bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 3505: Classification of cancer subtypes by cfDNA fragmentomics analysis
Authors: Zhu Lisha , Dai Chao , Gan Shuang , Jia Shidong , Du Pan 
Published: March 2024

Abstract
Introduction Fragmentation patterns of cell-free DNA (cfDNA) from whole genome sequencing offer insights into nucleosome occupancy, providing a tool to infer epigenomic and transcriptomic information. The nucleosome complex, the primary protector of cfDNA, is reflected in the size distribution of cfDNA fragments, with a mode fragment size of 167 bp corresponding to the wrapping of DNA around a single nucleosome. Nucleosome occupancy patterns at transcription factor binding sites (TFBS) exhibit differences between circulating tumor DNA in cancer patients and normal plasma controls, as well as among different tumor subtypes. Therefore, cfDNA fragmentomics can be employed for cancer subtyping analysis.
Methods We utilized the Griffin framework to classify tumor subtypes based on nucleosome profiling of cancer-specific TFBS and tumor subtype-specific chromatin accessibility regions from low-pass whole genome sequencing data of cfDNA. For prostate cancer subtyping, AR and ASCL1 binding sites were used to distinguish between androgen receptor-dependent prostate cancer (ARPC) and neuroendocrine prostate cancer (NEPC). ER and ERBB2 were employed for breast cancer subtyping to differentiate between ER-positive and ER-negative tumor subtypes.
Results Nucleosome profiling patterns at AR binding sites (ARBS) were compared among over 500 prostate cancer plasma samples and 42 normal plasma backgrounds. The ARBS nucleosome profiling abnormality score (ARBS score) was quantified by comparing its GC-corrected fragment central coverage with the normal background using a standard Z-score.Majority of prostate cancer samples with a high ARBS score could be classified as ARPC. For 12 samples with high tumor fraction but low ARBS score and no AR copy number amplification, we found that 8 had a high ASCL1 nucleosome profiling abnormality score. All these samples also exhibited RB1 copy number loss, suggesting that these 8 samples likely belong to NEPC. Nucleosome profiling of 171 breast cancer plasma samples at breast cancer-specific ER binding sites revealed that majority of samples had a high ERBS abnormality score, indicating likely ER-positive breast cancer.
Conclusion Our results demonstrate that nucleosome profiling of tumor cfDNA exhibits distinct patterns across different tumor types. Integrating nucleosome profiling at cancer-specific transcription factor binding sites can facilitate cancer subtyping, highlighting its potential clinical utility for patient stratification.
Citation Format: Lisha Zhu, Chao Dai, Shuang Gan, Shidong Jia, Pan Du. Classification of cancer subtypes by cfDNA fragmentomics 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 3505.
Abstract 3522: Application of large language models to nucleotide sequences for profiling signaling pathway disruptions in ovarian cancer patients
Authors: Chebanov Dmitrii K. , Tatevosova Nadezhda S. 
Published: March 2024

Abstract
Ovarian cancer is a challenging disease, often exhibiting recurrence after initial treatment. The complexity arises from simultaneous disruptions in multiple signaling pathways within tumor cells, rendering targeted inhibitors ineffective in providing sustained therapeutic impact. While targeted sequencing panels can identify specific therapeutically relevant events, they often fail to capture the complete picture of targetable tumor driving aberrations, and provide biomarkers relevant to disease resistance and/or recurrence. A potential solution lies in comprehensively defining the landscape of signaling pathway alterations by identifying molecular triggers underlying their dysregulation. While individual DNA mutations sometimes may not lead to carcinogenesis or changes in tumor cell behavior, combination of deactivating mutations in suppressor genes, can influence tumor development and prognosis, warranting a comprehensive delineation of their effect on the broad signaling network level. To this end, we propose the Large Language Models (LLMs), which have been successful in natural language processing, particularly in understanding context. Such tools are essential for discerning the effect of mutation combinations, their physical proximity to other mutations, and functional gene regions. We obtained mutation annotation files for 177 ovarian cancer patients from the TCGA database, reconstructing consensus nucleotide sequences using a reference genome. To input this data into LLMs, we generated embeddings for these sequences using the Nucleotide Transformer algorithm, pre-trained on 500 million variants of the reference genome. Additionally, we extracted data on detected disruptions in 10 signaling pathways (Cell Cycle, HIPPO, MYC, NOTCH, NRF2, PI3K, RTK RAS, TP53, TGF-Beta, WNT) for these patients from TCGA. Each pathway was associated with the percentage of its alteration. We trained a deep learning algorithm on these data to predict a combination of 10 numerical values, each corresponding to the percentage of disruption for a given signaling pathway. Our findings revealed that nearly half of the patients exhibited disruptions in 5 or more signaling pathways. The algorithm developed enables the determination of the extent of disruption in each pathway based on whole exome sequencing results. This approach facilitates more informed treatment strategy planning and enhances the efficient development of new drugs for ovarian cancer treatment.
Citation Format: Dmitrii K. Chebanov, Nadezhda S. Tatevosova. Application of large language models to nucleotide sequences for profiling signaling pathway disruptions in ovarian cancer patients [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 3522.
Abstract 3528: Constructing knowledge graph of N6-methyladnosion regulations in cancer from literature using ChatGPT-4
Authors: Jo Sumin , Wu Xidong , Zeng Yiming , Das Arun , Zhang Ting-He , Spellman David Alexander , Ferris Adam , Gao Shou-Jiang , Zhang Jianqiu (Michelle) , Chiu Yu-Chiao , Huang Yufei 
Published: March 2024

