bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 7585: Targeting of 7 or 24 dehydrocholesterol reductase decreases cancer progression in head and neck cancer
Authors: Lee Bok-Soon , Choi Ji-Hye , Kim Hyo Jeong , Roh Jin , Padilla Joselyn , Brock Kennedy Davis , Jang Jeon Yeob , Woo Hyun Goo , Kim Chul-Ho , Lee Jiyoung 
Published: March 2024

Abstract
The elevated expression of cholesterol metabolism has been associated with many cancer including head and neck squamous cell carcinoma (HNSCC). However, the molecular mechanisms between cholesterol biosynthesis and head and neck cancer have not yet been studied. In this study, we identified upregulation of dehydrocholesterol reductase such as DHCR7 or DHCR24 in HNSCC compared with adjacent normal tissues from the same patients using RNA sequencing data. We observed protein expression level of DHCR7 or DHCR24 in head and neck cancer cell lines through Western blotting. We also checked high expression of DHCR7 or DHCR24 in HNSCC tissues from patients using immunohistochemistry assays. Yet, when DHCR7 or DHCR24 was knocked down using siRNA, the viability of HNSCC cells was decreased in MTT assays. In parallel, we adapted small inhibitory drugs; AY9944 for DHCR7 inhibition and Triparanol or SH-42 for DHCR24 inhibition. Treatment of AY9944, Triparanol, or SH-42 also resulted in decreased viability of HNSCC cells. Moreover, when we used a cholesterol blocker, MCD, the cell viability was decreased, whereas the cells overcame cell death by adding the combination of cholesterol and LDL. Taken together, we concluded that head and neck cancer requires DHCR7 and DHCR24 activity involved in cholesterol metabolism for cancer growth. Our data suggest that targeting DHCR7 or DHCR24 using AY9944, Triparanol or SH-42 is one of the potential strategies that will definitely help with cancer treatment for head and neck cancer.
Citation Format: Bok-Soon Lee, Ji-Hye Choi, Hyo Jeong Kim, Jin Roh, Joselyn Padilla, Kennedy Davis Brock, Jeon Yeob Jang, Hyun Goo Woo, Chul-Ho Kim, Jiyoung Lee. Targeting of 7 or 24 dehydrocholesterol reductase decreases cancer progression in head and neck 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 7585.
Abstract 7597: Therapeutic potential of ketogenic diet as an adjuvant to lenvatinib treatment in hepatocellular carcinoma
Authors: Zhang Minghe , Lee Terence Kin-Wah , Ma Stephanie , Tong Man 
Published: March 2024

Abstract
Hepatocellular carcinoma (HCC) has been a major public health concern worldwide for decades because of its high mortality rates and poor prognosis, which are attributable to frequent tumor relapse and limited treatment regimens. Lenvatinib is one of the tyrosine kinase inhibitors approved for first-line treatment of advanced HCC, but its efficacy remains modest. Growing evidence suggest that the unsatisfactory survival benefits of lenvatinib could be attributed to the acquired drug resistance developed in HCC patients. In this study, we aim to explore the intrinsic metabolic vulnerability which could be exploited to enhance the treatment efficacy of lenvatinib in HCC. Our previous study showed that loss of tyrosine catabolism accompanied with a reduced downstream ketone metabolite beta-hydroxybutyrate (BHB) supports HCC initiation. To explore the therapeutic value of BHB in potentiating lenvatinib treatment in HCC, tyrosine catabolism-suppressing HCC cells were treated with BHB. We found that BHB treatment suppressed cell proliferation, increased cellular reactive oxygen species and potentiated the efficacy of lenvatinib treatment. Similar tumor-suppressive effect was observed when BHB was administered in vivo in mice bearing HCC patient derived xenograft. Combination treatment of lenvatinib and ketogenic diet which induces BHB level could drastically attenuate tumor development in immunocompetent mice bearing hepatic tumors. This study presents a potentially translatable combination treatment regimen to potentiate the therapeutic efficacy of lenvatinib in advanced HCC.
Citation Format: Minghe Zhang, Terence Kin-Wah Lee, Stephanie Ma, Man Tong. Therapeutic potential of ketogenic diet as an adjuvant to lenvatinib treatment in 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 7597.
Abstract 7640: Prognostic significance of S100A16 expression in patients with resected lung adenocarcinoma
Authors: Hung Jung-Jyh 
Published: March 2024

