bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 6673: B7-H3-targeted 4-1BB activation potentiates CD8 T cell-dependent antitumor immunity without systemic toxicity
Authors: You Gihoon , Lee Yangsoon , Kang Yeon-Woo , Park Han Wook , Park Kyeongsu , Jung Jaeho , Lee Seung-Woo 
Published: September 2020

Abstract
4-1BB is a costimulatory receptor on activated T and NK cells, and the stimulation of 4-1BB by the natural ligand or agonistic mAb enhances cellular proliferation and effector functions. Immunotherapy targeting 4-1BB has been tested for cancer patients; however, dose-limiting toxicities of 4-1BB agonists restrict further clinical development. B7-H3 (CD276) is overexpressed on the cell surface of multiple cancers and tumor-associated endothelial cells, yet barely on healthy adult tissues. To restrict 4-1BB stimulation activity in tumors, we have developed an FcγR-binding null bispecific IgG1 antibody consisting of B7-H3-targeting IgG and two anti-4-1BB single-chain variable fragments. The B7-H3x4-1BB bispecific antibody (B5x1D8) shows a potent in vitro T cell costimulatory activity in the presence of B7-H3 on the tumor cells. B5x1D8 rapidly accumulates in B7-H3-overexpressing tumors compared to 4-1BB agonistic mAb. B5x1D8 elicits a 4-1BB-dependent anti-tumor response in three different B7-H3-overexpressing murine tumor models. More importantly, in contrast to 4-1BB mAb, B5x1D8 does not induce any observable systemic abnormalities. Treatment of B5x1D8 increases the density, cytokine production, and proliferation of CD8 T cells in the tumor. Characterization of TILs indicates that B5x1D8 increases the number of PD-1+TIM-3+ “terminal effector” CD8 T cells for eliminating tumor cells. Furthermore, a combination of B5x1D8 and immune checkpoint blockade (ICB), anti-PD1, synergistically inhibits tumor growth. The human 4-1BB-targeting bispecific antibody also induces B7-H3-dependent 4-1BB costimulation and inhibits tumor growth in human 4-1BB transgenic mice. In sum, our data suggest that the B7-H3x4-1BB bispecific antibody represents an alternative form of IgG-based 4-1BB agonistic mAb for effective and safe cancer immunotherapy against B7-H3 positive cancers as monotherapy and combination therapy with other immunotherapy, like ICB.
Citation Format: Gihoon You, Yangsoon Lee, Yeon-Woo Kang, Han Wook Park, Kyeongsu Park, Jaeho Jung, Seung-Woo Lee. B7-H3-targeted 4-1BB activation potentiates CD8 T cell-dependent antitumor immunity without systemic toxicity [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 6673.
Abstract 6693: Metronomic treatment of orally available oxaliplatin and pemetrexed to potentiate the efficacy of anti-PD-1 by modulating tumor immunity
Authors: Maharjan Ruby 
Published: September 2020

Abstract
The rational combination of metronomic chemotherapy (MCT) with immunotherapy has the potential of immune modulation inducing enhancement of host immunity, thereby overcome tumor induced immunosuppressive defense. In this study, oral formulation of oxaliplatin (OXA) and pemetrexed (PMX) were developed by complexing ionically with Nα-deoxycholyl-l-lysyl-methylester (DCK) as intestinal permeability enhancer to form OXA/DCK-NE and PMX/DCK-OP respectively. OXA/DCK-NE and PMX/DCK-OP were combined for oral MCT and its anti-tumor effect was examined against maximum tolerated dose (MTD) of same drug combination by intravenous administration in colon cancer (CT26) syngeneic mouse model. MCT was able to modulate immunity in CT26 mouse model compared to MTD, such as enhancement of effector T-cell population in tumor, elevation of tumor antigen uptake, and activation of dendritic cells as analyzed by flow cytometry of tumor tissue and draining lymph nodes (TDLN). Moreover, cytotoxic T-cell and helper T-cell populations in lymphoid organs such as blood, spleen, and TDLN were found to be upregulated in MCT group but not in MTD group. Furthermore, on combining MCT with αPD-1, the tumor suppression was more effective showing 97.8% decrease in tumor volume compared to that of control leading to tumor free mice (95% complete response). Mice that achieved complete antitumor response upon re-challenge, demonstrated that combination therapy of MCT and αPD-1 induced memory response which was further confirmed by 1.5-fold enhanced antigen specific memory response compared to αPD-1 only treated group as detected by secretion of INF-γ.
In this study, we observed that combination of OXA/DCK-NE and PMX/DCK-OP with αPD-1 therapy synergized immunomodulatory effect by enhancing the populations of effector T-cells thereby inducing complete response and antigen specific memory response. Thus, this suggests the rationality of combination treatment with oral metronomic OXA and PMX with αPD-1 to elicit effective immune modulation.
Citation Format: Ruby Maharjan. Metronomic treatment of orally available oxaliplatin and pemetrexed to potentiate the efficacy of anti-PD-1 by modulating tumor immunity [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 6693.
Abstract 683: Identification of a macrocyclic peptide that blocks the CD47-SIRPα interaction and its antitumor effect
Authors: Hazama Daisuke , Murata Yoji , Okamoto Takeshi , Tanaka Daisuke , Sakamoto Mariko , Kakuchi Yuka , Saito Yasuyuki , Kotani Takenori , Nishimura Yoshihiro , Matozaki Takashi 
Published: September 2020

