bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 2315: AI-enabled precision oncology era: Advanced and interactive interpretation of next-gneneration sequencing (NGS) reports
Authors: Chen Hui , Xu Zanmei , Chen Lijuan , Wang Mingmin , Zhang Peng , Pang Fei , Wang Kai 
Published: March 2024

Abstract
Background: Next-generation sequencing (NGS) has been widely adopted in clinical practice, but there are still unmet needs among physicians regarding the interpretation and application of NGS reports. These needs include selecting multi-targeted and combination therapies, keeping up with evolving treatment options and clinical trial results, and incorporating personalized medicine based on real clinical cases.
Methods: To address these challenges, we propose using large language model (LLM) to analyze and interpret clinical questions, perform semantic searches in a high-quality database, and provide customized prompts and logical outputs. Our knowledge base includes diagnostic and treatment details for 600+ tumor subtypes, 620,000+ entries, 2,000+ drug details, and 440,000+ clinical trials. The database is updated automatically using a fact engine and medical literature sources. Personalized gene interpretation is incorporated using pretraining, cosine similarity, and context learning prompts.
Results: Our tool, named "SmartMTB", enables personalized interpretation of genomic testing reports for precision oncology. Users input the latest clinical features using a selection-based or question-and-answer approach. The tool identifies the treatment stage and genomic variations, provides information on mutation frequencies, sensitivity and resistance of targeted and immunotherapies, and priority selection strategies for multi-gene mutations. It matches similar clinical cases and provides real-time updates on treatment options and clinical trial literature. It also facilitates patient matching for clinical trials and considers economic factors.
Conclusion: We present a groundbreaking artificial intelligence (AI) tool in cancer care that revolutionizes precision oncology. This AI system, trained on extensive datasets, swiftly and accurately analyzes and interprets complex genomic and clinical data. It enables clinicians to make informed decisions about personalized treatment strategies, significantly improving patient outcomes in cancer therapy.
Citation Format: Hui Chen, Zanmei Xu, Lijuan Chen, Mingmin Wang, Peng Zhang, Fei Pang, Kai Wang. AI-enabled precision oncology era: Advanced and interactive interpretation of next-gneneration sequencing (NGS) reports [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 2315.
Abstract 2317: Development and validation of a colorectal cancer prediction model: A nationwide cohort-based study
Authors: Hayek Samah 
Published: March 2024

Abstract
Background: Colorectal cancer (CRC) ranks as the third most prevalent cancer globally and is a major cause of cancer-related mortality. Early diagnosis is critical to increase survival rates. While CRC screening has shown significant benefit, adherence remains low, and there is a need for better tools to identify high-risk patients. Risk prediction models were demonstrated to identify such patients.
Aim: To establish an individualized risk prediction model for CRC diagnosis based on Electronic Health Records (EHR).
Methods: This is a retrospective cohort study utilizing EHR data of Clalit Health Services (CHS) members aged 50-74 that were eligible for CRC screening, from January 2013 to January 2019. The model was trained to predict CRC diagnosis within two years using approximately 20,000 EHR features including socio-demographic information, laboratory results and medical history. Model performance, as a complementary screening method, was evaluated.
Results: The study included 2679 subjects with CRC diagnosis and 1,133,713 subjects without CRC diagnosis. The model was trained on subjects from 2013-2017, and performance was validated on subjects from 2019 and a cohort of subjects that underwent fecal occult blood test (FOBT). Incidence values of CRC among subjects in the top 1% risk scores were higher than baseline (2.3% vs. 0.3%; lift 8.38; P-value &lt;0.001). Characteristics of subjects by risk scores percentiles are presented in Table 1. Cumulative event probabilities increased with higher model scores, indicating a correlation between predicted and actual risk of CRC diagnosis. Model-based risk stratification among subjects with a positive FOBT, identified subjects with more than twice the risk for CRC compared to FOBT alone.
