis (osteoclast activation) that increases bone resorption, pain, pathological fractures, spinal cord compression, and hypercalcemia. The present study was designed to determine the effect of a cruciferous vegetable component (Sulforaphane; SFN) on breast cancer-induced osteoclastogenesis. Osteoclast differentiation in mouse bone marrow monocytes (BMM) was inhibited significantly upon the addition of 10% conditioned-media (CM) from SFN-treated breast cancer cells belonging to different subtypes, including MDA-MB-231, MCF-7, and SK-BR-3, in comparison with corresponding control. PCR-based gene expression profiling identified a common set of genes downregulated by SFN treatment compared to vehicle-treated control in MDA-MB-231, MCF-7, and SK-BR-3 cells, including osteoclastogenesis promoting transcription factors (RUNX2, NF-κB, and SOX-9) and certain soluble molecules (MMP9, TNFα, and Cathepsin K). Many of these gene expression changes were confirmed in MDA-MB-231, MCF-7, and SK-BR-3 cells by RT-PCR or western blotting. To determine the in vivo efficacy, SFN was administrated orally (1 mg per mouse; three times/week) to athymic mice intracardially injected with MDA-MB-231-Luc cells. SFN administration significantly inhibited the multiplicity of bone metastases and increased the bone volume relative to total volume. SFN-mediated prevention of osteoclastogenesis was associated with a significant decrease in TRAP-positive osteoclasts in bones and the levels of Cathepsin K, IL-8, and RANKL in serum. Altogether, this study demonstrates, for the first time to the best of our knowledge, that SFN is a potent inhibitor of breast cancer-induced osteoclastogenesis and bone resorption in vitro and in vivo. This study was partly funded by a pilot project grant from the UPMC Hillman Cancer (NCI grant P30 CA047904; Robert L. Ferris- Principal Investigator) and RO1 CA142604 and CA129347 awarded by the National Cancer Institute (Shivendra Singh- Principal Investigator).
Citation Format: Subrata K. Pore, Joseph D. Latoche, Carolyn J. Anderson, Juraj Adamik, Deborah L. Galson, Kurt R. Weiss, Boeun Lee, Rebecca J. Watters, Prashant N. Kumta, Shivendra V. Singh. Sulforaphane is a novel inhibitor of breast cancer-induced osteolytic bone resorption [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 5070.
Abstract 5081: Leelamine is a novel inhibitor of fatty acid synthesis in prostate cancer
Authors: Singh Krishna B. , Singh Shivendra V. 
Published: August 2019

Abstract
Increased beta-oxidation of fatty acids to meet energy demand is a rather unique characteristic of a subset of human prostate cancers. A safe and effective intervention for inhibition of fatty acid synthesis is still a clinically unmet need. We have shown previously that leelamine (LLM), a phytochemical derived from pine tree bark, suppresses expression and activity of full-length androgen receptor (AR) and its splice variants in vitro and in vivo in preclinical models of prostate cancer. Because AR is implicated in regulation of fatty acid metabolism, the present study was undertaken to determine the effect of LLM on this metabolic pathway. Treatment of a castration-resistant (22Rv1) as well as an androgen-responsive (LNCaP) human prostate cancer cell line with LLM (2.5 and 5 µmol/L) resulted in downregulation of key fatty acid synthesis enzyme proteins including ATP citrate lyase (ACLY), acetyl-CoA carboxylase 1 (ACC1), fatty acid synthase (FASN), and sterol regulatory element-binding protein 1 (SREBP1). LLM treatment also decreased intracellular levels of total free fatty acids and neutral lipid droplets in LNCaP and 22Rv1 cells. Consistent with the in vitro results, we also observed a significant decrease in ACLY and SREBP1 protein expression and number of neutral lipid droplets in vivo in tumor tissue sections of 22Rv1 xenografts after intraperitoneal administration of LLM (9.1 mg/kg bw/ day, 5 times/week) compared to controls. Studies are in progress to determine if overexpression of AR and/or SREBP1 confers protection against fatty acid synthesis inhibition by LLM. In conclusion, it is reasonable to postulate that suppression of AR-SREBP1 regulated fatty acid metabolism is an important mechanism in prostate cancer inhibition by LLM. This study was supported by the grant RO1 CA101753 awarded by the National Cancer Institute.
Citation Format: Krishna B. Singh, Shivendra V. Singh. Leelamine is a novel inhibitor of fatty acid synthesis in prostate 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 5081.
Abstract 5086: Acetyl-L-carnitine (ALCAR) inhibits angiogenesis, migration and macrophage recruitment in prostatic cancer cells
Authors: Albini Adriana , Bruno Antonino , Baci Denisa , Gallazzi Matteo , Tramacere Matilde 
Published: August 2019

