bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract LB-182: Combination of sulforaphane and 5-aza-2′-deoxycytidine slows the growth and upregulates the chemoattractant CCL5 in metastatic melanoma
Authors: Chiang Tung-Chin , Koss Brian , Storey Aaron J. , Zabaleta Jovanny , Choudhury Samrat Roy , Shields Bradley D. , Draper Elizabeth C. , Su L Joseph , Tackett Alan J. 
Published: September 2020

Abstract
UV exposure-induced oxidative stress is implicated as a driving mechanism for melanoma. Increased oxidative stress results in DNA damage and epigenetic dysregulation. We previously found that a low dose of the antioxidant sulforaphane (SFN) in combination with the epigenetic drug 5 –aza –2 ′–deoxycytidine (DAC) slowed cell growth in melanoma at a greater rate than DAC alone. Here, we report a multi –omics analysis with combination and single drug treatment. We observed a significant increase in global protein abundance with an altered level of expression in the combination treatment as compared to individual treatment. Proteins associated with methylation patterns and with reactive oxidative species were found dysregulated in combination treatment. A comparison analysis of proteomic and transcriptomic data with Ingenuity Pathway Analysis showed common canonical pathways involved in phosphorylation (ERK/MAPK signaling) and immune response with combination treatment. The global DNA methylation pattern was analyzed to explore the impact of combination and single drug treatment on methylation status. Interestingly, we observed an increase in the levels of a critical immuno-regulator cytokine, C –C motif ligand 5 (CCL5), which functions as a chemoattractant for natural killer (NK) cells. Animal studies showed the level of CCL5in blood and tumor was increased with DAC and SFN combination treatment compared to that of control, which coincided with tumor reduction. Our data demonstrate the impact of SFN on the effectiveness of the FDA –approved drug DAC to reduce metastatic melanoma tumors.
Citation Format: Tung-Chin Chiang, Brian Koss, Aaron J. Storey, Jovanny Zabaleta, Samrat Roy Choudhury, Bradley D. Shields, Elizabeth C. Draper, L Joseph Su, Alan J. Tackett. Combination of sulforaphane and 5-aza-2′-deoxycytidine slows the growth and upregulates the chemoattractant CCL5 in metastatic melanoma [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 LB-182.
Abstract LB-230: Nanotherapy targeting metastatic factor RHAMMB-positive pancreatic neuroendocrine tumors
Authors: Lee Seung Koo , Chen Xiang , Du Yi-Chieh Nancy , Tung Ching-Hsuan 
Published: September 2020

Abstract
Pancreatic neuroendocrine tumors (PNETs) represent one-third of gastroenteropancreatic neuroendocrine tumors and are the second malignancy of the pancreas. The 5-year survival rate of metastatic PNETs is only about 15%. We have recently demonstrated that receptor for hyaluronic acid-mediated motility isoform B (RHAMMB) is upregulated in PNETs and promotes metastasis of PNETs. We have also reported that Bcl-xL accelerates the formation of PNETs with invasive properties. Here, we propose a RHAMM-targeted combination therapy (RCT), as a novel treatment for PNETs. RCT is designed to accomplish a synergistic apoptotic/anticancer effect, by aiming RHAMMB, a receptor for hyaluronic acid (HA), and codelivering Bcl-xL siRNA with mitochondria-fusing peptide to kill PNET cells. Yet, it is a substantial challenge to codeliver multiple drugs to RHAMM-expressing tumors. To simultaneously transport these unconventional therapeutics for a synergistic effect, a siRNA for Bcl-xL and a mitochondria-fusing peptide are layered onto a nanocore by charge-charge interaction, followed by a layer of HA for targeting RHAMMB. We showed that the prepared RCT is efficiently internalized by RHAMMB-positive cells, but not by RHAMMB-negative cells. We have found that the encapsulated Bcl-xL siRNA and the mitochondria-fusing peptide are released inside cells to silence Bcl-xL gene expression and induce apoptosis by fusing the mitochondrial membrane. A synergistic cell killing effect was achieved in vitro (&gt; 83% cell death). In a preclinical mouse model, we demonstrated that systemically-injected RCT significantly reduced tumor burden (&gt; 65% reduction) and sustained the blood glucose levels of mice bearing RHAMMB-positive PNETs. Together, the developed RCT can serve as a promising drug delivery system for RHAMMB-overexpressed cancer treatments.
Citation Format: Seung Koo Lee, Xiang Chen, Yi-Chieh Nancy Du, Ching-Hsuan Tung. Nanotherapy targeting metastatic factor RHAMMB-positive pancreatic neuroendocrine tumors [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 LB-230.
Abstract LB-249: PiRADL: An advanced deep learning tool for piRNA detection and its application in cancer biomarker identification
Authors: Chen Shaoqiu , Deng Youping 
Published: September 2020