Abstract
Background: Lung cancer is the leading cause of cancer death worldwide. Tumor recurrence is the most common cause of treatment failure after surgical resection. The S100 protein family is a multigenic group of cytoplasmic calcium-binding proteins. S100A16 has to be reported to be an independent prognostic indicator of poor overall survival in lung adenocarcinoma recently. However, the prognostic value of S100A16 in recurrence-free survival (RFS) in patients with resected lung adenocarcinoma has not been well demonstrated. The relationship between S100A16 expression and the histological subtypes of lung adenocarcinoma remained unknown.
Methods: A total of 79 patients with resected lung adenocarcinoma were included in the study. S100A16 expression was determined by immunohistochemistry in tumor specimens. The prognostic value of S100A16 overexpression and its relationship with clinicopathological variables, including histology subtypes, were investigated.
Results: S100A16 overexpression tended to be significantly associated with order age (P = 0.074). There was no significant association between S100A16 overexpression and predominant pattern group (lepidic/acinar/papillary vs. micropapillary/solid) (P = 0.447). Univariate analysis indicated that pathologic stage (II or III vs. I) (P = 0.001) and S100A16 overexpression (P = 0.005) were significant prognostic factors for worse recurrence-free survival (RFS). Predominant pattern group (lepidic/acinar/papillary vs. micropapillary/solid) (P = 0.064) tended to be a significant prognostic factor for worse RFS. In multivariate analysis, pathologic stage (II or III vs. I) (HR, 27.847; 95% CI, 4.173 to 185.831; P = 0.001), and S100A16 overexpression (HR, 21.740; 95% CI, 2.516 to 187.870; P = 0.005) were still significant prognostic factors for worse RFS.
Conclusions: S100A16 overexpression was a significant prognostic factor for worse RFS in patients with resected lung adenocarcinoma. This information is useful to stratify high-risk patients of recurrence after resection of lung adenocarcinoma.
Citation Format: Jung-Jyh Hung. Prognostic significance of S100A16 expression in patients with resected 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 7640.
Abstract 80: Toward best practices for B-cell receptor repertoire profiling
Authors: Xiao Wenming ,   BCR-SEQC consortium
Published: March 2024

Abstract
FDA anticipates that immune cell receptor profiling will become crucial to the Agency’s evaluation of the efficacy and safety of emerging cancer immunotherapies and immunomodulatory drugs by advancing precision medicine and providing orthogonal, functional evidence. High-throughput sequencing of B-cell receptor sequencing (BCR) gene rearrangements and downstream analysis empowers researchers to define the B-cell clonal landscape, as well as identify biomarkers for minimal residual disease (MRD). Although this field has made vast strides over the last decade, it lacks standardized assay controls, adequate sensitivity and specificity in gene mapping/alignment, and appropriately qualified reference data sets, materials, and validation methods. Moreover, detailed comparisons of wet-bench analytical methods or bioinformatic procedures have not appeared. The FDA led B-cell receptor sequencing quality control (BCR-SEQC) consortium is establishing protocols for the development of reference materials and data sets for the evaluation of NGS-based BCR repertoire reconstruction. The consortium is also developing materials for performance bench-marking studies. To this end, we performed whole-genome sequencing, RNAseq, and BCR-seq on 50 B-cell lines to determine the clonotype, full-length transcripts, and expression abundance of BCR genes in each cell line. Two batches of reference materials (DNA, RNA, and cells) were generated from each of nine cell lines that were determined unambiguously to be monoclonal in BCR expression of both heavy and light chains. These materials were distributed to ten companies to test up to 15 BCR-seq products. Our benchmarking studies include six widely-used sequencing technologies. The overarching objectives of this research were to elucidate current capabilities and limitations, address fundamental technical needs, provide reference materials and data sets, and establish actionable best practices for reconstructing B cell receptor repertoires from NGS data.
Citation Format: Wenming Xiao, BCR-SEQC consortium. Toward best practices for B-cell receptor repertoire 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 80.
Abstract 835: Occupational-related exposure to benzene and risk of female genital cancers: Systematic review and meta-analysis
Authors: Shah Darshi , DeStefano Vincent , Shah Veer , Seyyedsalehi Monireh Sadat , Bonetti Mattia , Boffetta Paolo 
Published: March 2024

