bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 4887: Unveiling the functional impact of glioma non-coding germline variants using large language models
Authors: Alvarez-Torres Maria del Mar , Fu Xi , Sagatelian Romella K , Buendia Alejandro L , Rabadan Raul 
Published: March 2024

Abstract
Efforts to identify the risk for developing gliomas through Genome-Wide Association Studies (GWAS) face relevant challenges, such as accurately pinpointing the genomic positions of germline variants, primarily found in non-coding regions, with unclear functional implications in glioma development. In this context, the GET method, a large language model designed to unveil regulatory grammars across 213 human cell types, emerges as a valuable tool for elucidating the mechanisms of non-coding mutations.
Our aims include: I) Validate the efficacy of the GET method using a well-documented case and prioritize relevant variants for analysis; II) Refine the genomic positions of these variants and exploring their linkage disequilibrium (LD); III) Identify potential motifs and genes that might be influenced by these high-impact variants.
After examining published GWAS, 39 non-coding SNPs in gliomas were found. Adding 316 variants in high LD (R2 &gt; 0.8) expanded the dataset to 355 variants for analysis. Using the GET method, we calculated impact scores (ISs) for each variant, considering changes in motif binding score (ΔBM) and motif gradient (GM) across human astrocytes. We focused on rare variants (risk allele frequency &lt;0.25) with the higher odds ratios (OR).
The table presents GET results for a well-known case-control, showing its role in increasing glioma risk through OCT4 motif disruption and enhanced MYC expression, as previously was confirmed experimentally. Moreover, it details results concerning the 5 rare germline variants exhibiting the highest ISs, including precise genomic positions and the most affected motifs and genes that could potentially be impacted by each variant.
Using a large language model, this study overcomes the challenge of mere variant identification, aiming to establish a framework for uncovering the underlying mechanism of risk variants. While further validation is essential, this is a promising approach to explore the impact of germline mutations.
Variant ID Genomic Position Altered Motif Affected Gene Ref allele Alt allele Case-control rs55705857 Chr8:129633445 OCT4+SOX2:POU MYC A G H-I variant #1 rs78378222 Chr17:7668433 HD/18 PER1 T G H-I variant #2 rs518394 Chr9:22019673 KLF/SP/2 CDKN2A G C H-I variant #3 rs79495512 Chr15:76199995 KLF/SP/2 LINGO1 T C H-I variant #4 rs78185702 Chr15:76317893 SMAD LINGO1 C T H-I variant #5 rs2538067 Chr7:54907724 CTCF EGFR G A
Citation Format: Maria del Mar Alvarez-Torres, Xi Fu, Romella K Sagatelian, Alejandro L Buendia, Raul Rabadan. Unveiling the functional impact of glioma non-coding germline variants using large language 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 4887.
Abstract 489: Unlocking immune resistance and metastasis of PDAC via dual acting circularized nanodisc
Authors: Elzoghby Ahmed , Emam Hagar , Yim Seungbin , Xin Cuiyan , Nasr Mahmoud 
Published: March 2024

