bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 1615: Correlation between demographic factors, dietary habits and urinary micronutrient content among minority groups in New York City
Authors: Zambrano Cristina , Beeber Maayan , Panitz April , Wyka Katarzyna , Ibrahim Safa , Tan Yin , Ma Grace , Navder Khursheed , Yeh Ming-Chin , Ogunwobi Olorunseun 
Published: August 2019

Abstract
Cancer is the second leading cause of death in the United States. A diet low in fruits and vegetables increases the risk of certain cancer types. Moreover, socioeconomic factors contribute to limited access to fresh and healthy foods and limited opportunities for safe physical activity, leading to poor physical health. In this multidisciplinary, IRB approved study, we explored relationships between demographic factors, self-reported dietary behaviors and gallic acid, a polyphenolic micronutrient that correlates well with fruit and vegetable intake. We recruited participants at a senior center in East Harlem, New York City, a racially diverse and underserved community. The participants completed a NIH-validated survey through which we assessed their dietary habits and collected standardized demographic data and history of cancer. Urine samples from participants were analyzed for gallic acid. So far, 33 participants completed the survey and 25 of them provided urine samples for gallic acid analysis. We found an association between demographic information (Race/ethnicity and age) and intake of certain foods. Specifically, age was negatively associated with french fries/fried potatoes, cooked dried beans and tomato soup intake (p&lt;0.05) and Black/African American race was associated with increased intake of juice (p&lt;0.05). A positive association between self-reported food intake and gallic acid concentration was only observed with french fries/fried potatoes (R = 0.641, p&lt;0.001). Overall, the results suggest a significant association between dietary habits and demographic factors. Ongoing work include further recruitment of participants, and multivariate analysis of the data. This will be followed by an intervention consisting of active nutritional education, and urinary gallic acid analysis to assess the impact of nutritional education on dietary habits.
Citation Format: Cristina Zambrano, Maayan Beeber, April Panitz, Katarzyna Wyka, Safa Ibrahim, Yin Tan, Grace Ma, Khursheed Navder, Ming-Chin Yeh, Olorunseun Ogunwobi. Correlation between demographic factors, dietary habits and urinary micronutrient content among minority groups in New York City [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 1615.
Abstract 1616: Adjuvant supplementation of docosahexaenoic acid with 2-deoxyglucose, metformin, and aspirin on cellular metabolism in breast, lung, and prostate tumor cells
Authors: Ma Irvin V. , Mouradian Michael , Cooper Andrew R. , Vicente Erika D. , Pardini Ronald S. 
Published: August 2019

Abstract
A standard treatment for cancer often involves the use of adjuvant supplementation with targeted therapy to maximize tumor suppression. Recent studies have shown that docosahexaenoic acid (DHA; C22:6 n-3), a long chain omega-3 polyunsaturated fatty acid, acts on an array of molecular oncogenes that are often targeted by anti-tumorigenic drugs. After a careful review of DHA’s mechanistic properties, 2-deoxyglucose (2DG), metformin, and aspirin were identified as drugs that share similar molecular targets as DHA. 2DG and metformin both target cellular metabolism by inhibiting intracellular ATP, albeit different mechanisms. 2DG inhibits glycolysis, while metformin targets the mitochondria. Aspirin inhibits inflammation via irreversible acetylation of platelet COX. The current study investigates the effects of DHA in combination with each drug on numerous cancer subtypes to determine if concurrent treatments can synergistically inhibit tumor proliferation. In BT-474 human breast ductal carcinoma, MDA-MB-231 human breast adenocarcinoma, and A549 human lung adenocarcinoma, DHA was shown to enhance the tumor-inhibiting effects of 2DG via intracellular ATP reduction. In contrast, DHA showed no effect with metformin on the same tumor cell lines. With limited studies localizing DHA’s metabolic effects, these results suggest that DHA may have potential selectivity for ATP inhibition via glycolysis. In response to DHA + aspirin, the PC-3 prostate adenocarcinoma and MDA-MB-231 displayed synergistic inhibition of cell viability, while LNCaP prostate carcinoma showed an additive effect. With studies showing DHA’s ability to inhibit endothelial COX, further studies will be done to determine whether the synergistic effects of DHA + aspirin expand to COX expression as well. Moreover, downstream targets of 2DG and metformin will be studied to determine the extent of DHA enhancement on oncogenic targets, such as mTOR, 4EB-P1, and GRB10. The results of this study provide preliminary evidence for the efficacy of adjuvant supplementation with DHA in combination with anti-tumorigenic drugs. With ongoing analysis, the potential for adjuvant nutritional supplementation will allow for additional treatment strategies for future patients.
Citation Format: Irvin V. Ma, Michael Mouradian, Andrew R. Cooper, Erika D. Vicente, Ronald S. Pardini. Adjuvant supplementation of docosahexaenoic acid with 2-deoxyglucose, metformin, and aspirin on cellular metabolism in breast, lung, and prostate tumor 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 1616.
Abstract 162: ASB13 inhibits breast cancer progression and metastasis through SNAI2 degradation and transcriptional regulation of YAP
Authors: Fan Huijuan , Wang Xuxiang , Li Wenyang , Shen Minhong , Wei Yong , Zheng Hanqiu , Kang Yibin 
Published: August 2019

