bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 3521: Robust inference of PTEN deletion in prostate cancer from routine histopathology whole slide images using attention-based deep learning
Authors: Scarfò Federico , Omar Mohamed , Marchionni Luigi 
Published: March 2024

Abstract
The diagnostic workup of prostate cancer (PCa) requires a thorough manual histopathological inspection of prostate tissue specimens stained with hematoxylin and eosin (H&E). While immunohistochemistry and advanced molecular tests like FISH could uncover key genetic alterations, they are not cost-effective and often require special training, necessitating the development of more effective tools that can accurately infer the status of such alterations directly from the tissue morphology depicted in routine H&E-stained glass slides. PTEN deletion, which is present in 15-20% of PCa, is significantly associated with tumor aggressiveness and negative prognostic impact. Deep learning (DL) models can be trained to predict the presence of PTEN deletion on routine whole slide images (WSIs) and select patients that could benefit from further treatment. In this study, we trained and tested the performance of a weakly supervised, multiple-instance learning network on a cohort of 449 WSIs radical prostatectomy specimens stained with hematoxylin & eosin. Our model demonstrated robust performance in predicting PTEN loss based on tumor morphology alone. Through the model, we also generated attention heatmaps of the most informative tiles of each slide, allowing us to quantify the different cellular components, thus inferring the morphological features associated with PTEN loss. This study shows that DL is a potential tool that can assist the pathologist in accurately inferring the molecular status of key genetic alterations using the tissue morphology depicted in histopathology slides. Moreover, the morphological information that can be obtained by attention-based models can be leveraged to close the gap between the pathologist’s morphological analysis and the interpretability of artificial intelligence models.
Citation Format: Federico Scarfò, Mohamed Omar, Luigi Marchionni. Robust inference of PTEN deletion in prostate cancer from routine histopathology whole slide images using attention-based deep learning [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 3521.
Abstract 3533: Spatially Resolved Laser-Activated Cell Sorting (SLACS) and AI integration for enhanced pathological analysis and biomarker discovery
Authors: Jang Haewook , Lee Amos Chungwon , Lee Sumin , Kwon Sunghoon 
Published: March 2024

Abstract
This study focuses on the integration of Artificial Intelligence (AI) with Spatially Resolved Laser-Activated Cell Sorting (SLACS) for enhanced pathological analysis and biomarker discovery. The primary approach involves using AI to automatically analyze slide images, identifying critical areas associated with diseases such as cancer or cells emitting specific signals. AI algorithms efficiently segment these significant regions or cellular groups, facilitating targeted analysis.
Following the AI-driven segmentation, SLACS comes into play, isolating the identified cells from the slide for further examination. This precise sorting allows for the focused study of cells relevant to disease processes, enhancing the efficiency and accuracy of pathological analysis.
The separated samples obtained through SLACS are then subjected to detailed analysis. This step is crucial for understanding the cellular mechanisms at play in disease states and for the identification of potential biomarkers. These biomarkers are vital for developing targeted therapeutic strategies and advancing personalized medicine.
By combining AI's advanced image processing and pattern recognition capabilities with the precision of SLACS, this integrated approach significantly improves the identification and analysis of disease-related cellular characteristics. It streamlines the process of isolating and studying specific cell populations, contributing to a deeper understanding of disease mechanisms and progression.
In conclusion, the synergistic application of AI and SLACS represents a significant advancement in pathological research and biomarker discovery. This methodology not only enhances the accuracy of disease marker identification but also opens new avenues for research and clinical application.
Citation Format: Haewook Jang, Amos Chungwon Lee, Sumin Lee, Sunghoon Kwon. Spatially Resolved Laser-Activated Cell Sorting (SLACS) and AI integration for enhanced pathological analysis and biomarker discovery [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 3533.
Abstract 3545: Atlas of therapeutic targets: Evolution of druggable proteome
Authors: Babic Ivan , Nurmemmedov Elmar 
Published: March 2024