Conclusions: We developed an individualized risk prediction model for CRC that can be utilized as a complementary decision support tool for healthcare providers to precisely identify patients at high risk for CRC and refer them to confirmatory testing.
Citation Format: Samah Hayek. Development and validation of a colorectal cancer prediction model: A nationwide cohort-based study [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 2317.
Abstract 2335: MultiNMF: multiview factorization for joint modeling of spatial multi-omics and histology images
Authors: Bowie William , Wang Stacy , Strope Benjamin , Zhu Qian 
Published: March 2024

Abstract
An increasing number of cancer research studies employ spatially resolved transcriptomics (SRT) to investigate the composition of tumor microenvironment in a cancer type of interest. These studies have defined tumor microenvironment (TME) states and spatial domains based on clustering spatial gene expression patterns in SRT in an unbiased manner, yet a more thorough delineation of TME states requires the incorporation of the tumor’s histology image. Here, we develop MultiNMF, a multiview factorization approach that is suitable for cancer research studies where joint profiles of spatial multi-omics and tumor histology images are available. We apply MultiNMF to analyze a set of TNBC SRT primary tumor samples and reveal TME states in the stromal, epithelial, and immune enriched compartments, defined by distinct histomorphological features. We further illustrate the ability of the approach to extend to paired spatial ATAC-seq and histology dataset that is recently published on HER2 breast cancer. MultiNMF thus permits an automated and data-driven decomposition of SRT and spatial ATAC data supported by histomorphological evidence.
Context
In SRT by 10X Visium, the hematoxylin eosin (H&E) staining image is automatically aligned to the slide where the tissue section is mounted. Image patches can be easily extracted from areas under the SRT barcoded spots. For these images, I will apply a pre-trained convolutional neural network (CNN) model to extract high dimensional features from spot images. The image features can be added as a second view after gene expression view for joint analysis under MultiNMF.
Results
MultiNMF can factorize the SRT data into components well-supported by histological evidence. It has identified T-cell infiltrating regions, and EMT-enriched regions with distinct immune and stroma morphological characteristics. The T-cell infiltration neighbors a region that has an appearance of necrosis according to the pathologist evaluation. These domains further demonstrate enrichments of modules with motif enrichment (known as regulons) according to SCENIC analysis. Analysis of spatial ATAC illustrates not only HER2 but also its amplicon amplification. From MultiNMF components of spatial ATAC, we derive regulatory regions for T-cell/B-cell marker genes. Overall, MultiNMF is a novel model that has not been applied to SRT field. It provides an multiomic extension to the traditional NMF.
Citation Format: William Bowie, Stacy Wang, Benjamin Strope, Qian Zhu. MultiNMF: multiview factorization for joint modeling of spatial multi-omics and histology images [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 2335.
Abstract 2371: BGB-B167, a first-in-class 4-1BB/CEACAM5 bispecific antibody, exhibits potent in vitro and &lt; for vivo antitumor activity and &lt; for superior safety profile in preclinical models
Authors: Li Zhuo , Qu Liang , Chen Xin , Xue Liu , Li Jie , Liu Qi , Sun Jian , Sun Hanzi , Chen Yun , Xie Yuanyuan , Wang Wanyi , Zhu Lin , Wang Penghao , Zhou Xiaosui , Hou Hongjia , Chen Jie , Liu Xinyi , Zhang Yilu , Liu Ning , Liang Xinyi , Zhang Shuo , Tang Xiaoyan , Song Jing , Zhang Tong , Song Xiaomin , Liu Xuesong , Li Kang , Huang Chichi 
Published: March 2024

Abstract
4-1BB (CD137) is a key costimulatory immunoreceptor and a promising therapeutic target in cancer. CEACAM5 (CEA) is a well-established tumor associated antigen overexpressed in many cancers, including colorectal, gastric, lung, pancreatic cancer, liver, breast and thyroid cancers. BGB-B167 is a novel immunoglobulin G (IgG)-based bispecific antibody targeting 4-1BB and CEA and is under clinical development for the treatment of advanced or metastatic solid tumors in humans. BGB-B167 binds to its target proteins with high specificity and affinity. Potent and CEA-dependent functional activities were demonstrated using peripheral blood mononuclear cell (PBMC)-based immune cell activation and cytotoxicity assays. In humanized 4-1BB knock-in mice bearing human CEA-expressing tumors, BGB-B167 exhibited potent, dose-associated single-agent efficacy as well as synergistic antitumor activity in combination with anti-PD-1 antibody. BGB-B167 was well tolerated in 1-month repeat-dose toxicology study in cynomolgus monkeys. Here, we describe the characterization of BGB-B167 with regard to preclinical proof-of-concept and basic drug-like properties. The combined dataset provides an overview on the design, mode of action, preclinical pharmacology and safety profile of BGB-B167.
