bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract B022: Acinar-to-ductal metaplasia index (ADMI): A quantifiable measure of ADM
Authors: Aney Katherine J , Jeong Woo-Jeong , Koak Pal , Nissim Sahar 
Published: September 2024

Abstract
Acinar to ductal metaplasia (ADM) is thought to be a natural adaptive response to injury by which downregulation of digestive enzymes mitigates tissue damage and facilitates repair. However, this latent plasticity is also a critical gateway to cancer. In the context of a KRAS mutation – the driver mutation in pancreatic cancer – ADM is co-opted, causing acinar cells to progress into precancerous lesions instead of returning to a normal acinar state. ADM has various stages of severity marked by severe histologic changes replacing acinar morphology and markers with those of ductal epithelium. While this descriptive state of ADM is well established in pancreas biology, the field has lacked an objective measure to quantify various stages of deviation from the homeostatic acinar cell state. Here, we leverage FixNCut, a novel method for single-cell transcriptomic analysis of the pancreas, to develop an index tool that allows researchers to objectively measure the deviation of metaplastic acinar cells from normal acinar cell identity. The ADM Index, or ADMI, is based on 16 signature genes upregulated only in acinar cells in ADM, and is corroborated with GeoMx WTA data. We observe an increased ADMI score in acinar cells following pancreatitis, with perduring detectable elevation of ADM specific genes 14 days post-inflammation. ADMI was also validated in independent single-cell and bulk RNA-seq data, demonstrating the utility of scoring ADMI across studies and in diverse perturbations. We further apply ADMI to the context of recurrent weekly acute pancreatitis in mice and observe progressively increasing ADMI score sequentially at 2 week, 4 week, and 6 week timepoints. Our findings validate ADMI as a robust, objective standard that researchers can use in their own datasets to quantify deviation of individual acinar cells from normal identity, to investigate single-cell heterogeneity of these deviations, and to observe how these deviations change over time or following diverse stressors.
Citation Format: Katherine J Aney, Woo-Jeong Jeong, Pal Koak, Sahar Nissim. Acinar-to-ductal metaplasia index (ADMI): A quantifiable measure of ADM [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr B022.
Abstract B023: Unravelling fibroinflammatory and immune signatures for pancreatic cancer early detection
Authors: Fraser Kyra , Rosado Maria , Pereira Stephen P , Acedo Pilar 
Published: September 2024

Abstract
Pancreatic cancer is associated with late-stage diagnosis and poor patient prognosis. The disease is characterised by chronic dysregulated immunity and inflammation. Moreover, chronic pancreatitis is linked with an increased risk of pancreatic cancer. Tissue remodelling during disease progression involves a complex network of cells and a crosstalk between cancer cells, stromal components and immune cells, but it remains poorly explored. Thus, to improve the outcome of patients, there is a need for better understanding of biological changes associated with the initiation and progression of pancreatic cancer at the molecular and spatial levels. Using samples obtained from our Accelerated Diagnosis of neuroEndocrine and Pancreatic TumourS (ADEPTS) biobank, we have evaluated blood samples for the discovery of non- invasive biomarkers to discriminate patients with pancreatic cancer from symptomatic patients without cancer. In this pilot study, the transcriptome of peripheral blood mononuclear cells (PBMCs), isolated using a ficoll gradient was explored to identify PDAC-associated mRNA signatures. Moreover, the Olink® Explore Inflammation and Oncology panels were performed in a cohort of 40 patients with early stage (I-II) PDAC, 40 chronic pancreatitis samples and 40 control patients with benign symptomatic upper GI diseases. To validate and enrich these results, a panel of inflammatory and immune markers including immunoglobulins, cytokines and chemokines was also run using multiplex techniques. Symptoms observed in our cohort were also analysed together with clinical (e.g diabetic status, biochemistry profile) and demographic data (age, sex, ethnicity, post code etc). We have identified mRNA transcriptome signatures and differentially expressed proteins associated with inflammatory and immunological processes in PDAC compared to benign symptomatic samples. Our results offer potential diagnostic utility for early-stage disease. Additionally, our unique cohort of patient samples comprising of those with confirmed PDAC and those presenting to clinic with benign disease and similar non-specific symptoms, allows for the development of non-invasive diagnostic tests with better translational applicability. Future work will involve the validation of these findings in a larger patient cohort and the use of NanoString GeoMx® Digital Spatial Profiler to perform whole transcriptome and proteome profiling in human chronic pancreatic and pancreatic cancer tissue samples.
