bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 7662: Convolutional neural network algorithms and hematoxylin-and-eosin-stained images predict clinical outcomes in high-grade serous ovarian cancer patients with advanced age and stage
Authors: Ng Chun Wai , Wong Kwong-kwok , Lawson Berrett , Ferri-Borgogno Sammy , Mok Samuel 
Published: March 2024

Abstract
Ovarian cancer is the deadliest among gynecologic cancers. It is partly due to the mild symptoms in early stages and relatively high diagnostic age (about 63-year-old median age). These factors contribute to the short chemotherapy response time and survival time with about 18- and 36-months median progression-free survival and overall survival duration, respectively. The advancements of convolutional neural network (CNN) machine learning algorithms provide opportunities for the development of cost- and time-effective models which is urgently needed for ovarian cancer patients, especially for those with elevated risk. A CNN model was trained with hematoxylin and eosin (H&E) stained tumor whole slide images (WSIs) from ovarian cancer patients with advanced stages. 773 WSIs of 335 patients from TCGA-OV dataset were separated randomly for training and testing cohort. The model was then applied on testing cohort with advanced stage and age patients for H&E survival score. The c-index of the predicted risk on overall survival (OS) and progression-free survival (PFS) were 0.66 and 0.63, respectively. The log-rank p-values of patients between low and high risk were 0.00137 and 0.00138, respectively. The model was then further evaluated with an external dataset. The c-index of the predicted risk were 0.64 and 0.72 for OS and PFS, respectively. And the log-rank test between patients with low and high risk achieved p-values 0.017 and 0.0047 for OS and PFS, respectively. In conclusion, a novel prognostic CNN model was developed for ovarian cancer patients with advanced age and stage for better disease management.
Citation Format: Chun Wai Ng, Kwong-kwok Wong, Berrett Lawson, Sammy Ferri-Borgogno, Samuel Mok. Convolutional neural network algorithms and hematoxylin-and-eosin-stained images predict clinical outcomes in high-grade serous ovarian cancer patients with advanced age and stage [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 7662.
Abstract 784: Proteome-wide mendelian randomization identifies causal association between plasma proteins and lung cancer
Authors: Si Guojin , Li Wenxuan , Zhang Yacong , Lyu Zhangyan , Song Fengju , Chen Kexin 
Published: March 2024

Abstract
Background: Lung cancer is one of the world's main health issues, and the exploration of novel protein markers can help to understand the mechanisms of lung cancer development and the discovery of therapeutic targets. Based on this, we used proteome-wide Mendelian Randomization to determine causal associations between plasma protein levels and the risk of developing lung cancer and subtypes. Methods: We performed a two-sample Mendelian Randomization analysis using genome-wide association data for 4907 proteins from the deCODE genetics study and five lung cancer and its subtypes from the FinnGen database. Causal effects were estimated using the Wald-ratio method or the inverse variance weighted (IVW) method, with additional analyses using the other four common methods when the number of SNPs was more than one. The Steiger filter method was used to test the correct direction of the causal effect. Multiple corrections were utilized by the Benjamini-Hochberg method. Results: After filtering, we finally analyzed 4645 proteins for the causal association with lung cancer, and the F-statistic of the instrumental variable for each protein was more than 10. After multiple tests, the levels of four genetically predictive proteins were found to be associated with the risk of lung cancer, and one protein was associated with the risk of small-cell lung cancer. In detail, elevated levels of three genetically predicted proteins were significantly associated with increased risk of lung cancer, namely, SYSC (OR:4.75, 95%CI: 2.47-9.16, FDR=0.007), TM11D (OR: 1.34, 95%CI: 1.17-1.53, FDR=0.02), and PDC6I (OR: 2.35, 95%CI: 1.58-3.49, FDR=0.03). Elevated levels of FASLG (OR: 0.87, 95%CI: 0.82-0.92, FDR=0.007) were associated with a reduced risk of lung cancer. In addition, genetically predicted elevated levels of GRP (OR: 0.05, 95%CI: 0.01-0.17, FDR=0.02) were associated with an increased risk of small-cell lung cancer. The Steiger filter test showed that all directions were from proteins to lung cancer. Conclusion: In this study, we found that five proteins may have effects on the risk of lung cancer, in which genetically predicted SYSC, TM11D, and PDC6I were significantly associated with an increased risk of lung cancer, whereas FASLG and GRP were associated with a decreased risk of lung cancer and small-cell lung cancer, respectively. The above may help to explore the pathogenesis of lung cancer, but further comprehensive studies are still needed.
Citation Format: Guojin Si, Wenxuan Li, Yacong Zhang, Zhangyan Lyu, Fengju Song, Kexin Chen. Proteome-wide mendelian randomization identifies causal association between plasma proteins and lung 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 784.
Abstract 832: Gender differences in urinary tract cancer susceptibility associated with radon in water: A population based study from Finland
Authors: Li Peng Yunzhi 
Published: March 2024