Abstract
Metastasis is responsible for the majority of cancer-related deaths. The transcription factor SNAI2 promotes metastasis by facilitating tumor cell invasion and tumor initiating activity. However, its post-translational regulation is less studied. We performed a dual luciferase-based, genome-wide E3 ligase siRNA library screening and identified ASB13 as an E3 ubiquitin ligase that targets SNAI2 for ubiquitination and degradation. ASB13 knockout in breast cancer cells leads to increased cell migration and decreased F-actin polymerization; while overexpression of ASB13 suppresses lung metastasis formed by LM2 cells. Furthermore, we discovered that ASB13 knockout decreases YAP expression, which is dependent on increased SNAI2 protein level. YAP functions as a tumor suppressor gene in breast cancer, as YAP knockout increases tumorsphere formation, anchorage-independent colony formation, and cell migration. Clinical data analysis reveals that ASB13 expression is positively correlated with overall survival in breast cancer patients. These findings establish the ASB13-SNAI2-YAP axis as a regulatory mechanism for breast cancer migration and metastasis.
Citation Format: Huijuan Fan, Xuxiang Wang, Wenyang Li, Minhong Shen, Yong Wei, Hanqiu Zheng, Yibin Kang. ASB13 inhibits breast cancer progression and metastasis through SNAI2 degradation and transcriptional regulation of YAP [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 162.
Abstract 1624: Antigen-independent de novo prediction of cancer-associated TCR repertoire
Authors: Li Bo 
Published: August 2019

Abstract
Cancer-associated T cells play a critical role in mediating immune responses in the anti-tumor immunity. However, due to the complex nature of cancer antigens, and the limited experimental approaches for collecting antigen-specific T cells, it remains a difficult task in cancer immunology to detect cancer-associated T cells. In this project, we have developed an antigen-independent machine learning approach to perform de novo prediction of cancer-associated T cells. Specifically, we have discovered a signature in the CDR3 sequence that can distinguish cancer-associated T cells from others. We applied this signature to immune repertoire data to define a “cancer score”, which is related to the probability of a repertoire being cancer-associated. We applied this approach to investigate 15 sample cohorts from public domain. These cohorts cover healthy donors, viral infected individuals and cancer patients from both early and late stages. Surprisingly, we observed consistently and significantly higher cancer scores using the cancer patients’ immune repertoire data, while none of the non-cancer repertoire was significant compared to healthy donors. We therefore used repertoire cancer score as a single predictor for cancer status to distinguish cancer patients from healthy donors, and observed high prediction power measured by area under the ROC (AUROC) curves. The AUROC reached 0.90 for early breast cancer patients, which is better than a number of current early prediction methods based on cancer biomarkers, such as PSA, CA-125, CEA, etc. In addition, cancer scores derived from certain late-stage cancers are associated with patient response to checkpoint blockade therapies, suggesting that it may also be used in combination with existing biomarkers, such as PD-L1 expression or cancer neoantigen load, to improve the prediction of clinical outcome of these cancer types. We anticipate broad utilities of cancer scores in cancer diagnosis and immunotherapy prognosis with the rapidly accumulating TCR repertoire sequencing data in clinical studies.
Note: This abstract was not presented at the meeting.
Citation Format: Bo Li. Antigen-independent de novo prediction of cancer-associated TCR repertoire [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 1624.
Abstract 1627: Using internet-platform technology to train community-based researchers in developing countries: Lessons learned from a Caribbean study
Authors: Thomas-Purcell Kamilah B. , Primus-Jospeh Marva Primus , Richards Christine , Atkins Lydia S. , Blaze Julie , Wylie Patsy , James Ann Marie Burnie , Ashing Kimlin 
Published: August 2019