Citation Format: Kyra Fraser, Maria Rosado, Stephen P Pereira, Pilar Acedo. Unravelling fibroinflammatory and immune signatures for pancreatic cancer early detection [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr B023.
Abstract B026: Development of a Urinary miRNA-Based Classifier for Detecting Pancreatic Cancer in High-Risk Groups
Authors: Kawase Tomoya , Kusaka Hiroyasu , Yoshida Koji , Kato Yasutaka , Kita Junji , Kurahara Hiroshi , Kondo Shunsuke , Satomura Atsushi , Yoshida Yuji , Yamaguchi Hiroki , Ando Yoriko , Mikami Motiki , Mizunuma Mika , Ichikawa Yuki 
Published: September 2024

Abstract
Background Despite advances in treatment for cancer, the 5-year survival rate of pancreatic adenocarcinoma(PDAC) remains approximately 10% as of 2023. Early detection of PDAC is crucial for improving therapeutic outcomes. In order to detect PDAC at early stages, monitoring patients with clinical risk factors is important but relying solely on these risk factors is not sufficient. There is an urgent need for detection of biological markers such as microRNAs (miRNAs). In this study, we analyzed urinary miRNAs as a potential non-invasive biomarker and developed a machine learning classifier to detect pancreatic cancer from high-risk patients. Method A total of 165 participants with Stage0 to Ⅳ PDAC and 109 high-risk participants were analyzed. High-risk patients were defined as those who had at least one of the following diseases and/or risk factors: type 2 diabetes, chronic pancreatitis, intraductal papillary mucinous neoplasm, pancreatic cysts, dilation of main pancreatic duct and family history of PDAC. Urinary miRNA were sequenced by next generation sequencer. The data were split into 80% of training data and 20% of validation data for the development of a classifier. Machine learning based algorithm was deployed to construct an algorithm from the training data and its performance was evaluated by the remaining validation data. Results: Differential expression analysis revealed 12 upregulated and 7 downregulated miRNAs. Among downregulated miRNAs, PDAC related pathways such as growth signaling were significantly enriched. The performance of the classifier was AUC: 0.87, sensitivity: 0.80, and specificity 0.77. The sensitivity for early stage (Stage0-Ⅰ)PDAC was 0.80 and 0.88 especially when limited to StageⅠ. Conclusion: The classifier was able to detect PDAC at early stages with high sensitivity and specificity. This study demonstrated the potential for effective monitoring of high-risk patients for the early detection of PDAC, highlighting the feasibility and advantages of urine sampling due to its simplicity. To further evaluate the performance of the algorithm, a validation study is recommended.
Citation Format: Tomoya Kawase, Hiroyasu Kusaka, Koji Yoshida, Yasutaka Kato, Junji Kita, Hiroshi Kurahara, Shunsuke Kondo, Atsushi Satomura, Yuji Yoshida, Hiroki Yamaguchi, Yoriko Ando, Motiki Mikami, Mika Mizunuma, Yuki Ichikawa. Development of a Urinary miRNA-Based Classifier for Detecting Pancreatic Cancer in High-Risk Groups [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr B026.
