bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 7234: The second-generation high-fidelity base editor AccuBaseTM (ceBE) can proficiently modify multiple genes within a human primary T cell while minimizing Off-target effects
Authors: Ying Han , Li Guanglei , Wang Liling , Lu Chunju , Lai Qi , Yao Chao , Lv Fenghua , Zhu Shiguo , Xu Tianhong 
Published: March 2024

Abstract
Off target mutations, chromosomal rearrangements and Indels are the biggest limitation to the use of gene editing tools today. Base editing system provides an efficient method to modify the genome without double strand breaks (DSB). The biggest problem for base editing is the sgRNA-independent off-target. We utilize synthetic biology strategies to create a novel base editor AccuBaseTM (ceBE) that has the highest editing efficiency in the industry, with zero off target events. This was achieved by strategically inserting deaminases into Cas9 at specific, tolerance-verified sites, a process guided by an extensive transposon-based genetic screening, protein engineering, and direct evolution approach. AccuBaseTM protein forms a complex with the sgRNA, but it has not yet engaged with the target DNA and is not in contact with any non-targeting dsDNA. This mechanism avoids guide RNA independent indiscriminate off-target mutations that was a common problem of the first generation base editing tool. Upon guided by sgRNA, the AccuBaseTM protein associates with the target double-stranded DNA, triggering structural alterations within the AccuBaseTM protein. This results in the outward exposure of the deaminase domain, facilitating the editing of the target base. AccuBaseTM (ceBE) substantially reduces off-target effects while maintaining on-target editing when compared to BE4max. This improvement has been confirmed in multiple human cell lines, mouse cell lines, human primary T and NK cells, human embryonic stem cells, and in animal embryos. In this study, we apply AccuBaseTM to achieve multiplex gene modifications in primary human T cells, demonstrating its capacity for highly efficient multiplex gene disruption without inducing off-target effects. Importantly, the multiplex base-edited T cells exhibit enhanced ex-vivo cell expansion and do not exhibit the double-strand break-induced translocations observed in T cells edited with Cas9 nuclease.
Citation Format: Han Ying, Guanglei Li, Liling Wang, Chunju Lu, Qi Lai, Chao Yao, Fenghua Lv, Shiguo Zhu, Tianhong Xu. The second-generation high-fidelity base editor AccuBaseTM (ceBE) can proficiently modify multiple genes within a human primary T cell while minimizing Off-target effects [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7234.
Abstract 7242: Immunostimulatory gene therapy co-expressing immune activators TMZ-CD40L and 4-1BBL with a PD-L1-targeting Affibody molecule to reactivate T cell immunity
Authors: White Luke Adam , Vaask Iris , Härdin Jonas , Eriksson Emma , Sandegren Anna , Frejd Fredrik Y. , Lövgren Tanja , Loskog Angelica 
Published: March 2024

Abstract
Immunotherapy has been used to make great strides in helping cancer patients, and new approaches to reactivate patient immunity are constantly being developed. We are developing immunostimulatory gene therapy using adenoviruses as gene vehicles to inflame the tumor microenvironment and drive anti-tumor immunity. One consequence of immune activation is PD-L1 upregulation, so to optimize our treatment, we are using a novel gene therapy vehicle expressing both immune activators and a PD-L1-blocking Affibody molecule. Affibody molecules are tri-helical proteins engineered to bind specific target molecules with high affinity. These molecules have successfully been used to image cancer cells in the body and can block target proteins. Affibody molecules are advantageous over other affinity molecules due to their small size, averaging about 6 kDa, and their high stability. The small size makes an Affibody molecule an ideal candidate for use in viral vectors, where genetic space is limited. In the present study, we tested the biological activity of a PD-L1 targeted Affibody molecule expressed from a viral vehicle compared to the synthetic protein, using a Bio-IC in vitro luciferase assay. Further, the capacity of the PD-L1-binding Affibody molecule to prevent inhibitory signaling was evaluated in an ovarian cancer/immune cell co-culture assay.
Our results demonstrate that cell lines infected with viral gene vehicles successfully express and release the Affibody molecule as demonstrated with PCR and ELISA. Furthermore, the supernatants containing the Affibody molecule and synthetic Affibody molecule inhibited the activity of PD-L1 in the BioIC assay. Next, an adenovirus vector containing trimerized-CD40L and 4-1BBL (LOAd703), known to upregulate PD-L1 in stimulated dendritic cells, was modified to co-express the anti-PD-L1 Affibody molecule (LOAd724). Gene expression was confirmed using PCR, flow cytometry (CD40L, 4-1BBL) and ELISA (anti-PD-L1 Affibody molecule). The tumor/immune cell co-culture is underway. In summary, Affibody molecules can be expressed from adenoviral gene vehicles alone or in combination with immunostimulatory genes. Further studies are warranted to understand the role of local PD-L1 blockade in the tumor microenvironment following LOAd724 therapy.
Citation Format: Luke Adam White, Iris Vaask, Jonas Härdin, Emma Eriksson, Anna Sandegren, Fredrik Y. Frejd, Tanja Lövgren, Angelica Loskog. Immunostimulatory gene therapy co-expressing immune activators TMZ-CD40L and 4-1BBL with a PD-L1-targeting Affibody molecule to reactivate T cell immunity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7242.
Abstract 7246: Enhancing PDAC therapy through in vivo correction of KRAS
G12D using enIscB-directed base editor combined with ICB
Authors: Yang Chao , Fang Qingxiao , Zhou Tianxing , Xie Yongjie , Yu Jun , Hao Jihui 
Published: March 2024