Citation Format: Zhuo Li, Liang Qu, Xin Chen, Liu Xue, Jie Li, Qi Liu, Jian Sun, Hanzi Sun, Yun Chen, Yuanyuan Xie, Wanyi Wang, Lin Zhu, Penghao Wang, Xiaosui Zhou, Hongjia Hou, Jie Chen, Xinyi Liu, Yilu Zhang, Ning Liu, Xinyi Liang, Shuo Zhang, Xiaoyan Tang, Jing Song, Tong Zhang, Xiaomin Song, Xuesong Liu, Kang Li, Chichi Huang. BGB-B167, a first-in-class 4-1BB/CEACAM5 bispecific antibody, exhibits potent in vitro and &lt; for vivo antitumor activity and &lt; for superior safety profile in preclinical models [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 2371.
Abstract 2410: Comparative genomic profiling of unresectable NSCLC patients in the U.S. and China using a globally harmonized liquid biopsy assay platform
Authors: Tang Haoran , Jia Cancan , Xie Feng , Zhang Yue , Dong Xiaoxi , Huang Yong , Jia Shading 
Published: March 2024

Abstract
Background: Non-Small Cell Lung Cancer (NSCLC), accounting for over 80% of all lung cancers, benefits from targeted therapies based on genetic tests like EGFR, KRAS, ALK, and ERBB2. While prior studies focused on molecular characteristics through tissue biopsies, limited research has explored NSCLC molecular profiles via liquid biopsy, especially across different human races. This study presents a comprehensive genomic profiling analysis of unresectable NSCLC patients in the U.S. and China, using a globally harmonized liquid biopsy assay.
Methods: As part of Predicine's Phoenix Program, a global molecular biomarkers screening initiative, 61 patients in the U.S. and 352 patients in China with unresectable advanced NSCLC were enrolled. All were treatment-naïve or recurred after 1st-line targeted therapies. Blood samples (10ml) were tested using PredicineCARE, an NGS-based liquid biopsy assay, to profile somatic mutations, copy number variations, and gene fusions.
Results: The assay, validated in both regions using the same reference materials, achieved a Limit of Detection (LOD) of 0.25% mutation allele frequency, with a positive predictive value exceeding 99%. Profiling NSCLC patients in the U.S. and China revealed common genes like TP53, CDKN2A, EGFR, KRAS, RB1, and PIK3CA. TP53 and PIK3CA variations showed equivalent prevalence. However, EGFR variations were significantly higher in China (p&lt;0.05), while CDKN2A (p&lt;0.001), KRAS (p&lt;0.01), and RB1 (p&lt;0.01) variations were notably higher in the U.S.
Conclusions: This study unveils the mutational landscape of advanced NSCLC through liquid biopsy. Unique prevalence patterns between U.S. and China cohorts suggest novel biomarkers for clinical diagnosis and provide insights for targeted therapy mechanism studies.
