Pancreatic Ductal Adenocarcinoma Group Research Articles

Comprehensive Assessment of the Intrinsic Pancreatic Microbiome.

We sought to comprehensively profile tissue and cyst fluid in patients with benign, precancerous, and cancerous conditions of the pancreas to characterize the intrinsic pancreatic microbiome. Small studies in pancreatic ductal adenocarcinoma (PDAC) and intraductal papillary mucinous neoplasm (IPMN) have suggested that intra-pancreatic microbial dysbiosis may drive malignant transformation. Pancreatic samples were collected at the time of resection from 109 patients. Samples included tumor tissue (control, n=20; IPMN, n=20; PDAC, n=19) and pancreatic cyst fluid (IPMN, n=30; SCA, n=10; MCN, n=10). Assessment of bacterial DNA by quantitative PCR and 16S ribosomal RNA gene sequencing was performed. Downstream analyses determined the relative abundances of individual taxa between groups and compared intergroup diversity. Whole-genome sequencing data from 140 patients with PDAC in the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) were analyzed to validate findings. Sequencing of pancreatic tissue yielded few microbial reads regardless of diagnosis, and analysis of pancreatic tissue showed no difference in the abundance and composition of bacterial taxa between normal pancreas, IPMN, or PDAC groups. Low-grade dysplasia (LGD) and high-grade dysplasia (HGD) IPMN were characterized by low bacterial abundances with no difference in tissue composition and a slight increase in Pseudomonas and Sediminibacterium in HGD cyst fluid. Decontamination analysis using the CPTAC database confirmed a low-biomass, low-diversity intrinsic pancreatic microbiome that did not differ by pathology. Our analysis of the pancreatic microbiome demonstrated very low intrinsic biomass that is relatively conserved across diverse neoplastic conditions and thus unlikely to drive malignant transformation.

Evaluations of the combined use of blood- and tissue-based protein biomarkers for pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with a low 5-year survival rate. Biomarkers may be of value for the early diagnosis of pancreatic cancer. This study assessed blood- and tumour tissue-based biomarkers associated with pancreatic cancer. We studied 61 patients who underwent pancreatic resection. Of these 61 patients, 46 patients had PDAC, and 15 patients had inflammatory tumours. Blood and tumour tissue levels of VEGF, hypoxia-inducible factor 1α (HIF-1α) and glucose transporter 1 (GLUT1) were measured. Blood concentrations of VEGF (p < 0.000001) and HIF-1α (p = 0.000002) were significantly higher in the PDAC group than in the inflammatory tumour group. Tumour tissue concentrations of VEGF (p < 0.000001), HIF-1α (p = 0.000005) and GLUT1 (0.000002) were also significantly higher in the PDAC group. Univariate analyses revealed that age, BMI, and blood levels of CA19-9, VEGF, and HIF-1α were potential predictors of PDAC. Potential predictors of PDAC in tumour tissue were VEGF, HIF-1α and GLUT1. Multivariate analyses found that VEGF was the most powerful independent predictor of PDAC in blood (OR = 1.016; 95% CI: 1.007-1.025; 0.001) and tumour tissue (OR = 1.02; 95% CI: 1.008-1.032, p = 0.001). The cut-off point for blood VEGF was 134.56 pg/ml, with a sensitivity of 97.8%, specificity of 86.7%, PPV of 95.7%, and NPV of 92.9%. The cut-off point for tissue tumour VEGF in PDAC was 208.59 pg/mg, with a sensitivity, specificity, PPV and NPV of 97.7%, 92.9%, 97.7%, and 92.9%, respectively. There are significant differences in blood-based biomarkers for differentiating between PDAC and inflammatory tumours of the pancreas. VEGF was an independent predictor of PDAC independent of its addition to the routinely used tumour marker CA19-9 antigen.