Abstract
PIWI-interacting RNAs (piRNAs) are a large class of non-coding RNAs expressed in animal cells. Although many programs involved in identifying piRNAs from small RNA sequencing datasets, the boundaries are still hard to overcome. Here we developed a new tool called piRADL, by using deep learning to identify piRNA sequences. We transform the sequences into the 3D Numpy arrays(16X32X256), then the conventional neural network classifier was applied to train on the datasets from four different species (rat, human Caenorhabditis elegans, and Drosophila melanogaster). In the validation dataset, the piRADL shows better performance in comparison with other programs in accuracy, precision, sensitivity, and specificity. It is user-friendly, the user only needs to submit the sequences to the software. Based on this tool, we have found some interesting PiRNA prognosis biomarkers to predict colorectal, lung and breast cancer patient survival.
Citation Format: Shaoqiu Chen, Youping Deng. PiRADL: An advanced deep learning tool for piRNA detection and its application in cancer biomarker identification [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 LB-249.
Abstract LB-251: Investigation of few-shot deep learning techniques on a small, high dimensional head and neck carcinoma dataset for prediction of cancer recurrence
Authors: Ondrey Frank G. , Schutte Dalton 
Published: September 2020

Abstract
Introduction: Artificial Intelligence (AI) techniques may be useful in predictive oncology. We examined the feasibility of applying open source Few-shot learning to genomics data for the purpose of predicting head and neck cancer recurrences. We compared basic feed forward neural networks (vanilla networks) to a special architecture designed for few-shot learning called a Siamese network. Methods: Affymetrix U133A gene chip data (D=22,215, 41 tumor/13 normal) of patients with non-recurrent (46.34%) and recurrent (53.66%) head and neck carcinomas (nRHNC and RHNC, respectively) was analyzed. Shallow (one-layer) and deep (three-layer) variants were examined for both vanilla and Siamese networks to predict RHNC using the PyTorch framework. Four models were built using PyTorch: one shallow vanilla (SV) network with a single layer of 100 neurons, based on the top performing model from the authors' previous work, one deep vanilla (DV) network with layers of 256, 64, and 8 neurons, one shallow Siamese (SS) network with a single layer of 100 neurons, and one deep Siamese (DS) network with layers of 256, 64, and 8 neurons. Each model was initialized, trained, and evaluated 11 times. Kaiming initialization and Stochastic Gradient Descent with Nesterov Momentum (0.5) was employed with typical assumptions. Results: The model with the highest mean F1 score, AUC, and sensitivity was the SV network (0.7001, 0.6667, and 0.6800 respectively) the model with the highest sensitivity was DV (0.7217). When comparing the mean metrics for the following pairs: SV/DV, SS/DS, SV/SS, and DV/DS, the only difference of means that were found to be statistically significant was the SV/SS pairing for F1, AUC, and specificity. Conclusion: The simplest model, with the least representational capacity, performed best but not in a statistically significant manner in nearly all cases. Interestingly, the DS model performed better than the SS model AUC and specificity while the SV model performed better than its deep counterpart in those metrics. In this initial analysis, we conclude that it is feasible to apply PyTorch AI to Affymetrix gene chips to obtain meaningful data about head and neck cancer recurrences.
Citation Format: Frank G. Ondrey, Dalton Schutte. Investigation of few-shot deep learning techniques on a small, high dimensional head and neck carcinoma dataset for prediction of cancer recurrence [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 LB-251.
Abstract LB-267: Detection of lung cancer by metabolomics of exhaled breath and machine learning
Authors: Qiu Mantang , Li Hang , Meng Shushi , Li Qingyun , Zhou Zuli , Wang Jun 
Published: September 2020