Abstract
Purpose: The IARC monograph has classified benzene as a class I carcinogen, as evidenced by its association with hematological malignancies. However, its association with female genital cancers (ovarian, uterine, cervical) has not been characterized. Therefore, a meta-analysis of cohort studies was performed to determine the possible correlation between occupational benzene exposure and the risk of female genital cancers.
Methods: A systematic literature review was conducted that identified 7221 relevant publications. A total of 9 cohort studies detailing occupational exposure to benzene and correlated risk of ovarian, uterine, and cervical cancers were retained, and a meta-analysis (using fixed effects model) was performed. PECOS criteria and STROBE guidelines were followed, and the study protocol was registered in the PROSPERO database (Reg. No. 379720). Study quality was assessed using a modified Newcastle-Ottawa scale (NOS).
Results: The summary relative risk (RR) for ever-benzene exposure was 1.22 (95% CI=1.03-1.44, I2&lt;0.001%, n=19); corresponding RR results for female genital cancers incidence and mortality were 1.09 (95% CI=0.92-1.29, I2=5.0%, n=17) and 1.69 (95% CI=1.18-2.4, I2&lt;0.001%, n=9), respectively. These findings were confirmed in sub-analyses by geographical area, industry type, publication year, and levels of benzene exposure. Heterogeneity was detected amongst industries of employment (p-het=0.012) which included petroleum, chemical, printing, and mixed industries categories. Notably, the printing industry (compositors, press operators, and bookbinders) had a summary RR of 2.56 (95% CI=1.45-4.52, I2=35.0%, n=2). However, no heterogeneity was observed for geographical regions (p-het=0.77), duration of benzene exposure (p-het=0.73), publication year (p-het=0.89), or among varying study quality (&lt; or ≥ median NOS score; p-het=0.14). No publication bias was detected (p=0.43).
Conclusions: Our meta-analysis identified an association between occupational benzene exposure and the risk of female genital cancers. The association was restricted to the industry of employment. Effects of residual confounding variables cannot be excluded, however, given the present association, there is a potential for causality, especially in workers employed in the printing industry.
Citation Format: Darshi Shah, Vincent DeStefano, Veer Shah, Monireh Sadat Seyyedsalehi, Mattia Bonetti, Paolo Boffetta. Occupational-related exposure to benzene and risk of female genital cancers: Systematic review and meta-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 835.
Abstract 876: spatialGE: Empowering researchers to study the tumor microenvironment leveraging spatial transcriptomics
Authors: Ospina Oscar , Manjarres-Betancur Roberto , Gonzalez-Calderon Guillermo , Soupir Alex C. , Smalley Inna , Tsai Kenneth , Markowitz Joseph , Berglund Anders , Yu Xiaoqing , Fridley Brooke L. 
Published: March 2024

Abstract
The use of single-cell RNA-seq (scRNA-seq) to understand the complexity of the tumor microenvironment (TME) has been pivotal in advancing cancer research and treatment strategies. Nevertheless, locating the individual TME components in their spatial contexts is essential to understanding the complexity of the TME, tumorigenesis, metastasis, and drug response. The recent popularity of spatially resolved transcriptomics (SRT) allows researchers to describe the TME and their spatial neighborhoods; however, SRT data analysis can be challenging for non-data scientists. Thus, we have developed spatialGE, a point-and-click web application designed to empower researchers in the analysis of SRT data. Our application offers a suite of functionalities, including the visualization of spatial gene expression activity maps, quantification of the spatial heterogeneity, testing of pathway-level spatial aggregation, tissue domain/niche detection, detection of spatial gradients in expression patterns, and differential expression analysis. Furthermore, spatialGE allows direct comparative analysis of gene expression with the morphology in tissue images, as well as methods for cell/domain phenotyping. Data input has been streamlined and made flexible to receive outputs from multiple SRT platforms. Analyses and results are stored within user-defined projects to facilitate accessibility at any time. We demonstrate the usefulness of spatialGE through its application to leptomeningeal metastatic melanoma (LMM) tissues assayed with the Visium platform. The gene expression tissue domain detection and gradient analysis in spatialGE indicated that SERPINA3 expression is higher in tumor regions closer to stroma. This finding led to cell culture experiments indicating that knocking down SERPINA3 results in the sensitization of tumors to MAPK-targeting therapy. These results highlight the utility of spatialGE for exploring SRT data and the empowerment of cancer researchers to explore the spatial architecture of the TME.
Citation Format: Oscar Ospina, Roberto Manjarres-Betancur, Guillermo Gonzalez-Calderon, Alex C. Soupir, Inna Smalley, Kenneth Tsai, Joseph Markowitz, Anders Berglund, Xiaoqing Yu, Brooke L. Fridley. spatialGE: Empowering researchers to study the tumor microenvironment leveraging spatial transcriptomics [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 876.
Abstract 899: Development of a deep learning model for cell type mapping in colorectal cancer using H&E images leveraging image-based spatial transcriptomics data
Authors: Cook Seungho , Lee Dongjoo , Lim Myunghyun , Lee Jae Eun , Lee Daeseung , Im Hyung-Jun , Pyo Jung-Soo , Na Kwon Joong , Choi Hongyoon 
Published: March 2024