Abstract
Much recent attention has focused on the modification of immune responses as a basis for new cancer treatments. Immune checkpoint inhibitors, such as monoclonal antibodies (mAbs) to CTLA-4, PD-1, and PD-L1, have provided clinical benefit in patients with a variety of types of cancer, and a growing number of agents targeting immune checkpoints are in preclinical and clinical development. SIRPα is a transmembrane protein that is abundant in myeloid cells, such as macrophages and dendritic cells. SIRPα interacts with another transmembrane protein CD47 through their ectodomains, and they constitute the cell-cell communication system (the CD47-SIRPα system). We previously demonstrated that the interaction of SIRPα on macrophages with CD47 on tumor cells prevented the phagocytosis of tumor cells by macrophages. In contrast, blockage by anti-SIRPα antibodies of the CD47-SIRPα interaction enhanced the phagocytic activity of macrophages against antibody-opsonized tumor cells, suggesting that the CD47-SIRPα axis acts as an immune checkpoint pathway involved in tumor escape from immunosurveillance. Here, by the use of random nonstandard peptide integrated discovery (RaPID) system, we have identified novel macrocyclic peptides that bind to the extracellular domain of SIRPα with high affinity. These SIRPα-binding macrocyclic peptides inhibited the CD47-SIRPα interaction. Moreover, one of the SIRPα-binding macrocycles promoted the phagocytosis of antibody-opsonized tumor cells by macrophages in vitro and enhanced the antitumor effect of mAbs against tumor antigens in xenograft or syngeneic mouse models of cancer. Our results thus suggest that macrocyclic peptides against SIRPα hold promise as a novel tool for cancer immunotherapy.
Citation Format: Daisuke Hazama, Yoji Murata, Takeshi Okamoto, Daisuke Tanaka, Mariko Sakamoto, Yuka Kakuchi, Yasuyuki Saito, Takenori Kotani, Yoshihiro Nishimura, Takashi Matozaki. Identification of a macrocyclic peptide that blocks the CD47-SIRPα interaction and its antitumor effect [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 683.
Abstract 707: Combinatorial assessment of ctDNA release and mutational burden predicts clinical outcome from anti-PD-(L)1 therapies in non-small-cell lung cancer
Authors: Fang Wenfeng , Ma Yuxiang , Yin Jiani C. , Zhou Huaqiang , Wang Fufeng , Bao Hua , Wang Ao , Wu Xue , Hong Shaodong , Yang Yunpeng , Huang Yan , Zhao Hongyun , Shao Yang W. , Zhang Li 
Published: September 2020

