bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 479: Enhanced payload delivery for acute myeloid leukemia treatment through CD37-targeting DNA nanobots
Authors: Vantangoli Nicholas , Halley Patrick D. , Spitzner Jeffrey R. , Byrd John C. , Larkin Karilyn T. , Castro Carlos E. , Lucas Christopher R. 
Published: March 2024

Abstract
Acute Myeloid Leukemia (AML) treatment faces considerable challenges, in particular the delivery of therapeutics with sufficient potency and specificity. Traditional Antibody-Drug Conjugates (ADCs) offer a strategic approach to cancer therapy by combining the targeting capabilities of monoclonal antibodies with the cell-killing effect of cytotoxic drugs. However, the limitations in drug-to-antibody ratio (DAR) and the challenges in controlling the release kinetics have often hindered their therapeutic window suggesting novel approaches are necessary. Our approach integrates DNA nanobot technology to facilitate targeted delivery of anthracycline agents directly to CD37-expressing leukemic cells. CD37, a tetraspanin superfamily antigen, has shown promise as a target for AML due to its selective expression on immune cells and favorable internalization properties. By harnessing DNA origami assembly techniques, we developed a customizable platform capable of accommodating a significantly higher payload of chemotherapeutic agents than traditional ADCs. This increased payload potential, paired with the precision targeting of CD37, presents a potent therapeutic strategy with the possibility of reduced systemic toxicity. Here we present preclinical evidence demonstrating targeted delivery of our DNA nanobot drug delivery device and anthracycline payload to CD37+ AML target cells in vitro. In addition, we show targeted efficacy in vitro and in vivo of our anthracycline-loaded DNA nanobot-enabled delivery system, which significantly outperformed free anthracycline in AML target cells. Collectively, our findings suggest an improvement in the therapeutic index of anthracycline and antimitotic agents as well as the ability to combine multiple drug pathways in a single delivery device. The implications of this work extend beyond AML, offering a versatile platform that could revolutionize ADC chemistry and targeted drug delivery. We propose that our DNA nanobot delivery system signifies a pivotal advancement in the field of targeted cancer therapy, with the potential to overcome longstanding barriers in the treatment of AML and potentially other malignancies.
Citation Format: Nicholas Vantangoli, Patrick D. Halley, Jeffrey R. Spitzner, John C. Byrd, Karilyn T. Larkin, Carlos E. Castro, Christopher R. Lucas. Enhanced payload delivery for acute myeloid leukemia treatment through CD37-targeting DNA nanobots [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 479.
Abstract 4802: Accuracy of the GALAD serologic model in the diagnosis of hepatocellular carcinoma in Ghanaian patients
Authors: Nartey Yvonne Ayerki , Yang Ju Dong , Zemla Tyler , Ayawin Joshua , Asibey Shadrack Osei , El-Kassas Mohamed , Bampoh Sally Afua , Duah Amoako , Agyei-Nkansah Adwoa , Awuku Yaw Asante , Afihene Mary Yeboah , Yamada Hiroyuki , Yin Jun , Plymoth Amelie , Roberts Lewis Rowland 
Published: March 2024

Abstract
Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality worldwide. In sub-Saharan Africa, HCC is the second most common cancer in men and the sixth in women. African patients present with HCC at a young age and at advanced stage of disease. Improved surveillance can increase early HCC detection. The GALAD score has good sensitivity in the diagnosis of HCC in Asia, Europe and North America, however, it has not been validated in an African cohort. The aim of this study was to assess the performance of the GALAD score in the diagnosis of HCC in Ghanaian patients.
Methods: Clinical data from patients with cirrhosis (n=93) or HCC (n=78) from out-patient hepatology clinics at 3 teaching hospitals in Ghana were abstracted, and serum samples analyzed. A logistic regression model predicting HCC status based on GALAD score was constructed to obtain the Receiver Operating Characteristics (ROC) curve for GALAD. The area under the curve (AUC) with 95% confidence interval was calculated and used to assess the performance of the GALAD score in surveillance for HCC.
