bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 501: Acute kidney injury induced by allogeneic hematopoietic stem cell transplantation: disease understanding and treatment with nanotechnology-based therapeutics
Authors: Panagiotakopoulou Magdalini , Grabarnik Emma , Jaimes Edgar A. , van den Brink Marcel R. , Heller Daniel A. 
Published: June 2022

Abstract
Allogeneic hematopoietic stem cell transplantation (allo-HCT) is a highly effective treatment for a variety of hematologic malignancies, but also associated with serious complications, such as graft-versus-host-disease (GVHD), a severe immune condition that manifests in multiple organs including the kidneys. Kidney injury in the setting of allo-HSCT has an incidence as high as 80% and leads to high patient morbidity and mortality, however, its progression and mechanisms remain poorly characterized. Moreover, no approved drugs for AKI of any etiology exist, as most experimental therapies have poor pharmacokinetic profiles and minimal efficacy in treating AKI in humans. We have characterized the pathology, genetic and molecular signatures of GVHD-mediated AKI in a murine model of HCT. GVHD results in AKI characterized predominantly by T-cell infiltration and activation of inflammatory pathways in both donor infiltrating inflammatory cells, as well as resident cells (i.e. tubules). By RNA sequencing, we have identified specific inflammatory pathways that are differentially expressed between healthy and GVHD mice and can serve as targets for therapeutic intervention. Moreover, we have synthesized nanoparticles that can specifically target proteins overexpressed in GVHD-kidneys and can encapsulate a variety of small molecule drugs. The complete understanding of this disease and development of this technology has a high potential to improve clinical practice and patient prognosis after allo-HCT, but it can also serve as a platform for treating other forms of cancer-associated kidney injury (e.g. induced by nephrotoxic chemotherapy), thus affecting the life of numerous patients.
Citation Format: Magdalini Panagiotakopoulou, Emma Grabarnik, Edgar A. Jaimes, Marcel R. van den Brink, Daniel A. Heller. Acute kidney injury induced by allogeneic hematopoietic stem cell transplantation: disease understanding and treatment with nanotechnology-based therapeutics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 501.
Abstract 5011: Meta-analysis of human cancer single cell RNAseq datasets using the fully integrated IMMUcan database
Authors: Camps Jordi , Noel Floriane , Liechti Robin , Goetz Lou , Hoffmann Caroline , Massenet-Regad Lucile , Amblard Elise , Saichi Melissa , Ibrahim Mahmoud M. , Pollard Jack , Medvedovic Jasna , Roider Helge G. , Soumelis Vassili 
Published: June 2022

Abstract
The development of single cell RNA-sequencing (scRNAseq) technologies has greatly contributed to deciphering the tumor microenvironment (TME) landscape, and a wealth of biological data is now publicly accessible. This represents a very valuable resource to researchers in the field, offering a reference for comparison of novel results, as well as opportunities for original meta-analysis studies. However, the massive amount of biological information renders its exploitation difficult in the absence of a well-structured and annotated resource. Marked heterogeneity and variability between studies in terms of cancer type, clinical context, technological platform, data quality, number and type of cells, create additional bottlenecks. We have developed a fully integrated scRNAseq database exclusively dedicated to human cancer. It gathers 134 datasets on 51 different cancer types, annotated in 50 fields containing precise clinical, technological and biological information. We developed an original data processing pipeline organized in 4 steps: first, data collection; second, data processing, which includes quality control, sample integration, cell clustering; third, cell ontology tree of the TME, built and used to annotate the clusters in a supervised and manual manner; and fourth, interface to analyze TME in a cancer type-specific or global manner. This integrated, accessible and user-friendly resource should be of great value to the biomedical community. It represents an unprecedented level of detailed annotation, offering vast possibilities for downstream exploitation of human cancer scRNAseq data for discovery and validation studies. The database is freely accessible at: https://immucanscdb.vital-it.ch.
Citation Format: Jordi Camps, Floriane Noel, Robin Liechti, Lou Goetz, Caroline Hoffmann, Lucile Massenet-Regad, Elise Amblard, Melissa Saichi, Mahmoud M. Ibrahim, Jack Pollard, Jasna Medvedovic, Helge G. Roider, Vassili Soumelis. Meta-analysis of human cancer single cell RNAseq datasets using the fully integrated IMMUcan database [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5011.
Abstract 5024: Aging patterns in five disparate cancer entities indicate a novel senescent cell population with therapeutic targetability
Authors: Saul Dominik , Kosinsky Robyn Laura 
Published: June 2022