Abstract
N6-methyladenosine (m6A) is the most abundant mRNA modification with a crucial role in cellular processes. Its involvement in cancers is well-established. Dysregulation of m6A is linked to cancer initiation, progression, and therapy resistance, impacting the tumor microenvironment (TME) and promoting metastasis. Understanding m6A's regulatory role in cancer is essential for identifying new therapeutic targets. We considered the construction of a knowledge graph (KG) representing molecular regulatory mechanisms (MRMs) of m6A in healthy conditions and different cancers from PubMed papers. Such a KG consists of interconnected triplets of (head node, regulatory relationship, tail node), each denoting regulation between genes or a gene and a phenotype/process. Advances in natural language processing (NLP) have provided tools such as SemRep and GNBR to automate the extraction of these triplets from the literature. However, these methods don’t extract contextual information, such as the type of cancers or cells in which these MRMs occur, leading to contradictory regulations in the constructed KG. In addition, existing approaches were designed to extract generic regulatory relations and therefore struggled in properly capturing unique concepts associated with m6A regulatory mechanisms. To tackle this challenge, we designed a novel ChatGPT-4 prompt to extract relational graphs from papers tailored for m6A regulatory mechanisms. An annotated dataset of 400 titles on m6A-related MRMs were created to evaluate the performance. We then applied the prompt to 1023 papers to create a m6A Molecular Regulatory Mechanism Knowledge Graph (m6A-MRM-KG). This graph illuminates m6A's roles in gene expression regulation, especially in cancer and immunity. Anticipated to enhance our understanding of cancer development, it provides insights into potential therapeutic strategies. The m6A-MRM-KG not only organizes information but also empowers researchers to uncover novel insights in the dynamic relationships of m6A modifications in diverse biological contexts.
Citation Format: Sumin Jo, Xidong Wu, Yiming Zeng, Arun Das, Ting-He Zhang, David Alexander Spellman, Adam Ferris, Shou-Jiang Gao, Jianqiu (Michelle) Zhang, Yu-Chiao Chiu, Yufei Huang. Constructing knowledge graph of N6-methyladnosion regulations in cancer from literature using ChatGPT-4 [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 3528.
Abstract 3536: Paracell: A high throughput, deep learning-based pipeline for single-cell phenotypic profiling
Authors: Nguyen David L. , Chao Jesse T. 
Published: March 2024

Abstract
Variants of uncertain significance constitute the vast majority of missense variants, hampering the utility of genetic testing, despite its remarkable impacts on clinical diagnosis and decision-making. Although the sequencing and interpretation of genetic variants have been drastically improved in recent years, only about 2% of novel missense variants are currently clinically actionable. Therefore, a robust and scalable pipeline is desperately needed to provide functional evidence critical for variant interpretation. In this work, we demonstrate Paracell, a deep learning-based phenotypic profiling pipeline that leverages subcellular segmentation, high-dimensional single-cell phenotyping, and machine learning to classify the functional impact of variants. Paracell captures heterogeneity in cellular phenotypes and protein colocalization between variants and their signaling partners, as well as subcellular markers, and enables analysis at a single-cell resolution. Using a classification model trained on single-cell features extracted by Paracell, we were able to accurately classify cells from loss-of-function (LoF) variants based on their impact. After aggregating cellular profiles on a variant level, our pipeline was able to distinguish LoF variants from functional ones with high sensitivity and specificity. Our work demonstrates that Paracell is a robust and scalable method that can sensitively detect differences in single-cell phenotypic profiles. The systematic application of this pipeline will provide valuable functional evidence for variant interpretation, enhancing their clinical utility and accelerating personalized cancer care.
Citation Format: David L. Nguyen, Jesse T. Chao. Paracell: A high throughput, deep learning-based pipeline for single-cell phenotypic profiling [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 3536.
Abstract 3559: A validated bioinformatics tool-set for predicting TCR specificity
Authors: Wilm Andreas , Schmidt Florian , Wirawan Melissa , Cooray Manisha , Wu Kan Xing , Fink Katja , MacLeod Dan 
Published: March 2024