Abstract CT081: A phase 1/2 study investigating the safety and efficacy of autologous TAC T cells in subjects with unresectable, locally advanced, or metastatic claudin 18.2+ solid tumors

Abstract Background: CLDN18.2 is a tight junction protein found in differentiated gastric epithelial cells, which can become abnormally or ectopically expressed in a number of solid tumor indications. This exposure profile illustrates the potential of CLDN18.2 as a candidate for targeted therapy. To date, there are no approved therapeutic agents directed against this target. The T cell antigen coupler (TAC) technology is an approach to modifying T cells ex vivo, which allows recognition and cytotoxicity of tumor cells by co-opting the natural T cell receptor. TAC T cells demonstrate a safer profile than chimeric antigen receptor T cells. TAC01-CLDN18.2 is an autologous T-cell product comprising T cells expressing CLDN18.2 TAC. Methods: This is a first-in-human study (NCT05862324) to investigate the safety and preliminary anti-tumor activity of TAC01-CLDN18.2 in CLDN18.2+/HER2- solid tumors. Subjects will undergo leukapheresis and may receive bridging anticancer therapy, if deemed necessary by the Investigator, during cell manufacturing. Prior to TAC01-CLDN18.2 infusion, subjects will undergo low-intensity lymphodepletion chemotherapy. In Phase I dose escalation, TAC01-CLDN18.2 will be administered at increasing doses (Cohorts 1-3) in adult subjects after ≥2 lines of therapy using the classic 3+3 dose escalation study design. Subjects with pancreatic ductal adenocarcinoma (PDAC) may have been treated with 1 line of prior therapy. CLDN18.2 expression levels will be determined centrally using a clinical trial assay validated across relevant indications. Dose limiting toxicities will be assessed up to 28 days from TAC01-CLDN18.2 infusion. A second dose may be administered, according to preidentified clinical and safety criteria. In Phase II, dose expansion groups will further evaluate the efficacy, safety, and pharmacokinetics of the optimal TAC01-CLDN18.2 dose, with the option of redosing. Indications will include gastric and esophageal adenocarcinoma (group A), PDAC (group B) and ovarian and non-small cell lung cancer (group C) in subjects after ≥2 and &amp;lt;4 lines of prior therapy (subjects with PDAC may have been exposed to 1 therapy). Definitions of eligible CLDN18.2 expression levels will be based on retrospective analysis of data from Phase 1 in association with clinical efficacy. A Simon 2-stage design will be used to enroll ≤57 subjects in Group A and ≤22 subjects in Group C. Group B will be exploratory and will enroll ≤10 subjects with an opportunity of cohort enrichment based on clinical efficacy data. The first patient was enrolled in December and continues to participate in the trial. Citation Format: Ecaterina E. Dumbrava, Daniel Olson, Samuel Saibil, Davendra P. Sohal, Syma Iqbal, Alejandro Urgelles, Maria Apostolopoulou, Amy Mueller, Kara Moss, Deyaa Adib, Ben Schlecter. A phase 1/2 study investigating the safety and efficacy of autologous TAC T cells in subjects with unresectable, locally advanced, or metastatic claudin 18.2+ solid tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr CT081.

Tumor location, clinicopathological features, and perioperative and prognostic outcomes in patients who underwent pancreatic resection following neoadjuvant chemoradiotherapy for resectable pancreatic cancer: A retrospective study

Background/Objective: Preoperative treatment of resectable pancreatic ductal adenocarcinoma (PDAC) is gaining popularity worldwide. However, the characteristics of tumors located in the pancreatic head (Ph), or those in the body or tail (Pbt), after surgery following neoadjuvant chemoradiotherapy (NACRT) remain unclear. This study aimed to evaluate and compare the clinicopathological features, perioperative outcomes, and prognosis of patients with resectable PDAC who underwent NACRT followed by curative pancreatic resection, focusing on distinguishing between Ph and Pbt PDACs. MethodsWe included 107 patients with resectable PDAC who underwent curative resection following NACRT between 2009 and 2023. Clinicopathological features, perioperative and prognostic outcomes, recurrence patterns, and prognoses were compared between Ph and Pbt PDAC groups. ResultsTumors were found in the Ph and Pbt in 64 and 43 patients, respectively. Albumin levels and lymphocyte-to-monocyte ratios after NACRT were significantly lower in the Ph group than in the Pbt group. The Pbt group showed significantly higher rates of positive peritoneal lavage cytology and serosal, arterial, and portal vein invasion than the Ph group did. Overall and recurrence-free survival were similar between the two groups. The most common site of initial postoperative recurrence was the lung only in both groups; however, the rate of peritoneal dissemination only was significantly higher in the Pbt group than in the Ph group. ConclusionsThe prognoses based on tumor locations in the Ph and Pbt after surgery following NACRT are similar. Following the resection of resectable Pbt PDAC, the possibility of peritoneal dissemination recurrence should be considered.

