bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 485: Cancer killing effect of ultrasound-responsive mechanophore nanoparticles
Authors: Wang Jian , Zhao Shensheng , Yi Junxi , Sun Yunyan , Agrawal Megha , Li King , Moore Jeffrey S. , Oelze Michael , Chen Yun-Sheng 
Published: March 2024

Abstract
Radiotherapy, a mainstay in cancer treatment, often inadvertently damages healthy tissues while targeting malignancies with free radicals. As an alternative, Photodynamic Therapy (PDT) employs non-ionizing photons to generate reactive oxygen species (ROS) in tumors, offering precise targeting with minimal long-term effects. However, its efficacy is constrained to superficial tumors due to the limited penetration of light. Sonodynamic Therapy (SDT), an evolution of PDT, uses ultrasound to enhance tissue penetration while maintaining its non-invasive nature. Initial SDT studies suggest that ultrasound can activate PDT's photosensitizers to produce ROS, offering a potential pathway for tumor eradication. Nevertheless, the principal mechanisms of ROS generation in SDT, namely pyrolysis and sonoluminescence, raise concerns about potential tissue injury. In our research, we introduce an innovative approach employing mechanophores into nanoparticles. Mechanophores, molecular units within polymers sensitive to mechanical stress, undergo transformations under such conditions, including the release of small molecules or free radicals. We have developed injectable nano-sized mechanophore nanoparticles, designed to respond to high-frequency ultrasound (&gt;100 kHz) more efficiently and with reduced energy requirements. This study focuses on nano-engineering the echogenicity of these mechanophore nanoparticles to enhance ROS generation. When agitated with focused ultrasound (FUS), these nanoparticles successfully induced ROS, leading to significant cell death, a result not observed with FUS alone in the absence of nanoparticles. In vivo experiments using mouse orthotopic cancer models demonstrated these nanoparticles' ability to inhibit tumor growth effectively. Under biocompatible ultrasound conditions, the nanoparticles cause effective cancer cell eradication with minimal impact on surrounding healthy tissues. Our findings not only introduce a novel approach to cancer treatment but also pave the way for broader applications of mechanophore nanoparticles in biomedicine. This study underscores the potential of ultrasound-activatable mechanophore nanoparticles as a versatile, effective, and safe option for cancer therapy.
Citation Format: Jian Wang, Shensheng Zhao, Junxi Yi, Yunyan Sun, Megha Agrawal, King Li, Jeffrey S. Moore, Michael Oelze, Yun-Sheng Chen. Cancer killing effect of ultrasound-responsive mechanophore nanoparticles [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 485.
Abstract 4864: Epidemiological characteristics and treatment strategies of gastric cancer with neuroendocrine differentiation (NED)
Authors: Huang Zhen , Wei Jinlai , Shen Yifan , Zhang Jun 
Published: March 2024

Abstract
Background: Gastric cancer with neuroendocrine differentiation (NED) occurs in 10% to 40% of cases, a significant proportion that is under-researched. The lack of clinical guidelines for this subgroup necessitates the development of effective treatment protocols.
Methods: This study enrolled 149 gastric cancer patients, analyzing retained surgical wax blocks via immunohistochemistry for syn, CgA, and CD56 expressions. Patients were categorized into NED-positive and NED-negative groups for analysis, employing continuous variable analysis, categorical variable analysis, clustering, principal component analysis (PCA), feature importance analysis, heatmap analysis, and survival prognosis. Moreover, the clinical efficacy and safety of first-line (surufatinib + SOX + anti PD-1 antibody, n=3) and second-line (surufatinib + anti PD-1 antibody, n=3) treatments were observed in six advanced gastric cancer with NED patients, exploring the epidemiological characteristics, feasibility, and importance of clinical treatments for NED.