Abstract
In this study, we aim to investigate the therapeutic potential of in vivo genetic therapy using AAV-mediated adenine base editor (ABE) delivery for pancreatic ductal adenocarcinoma (PDAC) carrying the KRASG12D mutation (PDACG12D), and further explore its potential when combined with immune checkpoint blockade (ICB). The KRASG12D mutation is a dominant genetic alteration representing a highly sought-after and effective therapeutic target in PDAC. However, the in vivo genetic therapy for KRASG12D in PDAC has been rarely explored. Programmable base editors have been shown to install genetic mutations precisely and have significant therapeutic value, providing a promising strategy for genetic therapy of PDACG12D. Here, we engineered ABEs derived from various programmable nucleases, considering the protospacer adjacent motif (PAM) or target adjacent motif (TAM) sequence. The hypercompact A8e-enIscB demonstrated the highest editing efficiency in HEK293TG12D and PANC-1 cell lines. Subsequently, A8e-enIscB was packaged into a single-AAV system and delivered to humanized mice subcutaneously transplanted with patient-derived organoidG12D (PDOG12D). We found the delayed tumor progression with BE-based genetic therapy. Additionally, a substantial editing efficiency in tumor tissues and lower off-target editing in normal tissues was observed. By comparing single-cell transcriptome sequencing data between BE-treated and non-treated samples, we observed a reprogrammed immune microenvironment characterized by elevated infiltration of cytotoxic CD8+ T cells and NK cells, as well as decreased recruitment of suppressive PMN-MDSCs, Tregs and TAMs upon correction of KRASG12D. Furthermore, AAV-directed ABE genetic therapy could significantly sensitize PDAC to ICB therapy (anti-PD-1, anti-CTLA4) in humanized PDAC PDO/PDX mouse models. Accordingly, an ongoing work in which the genetic therapy combined with ICB phase IA investigator-initialized trial will be designed to evaluate the safety of this regimen. In summary, our study demonstrates the efficiency and safety of innovative genetic therapy for correcting the KRASG12D mutation in PDAC. Furthermore, we demonstrated the therapeutic potential of combining this genetic therapy with ICB.
Citation Format: Chao Yang, Qingxiao Fang, Tianxing Zhou, Yongjie Xie, Jun Yu, Jihui Hao. Enhancing PDAC therapy through in vivo correction of KRAS G12D using enIscB-directed base editor combined with ICB [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7246.
Abstract 7258: Combinatorial effects of viral oncolysis and miRNA modulation as a novel therapeutic of pancreatic ductal adenocarcinoma
Authors: Ruschel Monique Kanitz , Davis Isaiah , Mills Hannah Taylor , Gourishetti Karthik , Bhere Deepak 
Published: March 2024

Abstract
Pancreatic cancer is a devastating illness that affects numerous individuals each year. Unfortunately, pancreatic ductal adenocarcinoma (PDAC) is an aggressive form of this disease that accounts for a disproportionate number of cancer-related deaths. Despite representing only 3.3% of all cancer cases in the United States, pancreatic cancer is responsible for a staggering 8.3% of cancer-related fatalities. In 2023, 64,050 patients were diagnosed with PDAC alone. Current treatment options, which are limited to surgery, radiation, and chemotherapy, have failed to improve clinical outcomes. Recent research has revealed miRNA (miR) as a contributor to the suppression and progression of various cancers, including PDAC. Specifically, dysregulation of miR-7 has been associated with PDAC. Downregulation of miR-7 is linked to more aggressive cases of the disease. Another potential therapeutic approach to treating cancer is the use of oncolytic viruses (OV). These viruses can replicate in cancerous cells while leaving healthy cells unharmed, making them an attractive treatment option. In this study, we utilized exosomes isolated from human Umbilical Cord stem cells (hUCs) enriched with miR-7 and combined with OHSV to examine their effect on established PDAC cell lines. We utilized cell viability assays, microscopy, and western blot analysis to evaluate the impact of the combinatorial approach on the underlying molecular mechanisms. Our findings suggest that the successful transfer of miR cargo and treatment with oHSV into the target PDAC cells arrested cell proliferation by targeting apoptotic pathways.
Citation Format: Monique Kanitz Ruschel, Isaiah Davis, Hannah Taylor Mills, Karthik Gourishetti, Deepak Bhere. Combinatorial effects of viral oncolysis and miRNA modulation as a novel therapeutic of pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7258.
Abstract 7362: Integrated analysis of scRNAseq and spatial omics in brain metastatic lung cancer tumor tissue
Authors: Hirose Haruka , Tsuji Takahiro , Kojima Yasuhiro , Yoshizawa Akihiko , Arakawa Yoshiki , Ozasa Hiroaki , Wake Hiroaki , Shimamura Teppei 
Published: March 2024