Abstract
Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive and deadliest forms of cancer. While major progress in immunotherapy has been made in recent years, it remains poorly effective in this malignancy. Two key players stand behind immune resistance of PDAC and inadequate efficacy of immunotherapy to suppress its metastasis. First, the immune-resistant pancreatic cancer stem cells (CSCs) evade immune surveillance acting as a source for tumor relapse and metastasis. This immune resistance stems from low immunogenicity of PDAC cells as well as the inaccessible CSCs that reside deep in the tumor core. Second, pancreatic cancer associated fibroblasts (CAFs) fuel metastasis of PDAC cells by suppressing their immune recognition and establishing pre-metastatic niche at distant organs to help their colonization. Our hypothesis is to overcome immune resistance of PDAC and inhibit early steps of its metastasis via simultaneous immunogenic eradication of immune resistant CSCs and switching immunosuppressive and pro-metastatic CAFs into quiescent ones. Therefore, we engineered ultrasmall circularized nanodiscs (cND) that encapsulate oncolytic peptide LTX-315 and fibroblast remodeling Notch signaling inhibitor. The engineered oncolytic LTX-315 loaded cND successfully induced immunogenic cell death (ICD) of both bulk PANC1 and pancreatic CSCs. Compared to non-treated and free LTX-315 treated cells, LTX-315 cND resulted in enhanced release of DAMPs including ATP and HMGB-1 as well as surface translocation of calreticulin. The ultrasmall size (9 nm) of cND enabled its deep tumor penetration to eradicate resistant CSCs, as revealed using 3D PDAC heterospheroids. In parallel, the Notch blocking cND promoted quiescence of the activated pancreatic stellate cells as denoted by downregulation of SMA, FAP and collagen. In KPC syngeneic PDAC mouse model, the dual oncolytic/Notch blocking cND combined with aPD-L1 therapy significantly suppressed tumor growth and prevented its metastasis to liver compared to vehicle-treated mice. Immunophenotyping study showed marked reduction in the abundance of CSCs and CAFs in the tumor treated with the cNDs. Moreover, the tumor infiltration of CD86+ MHCII+ dendritic cells and cytotoxic CD8+ T cells was enhanced after treatment with the cND. Overall, our dual strategy of targeting pancreatic CSCs and CAFs via oncolytic/Notch blocking cND boosted the anti-tumor and anti-metastatic efficacy of aPD-L1 therapy to eliminate primary PDAC tumor and suppress its metastasis.
Citation Format: Ahmed Elzoghby, Hagar Emam, Seungbin Yim, Cuiyan Xin, Mahmoud Nasr. Unlocking immune resistance and metastasis of PDAC via dual acting circularized nanodisc [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 489.
Abstract 4908: Comprehensive tool for pan-cancer consensus data mining of drugs, targets and biomarkers
Authors: Yoon Sukjoon 
Published: March 2024

Abstract
The rapid increase in collateral omics and phenotypic data has enabled data-driven studies for the discovery of drug targets and biomarkers. For this purpose, we have developed a convenient tool, Q-omics software, for general researchers and cancer scientists to carry out customized data mining without bioinformatics background. Q-omics provides a comprehensive interface and smart functions for facilitating pan cancer-wide association studies on mutations, RNA expression, protein abundance, shRNA/CRISPR efficacy, drugs, tumor-infiltrating cells, patient survival, clinical info. etc. Furthermore, consensus scores on associated data pairs from heterogeneous samples, help to predict the reproducibility of data mining results. Q-omics (download: http://qomics.io) improves the utility of cancer omics big data for non-computational scientists at all levels of cancer research.
Citation Format: Sukjoon Yoon. Comprehensive tool for pan-cancer consensus data mining of drugs, targets and biomarkers [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 4908.
Abstract 4950: Applying fragmentomics profiles of plasma cell-free DNA for breast cancer detection
Authors: Liu Yaxin , Gan Shuang , Dong Hang , Tang Haoran , Wang Nan , Gui Xinyu , Zhu Anjie , Zhang Ruyan , Du Pan , Jia Shidong 
Published: March 2024

Abstract
The liquid biopsy approach focusing on DNA molecules supports the non-invasive diagnosis of breast cancer. Low-pass whole genome sequencing(lpWGS) for breast cancer detection has been validated for accuracy and cost-effectiveness, especially with copy number variant (CNV) analysis. However, detecting breast cancer with cell-free DNA (cfDNA) CNV was limited by tumor fraction. Utilizing cell-free DNA fragmentation characteristics for cancer detection has shown potential in plasma with high precision and applicability with low tumor fraction. In this study, 217 patients with breast cancer and 10 healthy individuals were enrolled. The cfDNA was extracted and performed using PredicineSCORE, a low-coverage whole genome sequencing assay to identify tumor-specific features. The cfDNA fragmentomics profiles including the coverage at specific regions, length and end-motif of fragment were implemented. We also investigated the copy number variation and the inferred tumor fraction which was adjusted by the Expectation-Maximum (EM) algorithm. Combining, an ensembled machine learning model was constructed for the classification of breast cancer and normal control. We observed a high correlation between tumor fraction and features. The absolute correlation coefficient scores between tumor fraction with the LYL1, MAF, FOXA1, and GRHL2 bonding sites' relative depth were over 0.82, 0.71,0.89,0.87. The insert size ratio of short reads and the CCCA end motif were over 0.77 and 0.72, respectively. We then diluted the tumor samples of tumor fraction at 0.1% to 50% by manually mixing reads from tumor samples into normal samples. By applying the XGBoost algorithm, the performance (F1 score &gt; 0.95) showed a promising ability to distinguish tumors from normal samples. The limit of detection (LOD) was less than 1% which is more sensitive than previously proposed models. Those samples detected mutations with high through panel which can confirm the accuracy of the assay. Our results discovered that profiling the cell-free DNA fragment could facilitate the way of non-invasively detection of breast cancer with high precisions.
Citation Format: Yaxin Liu, Shuang Gan, Hang Dong, Haoran Tang, Nan Wang, Xinyu Gui, Anjie Zhu, Ruyan Zhang, Pan Du, Shidong Jia. Applying fragmentomics profiles of plasma cell-free DNA for breast cancer detection [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 4950.
Abstract 4953: A proposal to extend standardized organ mapping antibody panels (OMAPs) to integrate protein and RNA analysis in spatial biology
Authors: Müller Werner , Femel Julia , Neil Emily , Park Dongju , Hennessey Rebecca C. , DiBiasio Eric C. , DiBuono Michael , Lafayette Hanna , Lloyd Erica , Lo Hsinyi , Makrigiorgos Alex , Soliman Sameh , Mangiardi Dominic , Praveen Paurush , Rüberg Silvia , Staubach Fabian , Hindman Ryan , Rothmann Thomas , Meyer Hansueli , Wantenaar Tanya , Wang Jinling , Pinard Robert , Bosio Andreas 
Published: March 2024