Results: The study revealed a 10.1% incidence of NED positivity in gastric cancer patients. NED-positive patients exhibited a rapid decline in early survival probability, with a median relapse-free survival of 32.4 months compared to 41.5 months in NED-negative patients, indicating a poorer prognosis for NED-positive patients. In-depth analysis suggested significant correlations between NED status and factors such as gender, tumor location, pathological type, and AJCC staging, with the most significant associations observed in males, stage IIIA, and gastric angle location. In the six advanced gastric cancer with NED cases, effective tumor reduction was observed in both first-line (100% tumor shrinkage rate, ORR 66.7%, DCR 100%) and second-line treatments (66.7% tumor shrinkage rate, ORR 33.3%, DCR 100%), showing sustained partial responses (PR) exceeding 10 months in two patients and 7 months in one patient, respectively. The major grade 3 or higher TEAEs being neutropenia and thrombocytopenia in the first-line, and hypertension and proteinuria in the second-line therapy.
Conclusion: This study provides pivotal epidemiological and clinical treatment evidence for gastric cancer with NED, underscoring the necessity of personalized treatment strategies for this subgroup. Future research should focus on refining treatment protocols to enhance the quality of life and overall prognosis for these patients.
Citation Format: Zhen Huang, Jinlai Wei, Yifan Shen, Jun Zhang. Epidemiological characteristics and treatment strategies of gastric cancer with neuroendocrine differentiation (NED) [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 4864.
Abstract 487: Peptide amphiphiles hitchhike on endogenous biomolecules for enhanced drug delivery
Authors: Fischer Jared M. , Stewart Morgan , Dai Mingchong , Drennan Samuel , Sabuncu Sinan , Kingston Benjamin , Dengos Isabel , Xiang Li , Bonic Karla , Yi Xin , Ranganathan Srivathsan , Branchaud Bruce , Yildirim Adem 
Published: March 2024

Abstract
Peptide amphiphiles (PAs) have been applied as building blocks for self-assembled nanostructures with various morphologies. These self-assembled nanostructures are promising drug delivery platforms as they are naturally biodegradable and biocompatible with low immunogenicity. One primary focus in drug delivery using the PA nanostructures has been to develop strongly assembled structures to avoid disassembly in the blood and allow them to carry payloads to their intended targets. In this work, rather than developing stable self-assembled PA structures, we focused on weakly assembled PA nanostructures to investigate their interactions with blood components and to understand how these interactions affect their biodistribution. We found that weakly assembled PA nanostructures (SA-E) quickly disassembled in plasma and reassembled with lipoproteins (LPs), which significantly prolonged the blood circulation of SA-E in mice. We showed that SA-E crossed the endothelial barriers through transcytosis only when they were assembled with LPs. Cellular uptake studies showed that SA-E accumulated in cancer cells through lipid-raft-mediated uptake. Interestingly, their cellular internalization was independent of LP receptors, suggesting that SA-E assembles with different endogenous biomolecules in vivo. By exploiting these endogenous interactions, SA-E demonstrated high tumor accumulation in a myriad of small animal tumor models of breast, colon, pancreatic, brain, skin, prostate, or lung cancers. SA-E demonstrated large tumor-to-background signal ratios in live animals (&gt;5), extended retention (&gt;2 weeks), and excellent tissue distribution (tumor to liver signal &gt;3) in these tumor models. Encouraged by the excellent tumor accumulation of SA-E, we prepared SA-E drug conjugate using a highly potent and toxic chemotherapeutic agent, Monomethyl auristatin E (MMAE). SA-E-MMAE conjugate demonstrated strong antitumor efficacy in aggressive breast and glioma models in mice with reduced side effects. With its simple and modular design and universal tumor accumulation mechanism, SA-E represents a promising platform for broad applications in cancer therapy.
Citation Format: Jared M. Fischer, Morgan Stewart, Mingchong Dai, Samuel Drennan, Sinan Sabuncu, Benjamin Kingston, Isabel Dengos, Li Xiang, Karla Bonic, Xin Yi, Srivathsan Ranganathan, Bruce Branchaud, Adem Yildirim. Peptide amphiphiles hitchhike on endogenous biomolecules for enhanced drug delivery [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 487.