Abstract
Spatial omics technologies have made significant advances in recent years, improving our understanding of the spatial localization of cells and intercellular interactions. Intercellular interactions are essential for maintaining proper tissue function and homeostasis, and dysregulation of these interactions can contribute to various diseases, including cancer and autoimmune disorders. Ligand proteins, one of the signaling molecules responsible for cell-cell interactions, bind to cell surface receptors and trigger intracellular signaling pathways. The effective range of ligand signaling, i.e. the extent to which a cell spatially and directly transmits information via the ligand, has not been systematically studied, although it may determine the occurrence of cell-cell interactions. The authors have previously developed methodologies combining spatial transcriptomics and single-cell RNA sequencing to analyze intercellular interactions at the single-cell level in spatial dimensions. Additionally, we have devised a technique to model the spatial diffusion of ligands from spatial transcriptomics and estimate the spatial range of ligand action. In this study, we used these developed methods to elucidate events in the tumor microenvironment of brain metastatic lung cancer tissue. Specifically, an integrated analysis of metastatic brain tumor samples was performed using visium and single-cell data from the same patient. The results suggest the presence of a heterogeneous microglial subpopulation, including clusters enriched in cytokines and chemokines for angiogenesis and wound healing, clusters associated with immune tolerance via IL-10 signaling, clusters enriched in gene sets related to phagocytosis, antigen processing, and antigen presentation, and clusters related to clearance of pathogens and residues. The presence of homogeneous microglial subpopulations was suggested. Furthermore, these subpopulations exhibited different spatial distributions, suggesting that they interact with surrounding cancer cells through different mechanisms.
Citation Format: Haruka Hirose, Takahiro Tsuji, Yasuhiro Kojima, Akihiko Yoshizawa, Yoshiki Arakawa, Hiroaki Ozasa, Hiroaki Wake, Teppei Shimamura. Integrated analysis of scRNAseq and spatial omics in brain metastatic lung cancer tumor tissue [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7362.
Abstract 7400: AI-driven multimodal 3D tumor modeling with spatial molecular insights for predicting ICI response in gastric cancer
Authors: Choi Yoon Ho , Kim Minji , Redekop Katya , Jang Inyeop , Clemenceau Jean R. , Barnfather Isabel , Nakkireddy Sumanth Reddy , Hong Changjin , Park Sunho , Sung Ji-Youn , Park Jeong Hwan , Kohei Shitara , Yu Aoki , Lee Sung Hak , Wang Sam , Cheong Jaeho , Hwang Tae Hyun 
Published: March 2024