Abstract
The HubMap consortium has developed a new standard to report on normal histological samples using multicolor immunofluorescence imaging. An Organ Mapping Antibody Panel (OMAP) currently describes in a table the antibodies used, the cycle number assigned to a given reagent, and provides the rationale for using a particular antibody to better understand features of the tissue. This table is accompanied by antibody validation templates that show example images from the tissue described for the OMAP table. In addition, links to existing databases, gene symbols, the antibody features and colors are included. For each tissue, a further table called "Anatomical Structures, Cell Types, plus Biomarkers" (ASCT+B), which contains structures of the tissue, cell types in a given tissue structure, key RNA transcripts for a given cell type, and antibody stains as used in the OMAP table, is created. So far, this process has not been established for spatial multiomic datasets including protein and RNA detected on the same tissue section.
We have extended spatial biology methodology to combine cyclic RNA transcript detection with cyclic antibody-based protein detection using the MACSima Imaging Cyclic Staining (MICS) technology as well as H&E-staining on the same tissue section. For such datasets, the current OMAP data table needs to be extended by adding information on the RNA detection, cycle IDs and RNA validation templates, demonstrating the correct performance of a given RNA probe detection.
This poster will demonstrate a proposal to extend the OMAP table structure on an extended version of the “tonsil OMAP (OMAP-10)”, which was published on the Zenodo data repository page (https://zenodo.org/records/7875938). In addition, we will apply this extended OMAP to a colorectal cancer sample, to highlight the applicability and relevance of the OMAP structure for tumor tissue.
Citation Format: Werner Müller, Julia Femel, Emily Neil, Dongju Park, Rebecca C. Hennessey, Eric C. DiBiasio, Michael DiBuono, Hanna Lafayette, Erica Lloyd, Hsinyi Lo, Alex Makrigiorgos, Sameh Soliman, Dominic Mangiardi, Paurush Praveen, Silvia Rüberg, Fabian Staubach, Ryan Hindman, Thomas Rothmann, Hansueli Meyer, Tanya Wantenaar, Jinling Wang, Robert Pinard, Andreas Bosio. A proposal to extend standardized organ mapping antibody panels (OMAPs) to integrate protein and RNA analysis in spatial biology [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 4953.
Abstract 4971: Integrating artificial intelligence and functional precision oncology for individualized cancer therapies
Authors: Eguiarte-Solomon Fernando , Prendergast Rowan , Carapia Bianca , Rodriguez Javier , Buck Kristen , Gorospe Derrick , Valencia Elizabeth , Lopez-Ramos Jose , Gutierrez Itzel , Pippa Rafaella , Chien Yuan-Hung , Andrews Warren , Do Long , Sperry Jantzen , Nakashima Jonathan K. 
Published: March 2024