Citation Format: Haoran Tang, Cancan Jia, Feng Xie, Yue Zhang, Xiaoxi Dong, Yong Huang, Shading Jia. Comparative genomic profiling of unresectable NSCLC patients in the U.S. and China using a globally harmonized liquid biopsy assay 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 2410.
Abstract 2443: Improved detection of low frequency mutations in ovarian and endometrial cancers by utilizing a highly accurate sequencing platform
Authors: Revil Timothée , Pezeshkian Nairi , Gilbert Lucy , Sockell Alexandra , Ragoussis Jiannis 
Published: March 2024

Abstract
Ovarian and endometrial cancers come within the top-4 for incident cancers as well as deaths in North American women. Cure rates have not improved in 30 years as high-grade subtypes continue to be diagnosed in Stage III/IV. Attempts at early diagnosis have failed because high-grade cancer cells exfoliate and metastasize while the primary cancer is small and undetectable by existing tests based on imaging and blood-based tumor markers. DOvEEgene (Detecting Ovarian and Endometrial cancers Early using genomics) is a genomic uterine pap test developed by a McGill team to screen and detect these cancers while they are confined to the gynecologic organs and curable by surgery. The test identifies pathogenic somatic mutations in uterine brush samples. Here we tested the Onso system, a highly accurate sequencing technology from PacBio in order to potentially increase sensitivity while driving down sequencing costs by reducing required sequencing depth vs the current NGS standard. A highly sensitive, error-correcting capture technology (DOvEEgene-SureSelectHS) utilizing duplex error correction sequencing interrogated the exons of 23 genes involved in the development of sporadic and hereditary ovarian and endometrial cancers. We applied a combination of germline gene panel testing on saliva samples with deep duplex sequencing to detect somatic mutations at &lt;0.1% VAF, interrogation of microsatellite loci for instability, and low coverage WGS for copy number analysis of uterine brush samples. We sequenced 20 duplex Illumina sequencing libraries produced using the DovEE assay at PE 100bp mode and compared Onso data in non- duplex sequencing mode as well as duplex sequencing mode to the original Illumina duplex sequencing method. Here, we present this comparison and highlight the benefits of high accuracy sequencing for the detection of very low frequency (&lt;0.1%) somatic mutations. We observed improved mismatch rates for Onso data compared to Illumina, even after duplex error correction was applied. In addition, we found that more individuals are called as displaying microsatellite instablity from the Onso data, which may be due to improved sequencing performance in repetitive regions for Onso. Finally, we observed fewer potential false positive variant calls in the Onso data, highlighting the value of improved sequencing accuracy for rare variant detection.
Citation Format: Timothée Revil, Nairi Pezeshkian, Lucy Gilbert, Alexandra Sockell, Jiannis Ragoussis. Improved detection of low frequency mutations in ovarian and endometrial cancers by utilizing a highly accurate sequencing 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 2443.
Abstract 2550: Investigating glutathione peroxidases as potential prognostic factors in uterine corpus endometrial cancer
Authors: Dukesherer Alexis , Davison-Versagli Calli A. 
Published: March 2024

Abstract
Introduction: Uterine corpus endometrial cancer (UCEC) is one of the deadliest women’s reproductive cancers in the United States, and mortality rates have not improved in the last 30 years due to poor prognostic methodologies. Altered expression patterns of Glutathione Peroxidases (GPX1-8) have been connected to the outcome of many cancers. Still, GPX expression patterns and their use as prognostic biomarkers in UCEC have yet to be explored. This project aimed to evaluate the potential utility of GPX1-8 as prognostic biomarkers for UCEC.