Abstract 4880: Identification and clinicopathological analysis of potential p73-regulated biomarkers in colorectal cancer via integrative bioinformatics
Authors: Bareja Chanchal , Dwivedi Kountay , Uboveja Apoorva , Mathur Ankit , Kumar Naveen , Saluja Daman 
Published: March 2024

Abstract
This work aims to decipher p73-regulated biomarkers for a prompt diagnosis of colorectal cancer (CRC) by employing a combination of integrative bioinformatics and expression profiling technologies. Transcriptome profile of HCT116 cell line p53−/− p73+/+ and p53−/− p73 knockdown was performed to identify differentially expressed genes (DEGs) followed by cross-checking with three CRC tissue expression datasets available in Gene Expression Omnibus. KEGG and Gene ontology were performed on differentially expressed transcripts obtained via the transcriptome profile and intersected genes. The PPI network was constructed via Cytoscape to extract hub genes. KM plots assisted in investigating the prognostic significance of the hub genes. The clinicopathological relevance was explored using the GEPIA and UALCAN databases. Finally, machine/deep learning algorithms were employed to perform TNM-stage classification. Transcriptome profiling revealed 1,289 upregulated and 1,897 downregulated genes. When intersected with employed CRC datasets, 284 DEGs were obtained. The analysis of gene ontology and KEGG showed enrichment of the DEGs in metabolic process, fatty acid biosynthesis, etc. The PPI network constructed using these 284 genes assisted in identifying 20 hub genes. Kaplan Meier, GEPIA, and UALCAN analyses uncovered the prognostic and diagnostic relevance of these hub genes. Conclusively, the deep learning model achieved TNM-stage classification accuracy of 0.78 and 0.75 using 284 DEGs and 20 hub genes, respectively. This is a novel study utilizing transcriptomics, publicly available tissue datasets, and machine learning to unveil key CRC-relevant genes. These genes are found relevant regarding the patients’ prognosis and diagnosis. The unveiled biomarkers are also found robust in TNM-stage prediction. This is the first study where transcriptomics, publicly available tissue datasets, and machine learning are altogether employed to reveal key genes in CRC pathogenesis. Our research highlights the robust analysis of four independent data sets to find the key hub genes, further predicting the performance of the key genes in stage-wise classification.
Citation Format: Chanchal Bareja, Kountay Dwivedi, Apoorva Uboveja, Ankit Mathur, Naveen Kumar, Daman Saluja. Identification and clinicopathological analysis of potential p73-regulated biomarkers in colorectal cancer via integrative bioinformatics [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 4880.
Abstract 4910: AI predicts drug response from the genomic features of cells and the kinase activity changes induced by compounds
Authors: Janssens Rens , Rizzolo Kimberly , Artin Erin , Wagner Joel , Riester Markus , Korn Joshua , Park Jisoo 
Published: March 2024

Abstract
Human protein kinases are playing an important role in various biological activities including tumorigenesis and cancer progression, and for this reason, many kinase inhibitors have been developed as promising targeted therapies for cancer. To date, there are 72 FDA-approved treatments targeting about two dozen different protein kinases; 3 of them were approved in 2022. However, there are still critical challenges that can be addressed to improve the robustness and selectivity of the kinase inhibitors such as drug resistance, toxicity and compromised efficacy. To gain a deeper understanding of the biological mechanisms underlying the resistance to kinase inhibitors, we hereby design and train an artificial intelligence (AI) model that predicts in-vitro cell response to various kinase inhibition from the integration of the genomic features of cancer cell lines and the biochemical profiles of kinase activities. The inner-working of an optimized model provides important insights on the relevant kinase activities for the cell response. A major challenge for training such a model is that the training data for biochemical kinase activity is very sparse; the kinase panels are not consistent across different sources and each dataset contains many missing values. To address this, we train a secondary AI model to predict all the missing values for the kinase activity in our training data using the combination of the compound chemical structure and the kinase structure. The secondary model is independently validated to ensure the quality of the training data for the drug response prediction model. An investigation of the secondary model’s inner mechanism sheds light on the kinase binding sites and which chemical substructures are important for its inhibition. The model accurately predicts the cell response to different CDK inhibition. A focused analysis of the model characterizes off-target effects of the CDK inhibitors which may be linked to the drug resistance mechanisms, suggesting ways to improve the inhibitors or potential targets for combination therapies.