Abstract
Many efforts have been addressed on the identification of novel active compounds from natural sources, endowed with anti-proliferative, anti-oxidant, chemopreventive properties, and their ability to target the tumour microenvironment (TME). Through metabolomics approaches we previously found that in serum samples from prostate cancer (PCa) patients, three carnitine family members were significantly decreased, suggesting a potential protective role of carnitine against PCa. Acetyl-L-carnitine(ALCAR) is an aceticacid ester of carnitine with high bioavailability and is involved in the transportof fatty acids across the inner mitochondrial membrane. We have showed that ALCAR was able reduce angiogenesis in vitro, acting on the VEGF/VEGFR2 and CXCR4/CXCL12 axes. We also found that ALCAR inhibits inflammatory angiogenesis in vivo, by reducing endothelial cells and macrophage recruitment in the matrigel plugs. Here we investigated the ability of ALCAR to interfere with key functional steps of prostate carcinogenesis and identified the molecular mechanisms involved. The effects of ALCAR on PCa cells were investigated in vitro by functional assays (adhesion, migration and invasion assays), molecular and biochemical approaches (RT-PCR, FACS, Byoplex and western blot). We found that ALCAR reduces apoptosis on PCa cells (PC3, Du-145, LNcap) and inhibits crucial tumorigenic steps such as adhesion, migration and invasion. We than confirmed the results from functional assays at molecular levels, and we found that ALCAR blocks CXCR4/CXCL12 axes, a key regulator of malignant migratory/aggressive phenotype. In accordance with our published paper, we confirmed the anti-angiogenic and ant-inflammatory properties of ALCAR, that we found to lower VEGF and CXCL8 production in PCa cell lines. Furthermore, we found a significantly reduced expression of inflammatory-related chemokines/cytokines involved macrophage recruitment such as CCL2, IL-6 and TNFα in PCa cells. Our results highlight the angio/chemopreventive and anti-inflammatory properties of ALCAR and allow the identification of multiple and overlapping mechanisms of action through which ALCAR inhibits PCa progression / metastasis. Our findings provide the rational for the employment of ALCAR, as a possible supplement for approaches of chemoprevention in subjects at high risk to develop cancer.
Citation Format: Adriana Albini, Antonino Bruno, Denisa Baci, Matteo Gallazzi, Matilde Tramacere. Acetyl-L-carnitine (ALCAR) inhibits angiogenesis, migration and macrophage recruitment in prostatic cancer cells [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 5086.
Abstract 5094: SCOPE: A normalization and copy number estimation method for single-cell DNA sequencing
Authors: Wang Rujin , Lin Danyu , Jiang Yuchao 
Published: August 2019

Abstract
Whole genome single-cell DNA sequencing (scDNA-seq) enables characterization of copy number profiles at the cellular level. This circumvents the averaging effects associated with bulk-tissue sequencing and has increased resolution yet decreased ambiguity in deconvolving cancer subclones and elucidating cancer evolutionary history. ScDNA-seq data is, however, sparse, noisy, and highly variable even within a homogeneous cell population, due to the biases and artifacts that are introduced during the library preparation and sequencing procedure. Here, we propose SCOPE, a normalization and copy number estimation method for scDNA-seq data. The distinguishing features of SCOPE include: (i) utilization of cell-specific Gini coefficients for quality controls and for identification of normal/diploid cells, which are further used as negative control samples in a Poisson latent factor model for normalization; (ii) modeling of GC content bias using an expectation-maximization algorithm embedded in the Poisson generalized linear models, which accounts for the different copy number states along the genome; (iii) a cross-sample iterative segmentation procedure to identify breakpoints that are shared across cells from the same genetic background. We evaluate performance of SCOPE on real scDNA-seq data sets from cancer genomic studies. Compared to existing methods, SCOPE more accurately estimates subclonal copy number aberrations and is shown to have higher correlation with array-based copy number profiles of purified bulk samples from the same patient. We further demonstrate SCOPE on three recently released data sets using the 10X Genomics single-cell CNV pipeline and show that it can reliably recover 1% of the cancer cells from a background of normal and recover the cancer subclonal compositions of 10,000 breast cancer cells.
Citation Format: Rujin Wang, Danyu Lin, Yuchao Jiang. SCOPE: A normalization and copy number estimation method for single-cell DNA sequencing [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 5094.
Abstract 5095: Statistical modeling of transcriptional regulatory states in single-cell RNA-Seq data of tumor and infiltrated immune cells
Authors: Wan Changlin , Chang Wennan , Lu Xiaoyu , Sun Yifan , So Kaman , Cao Sha , Lu Xiongbin , Zhang Chi 
Published: August 2019