Abstract B027: A blood-based, multiplex protein biomarker signature shows superior performance in detection of early-stage pancreatic ductal adenocarcinoma (PDAC)
Authors: Palma Norma , Schiess Ralph , King Thomas , Athanasiou Alcibiade , Kureshi Natasha , Ford Lisa , Lucas Aimee , Katona Bryson , Brand Randall , Simeone Diane 
Published: September 2024

Abstract
BACKGROUND: A sensitive and specific blood biomarker test is needed to improve survival in PDAC through early detection. An initial discovery study analyzing more than 3000 proteins using both pre-selected immunoassays and the Olink proteomics platform identified 15 promising protein candidates, of which ten were successfully pre-verified for ELISA immunoassay analysis. This model-development study was conducted to develop and pre- validate a specific and sensitive biomarker signature for the early detection of PDAC. METHODS: This multicenter case-control study was performed using existing bio-banked pretreatment samples from patients with newly diagnosed PDAC (Stages I-II) and participants in PDAC high-risk surveillance programs as controls. Blinded quantitative measurement of protein biomarkers was performed using ELISA-based immunoassays. CA19-9 was measured on a Roche COBAS instrument. An AI-derived predictive mathematical biomarker signature incorporating multiple protein biomarkers was developed and assessed using standard cross- validation techniques. RESULTS: 128 Stage 1 and 2 PDACs and 495 high-risk control serum samples were analyzed with 10 predetermined biomarkers to develop a four-protein biomarker signature. The model predicted PDAC versus non-PDAC with 84% sensitivity at 98% specificity. Model performance was significantly better when compared to CA19-9 alone (sensitivity 65% at 98% specificity, p&lt;0.001). In those patients with low CA19-9 (&lt;37 U/ml), the model predicted PDAC with 62% sensitivity at 98% specificity, performing significantly better than CA19-9 alone (9% sensitivity at similar 98% specificity (p&lt;0.001). Additionally, in patients &gt;65 years old, the signature predicted PDAC with 90% sensitivity at 98% specificity, which was significantly more sensitive than CA19-9 alone (75% sensitivity at 98% specificity, p&lt;0.001). Test accuracy was not affected by diabetes status. CONCLUSIONS: This blood-based, multiplexed biomarker signature showed high sensitivity and specificity and significantly outperformed CA19-9 for early-stage PDAC. This biomarker model has the potential to improve outcomes in PDAC patients through early detection. Analytical and clinical validation studies are planned.
Citation Format: Norma Palma, Ralph Schiess, Thomas King, Alcibiade Athanasiou, Natasha Kureshi, Lisa Ford, Aimee Lucas, Bryson Katona, Randall Brand, Diane Simeone. A blood-based, multiplex protein biomarker signature shows superior performance in detection of early-stage pancreatic ductal adenocarcinoma (PDAC) [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr B027.
Abstract B029: Repurposing Digital PCR to Diagnose and Create a Novel Genomic Signature for Homologous Recombination Deficiency in Pancreatic Adenocarcinoma
Authors: Valerin Jennifer Brooke , Hasson Sophie , Rangel Valeria , Pannunzio Nicholas 
Published: September 2024

Abstract
Introduction: Germline mutations in DNA repair genes are often associated with increased risk of cancer. Mutations that affect the homologous recombination (HR) DNA repair pathway ( e.g., BRCA 1 and BRCA2) are known drivers of pancreatic adenocarcinoma. Early detection relies on identification of pathogenic mutations in patients with a family history of cancer. Next generation sequencing panels often use a select set of pathogenic variants for diagnosis, meaning that individuals with variants of uncertain significance may be missed, limiting testing of other family members for the potentially disease-causing variant. Here we describe a digital PCR assay developed to detect BRCA1 and BRCA2 variant that may eventually serve as a rapid, cost-effective alternative to sequencing that costs less than $5.00 per sample with results in 24 hours. This study highlights the need for better diagnostic tools, which will become critically important as we begin to study pathogenic mutations in more diverse populations. Methods: We have collected blood from 4 BRCA1 mutants and 5 BRCA2 mutants who have already undergone germline sequencing from commercially available assays. Clinicopathologic data has been collected as well. We designed two fluorescently labelled TaqMan probes, one that recognizes the wild-type BRCA alleles and one specific for mutant BRCA alleles. Briefly, 1 ng of genomic DNA is mixed with amplicon primers and fluorescent Taq-Man probes, then partitioned into 20,000 separate 517 picoliter wells. Quantification and detection are achieved based upon the fluorescent signal measured from each well. Results: We have successfully confirmed pathologic BRCA1 and BRCA2 mutations previously identified with next generation sequencing in 100% of samples analyzed. All three BRCA2 mutations generate a premature stop codon leading to a truncated protein and are known to be pathogenic. Two related patients have the same BRCA1 frameshift mutation that results in a premature stop codon, and one patient has a duplicated cytosine that leads to a frameshift mutation that is one of three characterized Ashkenazi Jewish founder mutations that increases cancer risk in this population . Conclusions: Here we report an innovative assay using digital PCR (dPCR) that provides a cost-effective, time-efficient method to identify MMR mutants and characterize VUS in patients and their families.