Abstract
Background: The T-cell receptor (TCR) is a transmembrane receptor on T cells, which is responsible for recognizing any foreign antigen derived peptide, presented on infected or abnormal cells by the MHC-I complex. Predicting TCR specificity computationally is a long-standing problem. Many methods leverage large libraries of TCR sequences and cognate epitopes as input for machine learning and predict specificity from sequence alone. Most methods generally do not perform very well, especially in the case of unseen peptides, i.e. for cases where peptides were not part of the input library.
Methods: Here we describe three independent but interacting in-silico modules for TCR discovery. 1) The first module predicts specificity from deep phenotypic T cell profiles. We produce these profiles using a high-throughput pipeline, which combines multiplexed CyTOF with subsequent VDJ CITE-Seq. The generated multiomics data is aggregated into a database called TCAPS, which is the basis for machine learning models. These models can predict T cell specificity from the deep phenotype alone, i.e. without TCR sequence information. The resolution however is limited to the target class and hence the actual epitope has to be determined through subsequent deorphanization. This deorphanization step is augmented by the other two modules also described here. 2) The second module enables ultrafast sequence similarity search, which is used to find similar TCRs with known epitope specificity 3) The third module ranks TCR candidates for prioritisation for in-vitro validation. This is achieved by running the Alphafold-based structure prediction program TCRDock.
Results: 1) We demonstrate how TCR specificity can be correctly predicted from multiomics deep T-cell profiles and specifically highlight two in-vitro validated predictions, for which the corresponding epitope/antigen were not part of the input panel. 2) Using a recently published TCR clustering benchmark TCRScapes, we demonstrate the superior performance of our ultrafast sequence similarity search. 3) We furthermore showcase TCRdock-directed experiments on TCRs specific for SF3B1mut-induced splice variants. TCRdock correctly ranked TCR candidates, including artificial combinations, whose functionality we prove through in-vitro validation.
Conclusions: We describe three modules for TCR discovery and demonstrate their utility by benchmarking and in-vitro validation of multiple predictions, including two examples against unseen epitopes/antigens.
Citation Format: Andreas Wilm, Florian Schmidt, Melissa Wirawan, Manisha Cooray, Kan Xing Wu, Katja Fink, Dan MacLeod. A validated bioinformatics tool-set for predicting TCR specificity [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 3559.
Abstract 360: Dissecting the role of oncogenic KRAS and ferroptosis within colorectal cancer
Authors: Rosa-Vázquez Alexandra , Bernard-Pagan Vincent , Chen Jasper , Taniguchi Cullen 
Published: March 2024

Abstract
Introduction: Ferroptosis is a novel iron-dependent regulated cell death mechanism triggered by a toxic accumulation of membrane lipid peroxidation. This mechanism is counteracted by ferroptosis defense systems, such as the GPX4/GSH system, which function as antioxidants that limit lipid peroxidation. Oncogenic KRAS (KRASmut) is a key driver for many aggressive cancers, including colorectal cancer (CRC). Previous studies have suggested that oncogenic KRAS plays a pleiotropic role in the ferroptosis pathway. Yet, the interplay between oncogenic KRAS and ferroptosis has not been fully elucidated in CRC. Here, we aimed to dissect the role of oncogenic KRAS in ferroptosis using CRC as a cancer model.
Methods: Cells from a KRAS-inducible model (iKAP) were seeded and treated with ferroptosis-inducing drugs (e.g., Erastin and RSL3) and/or a KRAS inhibitor (MRTX1133). Lipid peroxidation was then measured by BODIPY assay followed by flow cytometry to detect the quantity of BODIPY-stained cells. Protein expression of known ferroptosis defense mechanisms was also profiled (e.g., SLC7A11 [xCT] and GPX4).
Results: Results from this study demonstrate that KRASmut activation leads to a reduction in mean fluorescence intensity when compared to KRASWT of BODIPY-stained cells. This decreased mean fluorescence intensity is indicative of lower levels of lipid peroxidation and, consequently, ferroptosis. KRASmut cells were also found to be resistant to ferroptosis following treatment with ferroptosis inducers. To replicate a pharmacologically induced KRASWT phenotype, iKAP cells were treated with MRTX1133, which led to a significant decrease in viability when combined with RSL3. We observed a decreased protein expression of the membrane antiporter xCT following treatment with MRTX1133, which we believe works synergistically with the GPX4 inhibition of RSL3.
Conclusion: We observed that KRASmut may induce resistance to ferroptosis in vitro in CRC cells. Genetic and pharmacological inhibition of KRAS resulted in sensitization to ferroptosis inducers. Future work will uncover mechanisms through which oncogenic KRAS leads to ferroptosis resistance in CRC. More importantly, understanding the interaction between ferroptosis, oncogenic KRAS, and its antagonist systems has significant therapeutic potential in oncology.
Citation Format: Alexandra Rosa-Vázquez, Vincent Bernard-Pagan, Jasper Chen, Cullen Taniguchi. Dissecting the role of oncogenic KRAS and ferroptosis within colorectal 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 360.
Abstract 3710: Determining the efficacy of blood filtration in patients diagnosed with pancreatic cancer through the liquid biopsy
Authors: Suresh Divya D. , Courcoubetis George , Lin Emmeline , Mason Jeremy , Nissen Nicholas , Gaddam Srinivas , Lo Simon , Pandol Stephen , Kuhn Peter , Shishido Stephanie 
Published: March 2024