Abstract
Background: Exhaled breath-based test is an attractive option for cancer detection due to it is non-invasive and feasible nature. Here, we reported an effective approach to detect early-stage lung cancer (LC) by integration of metabolomics of exhaled breath and machine learning.
Methods: Healthy controls (HC) were adults who received low-dose CT for annual physical examination with no calcified pulmonary nodules and without history of cancer within 5 years. Treatment-naïve LC patients without history of cancer were enrolled. Exhaled breath samples of LC patients were collected before surgery. Exhaled breath samples were collected and stored in Tedla bags and detected by a high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS). A CO2 sensor was applied during exhaled breath collection to ensure only “alveolar air” was collected. Detection model construction and feature selection were performed by support vector machine (SVM).
Results: Firstly, an exploratory cohort of 119 HC and 118 LC was set up. Exhaled breath samples were directly detected by HPPI-TOFMS and 32500 features were extracted from each sample. 22 features were selected and subjected to SVM to construct detection model in the exploratory cohort, which was randomly divided into training (n=159) and test (n=78) set. This model achieved a sensitivity of 89.74%, specificity of 92.31%, and accuracy of 89.74% in the test set, suggesting exhaled breath is accurate for LC detection. Secondly, a quantitative detection method was established by introducing mixed internal standard gas at 15 mL/min controlled by a mass flow meter, by which individual violate organic compound could be quantified. Then, a prospective cohort of 53 LC and 129 HC was set up and detected by the quantitative method. For mass spectrometry data with inner standard gas, 21 features were selected by SVM. Similarly, the prospective cohort was randomly divided into training (n=122) and test (n=60) set. The 21 features-based quantified model was trained and tested in the prospective cohort and achieved a sensitivity of 88.24%, specificity of 100%, and accuracy of 96.67% in the test dataset.
Conclusions: Exhaled breath with machine learning is a highly accurate approach for early-stage LC detection and inner standard gas could improve detection performance.
Citation Format: Mantang Qiu, Hang Li, Shushi Meng, Qingyun Li, Zuli Zhou, Jun Wang. Detection of lung cancer by metabolomics of exhaled breath and machine learning [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 LB-267.
Abstract LB-295: Discovery of potent selective oral ACSS2 inhibitors for the treatment of acetate avid tumors
Authors: Goutopoulos Andreas , Erez Omri , Nakache Philippe , Alchanati Iris , Hay-Koren Avital , Paz Dikla , Kovalerchik Dimitri , Oppenheimer Hannah , Gottlieb Eyal , Botti Simone , Mor Inbal , Pastukh Nina 
Published: September 2020