Abstract
Background: Radon as a common source of environmental radiation, while its carcinogenic effect on urinary tract cancer has not been fully investigated.
Methods: Men and women who have continuously lived in Finland from year 1987 onward and reached 40+ years old at the beginning of 2017 were followed up for urinary tract cancer, including bladder cancer (ICD-O 3 code C67) and kidney cancer (C64-C66 and C68) for 5 years until the end of 2021. The residential location and type of buildings of the cohort members were traced from year 1987 until 2016 annually and linked to the average cumulative exposure of radon concentration in well water on municipality level. Newly diagnosed UTC cases were identified based on the Finnish cancer register. Pre-existing health conditions, such as hypertension, diabetes, chronic kidney disease, calculus of kidney and lower urinary tract, family history of cancer, as well as socioeconomic factors were ascertained from multiple registry databases. Data were analyzed with Cox proportional hazards model conditional on birth cohorts and adjusted with available covariates.
Findings: Among 1,019,331 men and 1,192,960 women with 30-years average radon exposure, 4206 and 2226 urinary tract cancer cases were diagnosed during follow-up period from 2017 until correspondingly. Men with higher radon exposure (&gt;500 Bq/m3) had about 14% higher risks of UTC in total (HR 1.14, 95% CI 1.07-1.21), 19% higher risks of bladder cancer (HR 1.19, 95%CI 1.09-1.32), 10% higher risks of kidney cancer (HR 1.10, 95%CI 1.02-1.20) comparing with lower exposed samples. Women with higher radon exposure had about 1% higher risks of UTC in total (HR 1.01, 95%CI 0.93-1.10), 3% higher risks of bladder cancer (HR 1.03, 95%CI 0.88-1.22), 1% higher risks of kidney cancer (HR 1.01, 95%CI 0.92-1.11). In the sub-population who only lived in houses during the 30-years exposure period, men with higher radon exposure had about 24% higher risks of UTC (HR 1.24, 95% CI 1.12- 1.36), 30% higher risks of bladder cancer (HR 1.30, 95%CI 1.12-1.51), 20% higher risks of kidney cancer (HR 1.20, 95%CI 1.06-1.36). No significant association was observed in women who only lived in houses during exposure period.
Conclusions: For men, higher radon exposure was significantly associated with elevated risks of UTC. No significant association was observed in women. Further investigates were needed to explore the mechanisms of gender differences in UTC susceptibility associated with radon exposure.
Keywords: Radon, Radiation, Cancer, Urinary tract cancer, Kidney cancer, Bladder cancer
Citation Format: Peng Yunzhi Li. Gender differences in urinary tract cancer susceptibility associated with radon in water: A population based study from Finland [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 832.
Abstract 849: Deciphering the link between aging, lobular involution, and breast cancer risk: The role of LIFR/pSTAT3 signaling
Authors: Lue Jaida C. , Radisky Derek , Stallings-Mann Melody , Sherman Mark , Degnim Amy , McCauley Bryan , Hoskin Tanya 
Published: March 2024

Abstract
Background: Age-related lobular involution (ARLI), characterized by regression of breast epithelial tissue during the peri-and post-menopausal period, plays a crucial role in breast cancer risk. Delayed or incomplete ARLI is linked with increased postmenopausal breast cancer susceptibility.This study focuses on unraveling the molecular mechanisms of ARLI, drawing parallels with the well-characterized postpartum involution (PPI). Our aim was to discover biomarkers differentiating women who progressed through ARLI from those with stagnant ARLI.
Methods: We utilized the Mayo Clinic Benign Breast Disease (BBD) cohort, analyzing 108 perimenopausal women aged 45-55 who had a subsequent benign biopsy between 2-15 years later. We assessed Lobular Involution (Ll) status by comparing the number and size of lobules in initial and subsequent biopsies. RNA profiling of initial biopsies was performed using NanoString 10360 and BC360, identifying differentially expressed genes between ARLI progression and stagnation. Immunohistochemistry (IHC) mapped the expression of these biomarkers in epithelial lobules.
Results: We identified 37 genes with significant differential expression, highlighting Leukemia Inhibitory Factor Receptor (LIFR) as upregulated in ARLI stagnant samples. LIFR, part of the IL-6 cytokine signaling pathway, is known for its role in the induction of apoptosis during PPI via pSTAT3 activation. Our IHC analyses of initial biopsies of women who differentially progress through ARLI confirmed differential expression of LIFR that is coordinately regulated with pSTAT3.
Conclusion: Our findings reveal significant parallels between ARLI and PPI. Future research will assess how LIFR/pSTAT3 signaling is linked to induction of apoptotic markers in perimenopausal women and will investigate how anomalies in the LIFR/pSTAT3 pathway might contribute to heightened breast cancer risk in women with delayed or incomplete ARLI. This study not only sheds light on the complex biological processes of aging in breast tissue but also opens new avenues for breast cancer risk assessment and prevention strategies.
Citation Format: Jaida C. Lue, Derek Radisky, Melody Stallings-Mann, Mark Sherman, Amy Degnim, Bryan McCauley, Tanya Hoskin. Deciphering the link between aging, lobular involution, and breast cancer risk: The role of LIFR/pSTAT3 signaling [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 849.
Abstract 886: Integrated deep learning model for predicting DNA methylation and tumor types from histopathology in central nervous system tumors
Authors: Shulman Eldad David , Hoang Danh-Tai , Turakulov Rust , Abdullaev Zied , Singh Omkar , Campagnolo Emma M. , Stone Eric A. , Nasrallah MacLean P. , Ruppin Eytan , Aldape Kenneth 
Published: March 2024