Abstract
Selection of therapeutic targets and modalities is of fundamental importance in drug discovery, as it can have a direct impact on the economics of drug discovery as well as its success rate. With the advancement of chem-bio and proteomics methods together with advent of artificial intelligence, the scope of the druggable proteome is expanding. The choice of drug targets gains further importance as we link it to potential clinical efficacy and safety, genetic variation and patient populations. Drug targets are often selected based on inadequate understanding of target biology, tractability by existing toolbox of therapeutic modalities and insufficient insight into possible clinical outcome. We have developed an “Atlas of Therapeutic Targets”, an interactive resource that provides classification of drug target families with linked information on clinical and investigational drugs. The atlas also provides key insights and score for tractability of targets with various therapeutic modalities. Particularly, our scoring system grades the targets on dimensions such as ligandability, involvement in protein-protein complexes, structural disorder, hotspot analysis and cross-referencing with known targets, thus yielding a total tractability score of 1-10. Dissection of targets based on such dimensions, enables critical evaluation therapeutic modalities as well tools and methodologies needed for their conversion into successful drug discovery programs. We curate 900+ human drug targets, analysis of which indicates the continued dominance of privileged target families across disease areas, but also the growth of novel first-in-class mechanisms. This atlas will be available as a resource for the wide drug discovery community.
Citation Format: Ivan Babic, Elmar Nurmemmedov. Atlas of therapeutic targets: Evolution of druggable proteome [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 3545.
Abstract 3549: PMed-TRIAL PDX database: Integrated multidimensional information to facilitate new drug development
Authors: Chen Xinlei , Guo Youbin , Van Loc , Caggiula Josh , Gu Xiaorong , Wen Danyi 
Published: March 2024

Abstract
PDX models play a crucial role in pre-clinical research for new drugs. In recent years, research based on PDX models has entered the 'Big Data Era.' On the one hand, there are numerous factors influencing drug responses, such as cancer types, genetic backgrounds, clinical treatment histories, and more. Therefore, selecting the appropriate PDX models for drug efficacy testing based on relevant information has become increasingly important. On the other hand, conducting in-depth genomics and transcriptomics analyses on PDX models allows us to gain a deeper understanding of the mechanisms of drug efficacy and drug sensitivity. After years of effort, LIDE have successfully built and maintained over 1800 PDX models from 40 different cancer types. And in this year (2023), we launched LIDE’s PMed-TRIAL PDX database website. As a comprehensive database of PDX models, it includes fundamental PDX clinical information, medication history, growth curves, and standard of care (SOC) data. It also contains whole exome sequencing data and RNA-seq data. Cancer is a complex and heterogeneous disease characterized by tumor heterogeneity, encompassing both inter-tumor diversity (between patients) and intra-tumor variability (within a single patient), thereby complicating drug responsiveness. PDX models preserve heterogeneity of individual patients, and a robust PDX cohort is essential for accurately representing inter-tumor heterogeneity. LIDE’s PDX cohorts effectively capture representative molecular subtypes across various major cancer types, highlighting their potential as a valuable resource for pre-clinical trials. LIDE’s PMed-TRIAL PDX database comes with a concise and efficient web user interface. Users can search and filter models using keywords, such as drug names, gene symbols, and other information. The website can now be accessed via the following URL (https://pmed.lidebiotech.com:8000/).
Citation Format: Xinlei Chen, Youbin Guo, Loc Van, Josh Caggiula, Xiaorong Gu, Danyi Wen. PMed-TRIAL PDX database: Integrated multidimensional information to facilitate new drug development [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 3549.
Abstract 3553: OralDB, A integrative data portal for oral and oropharyngeal cancer omics and clinical research
Authors: Wang Junlin , Lam Pui Ying , Su Yuxiong , Wang Junwen 
Published: March 2024

Abstract
Oral and oropharyngeal cancer is one of the more common cancers worldwide, with a poor prognosis in advanced stages and a higher incidence in Asian populations than in other regions. Its affected Affected areas include the lips, gums, tongue, the inside of the cheeks, the roof of the mouth, the floor of the mouth, the tonsils, and the oropharynx. Currently, the distribution of RNA expression data and clinical data in oral and oropharyngeal cancer is fragmented, impeding effective data mining for the discovery of novel biological markers and testing of new hypotheses. We constructed an oral and oropharyngeal cancer-specific database with expression dataRNA expression and clinical data from more than 4,356 patients in 41 studies. The RNA Expression data from 23 genome-wide platforms are carefully processed and quality-controlled, while clinical data is standardized and tightly managed. With the support of this oral and oropharyngeal cancer-specific database oral cancer-specific database, we have created an open-access web resource, the Oral and Oropharyngeal Cancer Browser, to facilitate researchers interested in oral and Oropharyngeal cancer to quickly access and analyze all currently published RNAoral cancer expression data and clinical data. Users can perform a meta-analysis of Oral and oropharyngeal oropharyngeal cancer to quickly understand the results of tumor versus non-malignant tissue (normal), differential gene expression, and expression-survival associations. Flexible options for survival analysis, comparative analysis, and correlation analysis based on a single dataset are also available. At the same time, the database also accepts the user's own input data and allows the user to do theircarry out their own analysis by combining the input data with the existing data in the database.
Citation Format: Junlin Wang, Pui Ying Lam, Yuxiong Su, Junwen Wang. OralDB, A integrative data portal for oral and oropharyngeal cancer omics and clinical research [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 3553.
Abstract 3563: Multi-modal mutational signatures elucidate the comprehensive consequences of mutational processes
Authors: Steele Christopher D. , Alexandrov Ludmil , Khandekar Azhar 
Published: March 2024