Single-cell RNA sequencing highlights epithelial and microenvironmental heterogeneity in malignant progression of pancreatic ductal adenocarcinoma

Intraductal papillary mucinous neoplasms (IPMNs) of the pancreas are bona fide precursor lesions of pancreatic ductal adenocarcinoma (PDAC). Single-cell transcriptomics provides a unique perspective for dissecting the epithelial and microenvironmental heterogeneity that accompanies progression from benign IPMNs to invasive PDAC. Single-cell RNA sequencing was performed through droplet-based sequencing on 35 693 cells from three high-grade IPMNs and two IPMN-derived PDACs (all surgically resected). Analysis of single-cell transcriptomes revealed heterogeneous alterations within the epithelium and the tumor microenvironment during the progression of noninvasive dysplasia to invasive cancer. For epithelial cells, we identified acinar–ductal cells and isthmus–pit cells enriched in IPMN lesions and profiled three types of PDAC-unique ductal cells. Notably, a proinflammatory immune component was distinctly observed in IPMNs, comprising CD4+ T cells, CD8+ T cells, and B cells, whereas M2 macrophages were significantly accumulated in PDAC. Through the analysis of cellular communication, the osteopontin gene (SPP1)–CD44 pathway between macrophages and epithelial cells were particularly strengthened in the PDAC group. Further prognostic analysis revealed that SPP1 is a biomarker of IPMN carcinogenesis for surveillance. This study demonstrates the ability to perform high-resolution profiling of single cellular transcriptomes during the progression of high-grade IPMNs to cancer. Notably, single-cell analysis provides an unparalleled insight into both epithelial and microenvironmental heterogeneity associated with early cancer pathogenesis and provides practical markers for surveillance and targets for cancer interception.

Abstract C011: Detection of circulating tumor cells for the early detection of pancreatic cancer

Abstract Introduction The majority of pancreatic cancer patients are diagnosed with advanced, metastatic disease. Biomarkers to detect pancreatic cancer at earlier stages could drastically improve the poor survival rates. Circulating tumor cells (CTCs) are tumor cells that disseminate or are shed into the vascular system and have been detected in patients at pre-malignant stages. However, detecting these rare cells accurately and reliably remains a challenge. The AccuCyte-CyteFinder system uses density-based enrichment of blood nucleated cells followed by detection of CTCs using fluorescence staining, whole slide scanning and automated image analysis. This study aimed to examine whether CTCs could be used to identify patients with pancreatic lesions likely to malignant. Methods 50 patients undergoing an endoscopic ultrasound procedure for a pancreatic mass were recruited at the Royal Prince Alfred Hospital, Sydney, Australia or Chris O’Brien Lifehouse, Sydney, Australia. Peripheral blood was collected prior to the procedure and processed for CTCs on the AccuCyte-CyteFinder platform using a CK+ EpCAM+ CD45- staining panel. Results 28% (14/50) were found to have an intraductal papillary mucinous neoplasm (IPMN), 10% (5/50) pancreatic ductal adenocarcinoma (PDAC), 6% (3/50) pancreatic neuroendocrine tumor (PNET), 42% (21/50) non-neoplastic pancreatic cyst, and 14% (7/50) normal (no mass found). CTCs were highest in the PDAC group with an average of 5 cells/ml, followed by the IPMN (3 cells/ml), pancreatic cyst (2.5 cells/ml), and PNET (0 cells/ml). 86% (12/14) of IPMN patients had detectable CTCs. 2 IPMN patients with CTC counts of more than 5 cells/ml resulted in a resected sample having high-grade dysplasia while 3 IPMN patients with no/low CTC counts resulted in a resected sample having low-grade dysplasia. There was no correlation with the size of the pancreatic mass and CTC number. Conclusions CTCs could be detected in patients with pre-malignant pancreatic cancer and could determine malignancy. CTCs could potentially be used to detect pancreatic cancer early however, larger patient numbers and longer follow-up are required to validate these findings. Citation Format: Dannel Yeo, Vera Klemm, Payal Saxena, Althea Bastian, Heidi Strauss, Charbel Sandroussi, Sara Wahlroos, Peter Grimison, John E.J. Rasko. Detection of circulating tumor cells for the early detection of pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr C011.