Abstract
Traditional cancer research methodologies predominantly focus on 2D tumor modeling, including spatial profiling and AI-driven analysis at the 2D level. To advance beyond these limitations, we implemented Holotomography (HT), a 3D, label-free optical imaging method. This technique, leveraging refractive index contrasts, allows for enhanced visualization of tumor tissues, organoids, and cells in three dimensions. Using serial H&E sections and unstained slides, we created detailed 3D tumor models in gastric cancer, providing insights into tumor, immune, and stromal composition, as well as cell-cell interactions and extracellular matrix characteristics. AI algorithms further enhanced our 3D tumor modeling, offering novel perspectives on the initiation and progression of tumor, immune, and stromal components at the subcellular level. Analyzing gastric tumors treated with immune checkpoint inhibitors before, during, and after treatment revealed dynamic changes in spatial composition over time, offering unique insights into the evolution of the 3D tumor ecosystem. Additionally, by integrating spatial transcriptomic and in situ molecular imaging data, we developed a novel Multimodal 3D Tumor Model. This model not only uncovers subcellular-level molecular characteristics but also elucidates cellular interactions during treatment, suggesting potential biomarkers and therapeutic approaches. Lastly, by constructing 3D cellular interaction networks incorporating true 3D interactions, we provided an in-depth view of cellular communication within the tumor microenvironment. This pioneering approach, representing the first Multimodal 3D Tumor Model at the subcellular level, offers novel insights into gastric cancer's spatial evolution, potentially revolutionizing our understanding and treatment of gastric cancer.
Citation Format: Yoon Ho Choi, Minji Kim, Katya Redekop, Inyeop Jang, Jean R. Clemenceau, Isabel Barnfather, Sumanth Reddy Nakkireddy, Changjin Hong, Sunho Park, Ji-Youn Sung, Jeong Hwan Park, Shitara Kohei, Aoki Yu, Sung Hak Lee, Sam Wang, Jaeho Cheong, Tae Hyun Hwang. AI-driven multimodal 3D tumor modeling with spatial molecular insights for predicting ICI response in gastric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7400.
Abstract 7406: Visualization and analysis of cancer genomics data using UCSC Xena
Authors: Goldman Mary , Craft Brian , Zhu Jingchun , Haussler David 
Published: March 2024

Abstract
UCSC Xena (http://xena.ucsc.edu/) is a web-based visual integration and exploration tool for multi-omic data and associated clinical and phenotypic annotations. Researchers can easily view and explore public data, their own private data, or both using the Xena Browser. Private data are kept on the researcher's computer and are never uploaded to our public servers. We support Mac, Windows, and Linux.
Questions Xena can help you answer:* Is overexpression of this gene associated with lower/higher survival?* What genes are differentially expressed between these two groups of samples?* What is the relationship between mutation, copy number, expression, etc for this gene?
Xena showcases seminal cancer genomics datasets from TCGA, the Pan-Cancer Atlas, GDC, PCAWG, ICGC, and more; a total of more than 1500 datasets across 50 cancer types. We support virtually any type of functional genomics data: SNPs, INDELs, copy number variation, gene expression, ATAC-seq, DNA methylation, exon-, transcript-, miRNA-, lncRNA-expression and structural variants. We also support clinical data such as phenotype information, subtype classifications and biomarkers. All of our data is available for download via python or R APIs, or using our URL links.
Our signature Visual Spreadsheet view shows multiple data types side-by-side enabling discovery of correlations across and within genes and genomic regions. We also have dynamic Kaplan-Meier survival analysis, powerful filtering and subgrouping, differential gene expression analysis, GSEA, charts, statistical analyses, genomic signatures, and the ability to generate URLs to live views.
New features include:
* Genome-wide differential gene expression analysis
* GSEA analysis
* Select samples directly from the screen for filtering and creating subgroups
* Loading of Microsoft Excel files
Our beta prototype site for visualizing single-cell data delivers million-cell-scale multi-omics data for interactive visualization in a web browser. Contact us for access to our beta prototype site.
If you use us please cite our publication in Nature Biotechnology: https://www.nature.com/articles/s41587-020-0546-8
Citation Format: Mary Goldman, Brian Craft, Jingchun Zhu, David Haussler. Visualization and analysis of cancer genomics data using UCSC Xena [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7406.
Abstract 7416: GeneSetR: A web server for gene set analysis based on genome-wide Perturb-Seq data
Authors: Kuzu Omer Faruk , Saatcioglu Fahri 
Published: March 2024

Abstract
Identification of genotype-phenotype relationships is of utmost importance and a core effort in biology. Recent developments in efficient and precise gene targeting approaches coupled to omics methods have significantly improved deciphering of molecular interactions and relationships. However, many single gene perturbations can affect the expression of hundreds of other genes and analysis of the resulting omics-derived gene lists currently remains a significant challenge. Here we present Perturb-Seq based Gene Set Analyzer (GeneSetR), a user-friendly web-server that can analyze user-defined gene lists based on the data from a recently published genome-wide Perturb-Seq study, which targeted 9,866 genes with 11,258 sgRNAs in the K562 cell line. Through this tool, users can cluster gene lists following dimensionality reduction by various algorithms, undertake network analysis from RNA sequencing data, identify key nodes among the submitted genes, perform gene signature analyses, and generate heatmaps based on perturbation or gene expression data. GeneSetR enables researchers to readily identify gene clusters associated with specific phenotypes or biological processes, providing insights into the potential functional roles of these clusters and the role of single genes in them. With robust analysis capabilities, GeneSetR is a powerful resource to facilitate the exploration of genotype-phenotype relationships.
Citation Format: Omer Faruk Kuzu, Fahri Saatcioglu. GeneSetR: A web server for gene set analysis based on genome-wide Perturb-Seq data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7416.