Abstract
The development of mutational signatures defined individually in the contexts of single/double base substitutions, insertions and deletions, structural variants and copy number variation have led to improved understanding of cancer mutagenesis and potential for improved patient stratification. We hypothesized that combining these contexts into a multi-modal mutational signature framework may improve the accuracy of assigning mutational processes to samples, and provide a holistic view of the full repertoire of mutations conferred from such processes. In order to develop multi-modal signatures, bespoke feature engineering was required for each mutational modality to enhance signal, in addition to individual modality matrix normalization so that the data could be harmonized and analyzed as a whole. These methods, and subsequent signature extraction were developed on 2,438 whole genome sequenced cancers from 21 distinct cancer types from the PCAWG consortium for which all modalities of data were available, incorporating a 348 channel signature definition. Deployment of these methods identified multi-modal signatures that encompass multiple mutation types known to associate with specific processes, for example SBS, indel and rearrangement channels defining a signature of homologous recombination deficiency. In contrast, some signatures remain defined by a single mutational modality, highlighting the distinction between broadly mutational processes, and more focused mutation processes. Multi-modal signatures show promise in increasing accuracy for difficult to assign signatures, such as those for homologous recombination deficiency, and therefore in improving patient stratification for life-saving treatment.
Citation Format: Christopher D. Steele, Ludmil Alexandrov, Azhar Khandekar. Multi-modal mutational signatures elucidate the comprehensive consequences of mutational processes [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 3563.
Abstract 357: Reducing reagent-resistant transferrin receptor 1 dimer is a specific marker for ferroptosis
Authors: He Jianping , Kelleher Mackenzie , Reynolds Lucas , Tanczos Hannah , Huang Zhengrong , McLaughlin Brett , Huang Wesley , Pope Lauren , Veeckmans Geraldine , Berghe Tom Vanden , Dixon Scott J. , Shah Yatrik , Schafer Zachary T. 
Published: March 2024

Abstract
Ferroptosis is an iron-dependent cell death caused by the catastrophic accumulation of lipid peroxidation. Despite the burgeoning ferroptosis research, there remains a void for an intrinsic and comprehensive ferroptosis indicator. Here we reported a simple and cost-effective way to detect ferroptotic cell death using reducing reagent-resistant (RRR) transferrin receptor 1 (TfR1). The TfR1 RRR dimer strongly correlates with ferroptosis induced by various ferroptosis-inducing conditions and relies on lipid peroxidation, which is not observed in other types of cell death. Through iron-overloading and tumor xenograft mouse models, we validate the in vivo ferroptosis detection using this technique. Furthermore, we identify the presence of TfR1 RRR dimers in extracellular vesicles isolated from the plasma of mice with ferroptosis-related diseases. Since TfR1 is a ubiquitously expressed protein, the increase of TfR1 RRR dimer can be used as a specific and endogenous ferroptosis marker with potential clinical applications.
Citation Format: Jianping He, Mackenzie Kelleher, Lucas Reynolds, Hannah Tanczos, Zhengrong Huang, Brett McLaughlin, Wesley Huang, Lauren Pope, Geraldine Veeckmans, Tom Vanden Berghe, Scott J. Dixon, Yatrik Shah, Zachary T. Schafer. Reducing reagent-resistant transferrin receptor 1 dimer is a specific marker for ferroptosis [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 357.
Abstract 3598: A new strategy for T cell therapy: T cells secreting TCR anti-CD3 bispecific T-cell engager
Authors: Sun Hanli , Huang Jiao , Zhan Kai , Wu Wanli , Tang Xianqing , Liu Min , Guo Shanshan , Zheng Hongjun , Huang Yingjie , Zhong Shi 
Published: March 2024