Identifying Symptoms Prior to Pancreatic Ductal Adenocarcinoma Diagnosis in Real-World Care Settings: Natural Language Processing Approach

Background Pancreatic cancer is the third leading cause of cancer deaths in the United States. Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer, accounting for up to 90% of all cases. Patient-reported symptoms are often the triggers of cancer diagnosis and therefore, understanding the PDAC-associated symptoms and the timing of symptom onset could facilitate early detection of PDAC. Objective This paper aims to develop a natural language processing (NLP) algorithm to capture symptoms associated with PDAC from clinical notes within a large integrated health care system. Methods We used unstructured data within 2 years prior to PDAC diagnosis between 2010 and 2019 and among matched patients without PDAC to identify 17 PDAC-related symptoms. Related terms and phrases were first compiled from publicly available resources and then recursively reviewed and enriched with input from clinicians and chart review. A computerized NLP algorithm was iteratively developed and fine-trained via multiple rounds of chart review followed by adjudication. Finally, the developed algorithm was applied to the validation data set to assess performance and to the study implementation notes. Results A total of 408,147 and 709,789 notes were retrieved from 2611 patients with PDAC and 10,085 matched patients without PDAC, respectively. In descending order, the symptom distribution of the study implementation notes ranged from 4.98% for abdominal or epigastric pain to 0.05% for upper extremity deep vein thrombosis in the PDAC group, and from 1.75% for back pain to 0.01% for pale stool in the non-PDAC group. Validation of the NLP algorithm against adjudicated chart review results of 1000 notes showed that precision ranged from 98.9% (jaundice) to 84% (upper extremity deep vein thrombosis), recall ranged from 98.1% (weight loss) to 82.8% (epigastric bloating), and F1-scores ranged from 0.97 (jaundice) to 0.86 (depression). Conclusions The developed and validated NLP algorithm could be used for the early detection of PDAC.

Blood-based protein biomarkers and red blood cell aggregation in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is highly malignant with a low 5-year survival rate. Blood biomarkers may be of value for the noninvasive diagnosis of pancreatic cancer. This study assessed blood-based biomarkers and disturbances in red blood cell aggregation associated with pancreatic cancer. We studied 61 patients who underwent pancreatic resection. Of these 61 patients, 46 patients had PDAC, and 15 patients had inflammatory tumours. Serum VEGF, hypoxia-inducible factor (HIF-1α), elastin-derived peptides (EDPs), total sialic acid (TSA) and resistin levels were measured. Red blood cell aggregation was assessed by a laser-assisted optical rotational cell analyser. VEGF (p < 0.000001), HIF-1α (p = 0.000002), resistin (p = 0.000349), EDP (p = 0.000089) and TSA (p = 0.000013) levels were significantly higher in the PDAC group than in the inflammatory tumour group. The aggregation index (AI), syllectogram amplitude (AMP) and threshold shear rate (γthr) were significantly higher in the PDAC group, whereas the aggregation half-time (t1/2) was lower than in the inflammatory tumour group. Multivariate analyses revealed that VEGF, TSA and EDP levels were variables that predicted PDAC. VEGF levels were the most powerful predictor of PDAC independent of CA 19-9 levels. The cut-off points for VEGF, TSA and EDP levels were 134.56 pg/ml, 109.11 mg/dl and 36.4 ng/ml, respectively, with sensitivities of 97.8%, 87% and 69.6%, respectively, and specificities of 86.7%, 86.7% and 93.3%, respectively. This study indicated that there are significant differences in blood-based biomarkers for differentiating between PDAC and inflammatory tumours of the pancreas. We also confirmed that PDAC is associated with the excessive aggregation of RBCs.