Abstract
Background: Blood-based detection of circulating tumor DNA (ctDNA) is a non-invasive and clinical accessible source of biomarkers that enables selection of non-small-cell lung cancer (NSCLC) patients for treatment with anti-PD-(L)1 therapies. However, new approaches are necessary to improve enrichment of responders.
Patients and Methods: Pretreatment plasma ctDNA from 97 advanced-stage NSCLC patients who underwent anti-PD-(L)1 therapy from December 2015 to August 2017, with matched tissue samples from 66 patients, were profiled using a targeted next-generation sequencing (NGS) panel (Geneseeq) encompassing 422 cancer-relevant genes. External validation was performed using two large randomized trials, POPLAR and OAK. Results: In the 72 patients with adequate ctDNA release (maximum somatic allele frequency, MSAF ≥2%), high blood tumor mutational burden (bTMB) was associated with significantly prolonged median PFS (110 vs 60 days, HR=0.54 [95%CI, 0.31-0.92]; log-rank p = 0.02) and a trend towards improved DCB (34.8% vs 22.5%, p=0.39). While a high concordance was observed between bTMB and tTMB (Spearman's ρ= 0.71), the greatest progression-free survival (PFS) improvement was achieved with concomitantly high bTMB and tTMB (mPFS, 156 vs 59 days, HR=0.47 [95%CI, 0.24-0.91], log-rank p=0.02). Remarkably, the subset of patients with low ctDNA release (MSAF&lt;2%), and consequently non-evaluable bTMB, derived similar PFS benefit as those with high bTMB. The additive predictive value of MSAF and bTMB was externally validated by results from the two large randomized trials.
Conclusions: Our findings validated panel-assessed bTMB as a promising biomarker that demonstrated added value over tissue testing alone for benefit with anti-PD-(L)-1 therapies. Our findings also provided the first clinical evidence suggesting that matched tissue and blood testing might be necessary to refine the predictive value of TMB. Moreover, we identified low ctDNA MSAF as an important and robust predictor of improved immunotherapy outcome.
Citation Format: Wenfeng Fang, Yuxiang Ma, Jiani C. Yin, Huaqiang Zhou, Fufeng Wang, Hua Bao, Ao Wang, Xue Wu, Shaodong Hong, Yunpeng Yang, Yan Huang, Hongyun Zhao, Yang W. Shao, Li Zhang. Combinatorial assessment of ctDNA release and mutational burden predicts clinical outcome from anti-PD-(L)1 therapies in non-small-cell lung 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 707.
Abstract 739: A disintegrin and metalloproteinase 8 as a potential blood biomarker for early diagnosis of gastric cancer
Authors: Choi Jae-Il , Park Joon Seong , Chung Hye Won , Lim Jong-Baeck 
Published: September 2020

Abstract
Purpose: We evaluate the clinical significance of A Disintegrin and Metalloproteinase 8 (ADAM 8) as a potential blood biomarker for gastric cancer (GC).
Materials & Methods: Blood ADAM 8 was measured by ELISA and CEA, Cytokines/chemokines (IL-23, SDF-1α/CXCL12, IL-8, and sCD40L) were measured by chemiluminescent immunoassay. They were compared among 5 groups; normal/gastritis, high-risk, early GC (EGC), advanced GC (AGC) without distant metastasis and AGC with distant metastasis by one-way analysis of variance in both training (n=80) and validation dataset (n=241). Clinicopathological features of GC and GC-associated cytokines were evaluated for their correlations with blood ADAM 8. To evaluate the diagnostic accuracy to predict GC, receiver operating characteristic (ROC) curve and logistic regression were used.
Results: In both training and validation dataset, blood ADAM 8 increased along the GC carcinogenesis (ANOVA, all of p&lt;0.01). Blood ADAM 8 were significantly higher in EGC and AGC compared with high-risk and normal (post-hoc Bonferroni, all of p&lt;0.01). However, there was no significant difference among cancer groups. Blood ADAM 8 was correlated with N-stage (Spearman's correlation, γs,=0.320, p=0.011), but not correlated with T-stage or M-stage. Pearson's correlations showed blood ADAM 8 was closely correlated with pre-inflammatory cytokines (IL-23, SDF-1α/CXCL12, all of p&lt;0.05), but not correlated with pro-angiogenic cytokines (IL-8, sCD40L). ROC curve and logistic regression demonstrated serum ADAM 8 showed higher diagnostic accuracy (sensitivity, 73.7%; specificity, 86.2%) than CEA (sensitivity, 23.1%; specificity, 91.4%). Combination of ADAM 8 and CEA more increased the diagnostic accuracy to predict GC (sensitivity, 81.8%; specificity, 84.0%).
Conclusion: Blood ADAM 8 is a promising biomarker for early detection of GC.
Citation Format: Jae-Il Choi, Joon Seong Park, Hye Won Chung, Jong-Baeck Lim. A disintegrin and metalloproteinase 8 as a potential blood biomarker for early diagnosis of gastric 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 739.
Abstract 809: nCounter for detection of clinically relevant alterations in exosomes of non-small cell lung cancer cells and patients
Authors: Capitán Ana Giménez , Bracht Jill , Huang Chung-Ying , Teixidó Cristina , Reguart Noemí , Boykin Rich , Warren Sarah , Beechem Joseph M , Ramirez Santiago Viteri , García Juan José , Aguilar Andrés , Costa Rafael Rosell , Gerlach Jay , Molina-Vila Miguel Angel 
Published: September 2020