Methods: To identify GPX1-8 expression alterations in UCEC tumors, the UCEC tissue samples (n=543) from the Cancer Genome Atlas Database (TCGA) dataset were analyzed using the University of Alabama at Birmingham Cancer Data Analysis portal (UALCAN) software to statistically compare mRNA levels between UCEC tumor samples and non-tumorigenic uterine tissue. To determine the impacts of GPX1-8 expression on overall survival (OS) and relapse-free survival (RFS), the UCEC TCGA dataset was analyzed using the pan-cancer RNA seq platform using Kaplan-Meier Plotter (KM plotter). The patient cohort was split into groups according to high GPX expression status and low GPX expression status using the trichotomization function in KM plotter. Hazard ratio (HR), 95% confidence interval (95% CI), and log-rank P value were calculated using the KM plotter platform.
Results: GPX1, GPX2, GPX4, GPX7, and GPX8 were differentially expressed in UCEC tumor tissue compared to non-cancerous uterine tissue. However, unfavorable OS in patients associated with low GPX4 expression in UCEC tumor samples (P=0.0043, HR=0.39, 95% CI=0.2-0.76) only. Unfavorable RFS in UCEC patients only correlated with low GPX1 expression (P=0.019, HR=0.43, 95% CI=0.21-0.89) or high GPX6 expression (P=0.0032, HR=2.37, 95% CI=1.31-4.27) in UCEC tumor samples.
Conclusion: Low GPX1 and GPX4 expression and high GPX6 expression may be novel unfavorable UCEC prognostic markers.
Citation Format: Alexis Dukesherer, Calli A. Davison-Versagli. Investigating glutathione peroxidases as potential prognostic factors in uterine corpus 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 2550.
Abstract 2581: Measuring interstitial fluid flow to predict and optimize spatial CAR-T dynamics in the clinic
Authors: Woodall Ryan T. , Esparza Cora , Brown Christine C. , Munson Jennifer M. , Rockne Russell C. 
Published: March 2024

Abstract
Interstitial fluid flow dynamics in brain tumors are known to be an important indicator for tumor invasion into healthy tissue, as cancerous cells follow the interstitial flow towards tumor-stimulating chemokines. These flow gradients not only carry tumor-stimulating chemokines, but also carry cytokines secreted by immune response. CAR-T cells are known to also depend highly on the cytokines present in the CSF and serum. For these reasons, we have developed a novel method, Localized Convolutional Function Regression (LCFR) for measuring interstitial fluid flow, perfusion, and diffusion using dynamic contrast-enhanced MRI. LCFR works by applying the known physics of contrast agent transport, and fitting the observed contrast agent dynamics to the known physics. From LCFR, we can recover standard Tofts-Kety perfusion dynamics, including Ktrans and Vp, and also recover the underlying flow field and diffusivity. These new measurements allow for the prediction of where tumors invade, and allow for us to model how CAR-T cells move within a tumor. In a case study, we model the spatial distribution of CAR-T cells and glioblastoma cells over time, assuming that the fluid velocity field drives CAR-T spread throughout the tumor. Using multiple delivery locations, we identify the optimal catheter placement to optimally distribute CAR-T cells through the tumor, improving the 50% kill rate of simulated tumor cells by over 25% from a naive delivery strategy. Further, we collect longitudinal cytokine levels from patients undergoing CAR-T therapy, allowing us to understand how interstitial flow, CAR-T dynamics, and tumor dynamics interact with each other. We report a case study wherein a patient is longitudinally followed through their treatment with CAR-Ts. Perfusion imaging reveals that the patient experienced a steady increase in perfusion and decrease in fluid leakage into surrounding tissue. VEGF and IL12 levels are observed to increase as tumor volume increases. To better understand this patient's dynamics, we use the fluid velocity field measured by LCFR to simulate the development of CAR-T cells within the tumor, using the cytokine profile as a surrogate measurement of CAR-T population. These results demonstrate the importance of fluid flow and cytokines in understanding how brain tumors develop and respond to treatment.
Citation Format: Ryan T. Woodall, Cora Esparza, Christine C. Brown, Jennifer M. Munson, Russell C. Rockne. Measuring interstitial fluid flow to predict and optimize spatial CAR-T dynamics in the clinic [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 2581.