Abstract
TCR-engineered T (TCR-T) cells and TCR-anti-CD3 bispecific T-cell engagers (TCEs) are potent TCR-based therapeutic agents with distinct advantages and limitations in tumor treatment. TCR-T cells offer durable persistence within patients but necessitate personalized manufacturing and lack the capacity to harness bystander T cells. Conversely, TCEs are readily available as "off-the-shelf" products and can recruit bystander T cells, yet they exhibit a shorter lifespan. In our study, we sought to merge the merits of both approaches by engineering T cells to secrete a TCR-anti-CD3 TCE specific for alpha fetoprotein (AFP), a tumor-associated antigen abundantly expressed in hepatocellular carcinoma (HCC). We initially identified a TCR with specificity for the AFP158-166 peptide bound to HLA-A*02:01 and enhanced its affinity to picomolar via phage display. To facilitate efficient secretion by T cells, we adapted the high-affinity TCR to a single-chain format (scTCR) and fused it with a CD3-specific single-chain antibody fragment (scAFP-TCE). Our findings demonstrated that scAFP-TCE effectively redirected bystander T cells to engage in the lysis of HCC cells. Moreover, scAFP-TCE could be secreted by T cells transduced with lentiviral particles encoding the TCE gene. These transduced T cells exhibited potent antitumor activity both independently and by enlisting bystander T cells. This innovative T cell strategy, combining the bystander-recruiting ability of fusion proteins with the durable persistence seen in T cell therapy after a single infusion, presents a promising alternative to conventional TCR-based therapeutic agents. We anticipate that this novel approach may hold substantial potential for enhancing HCC treatment and expanding the scope of TCR-based immunotherapies.
Citation Format: Hanli Sun, Jiao Huang, Kai Zhan, Wanli Wu, Xianqing Tang, Min Liu, Shanshan Guo, Hongjun Zheng, Yingjie Huang, Shi Zhong. A new strategy for T cell therapy: T cells secreting TCR anti-CD3 bispecific T-cell engager [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 3598.
Abstract 3599: Recombinant chimeric antibody-based gamma-delta TCR targeting GPC3 induces potent antitumor response against hepatocellular carcinoma
Authors: Li Dan , Liang Tianyuzhou , Duan Zhijian , Zhang Hongbing , Liu Cheng , Ho Mitchell 
Published: March 2024

Abstract
Background and Significance: Hepatocellular carcinoma (HCC), a major type of primary liver cancer, is the third leading cause of cancer-related death worldwide. While Glypican-3 (GPC3) is highly upregulated in HCC, it is minimally expressed in normal tissues. The research in our laboratory has been focused on the development of GPC3 as an immunotherapeutic target for liver cancer. Despite many clinical trials, there has not been any approved therapy targeting GPC3 largely due to the combination of antigen heterogeneity, poor tumor penetration, and the inability to induce a durable, potent immune response in the tumor microenvironment (TME). Here, we made a recombinant chimeric antibody-based T cell receptors (named AbTCR) targeting GPC3 and explored its anti-tumor efficacy in xenograft mouse models.
Methods: The AbTCR constructs were built by fusing an antigen recognition domain (hYP7 scFv or HN3 VH) specific for GPC3 to the gamma-delta TCR constant region along with a secondary T cell signaling activation motif. The NFAT-tdTomato reporter cell line was established and used to measure the AbTCR-triggered T cell signaling upon stimulation with tumor cells. Anti-tumor efficacy of AbTCR-T cells was determined via luciferase-based cell killing assay as well as multiple HCC xenograft mouse models.
Results: The hYP7 GPC3 AbTCR-T cell not only enhances antigen-binding capacity and T cell activity, but also induces infiltration and expansion of T cells in the TME, thereby causing robust liver tumor regression in mice.
Conclusion: Our data show that new hYP7-directed GPC3 AbTCR is a promising strategy for treating liver cancer. This work provides a rationale for the use of hYP7 GPC3 AbTCR in clinical studies for treating HCC and other GPC3-positive solid tumors.
Citation Format: Dan Li, Tianyuzhou Liang, Zhijian Duan, Hongbing Zhang, Cheng Liu, Mitchell Ho. Recombinant chimeric antibody-based gamma-delta TCR targeting GPC3 induces potent antitumor response against hepatocellular carcinoma [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 3599.