Citation Format: Jennifer Brooke Valerin, Sophie Hasson, Valeria Rangel, Nicholas Pannunzio. Repurposing Digital PCR to Diagnose and Create a Novel Genomic Signature for Homologous Recombination Deficiency in Pancreatic Adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr B029.
Abstract B032: Evaluating T cell-inflammation in pancreatic neuroendocrine tumors
Authors: Romero Joan Miguel , Vafiadis James , Aalto Tess , Fang Ruomeng , Wang Yifan , Zogopoulos George 
Published: September 2024

Abstract
Background: Pancreatic neuroendocrine tumors (pNETs) are rare tumors arising from neuroendocrine cells in the pancreas. Their clinical behaviour and prognosis are variable and durable responses are often difficult to achieve with conventional therapies. Immunotherapy approaches may be efficacious in a subgroup of pNETs. In a recent study that did not include pNETs, we showed that a four-chemokine signature (c-Score: CCL4, CCL5, CXCL9, CXCL10) predicts T cell-inflammation and response to immune checkpoint inhibitors across cancers. We have hypothesized that the c-Score will also identify pNET subgroups with T cell-inflammation. Methods: The c-Score and hallmarks of the cancer-immunity cycle are being evaluated in pNETs as a cohort study (n=100 cases) of the Marathon of Hope Cancer Centres Network whole genome and transcriptome tumor sequencing (WGTS) project. The cohort study will compare pNET profiles with those of other pancreatic and peri-ampullary tumors. Results: As of June 1, 2024, primary tumor data from 23 cases were available for analysis, including 16 pNETs, 2 pancreatic ductal adenocarcinomas (PDAC), 2 pancreatic adenosquamous carcinoma (PASC), 2 distal cholangiocarcinomas (dCCA), and 1 ampullary carcinoma (AC). Dimension reduction by principle component analysis (PCA) revealed clustering based on histological subtype. Interestingly, PCA using immune genes also demonstrate subgrouping that overlapped with the histological subtypes, suggesting that immunity of pancreatic/peri-ampullary neoplasms may be directed by histological types rather than the pancreas gland itself. These cancers showed a broad distribution of c-Score expression, with pNETs having the lowest c-Score. Tumors with high expression of the c-Score, including 2 pNETs, had increased expression of cancer-immunity cycle gene sets. DUX4, FAM186A, and IGFN1 were amongst the most common genes mutated in all tumors. Conclusions: Early data suggest that pNETs generally harbor low c-Score values and hallmarks of cancer-immunity, with the existence of a subgroup of pNETs displaying T cell-inflammation.
Citation Format: Joan Miguel Romero, James Vafiadis, Tess Aalto, Ruomeng Fang, Yifan Wang, George Zogopoulos. Evaluating T cell-inflammation in pancreatic neuroendocrine tumors [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr B032.