Abstract
Background: ALK, ROS1 and RET fusions and MET exon 14 skipping variant (METex14) are present in 10-15% of advanced non-small-cell lung cancer (NSCLC) patients and their accurate identification is critical to guide targeted therapies. In a significant number of cancer patients, the tumor tissue available is insufficient for genetic analysis and repeated tissue biopsies for monitoring the course of the disease and the emergence of resistance are not feasible. Liquid biopsies constitute the only alternative available in these cases, but NGS techniques have shown insufficient sensitivity for fusion detection in blood samples. The nCounter technology has been adapted to detect fusions and skipping variants in FFPE tumor biopsies and we aimed to validate it for exosomes.
Methods: Exosomes were purified using a miRCURY kit (Qiagen) and RNA was extracted using the TRI reagent (MRC Inc). A customized nCounter panel (Nanostring) for detection ALK, ROS1 and RET fusion transcripts and MET ex14 mRNA was used with a 10-cycles preamp step. First, proof-of-concept experiments were run by testing exosomes isolated from the culture medium of cell lines. Next, we tested exosomes isolated from the blood of NSCLC patients with know genotypes.
Results: nCounter fusion probes successfully detected ALK, RET and ROS1 fusion transcripts in exosomes isolated from the culture medium of the cell lines H3122 (EML4-ALKv1), H2228 (EML4-ALKv3), HCC78 (SLC34A2-ROS1) and LC/2-Ad (CCDC6-RET). Exosomes from a cell line established from a patient progressing to alectinib were also positive for EML4-ALKv1and showed high MET expression levels, while exosomes from the fusion-negative cell lines A549 and H23 tested negative. Finally, fusion transcripts were detected in exosomes purified from the blood of fusion positive NSCLC patients but not in fusion negative cases.
Conclusions: nCounter can detect ALK, RET and ROS1 fusion transcripts in exosomes purified from the blood of advanced NSCLC patients
Citation Format: Ana Giménez Capitán, Jill Bracht, Chung-Ying Huang, Cristina Teixidó, Noemí Reguart, Rich Boykin, Sarah Warren, Joseph M Beechem, Santiago Viteri Ramirez, Juan José García, Andrés Aguilar, Rafael Rosell Costa, Jay Gerlach, Miguel Angel Molina-Vila. nCounter for detection of clinically relevant alterations in exosomes of non-small cell lung cancer cells and patients [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 809.
Abstract 835: Novel framework for quantifying synergy in high-throughput drug combination cytotoxicity experiments
Authors: Thurman Robert , Arthur Bill 
Published: September 2020

Abstract
Combination therapies are a cornerstone of cancer treatment, enabling a strategy of engaging multiple mechanisms of action. The in vitro testing of drug combinations is therefore a key step in drug discovery, and plate-based assays and robotics now allow for high-throughput cytotoxicity experiments that test combinations at doses diluted at high resolution. While additivity models such as Bliss, Loewe and Highest Single Agent (HSA) are well-established for gauging the synergy or antagonism of combinations, new frameworks are required for incorporating these models in the context of hundreds of experiments testing dozens of dose combinations per experiment. We have developed a framework that compares observed cytotoxicity results against the predictions from additivity models on a dose-by-dose basis across an entire matrix of dose combinations. Visualizations readily highlight any synergistic or antagonistic deviations from additivity. Statistical tests are then performed using 3x3 blocks of adjacent combinations. Such an approach mutes the effects of outliers and rewards consistency of activity in nearby doses. To capture the overall trend in an experiment and to facilitate comparisons across multiple experiments, we propose a summary metric, TSDA (total significant deviation from additivity) that takes into account both the absolute deviations from additivity and their statistical significance. Generally, positive values of TSDA indicate overall synergy, while negative values indicate overall antagonism. Null distributions for TSDA have been generated using simulations of sham experiments (same drug for both axes in a combination). We have applied this framework to over 1500 individual combination experiments spanning a range of cell lines and therapeutic mechanisms. The framework has been used to successfully guide follow-up in vivo experiments and to inform clinical plans.
Citation Format: Robert Thurman, Bill Arthur. Novel framework for quantifying synergy in high-throughput drug combination cytotoxicity experiments [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 835.
Abstract 863: Transfer learning for gene expression prediction with deep neural networks
Authors: Arslan Emre , Rai Kunal 
Published: September 2020