Results: The median AFP (5010.2 vs. 3.4), L3% (28.3 vs. 0.3) and DCP (507.6 vs. 0.2) levels were all significantly higher among HCC cases than the controls (p&lt;0.0001). The median GALAD score was also higher among HCC patients (8.0 vs. -4.1, p&lt;0.0001). The AUC of the GALAD score in the diagnosis of HCC was 0.86 (95% CI 0.79 - 0.92). At a cut-off value of -0.37, the GALAD score had a sensitivity of 0.81 and a specificity of 0.86. In a re-fit of the logistic regression model, the most significant factors in the GALAD model in our cohort were AFP-L3% and log(DCP). The AUC of the new GALAD model after model selection was 0.89.
Conclusion: The GALAD score can be used to detect HCC in Ghana and has the potential to improve HCC surveillance in low-resource settings in sub-Saharan Africa.
Citation Format: Yvonne Ayerki Nartey, Ju Dong Yang, Tyler Zemla, Joshua Ayawin, Shadrack Osei Asibey, Mohamed El-Kassas, Sally Afua Bampoh, Amoako Duah, Adwoa Agyei-Nkansah, Yaw Asante Awuku, Mary Yeboah Afihene, Hiroyuki Yamada, Jun Yin, Amelie Plymoth, Lewis Rowland Roberts. Accuracy of the GALAD serologic model in the diagnosis of hepatocellular carcinoma in Ghanaian 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 4802.
Abstract 4844: Epidemiology of bone and soft-tissue sarcomas: A two-decade analysis of the surveillance, epidemiology, and end results program
Authors: El Beaino Marc , Baum Rachel , Connors Katherine M. , Masrouha Karim , Lozano-Calderon Santiago A. , Lin Patrick P. 
Published: March 2024

Abstract
In the United States, ~15000 cases of bone and soft-tissue sarcomas are diagnosed annually, representing roughly 1% of all incident cancers. While several studies have analyzed independent predictors of outcomes for these sarcomas, small samples and confounding bias have limited their ability to establish reliable conclusions. The present analysis provides a comprehensive epidemiological description of patients with any of 16 distinct bone and soft-tissue sarcomas by querying the Surveillance, Epidemiology, and End Results database between 2000 and 2020. Incidence, prevalence, incidence-based mortality, survival rates, and case-fatality ratios were computed. Using patient age, sex, race, and ethnicity, as well as tumor size, grade, and stage as covariates, multivariable survival Cox proportional hazards regression analyses were performed to model hazard ratios (HRs) and identify independent predictors of worse prognosis. Only patients with myosarcoma and fibrous histiocytoma witnessed a decrease in the prevalence of their disease over time (both, p&lt;0.05). The incidence of chondrosarcoma and synovial sarcoma increased in White and Asian patients but decreased in Black individuals (both, p&lt;0.001), while their case-fatality rates remained constant and increased, respectively, regardless of race and ethnicity (all, p&lt;0.05). Case-fatality rates only declined significantly for White and Non-Hispanic patients with solitary fibrous tumor (both, p&lt;0.05). Aside from alveolar soft-part sarcoma and clear cell sarcoma, a 10-year increase in age of patients with bone and soft-tissue sarcomas resulted in 18% to 61% higher likelihood of death (all, p&lt;0.05). For most malignancies, having a tumor size greater than 5 cm yielded a 37% to 189% higher likelihood of death. While incidence trends suggest improvements in diagnostic techniques and early tumor detection, survival and case-fatality trends indicate potential for advancement in treatment for most bone and soft-tissue sarcomas.
Citation Format: Marc El Beaino, Rachel Baum, Katherine M. Connors, Karim Masrouha, Santiago A. Lozano-Calderon, Patrick P. Lin. Epidemiology of bone and soft-tissue sarcomas: A two-decade analysis of the surveillance, epidemiology, and end results program [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 4844.