Abstract
Precision oncology seeks to tailor cancer treatments to individual patients. Patient-derived xenografts (PDXs) have emerged as a promising platform for selecting efficacious, personalized therapies and developing new oncology drugs. Here, we present a workflow for selecting and validating treatments for patients with colorectal cancer (CRC) that integrates orthotopic PDX (O-PDX) models, molecular profiling, machine learning, and in vivo pharmacological validation. Of the nine tumor biopsies collected, seven were successfully developed into O-PDX models (78%) with a median time to establishment of 119 days. Models were molecularly profiled for gene expression and were serially passaged to perform orthotopic pharmacology studies to validate test agents selected by oncologists as well those suggested by CertisAITM, a novel ensemble of machine learning models for the prediction of drug response in human cancers. The correlation between CertisAI's predicted therapy responses and the actual observed tumor growth inhibition (TGI) was r = 0.7, encompassing 37 distinct treatments across studies from six patients. This research collectively illustrates that the integration of artificial intelligence with functional in vivo assays offers a powerful platform for precision oncology, enabling the identification and validation of tailored treatments for individual cancer patients.
Citation Format: Fernando Eguiarte-Solomon, Rowan Prendergast, Bianca Carapia, Javier Rodriguez, Kristen Buck, Derrick Gorospe, Elizabeth Valencia, Jose Lopez-Ramos, Itzel Gutierrez, Rafaella Pippa, Yuan-Hung Chien, Warren Andrews, Long Do, Jantzen Sperry, Jonathan K. Nakashima. Integrating artificial intelligence and functional precision oncology for individualized cancer therapies [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 4971.
Abstract 4974: Development of an ovarian cancer diagnosis score using electronic health records
Authors: Phung Minh Tung , McLean Karen , Hanley Gillian E. , Pearce Celeste Leigh 
Published: March 2024

Abstract
Background: Diagnosis of invasive epithelial ovarian, fallopian tube, and primary peritoneal cancers (hereafter referred to as ovarian cancer) is often delayed, worsening both survival and quality of life for ovarian cancer patients. Diagnosis is delayed in ovarian cancer both because there is no effective screening method and because the symptoms commonly associated with ovarian cancer (i.e., abdominal/pelvic pain, bloating, loss of appetite, urinary symptoms) are non-specific to the disease. It would be beneficial to develop a flag in the electronic health record (EHR) when a patient’s healthcare utilization indicates further investigation for possible ovarian cancer is warranted. Thus, we used EHR data to develop a diagnosis score that identifies people who should be further assessed for potential ovarian cancer.
Methods: EHR data from 211,123 female patients, including 135 ovarian cancer patients, at the University of Michigan Medical System during 2012-2023 were analyzed. Time-varying Cox proportional hazard models were fit to identify the association between ovarian cancer and the diagnostic codes recorded for healthcare visits in the EHR, including the temporal sequence of the diagnostic codes. The beta coefficients for the diagnosis codes from the Cox models were summed to create a weighted diagnosis score for each patient. The weighted diagnosis score was tested for association with ovarian cancer in the same population.
Results: A total of 79 diagnostic codes were statistically significantly associated with ovarian cancer after applying a Bonferroni correction (p&lt;9x10-5). The temporal sequence of the diagnoses was not associated with ovarian cancer. There were 11 pairs of diagnosis codes that were correlated (correlation coefficient &gt;0.25); the diagnosis code with the higher p-value was excluded from further consideration. The remaining 68 codes were used to construct the diagnosis score. There was a statistically significant trend of increasing rate of ovarian cancer per quartile of the diagnosis score (hazard ratio=3.75, 95% confidence interval 3.50-4.03).
Conclusion: A score for ovarian cancer diagnosis was developed based on 68 diagnostic codes in the EHR. The next step is to validate the diagnosis score in an external dataset. This validation is underway using administrative data from British Columbia, Canada.
Citation Format: Minh Tung Phung, Karen McLean, Gillian E. Hanley, Celeste Leigh Pearce. Development of an ovarian cancer diagnosis score using electronic health records [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 4974.
Abstract 4976: The impact of obesity on survival in epithelial gynecological tumors
Authors: Badran Ahmed , Elshenawy Mahmoud , Fallatah Waleed , Hinkston Abdullaah K. , Ali Saad S. , Omar Mohammed Ali , Alaklabi Abdulaziz M. , Abdalla Hytham M. , Arabi Tarek Z. , Sabbah Belal N. , Alhusaini Hamed 
Published: March 2024