Abstract
Under the low oxygen and lipid-depleted conditions, often encountered in the tumor microenvironment, acetate is used as nutrient by cancer cells for the biosynthesis of fatty acids. ACSS2, the enzyme responsible for acetate capture within the cell and its conversion into Acetyl-CoA, is upregulated under metabolically stressed conditions and its silencing is shown to inhibit tumor growth in xenografts. Two independent HTS campaigns identified the same small molecule ACSS2 inhibitor series that exhibited uncompetitive binding behavior in respect to ATP. A co-crystal structure of human ACSS2 in complex with a congener of the series revealed that it binds in the acetate pocket and forms favorable interactions with ATP, which is bound adjacently. These compounds were over 1000-fold times selective for ACSS2 over the mitochondrial ACSS1 and other Acyl-CoA synthetases. Medicinal chemistry optimization of this series resulted in advanced compounds with sub-nanomolar biochemical potency and single digit nanomolar cellular EC50s in inhibiting de novo fatty acid synthesis from labeled acetate and excellent pharmacokinetic properties. In vivo, in mice, these compounds inhibited labeling of fatty acids in various tissues after administration of labelled acetate or ethanol. They exhibited in vivo anti-tumor efficacy after oral administration in xenograft models that are acetate avid, whereas had no effect in tumors that show low acetate uptake. MTB-9655, one of the compounds from this series with optimally balanced properties, is currently entering IND-enabling studies to allow its testing in cancer patients.
Citation Format: Andreas Goutopoulos, Omri Erez, Philippe Nakache, Iris Alchanati, Avital Hay-Koren, Dikla Paz, Dimitri Kovalerchik, Hannah Oppenheimer, Eyal Gottlieb, Simone Botti, Inbal Mor, Nina Pastukh. Discovery of potent selective oral ACSS2 inhibitors for the treatment of acetate avid tumors [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 LB-295.
Abstract LB-309: Comprehensive genomic insights into dynamic mechanism underlying the development of non-malignant human liver disease into hepatocellular carcinoma
Authors: Zhang Xiangyu , Cao Ran , Zhou Kaiqian , Wang Xinyu , Zhou Cheng , Chen Feiyu , Bao Hua , Wu Xue , Shao Yang W. , Fan Jia , Zhou Jian , Wang Zheng 
Published: September 2020

Abstract
Aim: Hepatocellular carcinoma (HCC) is a dynamically developing disease. However, the molecular and evolutionary mechanism underlying the development of human non-malignant liver disease into HCC remains largely unknown. Methods: We performed whole-genome sequencing studies on 3 different types of samples (e.g. liver cirrhosis (LC), dysplastic liver nodules (DN), and hepatocellular carcinoma (HCC)) which were collected simultaneously from each of 8 liver cancer patients. Bioinformatics analysis was performed on somatic mutations, mutational signatures, copy number alterations, structure variants, telomere length, and phylogenetic trees. Results: Surprisingly, few passenger mutations were shared across LC, DN and HCC samples within each patient. Compared with the non-malignant LC and DN samples, HCC was characterized by significantly greater number of somatic mutations (particularly oncogenic mutations), copy number alterations, genomic rearrangements, as well as shorter telomeres. HCC exhibited much similar mutational signatures as dysplastic nodules but different from background cirrhotic liver. Several mutational signatures (e.g. COSMIC signatures 16, 22, 24) were substantially more active in tumor and DN samples than LC, while some mutational signatures (e.g. signatures 5, 8) were universal and equally active in both non-malignant hepatocytes and HCC. Most somatic copy number alterations (SCNAs) were only observed in HCC and no overlap across different samples was found. In contrast to universal subclonal SCNAs, minimal subclonal diversification of mutations was identified in HCC, implying the majority of mutations are acquired at the earliest stages. Conclusions: HCC can be characterized by punctuated evolution of mutations in short bursts followed by subclonal diversification driven by SCNAs. Despite little overlap of mutations, the mutational context indicates a common mutagenesis mechanism underlying HCC and DN, which is different from LC.
Citation Format: Xiangyu Zhang, Ran Cao, Kaiqian Zhou, Xinyu Wang, Cheng Zhou, Feiyu Chen, Hua Bao, Xue Wu, Yang W. Shao, Jia Fan, Jian Zhou, Zheng Wang. Comprehensive genomic insights into dynamic mechanism underlying the development of non-malignant human liver disease into 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 LB-309.
Abstract LB-314: Development of reference material guaranteed copy number of DNA for liquid biopsy
Authors: Osaki Yusuke , Sato Yoshiharu , Ishii Miho , Yonekawa Yuki , Matoba Ryo 
Published: September 2020