Abstract
Human aging is associated with molecular changes and cellular degeneration, resulting in a significant increase in cancer incidence with age. Whether aging as its own entity can be initiator of putative cancerous developments, the transcriptional changes underlying the cancerous development itself are largely unknown. Here, we analyzed publicly available bulk RNA sequencing data of 328 control vs. 453 cancer samples in combination with single cell sequencing datasets from 49 control vs. 105 cancer patients. Using widely accepted aging-associated gene sets, we assembled key features of aging and their reflection in cancer development in five distinct cancer entities with an age-dependent increase of incidence, i.e. chronic myeloid leukemia (CML), colorectal cancer (CRC), hepatocellular carcinoma (HCC), lung cancer (LC) and pancreatic ductal adenocarcinoma (PDAC). After confirmation of the aging/senescence-induced gene (ASIG) expression to be upregulated in malignant diseases compared to healthy controls, we elucidated the importance of ASIGs during single cell development, making use of pseudotime analyses. In each entity, distinct temporally late enrichment states were carved out, and their transcriptional connectivity next to epigenetic control verified. Remarkably, we were able to demonstrate that all cancer entities analyzed in this study comprised cell populations expressing ASIGs. Except for pancreatic cancer, all entities accounted for subpopulations with aging associated patterns. Using a novel pipeline to repurpose FDA-approved drugs and combinations on a single cell level, we discovered a path for novel treatment regimen on a tumor-specific basis. These respective cellular subpopulations demonstrate pillars of susceptibility within the complex tumor microenvironment and serve as a basis for future studies on the role of aging and senescence in human malignancies.
Citation Format: Dominik Saul, Robyn Laura Kosinsky. Aging patterns in five disparate cancer entities indicate a novel senescent cell population with therapeutic targetability [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5024.
Abstract 5031: Improved deconvolution of combined bulk and single-cell RNA-sequencing data
Authors: Lei Haoyun , Guo Xiaoyan A. , Tao Yifeng , Ding Kai , Fu Xuecong , Oesterreich Steffi , Lee Adrian V. , Schwartz Russell 
Published: June 2022

Abstract
Identifying how cell types and their abundances evolve during tumor progression is critical to understanding the mechanisms and identifying predictors of metastasis. Single-cell RNA sequencing (scRNA-seq) has been especially promising in resolving heterogeneity of expression programs at the single cell level but is not always available, for example for large cohort studies or longitudinal analysis of archived samples. In such cases, cell subpopulations must be inferred by deconvolution, a process that can infer single-cell genomic data from bulk data but has limited ability to resolve fine clonal structure. We extend our previous bulk genomic deconvolution tool, Robust and Accurate Deconvolution (RAD), to establish a new method, scRAD, that can use reference scRNA-seq to interpret sample collections for which only bulk RNA-seq is available for some samples, e.g., clonally resolving archived primary (PRM) tissues using scRNA-seq from metastases (METs). We preprocess scRNA-seq data to accurately represent gene expression profiles, yielding a signature matrix S then extend our RAD method via a regularization term to deconvolve bulk data while maximizing consistency with S. We validate our method on semi-synthetic data derived from human PRM breast cancer cases and bone and ovary METs, showing that scRAD improves inference of single-cell gene expression profiles and their frequencies relative to the prior RAD with random initialization or initialization using the single-cell matrix S (Table 1). We then apply scRAD to a collection of paired PRM and MET tumors to quantify progression changes in common cell types. One-sided Kaplan-Meier analysis shows that tumors inferred to increase the mast cell fraction from PRM to MET exhibit lower overall survival (p&lt;0.05), consistent with the role of mast cells in metastatic growth and propagation. Tumors that show increased macrophage cell fraction from PRM to MET show improved overall survival (p&lt;0.04), consistent with the role of immune infiltration in survival.
mean square error (MSE) of gene expression and mixture fraction inference on semi-simulated data Method RAD with random initialization RAD with random initialization RAD with random initialization RAD initialized with S RAD initialized with S RAD initialized with S scRAD scRAD scRAD Sample number 2 4 8 2 4 8 2 4 8 Gene Expression MSE 0.41 0.28 0.37 0.31 0.27 0.37 0.22 0.15 0.13 Mixture fraction MSE 0.58 0.82 0.59 0.39 0.83 0.58 0.19 0.20 0.22
Citation Format: Haoyun Lei, Xiaoyan A. Guo, Yifeng Tao, Kai Ding, Xuecong Fu, Steffi Oesterreich, Adrian V. Lee, Russell Schwartz. Improved deconvolution of combined bulk and single-cell RNA-sequencing data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5031.
Abstract 504: Membrane-associated heat shock protein mHsp70 is involved in cancer cell invasion and tumor recurrence in malignant brain tumors
Authors: Shevtsov Maxim , Tagaeva Ruslana , Yudintceva Natalia , Bobkov Danila , Goncharova Darya , Kim Alexander , Fedorov Evgeniy , Nechaeva Anastasia , Mihrina Anastasia , Patruhina Anna , Fedorov Vyacheslav , Samochernykh Konstantin 
Published: June 2022