Citation Format: Rens Janssens, Kimberly Rizzolo, Erin Artin, Joel Wagner, Markus Riester, Joshua Korn, Jisoo Park. AI predicts drug response from the genomic features of cells and the kinase activity changes induced by compounds [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 4910.
Abstract 4914: Mapping the cancer immune landscape in colorectal adenocarcinoma using multiplexed imaging and AI based analysis
Authors: Agrawal Vasundhara , Arvidson Lisa , Smith Michael J. , White Katie O. , Heil-Chapdelaine Richard A. , Jensen Samuel , Bose Arindam 
Published: March 2024

Abstract
Colorectal cancer is a high burden cancer in the United States, leading to the second highest cause of cancer deaths. Despite chemotherapeutic intervention and surgical resection, recurrent disease may occur in some patients and typically, has a poorer prognosis. Thus, there is a demand for advanced therapies such as immunotherapies for improved clinical outcomes. An in-depth understanding of the colon cancer microenvironment is necessary to improve outcome in this subset of patients. Spatial biology approaches are well positioned to comprehensively uncover the molecular and biological mechanisms of cancer cell aggression in colorectal cancer. Here, we visualized nearly 30 biomarkers in colon adenocarcinoma (CAC) tissue using the Cell DIVE multiplexed imaging workflow and validated antibodies from Cell Signaling Technology (CST®). Using this approach, we were able to probe multiple pathways of interest in cancer progression, such as vascularization of tumor tissue, immune cell responses, and cell proliferation. Together, these data allow the creation of a spatial map of tumor cell aggression and offer predictive value for the progression of the disease. Deep molecular and architectural insights on this level are possible only with a spatial biology approach and offer multiple downstream hypotheses to further understand cancer progression and interrogate the heterogeneity within the tumor microenvironment.
Citation Format: Vasundhara Agrawal, Lisa Arvidson, Michael J. Smith, Katie O. White, Richard A. Heil-Chapdelaine, Samuel Jensen, Arindam Bose. Mapping the cancer immune landscape in colorectal adenocarcinoma using multiplexed imaging and AI based analysis [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 4914.
Abstract 4956: A fast and efficient bioinformatics analysis workflow for processing reads from single-cell multiomics assays captured on a microwell-based platform
Authors: Padmanabhan Raghavendra , Weichel Brent , Radenbaugh Amie , Jiang Yuefu , Weeks Charles , McCarthy Thomas , Kim Youngsook , Berno Anthony , Jensen Devon 
Published: March 2024

Abstract
Here, we present a significant update: version 2.0 of our sequencing analysis, it is a comprehensive primary analysis workflow that is up to 7X faster and consumes 2X less disk space than the previous release. We used it to process a library with 20,000 human PBMCs sequenced to a depth of 1.2 billion reads in 2.5 hours of wall-clock time. The pipeline can be used with reads from single-cell whole transcriptome, targeted mRNA, surface antigens (AbSeq), TCR/BCR and Sample Tag libraries captured on the BD Rhapsody™ System platform. The major processing steps include Quality Filtering, Cell Barcode Identification, Read Alignment, Feature Assignment, UMI Error Correction, Identification of Putative Cells, Sample De-Multiplexing, Dimensionality Reduction for Visualization and VDJ Contig Assembly and Annotation. Output files and metrics are available in easy-to-digest formats, including an html report with dynamic visualization. Integrated outputs in Seurat and Scanpy formats combine expression matrices and all cell annotation metadata (e.g., predicted cell type, sample assignment, TCR/BCR sequence and gene segments). These pre-generated files are ready to load into popular single-cell analysis tools. The pipeline uses CWL as the workflow manager and makes use of custom code written in C++ and Python along with various open-source packages for data processing. The pipeline can also process reads from non-human species and includes built-in support for specifying and building custom reference genome indices. In support of high-throughput multiomic discovery studies enabled by the BD Rhapsody™ HT Xpress System, this pipeline has been tested with datasets containing more than 800,000 putative cells and 14.5 billion reads.