Abstract 4900: Spatial Atlas of Human Anatomy (SAHA): A subcellular, multiscale spatial odyssey of immune and gastrointestinal tissues and tumors
Authors: Park Jiwoon , Gregorio Roberto , Hissong Erika , Patel Sanjay , Robinson Brian , Socciarelli Fabio , Metzger Evenlyn , Liang Yan , Reeves Jason , Beechem Joseph , Elemento Olivier , Alonso Alicia , Houlihan Shauna , Schwartz Robert , Mason Christopher E. 
Published: March 2024

Abstract
The Spatial Atlas of Human Anatomy (SAHA) is a foundational effort to map 250 million cells and transcriptomes and proteomes of 30 non-diseased organs from healthy adults at two spatial scales: whole transcriptome of histological features (50 µm to 2 mm), and 1,000-plex RNA and 64-plex protein panels at spatial subcellular resolution (50 nm across 1 cm2). The project aims to establish and validate best practices in experimental design, sample processing, data analysis, and data standards for high-content spatial analysis across multiple human organs at whole transcriptome and proteome levels. The profiled samples will capture variability across genders and ancestries. All results, including raw and processed data, will be made available to the scientific community through the SAHA data portal and AtoMxTM Spatial Informatics Platform.
Here we present the part of Phase I data collected specifically around immune (bone marrow and lymph node) and gastrointestinal tissues (ileum, appendix, colon, and liver), and compare these data to colon and liver cancer samples. On serial sections, we perform spatial whole transcriptome data (with GeoMx® Digital Spatial Profiler) and 1,000-plex RNA profiles and 64-plex protein profiles collected by the CosMx™ Spatial Molecular Imager (SMI) matched to the exact shape of functional histological organ features from H&E and immunofluorescence stainings. Our data, which spans ~1.5 million cells collected across ~1,100 fields of view, enables the highest-ever subcellular resolution maps of cell types, lineage states, metabolic capacity, cellular neighborhoods, subcellular movements of organelles, and spatially resolved (and novel) ligand-receptor interactions across normal and cancer tissues. Through comparing these data to several cancer samples collected across multiple spatial platforms, we show how spatial organ atlasing at multiple scales can uncover unique insights into organ development, health, and cancer. We also show how the SAHA data can serve as a benchmark reference for spatial precision medicine.
Citation Format: Jiwoon Park, Roberto Gregorio, Erika Hissong, Sanjay Patel, Brian Robinson, Fabio Socciarelli, Evenlyn Metzger, Yan Liang, Jason Reeves, Joseph Beechem, Olivier Elemento, Alicia Alonso, Shauna Houlihan, Robert Schwartz, Christopher E. Mason. Spatial Atlas of Human Anatomy (SAHA): A subcellular, multiscale spatial odyssey of immune and gastrointestinal tissues and tumors [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 4900.
Abstract 4923: A systematic framework for predicting adverse drug reaction signals using medical data
Authors: Jeon Junhyeok , Hong Eujin , Kim Hyun Uk 
Published: March 2024

Abstract
Prescribing a combination of medications is a common strategy to enhance the effectiveness of disease treatment. While this approach aims to improve therapeutic outcomes, it may lead to unintended adverse drug reactions (ADRs) due to interactions between prescribed drugs and individual factors. Recognizing the importance of ADRs, various machine learning models have been developed. However, these models face limitations, particularly in predicting reactions induced by three or more drugs and in accounting for individual patient factors, such as age and medical history. To address these limitations, we propose a systematic framework that leverages medical data for predicting ADR signals. In the framework, the MIMIC-IV database was systematically preprocessed, and a series of machine learning models were developed that predict the ADR signals. The model predictions were validated using the eICU collaborative research database. The machine learning models process multiple inputs, including information on administered drugs, age, gender, ethnicity, and underlying medical conditions to predict ADR signals. The ADR signals are determined by classifying abnormalities in 20 specific laboratory test values, such as hematocrit, creatinine, and hemoglobin. The machine learning models developed in this study hold promise as a valuable tool for assessing potential risks, such as ADRs, associated with the concurrent use of multiple drugs.