Abstract
Single-cell RNA-seq of tumor tissue has greatly strengthened our understanding of tumor development as well as the heterogeneous tissue micro-environment. Many models are dedicated to detect deferentially expressed genes among single cells of different conditions, or to discover (novel) sub-cell types via various clustering methods. However, current statistical models often neglect the zero/low expressions, and use one single modality to fit the rest of the non-zero ones. We believe that zero and low expressions are observed partly due to limited experimental resolution; on the other hand, occasionally, low expressions are falsely observed as non-zero due to the existence of incompletely degraded mRNA. In addition, the severe heterogeneity and fast dynamics of single cells makes it invalid to fit their expressions in one single modality. Here, we propose a left truncated mixture Gaussian (LTMG) distribution to uncover the multimodality in single cells’ gene expression while properly handling the zero and low expressions. LTMG is motivated by the kinetic relationships among the mRNA abundance, transcriptional regulatory signals (TRSs), and mRNA metabolism in a cell. Specifically, observations of a gene’s expression are fitted as mixture Gaussian distributions which assumes zero and low expressions as left censored data. By treating each mixing component as one state of the transcriptional regulators, we combined our LTMG model and our in-house bi-clustering algorithm to infer modules of genes co-regulated by certain (unknown) transcriptional regulatory signal, which is only shared by a subset of all the single cells. Application of the method to three high quality single-cell RNA-seq data sets of T cells extracted from liver, colon and lung cancer micro-environments, identified varied transcriptional regulations specific to subclasses of T cells.
Citation Format: Changlin Wan, Wennan Chang, Xiaoyu Lu, Yifan Sun, Kaman So, Sha Cao, Xiongbin Lu, Chi Zhang. Statistical modeling of transcriptional regulatory states in single-cell RNA-Seq data of tumor and infiltrated immune cells [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 5095.
Abstract 5104: Pan-cancer classification on gene expression data by neural network
Authors: Yu Kijin , Kim Bong-Hyun , Lee Peter Chang Whan 
Published: August 2019

Abstract
Cancer classification based on molecular signatures such as gene expression profiles has been provided insights on causes of the disease or provided guidance for selecting patient’s treatment regimen. We developed a cancer classifier that can identify 13 different types of cancers or a normal tissue based on bulk RNA-seq data as well as single cell RNA-seq data. Training was performed with 6,483 samples from 13 cancer tissues and 640 samples from tumor-adjacent normal tissues in The Cancer Genome Atlas based on 300 most significant genes for the binary classification of each cancer. Then, we compared Neural network, Support vector machine, Naïve Bayes and Random Forest methods. Neural network performed consistently better than other methods, even it took shorter time to train. We further applied our approach to bulked single cell data that is generated from single cell RNA-seq and found our model successfully classified cancer types and normal samples. Gene set enrichment analysis comparing cancer types found genes in cell cycle-related pathways, which highlights the differences between cancer types might be in their abilities to grow in different conditions. We also found genes that expressed in multiple cancers that can be novel biomarkers and therapeutic targets.
Citation Format: Kijin Yu, Bong-Hyun Kim, Peter Chang Whan Lee. Pan-cancer classification on gene expression data by neural network [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 5104.
Abstract 5107: Putative biomarkers for tumor sample purity prediction based on gene expression data
Authors: Li Yuanyuan 
Published: August 2019

Abstract
Tumor purity is the percent of cancer cells present in a sample of tumor tissue. The non-cancerous cells (immune cells, fibroblasts, etc.) have an important role in tumor biology. We applied a supervised machine learning method, XGBoost, to data from 33 TCGA tumor types to predict tumor purity using RNA-seq gene expression data. Across the 33 tumor types, the median correlation between observed and predicted tumor-purity ranged from 0.75 to 0.87 with small root mean square errors, suggesting that tumor purity can be accurately predicted using expression data. We further confirmed that a ten-gene set (CSF2RB, RHOH, C1S, CCDC69, CCL22, CYTIP, POU2AF1, FGR, CCL21, and IL7R) whose expression levels alone were predictive of tumor purity regardless of tumor type. We tested the predicative power of the ten-gene set on an independent dataset that was not from TCGA and showed that the expression levels of the ten genes can accurately predict the cancer cell proportions in those independent samples. The correlation coefficient between the predicted and experimentally obtained tumor purity was 0.88. Our analyses suggested that the ten-gene set may serve as a biomarker for tumor purity prediction using gene expression data.
Citation Format: Yuanyuan Li. Putative biomarkers for tumor sample purity prediction based on gene expression 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 5107.
Abstract 5111: HuPharm: An interactive data analysis platform for preclinical studies
Authors: Mao Binchen , Guol Sheng , Li Qixiang 
Published: August 2019