Abstract
Heat shock proteins (HSPs) constitute a large family of highly conserved proteins acting as molecular chaperones that play a key role in intracellular proteostasis, regulation of apoptosis, and protection from various stress factors (including hypoxia, thermal stress, oxidative stress). Apart from their intracellular localization, members of different HSPs families such as Hsp70 have been found to be localized on the plasma membrane of malignantly transformed cells, including multiforme glioblastoma (GBM). However, the role of the membrane-bound mHsp70 has not yet been elucidated in the pathophysiology of GBM. We have assessed the mHsp70 expression in biopsies obtained from neuro-oncological patients employing inverted confocal microscopy (Leica Microsystems) and CellVoyager CQ1 Benchtop High-Content Analysis System (Yokogawa). Live-cell imaging of the patient-derived biopsy revealed the increased cellular density of mHsp70-positive tumor cells particularly in the tissue along the tumor-brain margin (biopsy was derived 3 mm from contrast-enhancing margin and also within hyperintense FLAIR). Time-lapse analysis of the tumor sample further demonstrated the migration of mHsp70-positive cells into the surrounding culture medium. Subsequent high-powered multiplex immunofluorescence analysis (Akoya Biosystems Inc.) confirmed markedly increased single-cell infiltration of mHsp70(+) tumor cells (that were also co-immunofluorescently stained for SOX2, Nestin, and Oct4) in the normal brain tissues. In conclusion, expression of mHsp70 on the plasma membrane of tumor cells corresponds to the highly invasive potential of the GBM cells and recurrence of these tumors in neuro-oncological patients. This research was funded by the Ministry of Science and Higher Education of the Russian Federation (Agreement № 075-15-2020-901).
Citation Format: Maxim Shevtsov, Ruslana Tagaeva, Natalia Yudintceva, Danila Bobkov, Darya Goncharova, Alexander Kim, Evgeniy Fedorov, Anastasia Nechaeva, Anastasia Mihrina, Anna Patruhina, Vyacheslav Fedorov, Konstantin Samochernykh. Membrane-associated heat shock protein mHsp70 is involved in cancer cell invasion and tumor recurrence in malignant brain tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 504.
Abstract 5045: Genomics and pathology based deep learning to predict cancers of unknown primary
Authors: Wang Yanan , Zhang Guanjun , Liu Xi , Xi Yanfeng , Wang Pan , Zhang Yuman , Li Xing 
Published: June 2022