Abstract
Precision in diagnosis of diverse central nervous system (CNS) tumor types is crucial for optimal patient treatment. DNA methylation profiles, which capture the methylation status of thousands of individual CpG sites, are data-driven means to enhance diagnostic accuracy, but this technique is expensive, time-consuming, and not yet routinely available. To address this, we developed DEPLOY, a deep-learning model that predicts 10 major categories of CNS tumors from histopathology. DEPLOY integrates three distinct components: the first classifies CNS tumors directly from histopathology slide images (‘direct model’); the second initially generates predictions for DNA methylation beta values, which are subsequently used for tumor classification (‘indirect model’); and the third classifies tumor types directly from routinely available patient demographics. First, we find that DEPLOY accurately predicted beta values from histopathology images, suggesting that application of inferred methylation data is a promising approach for deep learning histopathology-based classifiers. Using a 10-class model trained on an internal dataset, we apply this model on two independent external test datasets of 1,522 and 348 cases, achieving top-1 accuracies of 96% and 94%, respectively on samples which are predicted with high confidence. Taken together, DEPLOY could assist pathologists in diagnosing CNS tumors, in an equitable very low-cost manner, within a clinically relevant short time frame.
Citation Format: Eldad David Shulman, Danh-Tai Hoang, Rust Turakulov, Zied Abdullaev, Omkar Singh, Emma M. Campagnolo, Eric A. Stone, MacLean P. Nasrallah, Eytan Ruppin, Kenneth Aldape. Integrated deep learning model for predicting DNA methylation and tumor types from histopathology in central nervous system 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 886.
Abstract 898: Deep learning-based cell types scores in tumor microenvironment estimated by H&E images associated with PD-L1 status in lung adenocarcinoma
Authors: Cook Seungho , Shin Haenara , Koh Jaemoon , Choi Hongyoon , Na Kwon Joong 
Published: March 2024

Abstract
Purpose: The spatial distribution of cell types in the tumor microenvironment (TME) is associated with functional status of tumor immunology and eventually affects the response to immuno-oncology treatment. We have developed a deep learning model that was trained by integrating H&E images of lung adenocarcinoma with spatial transcriptomic data. We applied this model to predict various cell types within the TME using only hematoxylin and eosin (H&E) images to correlate with the PD-L1 status.
Methods: A deep learning model to predict five cell types enrichment score maps from H&E images was trained by spatial transcriptomics data combined with matched H&E images. The five cell types of TME included B cells, T/NK cells, myeloid cells, fibroblasts, and epithelial cells. wAnother dataset based on tissue microarray (TMA) data of H&E images of lung cancer patients (n = 94) was used to predict cell type enrichment scores associated with PD-L1 status estimated by TPS score. The cell type scores derived from the H&E-stained lung cancer images were correlated with the PD-L1 status.
Results: The cell types predicted by the model using H&E image patches showed a significant correlation with those determined through spatial transcriptomic data, serving as an internal validation. All cell types (B cells, T/NK cells, myeloid cells, fibroblasts, and epithelial cells) from TMA cores showed significant differences according to the PD-L1 expression groups. The enrichment scores of B cells, T/NK cells, myeloid cells and fibroblasts were significantly higher in the PD-L1 high group.
Conclusions: Our research introduces a deep learning model for precise cell type mapping within the tumor microenvironment and applied to H&E-stained TMA cores. The relationship between PD-L1 status and the cell type enrichment scores within the tumor microenvironment, as predicted by the deep learning model analyzing H&E images, demonstrates the potential for using H&E-based characterization of the tumor microenvironment as a biomarker in immuno-oncology treatments.
Citation Format: Seungho Cook, Haenara Shin, Jaemoon Koh, Hongyoon Choi, Kwon Joong Na. Deep learning-based cell types scores in tumor microenvironment estimated by H&E images associated with PD-L1 status in lung adenocarcinoma [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 898.
Abstract 920: Insights from the National Cancer Institute’s request for information on existing data sharing processes for NIH-funded research
Authors: Ghosh Mousumi , Boyd Nathan , Jagu Subhashini 
Published: March 2024