Abstract
Background: The impact of obesity on the survival of patients with epithelial gynecological tumors is not yet clear. We aim to elucidate the impact of body mass index (BMI) on epithelial gynecological tumors in a large tertiary center at Saudi Arabia.
Methods: We retrospectively reviewed the medical files of all patients with pathologically confirmed epithelial gynecological tumors at our center. Then, we assessed the difference in overall survival (OS) according to primary tumor location, BMI, stage at presentation, and performance status (PS) at presentation using log-rank tests.
Results: 400 patients were included in the analysis. The mean age at diagnosis was 56.1±13 years. 242 (60.5%) patients were diagnosed with uterine carcinomas, 137 (34.3%) with ovarian carcinomas, and 21 (5.3%) with cervical carcinomas. 278 (69.5%) patients were obese at presentation, 74 (18.5%) were overweight, 43 (10.8%) were within normal range, and 7 (1.8%) were underweight. The PS at presentation was 0, 1, 2, 3, and 4 in 351 (87.8%), 17 (4.3%), 10 (2.5%), 16 (4%), and 6 (1.5%) patients, respectively. Cervical cancer was associated with long OS, while ovarian cancers demonstrated poor prognosis (p=0.0159). There was no difference in OS between different BMI groups (p=0.072). Patients with stage 4 cancers had significantly shorter OS (p&lt;0.0001). Patients with PS 4 at presentation was associated with the worst prognosis (p&lt;0.0001).
Interpretation: Shorter OS was associated with ovarian carcinomas, advanced staging, and poor PS at presentation. BMI classification had no impact on OS. Further studies are needed to confirm our findings.
Citation Format: Ahmed Badran, Mahmoud Elshenawy, Waleed Fallatah, Abdullaah K. Hinkston, Saad S. Ali, Mohammed Ali Omar, Abdulaziz M. Alaklabi, Hytham M. Abdalla, Tarek Z. Arabi, Belal N. Sabbah, Hamed Alhusaini. The impact of obesity on survival in epithelial gynecological 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 4976.
Abstract 4997: Auranofin enhances the efficacy of anti-PD1 immunotherapy
Authors: Zhao Zichen , Hu Shichuan , Zhang Yan 
Published: March 2024

Abstract
Anti-PD-1 immunotherapy brings hope for the treatment of malignant tumors, but its overall response rate is low. Therefore, how to sensitize anti-PD-1 therapy to make more patients benefit from immunotherapy is an urgent clinical demand. PKCι, a classical oncogene that affects cell proliferation and differentiation, has recently been found to be involved in the recruitment of immunosuppressive cells MDSCs and Tregs, which leads to anti-PD1 immunotherapy resistance. Our study is consistent with previously reported results that interfering with PKCι expression in tumor cells significantly enhances the anti-tumor effect of anti-PD1 therapy. Immunological mechanistic analyses demonstrated that the inhibition of PKCι expression significantly reduced the infiltration of tumor-associated macrophages (TAMs) rather than MDSCs or Tregs. Further studies revealed that PKCι enhanced the expression of macrophage chemokine CCL7 by promoting the formation of the YAP1/TEAD transcriptional complex. Interference in vitro and pharmacological inhibition of PKCι in tumor cells both significantly reduced CCL7 expression and TAMs invasion. Auranofin is a commonly used anti-inflammatory agent in clinical practice, which has been recently found that it directly induces tumor death by modulating the cellular redox system in tumor cells and remodels the tumor immunosuppressive microenvironment by specifically inhibiting PKCι expression. The results of our study showed that the anti-tumor effect of Auranofin combined with anti-PD1 therapy was superior to that of monotherapy in lung adenocarcinoma. Auranofin exerted synergistic anti-tumor effects with anti-PD1 immunotherapy by inhibiting the expression of PKCι-YAP1/TEAD-CCL7, reducing the recruitment of TAMs, and promoting the infiltration of effector CD8+ T cells. In summary, Auranofin is a potent candidate for sensitizing anti-PD-1 immunotherapy, reversing immunotherapy blunting by remodeling TIME, which provides new ideas and theoretical basis for immunotherapy of sensitizing malignant tumors.
Citation Format: Zichen Zhao, Shichuan Hu, Yan Zhang. Auranofin enhances the efficacy of anti-PD1 immunotherapy [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 4997.