Abstract
Background: Cancers of unknown primary (CUP) are a type of metastatic cancer. However, their primary anatomical site of origin cannot be clinically determined using routine history inquiries, laboratory tests, endoscopy, and imaging. CUP account for ~3-5% of cancers. Empirical chemotherapy (paclitaxel, carboplatin, etc.) is generally used, although the curative effect is poor. Finding the primary site of origin for CUP patients is of great significance for clinical treatment.
Method: Our study employed 10,001 pathological images and 9,775 gene detection data points based on whole-exome sequencing (WES) or YUANSU®(OrigiMed, Shanghai, China) for primary cancer within a database containing 32 common cancers. We applied machine learning algorithms (autoML, Transformers, attention) and constructed two diagnostic models. The models diagnosed by identifying pathological images (Model 1) and genomic data (Model 2). Accuracy for the two models was verified.
Result: Both models were evaluated using top-k differential diagnosis accuracy, in other words how often the ground truth label was found for k highest confidence predictions for the model. The pathological model (Model 1) achieved a top-3 accuracy of 83.38% and a top-5 accuracy of 90.36%. Using the same methodology, genomic model (Model 2) results were 87.5% and 92.2%, respectively.
Conclusion: Using deep learning, we developed diagnostic models for CUP based on pathological images and genomic data. Accuracy for both the pathological image model (Model 1) and the genomic data model (Model 2) was not satisfactory. To improve diagnostic accuracy, further studies on developing a diagnostic model that combines pathological imaging and genomic data are ongoing..
Citation Format: Yanan Wang, Guanjun Zhang, Xi Liu, Yanfeng Xi, Pan Wang, Yuman Zhang, Xing Li. Genomics and pathology based deep learning to predict cancers of unknown primary [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5045.
Abstract 5062: Identification of synthetic lethal gene interactions to discover novel cancer vulnerabilities and guide drug development
Authors: Parikh Mukti , García-Quismondo Manuel , Madhukar Neel S. , Gilvary Coryandar 
Published: June 2022

Abstract
Poor treatment responses in patients and drug resistance are main challenges in the clinic which can arise due an incomplete understanding of genetic dependencies within cancers and an ill defined patient population. Identifying synthetic lethal (SL) relationships between a gene pair, where loss of either of the participating genes has no effect on cancer cells, however simultaneous disruption of both genes leads to cancer cell death, can uncover major survival mechanisms and expose vulnerabilities within cancers. In spite of advances in CRISPR or RNAi based loss of function screening it is difficult to identify these gene interactions solely by experimentation due to the vast mutational landscape of cancers, inability of cell lines to capture clinically relevant mutations, and large number of possible SL gene combinations. Furthermore, large scale CRISPR/RNAi screens are prone to false positive results, leading to erroneous identification of gene interactions. We developed a machine learning model to identify SL genetic interactions by integrating pathway, ontology, genomic, and loss of function features on all gene pairs. This included features, which have been shown to be predictive of SL pairs such as, involvement in important cancer hallmark processes or signaling pathways, co-expression profile in patient cancers, and many more. The model was trained using SL pairs from published large scale experimental screens across several cancer types, ultimately enabling the prediction of cancer agnostic SL pairs. Non-SL pairs had to be computationally defined using post-perturbation gene expression profiles due to the lack of published data annotating non-SL pairs. The model showed significant predictive performance (AUROC = 0.94, AUPRC = 0.93, Specificity = 0.91), additionally, when tested on a gold standard, biologically verified set of SL gene pairs across several cancer types, the model showed good generalizability. We leveraged the model to identify key clinically relevant SL relationships and uncover complementary mechanisms tied to pro-survival processes. By identifying SL gene interacting pairs, our platform can help gain a better understanding of dominant mechanisms driving cancers and guide the discovery of novel therapies, biomarkers, and combination therapies early on during drug development.
Citation Format: Mukti Parikh, Manuel García-Quismondo, Neel S. Madhukar, Coryandar Gilvary. Identification of synthetic lethal gene interactions to discover novel cancer vulnerabilities and guide drug development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5062.
Abstract 5077: Proteomics analysis of extracellular vesicles from pancreatic cancer cells and cancer associated fibroblasts
Authors: Pan Sharon , Lai Lisa A. , Simeone Diane M. , Dawson David W. , Yan Yuanqing , Crnogorac-Jurcevic Tatjana , Chen Ru , Brentnall Teresa A. 
Published: June 2022