Citation Format: Junhyeok Jeon, Eujin Hong, Hyun Uk Kim. A systematic framework for predicting adverse drug reaction signals using medical data [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 4923.
Abstract 4931: The Cancer Proteome Atlas: Expanded datasets and empowered with an AI-driven chatbot
Authors: Liang Han , Liu Wei , Li Jun , Tang Yitao 
Published: March 2024

Abstract
Reverse-phase protein arrays (RPPAs) represent a powerful functional proteomic approach to elucidate cancer-related molecular mechanisms and develop novel cancer therapies. To facilitate community-based investigation of the large-scale protein expression data generated by this platform, we have developed a user-friendly, open-access bioinformatic resource, The Cancer Proteome Atlas (TCPA, http://tcpaportal.org), which contains several applications. The first one focuses on RPPA data of patient tumors, which contains &gt;8,000 samples of 32 cancer types from The Cancer Genome Atlas (TCGA) and other independent patient cohorts. The second application focuses on the RPPA data of &gt;1,500 cancer cell lines with publicly available, high-quality DNA, RNA and drug screening data. The third one focuses on perturbed RPPA profiles of &gt;14,000 samples given drug treatments. We recently expanded our RPPA protein set from 200 to 500 proteins Importantly, this highly selected set is enriched in therapeutic targets and biomarkers and covers key oncogenic pathways such as PI3K/AKT, RAS/MAPK, Src/FAK, TGFβ/SMAD, JAK/STAT, epithelial-mesenchymal transition, DNA damage repair, Hippo, Notch, cell cycle, apoptosis, and immune-oncology. Using this upgraded RPPA platform, we generated an harmonized, expanded RPPA dataset of 8000 TCGA patient samples and 1000 CCLE cell line samples. To further address the informatic challenges of analyzing such large, diverse datasets, in addition to the GUI interfaces, we recently developed a chatbot through which users can analyze the RPPA data through human nature languages and obtain the results and related analytic reports without a learning curve. Such a chatbot empowers a broad research community to explore high-quality RPPA datasets and generate testable hypotheses in an effective and intuitive manner, representing the direction of next-generation data analytics.
Citation Format: Han Liang, Wei Liu, Jun Li, Yitao Tang. The Cancer Proteome Atlas: Expanded datasets and empowered with an AI-driven chatbot [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 4931.
Abstract 4966: Machine learning and large language model approach to pancancer data elements
Authors: Niederhausern Andrew , Bahadur Nadia S. , Wallace Gary , Saadawi Gilan E. , Philip John 
Published: March 2024

Abstract
Introductory Statement: The goal is to use machine learning (ML) and large language model (LLM) to augment the manual curation of cancer data elements.
Introduction: Memorial Sloan Kettering Cancer Center (MSKCC) has ~100,000 cancer patients and counting with genomic testing. Clinicians use genomic data for research but lack clinical data to analyze together. We use a vendor, VASTA Global to hire curators to manually curate cancer patient’s core clinical data elements (CCDE) within unstructured/paragraph text in electronic medical record (EMR) notes.
CCDE encompasses 122 data elements that include a patient’s full cancer history that can take up to 1 working day to curate. We collaborated with the Realyze Intelligence Healthcare Solutions vendor to use their AI pipeline to generate the manual curated dataset.
Realyze generated the CCDE data elements such as histology, pathology site, MMR, TNM staging, ECOG, and KPS for a pilot lung cancer cohort of 150 patients. We manually validated the generated data for 74 out of 150 patients.