Abstract
Cancer becomes lethal as it spreads from the primary site to the rest of the body. Circulating tumor cells (CTCs) are considered a marker of disease progression and have been associated with decreased overall survival. Blood filtration is one such method of removing cancer cells from circulation to improve patient prognosis. This study utilizes the liquid biopsy to assess the efficacy of a proprietary filtration device on the blood of patients with pancreatic ductal adenocarcinoma (PDAC) using the third generation high-definition single cell assay (HDSCA3.0) workflow. Blood samples were collected and analyzed to characterize the CTCs and other rare cells present before and after filtration. Examination of 6 paired portal (PoVB) samples demonstrated a statistically significant decrease in total rare cells, total CK+ cells, and epi.CTCs across all patients due to filtration of the blood. Furthermore, analysis of 2 paired peripheral blood (PB) samples showed a decrease in total rare cells, total CK+ cells, and specific phenotypes of rare cells upon filtration. These preliminary results demonstrate initial proof of concept that this filtration device can remove CTCs from the circulation, which may be used as a potential therapeutic device for PDAC patient care.
Citation Format: Divya D. Suresh, George Courcoubetis, Emmeline Lin, Jeremy Mason, Nicholas Nissen, Srinivas Gaddam, Simon Lo, Stephen Pandol, Peter Kuhn, Stephanie Shishido. Determining the efficacy of blood filtration in patients diagnosed with pancreatic cancer through the liquid biopsy [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 3710.
Abstract 3748: Evaluation of immune cells from patient-derived tumor tissue and peritoneal washing cytology in correlation with immunohistochemical markers in endometrial cancer
Authors: Shin Changmin , Lee Seungmee , Chung Hyewon , Kim Jin Young , Ha Eunyoung , Seo Ji Hae , Shin Sojin 
Published: March 2024

Abstract
Background This study aims to evaluate the characteristics and clinical correlations of tumor-infiltrating lymphocytes (TILs) in endometrial cancer, derived from fresh tumor tissue and peritoneal washing cytology. Furthermore, it seeks to explore the correlation between TILs and immunohistochemical markers in endometrial cancer.
Methods In this study, we investigated the nature of TILs extracted from endometrial tissue and peritoneal washing cytology, along with their correlation with immunohistochemical markers. Samples collected from endometrial cancer patients during surgery, with informed consent, were analyzed for immune cell composition using flow cytometry. We examined the association between pathological findings, a range of immunohistochemical markers (including LVSI, PD-L1, ER, P53, HER2, MLH1, MSH2, MSH6, PMS2, MSI), and the status of tumor-infiltrating immune cells (such as CD3-CD19+ B Cells, CD3-CD16+CD56+ NK Cells, CD4+CD25+ regulatory T cells (Treg), and CD3+CD8+ cytotoxic T cells).
Results Our analysis encompassed 25 endometrial cancer-derived samples. Notable observations include an increase in CD3-CD19+ B cells in the LVSI-positive group and a decrease in the ER-positive group (p&lt;0.05, respectively). CD3-CD16+CD56+ NK Cells decreased in the ER-positive group, while CD3+CD8+ cytotoxic T cells were decreased in the HER2-positive group (p&lt;0.001, p&lt;0.05, respectively). Other immunohistochemical markers showed no significant differences.
Conclusion The study suggests that tumor-infiltrating immune cells from patient-derived tissue and peritoneal washing cytology may reflect the molecular and clinical characteristics of endometrial cancer. However, further research with an expanded sample size and correlation with oncologic outcomes is warranted to validate these findings.
Citation Format: Changmin Shin, Seungmee Lee, Hyewon Chung, Jin Young Kim, Eunyoung Ha, Ji Hae Seo, Sojin Shin. Evaluation of immune cells from patient-derived tumor tissue and peritoneal washing cytology in correlation with immunohistochemical markers in endometrial 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 3748.