Abstract B033: MUC16 - Potential Target for Antibody-based Immunotherapy in Pancreatic Cancer
Authors: Brooks Cory L , Radhakrishnan Prakash 
Published: September 2024

Abstract
Pancreatic ductal adenocarcinoma (PDAC) is the most devastating and lethal cancer. PDAC patients with the current standard of care produce dismal overall survival benefits with high toxicities. Thus, there is an unmet clinical need to develop targeted therapeutics against PDAC. Approximately 50 to 70% of PDAC patients overexpress MUC16, which is associated with disease progression, metastasis, drug resistance, and poor patient survival. We demonstrated that MUC16 enhances PDAC tumor growth via binding and activating epidermal growth factor (EGF) receptor family members and its downstream target PI3K/AKT signaling pathways. Here, we show the efficacy of humanized anti-MUC16 antibody (huAR9.6) against MUC16-positive PDAC. Methods: We tested the in vitro efficacy of humanized anti-MUC16 antibody (huAR9.6) against MUC16+ PDAC cells, patient-derived xenografts (PDXs), and patient-derived organoids (PDOs). We evaluated the in vivo therapeutic efficacy of the huAR9.6 antibody in MUC16+ PDAC cells implanted in subcutaneous and orthotopic tumor models. Fucosylated and afucosylated huAR9.6 induced direct tumor-killing activity was evaluated by antibody-dependent cellular cytotoxicity (ADCC) and complement-dependent cytotoxicity (CDC) with MUC16 positive PDAC cells. Results: Treatment with huAR9.6 reduced the in vitro growth, migration, invasion, and clonogenicity of MUC16+ PDAC cells and patient-derived organoids (PDOs). HuAR9.6 blocked MUC16-mediated ErbB and AKT activation in PDAC, PDOs, and PDX-derived cells. HuAR9.6 treatment induces ADCC and CDC against MUC16+ cells. Removing fucose residue on the N-glycans of the huAR9.6-IgG substantially increased huAR9.6’s ADCC activity. More importantly, huAR9.6 treatment caused PDAC regression in subcutaneous and orthotopic tumor models. Conclusion: The results of this study validate the translational therapeutic potential of huAR9.6 against MUC16-positive PDACs (detected in ∼ 50-70% of patients).
Citation Format: Cory L Brooks, Prakash Radhakrishnan. MUC16 - Potential Target for Antibody-based Immunotherapy in Pancreatic Cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr B033.
Abstract B034: Myeloperoxidase limits tumor-associated neutrophils mediated immunosuppression in pancreatic cancer
Authors: Basnet Angisha , Boone Brain , Liu Tracy 
Published: September 2024

Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease with a survival rate under 12%. Treatments like immunotherapy and chemotherapy are ineffective in PDAC due to the immunosuppressive tumor microenvironment, in which tumor-associated neutrophils (TANs) are a major component. TAN immunosuppression has been shown to depend upon increased production of reactive oxygen species, with myeloperoxidase (MPO) being a major source of it. We investigated the contribution of MPO in mediating TAN immunosuppression using orthotopic PDAC models in wild-type and syngeneic MPO-deficient mice. We observed a significant delay in tumor growth and improved clinical scores when MPO was deficient. Additionally, treatment with the MPO-specific inhibitor Verdiperstat also delayed tumor growth. Flow cytometry analysis of the tumor microenvironment further confirmed a significant increase in CD8 T and NK cells, along with an increase in granzyme B level in both subsets, when MPO was deficient. Additionally, there was an increase in activation marker (CD25) and memory subset among both CD8 and CD4 T cells in MPO-deficient mice. Ex vivo studies with isolated TANs from tumor-bearing mice showed reduced CD8 T and NK cell suppression, and reactive oxygen species level in absence of MPO, supporting the contribution of MPO in TAN immunosuppression. These data suggest that loss of MPO in TANs has decreased immunosuppressive functions resulting in increased cytotoxic and activated T cells and NK cells with increased memory response within the tumor microenvironment. Our results demonstrate the contribution of MPO in regulating tumor progression, TAN immunosuppression, and immune cell infiltration using an orthotopic PDAC model.
Citation Format: Angisha Basnet, Brain Boone, Tracy Liu. Myeloperoxidase limits tumor-associated neutrophils mediated immunosuppression in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr B034.