Abstract
cfDNA (cell free DNA) analysis using liquid biopsy plays an important role in cancer research. In particular NGS (next-generation sequencer)-based assay has attracted wide interests, because it can not only screen many cancer genes but also can detect ultra-low-concentration mutations at the same time. However, there is no reference material to evaluate correctly its performance. Currently, most researchers use diluted human genomic DNA that is commercially available as reference, but adjusting its concentration is difficult because of the Poisson distribution. Herein, we have developed a method to dispense cells with counting a cell one by one using inkjet technology (Manabu Seo, et. Al., 2019, Anal. Chem.). By lysing cells after dispensing, the defined copy number of DNA is prepared with a single molecule level. In this study, we assessed the feasibility of our reference material prepared by inkjet as a control for rare mutation detection (e.g. minimal sampling bias and high reproducibility). Spike-in control samples with exact allele frequencies with 0.07%, 0.53%, 0.64%, and 1.0%, were prepared by accurately dispensing with inkjet the cells that contain a sequence of exons 18, 19, 20, and 21 of EGFR. The EGFR sequence has G719A, E746_A750del, T790M, C797S, L858R, and L861Q mutations. For comparison, a few samples were also prepared by manual dilution process. The EGFR mutations were analyzed by the method of high-sensitivity procedure using NGS (Yoji Kukita, et. Al., 2013, PLoS ONE.). Result of the inkjet method had less variation than the manual dilution method, and high repeatability was obtained. Especially in E746_A750del (0.53%) the inkjet samples were tested three times (n = 24 each), and their CVs (coefficient of variation) were 17%, 17%, and 12%. In contrast, the manual sample's one was 33%. In addition, linearity of T790M and L858R were R2 = 0.98 and R2 = 0.96, respectively. The reference material produced by inkjet method can be provided under the same conditions at each facility and make it possible to improve the reliability of liquid biopsy. Therefore, the reference material could be appropriate standard for external quality control for EGFR mutant detection of liquid biopsy.
Citation Format: Yusuke Osaki, Yoshiharu Sato, Miho Ishii, Yuki Yonekawa, Ryo Matoba. Development of reference material guaranteed copy number of DNA for liquid biopsy [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 LB-314.
Abstract LB-326: The role of HER3 in HER2-amplified cancers other than breast cancers
Authors: Majumder Avisek , Steri Veronica , Salangsang Fernando , Moasser Mark 
Published: September 2020

Abstract
Introduction: Although recent studies showed that HER3 plays a key role in the pathophysiology of HER2-amplified breast cancers, its role in other cancers driven by HER2 amplification remains unknown. This study aimed to define the importance of HER3 signaling in other types of HER2-amplified cancer. Method: The expression and signaling activity of HER2, HER3, and downstream pathway proteins were studied in cell panels representing HER2-amplified cancers of the breast, bladder, colon and rectal, stomach, esophagus, lung, tongue, and endometrium along with controls lacking HER2 amplification. The time-dependent compensatory upregulation of HER3 in response to HER2 inhibitor therapy was assayed by western blotting and the apoptotic effects of HER2 inhibition were assayed by flow cytometry. The functional significance of HER3 in promoting tumorigenic growth in xenograft assays was determined in selected HER2-amplified cancer cell types by shRNA knockdown. Results: Many HER2-amplified cancers of the breast (HCC1569, HCC1954, HCC1419, UACC732, SUM190), bladder (CLS-439), stomach (N87, OE19), esophagus (TE4, KYSE410), lung (Calu3), and endometrium (USPC-ARC1, USPC-ARC2) showed HER3 activation, whereas HER2-nonamplified cancers of the same organs did not. With some exceptions, all HER2-amplified cancer cells undergo apoptosis when HER2 signaling is inhibited by lapatinib. Lapatinib induced apoptosis correlated with HER3 activation. Tumor growth was reduced upon HER3 knockdown in HER2-amplified cancers other than breast cancers, particularly cancers with constitutive HER3 phosphorylation. Significance: These results suggest that HER3 plays a functionally significant role in HER2 amplified cancers other than breast cancers, and may underlie resistance to HER2-targeting in these cancers. Combined HER2-HER3 targeting may be of value across the spectrum of different types of cancers.
Citation Format: Avisek Majumder, Veronica Steri, Fernando Salangsang, Mark Moasser. The role of HER3 in HER2-amplified cancers other than breast cancers [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 LB-326.