Abstract
Cancer is increasing as a health crisis in the Caribbean region. In the U.S. advocate engagement and Community Based Participatory Research (CBPR) have been used successfully to partner with community members in each stage of the research process thereby utilizing the knowledge and experience of those individuals for improved community responsive research and greater societal benefit. We utilized advocate engagement and CBPR towards building a cancer prevention program of research between U.S. and Caribbean researchers. In 2016-2017, a two-phased CBPR research study was initiated to address breast and cervical cancer screening in the four countries of the English-speaking Windward Islands of the Caribbean. The aims were to determine 1) community member’s perceptions of breast and cervical cancer screening and 2) gatekeepers’ perceptions of the quality and availability of breast and cervical cancer screening and treatment services. Mobile communication and internet-platform technologies were used to successfully train Caribbean community researchers on qualitative data collection and analysis methods. Our results showed that the incorporation of internet-based technology facilitated training, communication, social interactions, and alleviated transportation challenges. In particular, mobile platforms including GoToTraining, WhatsApp, Dedoose, Google Drive, and email were to used to deliver interactive training modules to community researchers and facilitate the collaborative development of recruitment materials, data collection instruments, data management, reports, manuscripts and results dissemination. Participatory research studies of this nature not only shed light on the cancer burden in developing nations but also builds community trust and capacity to engage in future international research.
Citation Format: Kamilah B. Thomas-Purcell, Marva Primus Primus-Jospeh, Christine Richards, Lydia S. Atkins, Julie Blaze, Patsy Wylie, Ann Marie Burnie James, Kimlin Ashing. Using internet-platform technology to train community-based researchers in developing countries: Lessons learned from a Caribbean study [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 1627.
Abstract 1629: Classification of cancer histology images using deep learning
Authors: Xie Weidong 
Published: August 2019

Abstract
Pathological evaluation of tumor tissue images stained with hematoxylin and eosin (H&E) is pivotal and specialists often disagree on the final diagnosis. Meanwhile, automated image analysis approaches have great potential to increase precision of diagnosis, help reduce human error and cut cost. In this study, we utilize various computational methods based on deep learning framework and build a stand-alone diagnostic tool to effectively classify different histopathology images across different types. The dataset of tissue slides from eligible patients with breast cancer/ thyroid cancer consists of 45,000 samples in our hospital. Available tissue samples above were split into a training set, for learning the CNN parameters, and test set, for evaluating its performance. An accuracy of 94% was obtained for non-cancer (i. e. normal or benign) vs malignant (i. e. invasive carcinoma or papillary thyroid carcinoma). In addition, we also demonstrated the utility of our model to discriminate between two subtypes of lung cancer. It provides pathologists or medical technicians a straightforward platform to use without requiring sophisticated computational knowledge, and cancerization would be identified which is not visible under a single microscope.
Citation Format: Weidong Xie. Classification of cancer histology images using deep learning [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 1629.
Abstract 163: Identification and characterization of TGFBI in circulating tumor cell subline from pancreatic cancer cell line
Authors: Muramatsu Tomoki , Sato Taku , Tanabe Minoru , Inazawa Johji 
Published: August 2019

Abstract
Distant metastasis to liver, lung, brain, or bone occurs via circulating tumor cells (CTCs). We hypothesized that a subset of CTCs had more malignant features than tumor cells at the primary site. We established a highly malignant cell line, Panc-1-CTC, derived from the human pancreatic cancer cell line Panc-1 (Panc-1-P) using an in vivo selection method. Panc-1-CTC cells exhibited more profound migratory and invasive abilities than Panc-1-P cells in vitro. In addition, Panc-1-CTC cells had a higher tumor-forming ability than Panc-1-P cells in vivo. To investigate whether a difference in malignant phenotypes exists between Panc-1-P and -CTC cells, we performed comprehensive gene expression array analysis. As a result, Panc-1-CTC significantly expressed transforming growth factor beta-induced (TGFBI), an extracellular matrix protein, more abundantly than did Panc-1-P cells. TGFBI is considered to regulate cell adhesion, but its functions remain unclear. In the present study, knockdown of TGFBI reduced cell migration and invasion abilities, whereas overexpression of TGFBI increased both abilities. Moreover, elevated expression of TGFBI was associated with poor prognosis in patients with pancreatic cancer. Taken together, TGFBI might be targeted for cancer therapy and its expression could be a biomarker for the prediction of prognosis in pancreatic cancer.
Citation Format: Tomoki Muramatsu, Taku Sato, Minoru Tanabe, Johji Inazawa. Identification and characterization of TGFBI in circulating tumor cell subline from pancreatic cancer cell line [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 163.
Abstract 1632: Deep learning functional associations using histopathology images
Authors: Noorbakhsh Javad , Farahmand Saman , Ha Mohammad Soltanieh , Namburi Sandeep , Zarringhalam Kourosh , Chuang Jeff 
Published: August 2019