For Research Use Only. Not for use in diagnostic or therapeutic procedures. BD, the BD Logo and BD Rhapsody are trademarks of Becton, Dickinson and Company or its affiliates. © 2023 BD. All rights reserved. NPM-2535 (v1.0) 1023
Citation Format: Raghavendra Padmanabhan, Brent Weichel, Amie Radenbaugh, Yuefu Jiang, Charles Weeks, Thomas McCarthy, Youngsook Kim, Anthony Berno, Devon Jensen. A fast and efficient bioinformatics analysis workflow for processing reads from single-cell multiomics assays captured on a microwell-based platform [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 4956.
Abstract 4989: Targeting IL-1β mitigates radiation induced MDSC expansion & improves survival in glioblastoma
Authors: Ghosh Subhajit , Huang Jiayi , DeNardo David , Hallahan Dennis , Jaboin Jerry , Thotala Dinesh 
Published: March 2024

Abstract
Background: Our previous research demonstrated that radiation therapy (RT) for glioblastoma (GBM) induces aberrant myelopoiesis, increasing production of myeloid derived suppressor cells (MDSCs) and exacerbating T-cell suppression. However, the precise biological mechanisms underlying this phenomenon remain unclear. This study aims to elucidate the role of the pro-inflammatory cytokine interleukin-1beta (IL-1β) in RT-induced myelopoiesis and its correlation with poor prognosis in GBM patients.
Methods: We conducted an extensive analysis of circulatory immune cells and pro-inflammatory cytokines in GBM patients treated with chemoradiotherapy. Using an orthotopic mouse model of GBM, we investigated the impact of IL-1β on myelopoiesis and survival. Functional assays were employed to examine MDSC expansion. Multi-color flow cytometry was used for immune profiling.
Results: We found that RT elevated the plasma IL-1β concentration in both GBM patients and GBM-bearing mice, which also corresponded with an increase of MDSC in blood. In the murine GBM model, RT appeared to promote hematopoietic stem (HSPC) differentiation towards myelopoiesis. This led to increased MDSC precursor cells in the bone marrow, elevated circulating MDSCs in blood, and more infiltration of MDSCs in the tumor microenvironment (TME). Similar effects were observed in GBM-bearing mice following the administration of exogenous IL-1β. In contrast, inhibition of IL-1β using anti-IL-1β antibody during RT led to reduced myelopoiesis. When anti-IL-1β antibody was combined with functional inhibition of MDSCs using phosphodiesterase-5 inhibitor-tadalafil during RT, we observed decreased MDSC infiltration and increased CD8+ T cell infiltration in the TME. The combination approach also extended the survival of GBM-bearing mice after RT longer than either drug alone.
Conclusions: Irradiation of GBM appears to induce IL-1β to promote aberrant myelopoiesis and MDSC expansion. A multi-pronged approach to inhibit MDSC production using IL-1β inhibitor and MDSC function using tadalafil during RT may be a promising approach to overcome the immune-suppressive TME of GBM.
Citation Format: Subhajit Ghosh, Jiayi Huang, David DeNardo, Dennis Hallahan, Jerry Jaboin, Dinesh Thotala. Targeting IL-1β mitigates radiation induced MDSC expansion & improves survival in glioblastoma [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 4989.