Abstract
Purpose The tumor microenvironment (TME) is crucial in colorectal cancer as it influences disease progression, treatment response, and patient outcomes, providing valuable insights for personalized therapies and prognostic assessments. Here, we have developed a deep learning model by integrating hematoxylin and eosin (H&E) stained images of colorectal cancer and image-based spatial transcriptomics (Xenium) to infer spatial mapping of cell types in TME only using H&E images.
Methods A total of 30 H&E images of colorectal cancer obtained by tissue microarray were registered with image-based spatial transcriptomics data (Xenium). Utilizing a Variational Autoencoder (VAE) based model and leveraging reference single-cell data enables the acquisition of cell types for individual cells in image-based spatial transcriptomics. A convolutional neural network (CNN) model was developed using H&E image as inputs to predict cell types in H&E-stained tissue image patches of colorectal cancer collected from various patients. The model also estimated the cell types from H&E-stained whole slide tissue image of colorectal cancer of The Cancer Genome Atlas (TCGA-COAD).
Results The accuracy of the model's predictions for cell types using H&E image patches was notably high and exhibited a significant concordance with the results obtained through the validation. The Intersection over Union (IoU) metric for image segmentation indicated a value of 0.66 for epithelial cells and 0.44 for TNK cells. The output of deep learning model for epithelial cells and T/NK cells from TCGA-COAD tissue images showed a correlation with human-labeled regions of cancer epithelium and immune cells.
Conclusions Leveraging image-based spatial transcriptomics, we developed a deep learning model capable of discerning various cell types within the tumor microenvironment solely from H&E images. This clinically translatable approach is valuable for investigating tumor microenvironment to develop biomarkers associated with various cancer therapeutics particularly immuno-oncology drugs. This approach can yield objective deep learning-based models without human labels for characterizing the tumor microenvironment in single-cell resolution, particularly regarding spatial immune distribution.
Citation Format: Seungho Cook, Dongjoo Lee, Myunghyun Lim, Jae Eun Lee, Daeseung Lee, Hyung-Jun Im, Jung-Soo Pyo, Kwon Joong Na, Hongyoon Choi. Development of a deep learning model for cell type mapping in colorectal cancer using H&E images leveraging image-based spatial transcriptomics 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 899.
Abstract 910: Clinical inference and biological dissection of tumor ploidy and heterogeneity in cutaneous melanoma for immunotherapy response using deep learning
Authors: Glettig Marc , Tarantino Giuseppe , Aprati Tyler , Elmarakeby Haitham , Liu David 
Published: March 2024

Abstract
Background: Tumor ploidy and heterogeneity demonstrated to be pivotal in predicting immunotherapy response in cutaneous melanoma in several independent cohorts (Liu NatMed2019, Tarantino BioRXiv2022). Their estimation can guide more personalized and rational utilization of these immunotherapies. However, 1) the biology underpinning ploidy and heterogeneity is unknown; and 2) these findings were derived in patients using retrospective research tumor-normal paired whole exome sequencing which is not performed for clinical management.
Methods: This study addresses this gap by employing deep learning models to predict these crucial markers using routinely available clinical assays, including H&E images. Attention-based computer vision models enable the identification of key morphological features in H&E slides. Segmentation of tumor tissue to perform automated masking, enhances ploidy inference.Moreover, biologically informed neural networks (P-Net, Elmarakeby Nature2021) uncover transcriptional and genomic features linked to ploidy and tumor heterogeneity. Our models are trained on publicly available data (e.g., Liu et al Nature Medicine 2019; TCGA SKCM) from melanoma cohorts and further validated in independent cohorts to ensure robustness.
Results: We developed and validated automated tumor tissue masking, enabling the prediction of Whole Genome Doubling (WGD) from H&E Slides with an AUC &gt; 0.75. Attention-based models identify distinct Tumor Microenvironment (TME) structures predictive of high tumor heterogeneity. P-Net application revealed the Calmodulin pathway, previously associated with regulating proliferation in cancer and targeted with chemotherapy, as intricately linked to high tumor heterogeneity, providing valuable insights into underlying mechanisms.
Conclusion: In conclusion, our study strategically harnesses and integrates existing datasets to rigorously test, refine, and validate hypotheses concerning the biological and therapeutic implications of genomic heterogeneity and ploidy. Notably, our predictive models with automated tumor masking demonstrate a remarkable AUC &gt;0.75 for biomarkers traditionally challenging to derive from clinical assays. This breakthrough opens avenues for novel therapeutic strategies targeting genomic heterogeneity and ploidy, providing a transformative potential to enhance patient care and outcomes.
Citation Format: Marc Glettig, Giuseppe Tarantino, Tyler Aprati, Haitham Elmarakeby, David Liu. Clinical inference and biological dissection of tumor ploidy and heterogeneity in cutaneous melanoma for immunotherapy response using 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 910.