Abstract
Conventional machine learning approaches try to solve problems in an isolated manner whereas humans utilize previous experiences between similar tasks. Transfer learning (TL) imitates this efficient approach and transfer knowledge that was learned in a source/domain task and employ that knowledge to improve learning in a related target task. We propose a deep convolutional neural networks (CNNs) structure to predict gene expression via epigenomic signals around the TSS. This structure allows to transfer knowledge that we learn in a cancer type to another one. We use H3K4me1, H3K4me3, H3K27Ac, H3K4me3, H3K27me3 histone modification marks for breast, lung, colon, glioma, ovarian, melanoma, and pancreas cancers as input and predict gene expression for each sample. Our model with transfer learning has improved prediction rates for various cancer types compared to the other established methods like Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). We also compared performances for different percentages of data with previously proposed DeepChrome algorithm. We checked normal datasets contribution for cancer samples as well. For this purpose, we train our model with normal samples from Roadmap Epigenome Project (REMC) repository first and passed the learned parameters to cancer datasets. It seems normal samples do not have common epigenomic patterns with cancer ones as we do not see any prediction of gene expression improvement. To our knowledge, there is no proposed transfer learning method in epigenetic data analysis. The proposed transfer learning mechanism will lead to avoid generating a significant amount of epigenomic data for different cancer types and will accelerate epigenomic studies in different cancer types.
Citation Format: Emre Arslan, Kunal Rai. Transfer learning for gene expression prediction with deep neural networks [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 863.
Abstract 865: Using machine learning to detect heterogeneity in single cell multi-omics datasets
Authors: Gulati Saurabh , Wang Shu , Parikh Saurabh , Liu Ben , Gokhale Kaustubh , Manivannan Manimozhi , Sahu Sombeet , Kim Dong , Parikh Anup 
Published: September 2020

Abstract
Background: While scRNA-seq helps in obtaining high-resolution views of single-cell heterogeneity through characterization of the functional state of cells, our understanding of the cellular properties and population architectures of heterogeneous tissues will be greatly advanced by the multi-omics investigation of single cells. Recently, several computational methods have been developed to integrate data from multiple different single cell experiments measuring individual analytes. But unambiguous inference that a cellular phenotype is caused by a genotype can only be achieved by their measurement from the same single cell. To address this gap, we have developed the Tapestri multi-omics workflow to analyze the RNA and DNA information from the same cell.
Methods: After pre-processing of the reads, cell calling is done by identifying the good barcodes using both DNA and RNA reads. DNA reads are processed to identify genetic variants in each cell. The variant cell matrix is then filtered for data completeness to ensure only high-quality data is used in downstream processing. Ploidy is estimated using by normalizing DNA reads, genetic variants and ploidy information together is used to identify subclones. The RNA reads are log normalized and scaled within each cell. Next we set the mean expression of each transcript to 0 and scale the variance to 1. This avoids downstream analysis being skewed by high expressors. We then trained a random forest classifier to identify significantly differentially expressed transcripts across subclones which were identified using genetic variants and ploidy information.
Results: Using the top differentially expressed transcripts we performed dimensionality reduction followed by clustering of cell types. The resulting visualization showed how well the genotypic and transcriptomic datasets integrated with one another. We tested this method on a model system with Raji and KG1 cell lines titrated at 50:50 ratio and were clearly able to associate the transcriptional variation with the genotypic variation of the 2 different cell lines. The method was also validated on a PBMC sample to ensure robustness of methods. We were able to identify the different cell types present the sample and were able to overlay that information with genetic variants to identify sub-clones in the identified cell types.
Citation Format: Saurabh Gulati, Shu Wang, Saurabh Parikh, Ben Liu, Kaustubh Gokhale, Manimozhi Manivannan, Sombeet Sahu, Dong Kim, Anup Parikh. Using machine learning to detect heterogeneity in single cell multi-omics 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 865.