Abstract
Data sharing plays a crucial role in accelerating biomedical research discoveries, promoting rigorous and reproducible research, enabling research data reuse, and fostering public trust. To facilitate data sharing, a coordinated, sustainable, and streamlined infrastructure is required to store, preserve, retrieve, and make data findable, accessible, interoperable, and reusable. Starting January 25, 2023, researchers funded by the National Institutes of Health (NIH) are required to comply with the NIH Data Management and Sharing Policy (https://sharing.nih.gov/data-management-and-sharing-policy/about-data-management-and-sharing-policies/data-management-and-sharing-policy-overview) and indicate how research data funded by the NIH will be managed and broadly shared. As institutions prepare to meet these requirements, the National Cancer Institute (NCI) is looking to understand the existing processes of data storage, preservation, and sharing including what infrastructure and services are available to facilitate data sharing. To carry out this goal, the NCI’s Office of Data Sharing (ODS), dedicated to promoting responsible and efficient sharing of cancer research data, issued a Request for Information (RFI) to understand the existing data sharing processes for NIH-Funded Research. The RFI was designed to collect information on services (e.g. guidance on data standards and file formats), technologies (e.g. storage, compression, retrieval, archival, analysis tools, workbenches), and processes (e.g. governance, communication, education, outreach) that promote data sharing. ODS utilized the RFI to gather input from stakeholders involved in facilitating data sharing. This included personnel from various fields such as research laboratories, scientific instrumentation core facilities, offices of research and/or sponsored programs, provost offices or other research administration, libraries, information technology, and institutional review boards. We also engaged with bioinformaticians and data scientists who are actively contributing to data curation, formatting, and analysis. Here, we present analysis of the responses received, including insights relating to gaps and challenges in data sharing, and proposed next steps and recommendations.
Citation Format: Mousumi Ghosh, Nathan Boyd, Subhashini Jagu. Insights from the National Cancer Institute’s request for information on existing data sharing processes for NIH-funded 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 920.
Abstract 93: Validation of an impedance-based in vitro potency assay for repeatability and precision
Authors: Califano Danielle , Jiang Jianjie , Windgassen Dirk , Flor Caitlyn De La , Chvatal Stacie , Sullivan Denise , Millard Daniel 
Published: March 2024

Abstract
Cell-based immunotherapies, such as CAR T cells, are emerging as a promising approach to therapeutic intervention against cancer. Developing cell and gene therapy products requires reliable and reproducible methods of quantifying critical quality attributes, such as potency, to ensure product strength and consistency. A variety of assays can be used to assess potency; however, it is crucial that these tests are qualified for assay performance to ensure intra- and inter-assay precision. Although inherently variable, biological assays are often the best methods to investigate a product’s mechanism of action and predict clinical outcomes. Previous work has shown that impedance-based potency assays provide a non-invasive, real-time measure of effector cell-mediated cytotoxicity, thus reducing cell manipulation that impacts data reliability. Here, we describe an in vitro potency assay that demonstrates low variability and high precision across replicates, analysts, and days. Effector cell-mediated cytotoxicity was performed using a CD19-positive liquid tumor cell line (Raji) tethered to the well surface using an anti-CD40 antibody. CD19-specific CAR T effector cells were added 24 hours after the target cells and co-cultured with the target cells across a range of E: T ratios, and cytolysis was monitored for three days. Real-time measurement of cell-mediated cytotoxicity also allowed for the calculation of kill time 50 (KT50), defined as the time required for 50% cytolysis of the target cells. Repeatability was calculated across three replicates per E:T ratio. The co-culture experiment was performed in duplicate by a second analyst and on two subsequent days for a comparison of f three plates as well as two operators to evaluate inter-assay precision. In addition to this, CAR T-mediated cell killing in a multiplate impedance system was also investigated for plate-to-plate variability. The percent coefficient of variation (% CV), a statistical measure that describes the precision and repeatability of an assay, was calculated to assess variability between assay replicates, operators, and plate replicates. In all conditions tested, the percent CV for all E:T ratios was below 20%, illustrating the repeatability and precision of the assay. These data support the use of an impedance-based potency assay for evaluating and characterizing immunotherapy products.
Citation Format: Danielle Califano, Jianjie Jiang, Dirk Windgassen, Caitlyn De La Flor, Stacie Chvatal, Denise Sullivan, Daniel Millard. Validation of an impedance-based in vitro potency assay for repeatability and precision [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 93.