Abstract
Histopathological images have critical value for understanding cancer subtype, local structure, and malignancy. Manual interpretation of such images can be time-consuming and error-prone, while also failing to make use of the power of image aggregation for coordinated data mining. Large-scale patient cohorts such as TCGA provide high-resolution H&E images that complement other -omics data, with the important advantage that images provide spatial information about heterogeneity and stromal cell interactions. We implemented a convolutional neural network (CNN) to classify H&E slides according to tissue type, cancer subtype, and genomic mutations. We modified the CNN architecture of Inception v3 according to our problem and trained it with tiled whole slide images from 31 solid cancers in TCGA, and evaluated our algorithm on holdout data. We first classified tumor/normal status from the images and determined AUC values for each cancer type. We then applied our method for tissue classification in a multilabel classification scenario. Furthermore, in few cancers (e.g. gastric cancer) we determined cancer subtypes using supervised learning. Finally we evaluated our model for classifying mutational status of cancer-related genes. To improve classification accuracy, we modified Inception architecture for multi-task classification by replacing the final fully-connected layer by a shared layer which provides the input to individual task layers corresponding to each gene mutation. Furthermore, we adopted image segmentation approaches (e.g.. auto-thresholding) to improve sample background removal, and used several approaches (e.g. resampling, cross entropy weighting) to rebalance the data. In some cases we achieved high classification results (e.g. AUC=0.7 for TP53 in lung adenocarcinoma). Our study suggests that histological images can be used to classify tumors according to their mutational status, and can provide biomarkers for molecular subtyping.
Citation Format: Javad Noorbakhsh, Saman Farahmand, Mohammad Soltanieh Ha, Sandeep Namburi, Kourosh Zarringhalam, Jeff Chuang. Deep learning functional associations using histopathology images [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 1632.
Abstract 1645: Data science for cancer research
Authors: Zheng W. Jim 
Published: August 2019

Abstract
Cancer has been studied at all levels, ranging from molecules, to cells, to individuals, to populations. However, cancer research faces significant challenges such as enormous volume and heterogeneity of the data. Furthermore, new technologies applied to cancer research can generate data at extremely high speeds. These “big data” pose a significant challenge to conventional data analysis methodologies. We established a Data Science and Informatics Core Facility for Cancer Research (DSICCR) to analyze big data and provides integrative solutions to analyze cancer data at all levels. DSICCR supports the cancer research community by establishing a powerful “big data” infrastructure using data science-oriented high-performance computing, deployed with analytic and mining algorithms and software built upon DSICCR faculty’s cutting-edge data science and informatics research. This infrastructure will support DSICCR’s data science and informatics services to the cancer research community through collaborative research, data analysis services, and outreach and education programs. DSICCR is uniquely positioned to leverage existing research networks and make this vast amount of information more available to the cancer research community - an unprecedented opportunity to support multi-scale cancer research statewide.
Citation Format: W. Jim Zheng. Data science for cancer research [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 1645.
Abstract 1654: Detection of structural variations in a cancer reference sample with multiple NGS platforms
Authors: Zhao Yongmei , Xiao Wenming ,   on behalf of Somatic Mutation Working Group of theSEQC2 Consortium
Published: August 2019

Abstract
Structural Variations (SVs) are genetic variations or rearrangements in the structure of chromosome. SVs study plays an important role in cancer research as cancer genome is commonly altered with thousands of structural rearrangements such as insertions, deletions, translocation, inversions, duplications, copy number variations (CNVs). Next-generation sequencing (NGS) technology has provided a cost-effective approach for the comprehensive detection of SVs in human cancer cells.
In this study, we systematically investigated the somatic structural variations in a pair of breast cancer cell lines with multiple NGS platforms including Illumina short-read, 10x genomics linked-read, PacBio long-read, and high-throughput chromosome conformation capture HiC. We systematically evaluated the factors that impact the SVs detection accuracy. We measured the reproductivity of somatic SV detections across platforms and benchmarked the different software tools’ performance. We performed SV integration from different platforms and established a high-confident SV call set for the reference samples. To independently evaluate the accuracy of our SV call set, we used orthogonal methods such as cytogenetic array as well as fusion gene detected from RNA-seq for cross validation. From this study, we identified the unique strengths of each NGS technologies and provide a practical guide to integrate multiple platform data to improve SV detection sensitivity and accuracy in cancer genome.
Note: This abstract was not presented at the meeting.
Citation Format: Yongmei Zhao, Wenming Xiao, on behalf of Somatic Mutation Working Group of theSEQC2 Consortium. Detection of structural variations in a cancer reference sample with multiple NGS platforms [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 1654.