Abstract
Extracellular vesicles (EVs) are comprised of lipid bound vesicles secreted by cells into the extracellular environment with various roles in cell-to-cell communication. Recent studies have implicated EVs in cell proliferation, epithelial-mesenchymal transition (EMT), metastasis, angiogenesis, and mediating the interaction of tumor cells and tumor microenvironment. To systematically characterize the EVs associated with pancreatic cancer, we performed proteomic and functional analyses on the protein contents of EVs released from pancreatic cancer lines, CAF cell lines and a normal pancreatic epithelial cell line. More than 1400 non-redundant proteins were identified in each EV proteome derived from these cell lines. The common EV proteins identified in these cell lines were enriched in biological processes such as vesicle-mediated transport and exocytosis. Protein networks relevant to pancreatic tumorigenesis, such as of EMT, complement and coagulation components, were significantly enriched in the EVs from cancer cells or CAFs in comparison to normal pancreatic epithelial cells. These findings support the roles of EVs as a potential mediator in transmitting EMT signals and complement response in the tumor microenvironment and possibly contributing to coagulation defects related to cancer development.
Citation Format: Sharon Pan, Lisa A. Lai, Diane M. Simeone, David W. Dawson, Yuanqing Yan, Tatjana Crnogorac-Jurcevic, Ru Chen, Teresa A. Brentnall. Proteomics analysis of extracellular vesicles from pancreatic cancer cells and cancer associated fibroblasts [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5077.
Abstract 5104: Extracellular vesicles expressing CEACAM proteins in the urine of bladder cancer patients
Authors: Igami Ko , Uchiumi Takeshi , Shiota Masaki , Ueda Saori , Eto Masatoshi , Kang Dongchon 
Published: June 2022

Abstract
Purpose: Early detection and long-term monitoring are important for urothelial carcinoma of the bladder (UCB). Urine cytology and existing markers have insufficient diagnostic performance. Here, we used medium-sized extracellular vesicles (EVs) in urine to find specific markers for UCB and evaluate their usefulness as diagnostic material.
Experimental Design: To search for specific markers in urinary EVs derived from UCB, we performed a comparative analysis using shotgun proteomics with urine from four UCB patients and four healthy subjects. Next, we used male 24 healthy specimens, 18 non-cancer specimens, and 29 UCB specimens for analysis of urinary EVs using a flow cytometer to evaluate the diagnostic performance of UCB-specific EVs.
Results. NTA analysis showed that the size of EVs extracted from urine by centrifugation was mostly less than 400 nm. By shotgun analysis of EVs, we detected several proteins characteristic of UCB and found that CEACAM proteins was increased in patients. Flow cytometric analysis revealed that the degree of expression of CEACAM1, 5, and 6 antigens on the surface of EVs varied among patients. EVs expressing CEACAM antigens also expressed MUC1, suggesting that they were derived from tumorigenic uroepithelial cells. The number of EVs expressing CEACAM1, 5, and 6 proteins (CEACAMs+EVs) was significantly increased in UCB (Mean9.5%±SD14%), non-bladder cancer (0.69±0.46), and healthy (0.37±0.31) by flow cytometry. The results of ROC analysis showed a good score of AUC=0.929. A provisional CEACAMs+EVs cutoff % was established with a sensitivity of 83% and specificity of 98%. Here, seven out of nine cases of negative urine cytology (class 2) were determined to be positive.
Conclusions: The presence of CEA antigen in the urine of patients with bladder cancer is known, but it has not yet been practically used for diagnosis. We have discovered EVs that specifically express these antigens and have obtained useful diagnostic performance. EVs expressing CEACAM proteins in urine are expecting target as a new liquid biopsy for UCB patients.
Citation Format: Ko Igami, Takeshi Uchiumi, Masaki Shiota, Saori Ueda, Masatoshi Eto, Dongchon Kang. Extracellular vesicles expressing CEACAM proteins in the urine of bladder cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5104.