Methods:The Realyze platform uses a combination of LLMs, ML algorithms and standard terminologies to create a cancer patient model. These models are flexible enough to address the unique needs and challenges of a pan-cancer oncology model. By using standardized FHIR export, results were delivered to a data lake solution and written into a REDCap database to enable human review.
Summary:We manually assessed 74 patients. The NLP gave concordant values for MMR, KPS and TNM staging for 100% of the instances. For MMR these were all null values with false negative (FN) of 100% accuracy. Pathology site had 92.15% accuracy while histology has 97.5% accuracy.
Conclusion:Will work on refining pathology site and histology’s ICDO3 list to increase the percentage of accuracy. Once Realyze refines their model for these data elements we will re-run it on a larger cohort of cancer patients and calculate the accuracy.
Accuracy Results Clinical data elements 74 patients assessed: Accuracy % ECOG 98.6 KPS 100 T (path) 100 T (clinical) 100 N (path) 100 N (clinical) 100 M (path) 100 M(clinical) 100 MMR 100 Histology (path) 97.5 Path site 92.15
Citation Format: Andrew Niederhausern, Nadia S. Bahadur, Gary Wallace, Gilan E. Saadawi, John Philip. Machine learning and large language model approach to pancancer data elements [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 4966.
Abstract 4968: Discovery of cancer-associated factors in 293.5 million diagnosis records using statistical machine learning analysis
Authors: Alam Md Ashad , Duesbery Nick , Fort Daniel Daniel 
Published: March 2024

Abstract
Cancer, a leading global public health issue, ranks as the second leading cause of death in the United States, with projections indicating 1,958,310 new cases and 609,820 deaths in 2023. Accurately quantifying specific pre-disease diagnosis factors associated with cancer, as well as predicting cancer through disease trajectories and imaging modalities, presents a complex and formidable challenge. A comprehensive temporal trajectory analysis of cancer diseases, using disease trajectories as an insight within large cohorts in the USA, has not yet been developed. Our analysis used a comprehensive EHR dataset stretching back to 1999, encompassing 293,501,891 records and involving 3,019,978 patients within Ochsner Health, a large integrated health system across the State of Louisiana. According to our investigation, those diagnosed at the ICD chapter level with diseases of the genitourinary system (genitourinary diseases, ICD-10 Chapter 14, relative risk (RR=1.65)) or endocrine nutritional and metabolic diseases (obesity, ICD-10 Chapter 4, RR=1.35) have an elevated risk of being diagnosed with cancer in the subsequent five years. The top four individual diagnosis ICD-10 codes associated with increased risk are N60 (Benign Mammary dysplasia, RR=7.80), M34 (Systemic sclerosis, RR=7.72), R43 (Disturbances of smell and taste, RR=6.93), and R92 (Abnormal and inconclusive findings on diagnostic imaging of the breast, RR=6.59). Furthermore, in our investigation of specific types of cancer risk across 17 cancer types (Breast, Skin, Prostate, Lung, Pancreatic, etc.), we observed that the most common diseases associated with breast and skin cancer involved immune mechanisms (RR=8.35 and 8.68), genitourinary issues (RR=4.13 and 2.48), obesity (RR=3.42 , 3.35), and cardiovascular conditions (ICD-10 Chapter 9, RR=1.73 and 2.19). For prostate cancer, associations were found with obesity (RR=3.50), human immunodeficiency virus (HIV, ICD-10 Chapter 1, RR=2.39). Across lung cancer, involvement with chronic viral hepatitis (ICD-10 Chapter 1, RR=4.00), cardiovascular issues (RR=3.52), genitourinary problems (RR=4.13), and obesity (RR=3.42) were indicated. Our comprehensive analysis has the potential to contribute to the early detection, diagnosis trajectories, and improved understanding of the pathological processes underlying cancer in patients worldwide.
Citation Format: Md Ashad Alam, Nick Duesbery, Daniel Daniel Fort. Discovery of cancer-associated factors in 293.5 million diagnosis records using statistical machine learning 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 4968.