Abstract
Background: Preclinical studies, the first stage of the drug development process, must always have proper planning, execution, analysis and reporting in order to preserve scientific integrity. The decision to advance to human clinical trials depends heavily on results of these studies. For To facilitateing in vivo pharmacology data analysis and interpretation, we developed a web platform (HuPharm) that automates statistical pipelines and report generation. Its intuitive and interactive interface will greatly help researchers, who more often than not are non-statisticians, on data validation, exploratory data analysis and statistical analysis.
Methods: We use RStudio’s shiny package to build an interactive web application (HuPharm), and we use knitr and rmarkdown packages to generate a highly informative data analysis report. HuPharm is deployed on the web using Shinyapps.io developed by RStudio. Based on the distribution of input data, HuPharm will automatically performs parametric tests such as ANOVA and Welch’s t-test or non-parametric tests such as Kruskal-Wallis test and Mann-Whitney U test. Additionally, HuPharm is capable of conducting survival analysis, including both non-parametric Kaplan-Meier analysis and Ccox regression. Aside from classical tests, we have also implemented a number of state-of-the-art analytics on HuPharm, such as multilevel/hierarchical linear models, frailty analysis and joint modeling of longitudinal and survival data. Last by not the least, power analysis module in HuPharm can help researchers with their study design by power and sample size calculation.
Results: Based upon user inputs, HuPharm will dynamically generate a series of tables and figures to facilitate preclinical data analysis, such as tumor volume summary statistic table and plots, tumor growth curves, omnibus group comparison plots, all pairwise group comparison table and post hoc analysis results table. In addition, HuPharm can generate one nicely formatted study report including all those aforementioned figures and tables in both html and pdf format. Powered by shiny server’s interactive design, users can easily adjust the parameters to explore their data in a highly flexible and responsive way.
Conclusions: HuPharm is a convenient and powerful tool to aid researchers in the field of preclinical trials studies for their study design and data analysis. It will help to add rigor and improve the quality of preclinical studies.
Citation Format: Binchen Mao, Sheng Guol, Qixiang Li. HuPharm: An interactive data analysis platform for preclinical studies [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 5111.
Abstract 5113: A flexible pipeline for precision medicine biomarker detection and prediction of treatment response
Authors: Hughitt V Keith , Gorjifard Sayeh , Michalowski Aleksandra M. , Simmons John K. , Dale Ryan , Polley Eric C. , Keats Jonathan J. , Mock Beverly A. 
Published: August 2019

Abstract
Recent years have seen an explosion in the availability of paired molecular profiling and drug screen data, providing an unprecedented opportunity for the development of targeted therapies based on an individual’s genetic background. Despite a number of recent successes in diseases ranging from cystic fibrosis to cancer, significant hurdles remain in our ability to accurately predict treatments based on molecular profiling data. In particular, few such tools exist that allow the integration of heterogeneous data types (e.g. genomic, transcriptomic, and somatic mutations), along with high-throughput drug screen data to make predictions about treatment efficacy. Here, we describe a generalized open-source pipeline developed for the analysis of precision medicine data, Pharmacogenomics Prediction Pipeline, or “P3”. The modular design of P3 enables the inclusion of arbitrary input data types and the selection from multiple alternative machine learning algorithms, while automated statistical and visualization reporting steps incorporated throughout the pipeline assist in parameter tuning and early detection of problematic data components. Molecular profiling data is further enriched by the incorporation of external biological information in the form of pathway and gene set annotations such and Gene Ontology (GO) and The Molecular Signatures Database (MSigDB). To demonstrate the use of P3 for preclinical biomarker prediction, we applied P3 to an unpublished multiple myeloma dataset consisting of exome, RNA-Seq, CNV, and drug screen data for 1,912 compounds across 47 tumor cell lines. Specifically, P3 was used to predict molecular features associated with response to treatment for all drugs where a differential response to treatment was observed across patients. Furthermore, molecular profiling and drug screen data for 267 drugs and over a thousand cell lines spanning multiple cancer types from the Genomics of Drug Sensitivity in Cancer (GDSC) project were analyzed using P3, providing insights into shared mechanisms of drug sensitivity and resistance across different cancer and treatment types.
Citation Format: V Keith Hughitt, Sayeh Gorjifard, Aleksandra M. Michalowski, John K. Simmons, Ryan Dale, Eric C. Polley, Jonathan J. Keats, Beverly A. Mock. A flexible pipeline for precision medicine biomarker detection and prediction of treatment response [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 5113.