Levels of retinol and retinoic acid in pancreatic cancer, type-2 diabetes and chronic pancreatitis.

Retinoids participate in multiple key processes in the human body e.g., vision, cell differentiation and embryonic development. There is growing evidence of the relationship between retinol, its active metabolite- all-trans retinoic acid (ATRA) - and several pancreatic disorders. Although low levels of ATRA in pancreatic ductal adenocarcinoma (PDAC) tissue have been reported, data on serum levels of ATRA in PDAC is still limited. The aim of our work was to determine serum concentrations of retinol and ATRA in patients with PDAC, type-2 diabetes mellitus (T2DM), chronic pancreatitis (CHP) and healthy controls. High performance liquid chromatography with UV detection (HPLC) was used to measure serum levels of retinol and ATRA in 246 patients with different stages of PDAC, T2DM, CHP and healthy controls. We found a significant decrease in the retinol concentration in PDAC (0.44+/-0.18 mg/L) compared to T2DM (0.65+/-0.19 mg/L, P<0.001), CHP (0.60+/-0.18 mg/L, P< 0.001) and healthy controls (0.61+/-0.15 mg/L, P<0.001), significant decrease of ATRA levels in PDAC (1.14+/-0.49 ug/L) compared to T2DM (1.37+/-0.56 ug/L, P<0.001) and healthy controls(1.43+/-0.55 ug/L, P<0.001). Differences between early stages (I+II) of PDAC and non-carcinoma groups were not significant. We describe correlations between retinol, prealbumin and transferrin, and correlation of ATRA and IGFBP-2. Significant decrease in retinol and ATRA levels in PDAC compared to T2DM, healthy individuals and/or CHP supports existing evidence of the role of retinoids in PDAC. However, neither ATRA nor retinol are suitable for detection of early PDAC. Correlation of ATRA levels and IGFBP-2 provides new information about a possible IGF and retinol relationship.

Abstract C028: A phase 1/2 study investigating the safety and efficacy of autologous TAC T cells in subjects with unresectable, locally advanced or metastatic claudin 18.2+ solid tumors

Abstract CLDN18.2 is a tight junction protein found in differentiated gastric epithelial cells which can become abnormally expressed in a number of solid malignancies. This exposure profile illustrates the potential of CLDN18.2 as a candidate for targeted therapy. To date, there are no approved therapeutic agents directed against this target but promising data exist using monoclonal antibodies and conventional chimeric antigen receptor (CAR) T therapy. The T cell antigen coupler (TAC) technology is an approach to modifying T cells ex vivo, which allows recognition and cytotoxicity of tumor cells by co-opting the natural T cell receptor. TAC T cells demonstrate a safer profile than CAR T treatments. TAC01-CLDN18.2 is an autologous T-cell product comprising T cells expressing CLDN18.2 TAC. This is a first-in-human study (NCT05862324) to investigate the safety and preliminary anti-tumor activity of TAC01-CLDN18.2 in CLDN18.2+/HER2- solid tumors. Subjects will undergo leukapheresis and may receive bridging anticancer therapy as appropriate during cell manufacturing. Prior to TAC01-CLDN18.2 infusion, subjects will undergo low-intensity lymphodepletion chemotherapy. In phase I dose escalation, TAC01-CLDN18.2 will be administered at increasing doses (Cohorts 1-3) in adult subjects after ≥2 lines of therapy using the classic 3+3 dose escalation study design. Subjects with pancreatic ductal adenocarcinoma (PDAC) may have been treated with 1 line of prior therapy. CLDN18.2 expression levels will be determined centrally using a clinical trial assay validated across relevant indications. Dose limiting toxicities will be assessed up to 28 days from TAC01-CLDN18.2 infusion. A second dose may be administered, according to preidentified clinical and safety criteria. In Phase II, dose expansion groups will further evaluate the efficacy, safety, and pharmacokinetics of the optimal TAC01-CLDN18.2 dose, with the option of redosing. Indications will include gastric and esophageal adenocarcinoma (group A), PDAC (group B) and ovarian and non-small cell lung cancer (group C) in subjects after ≥2 and &amp;lt;4 lines of prior therapy (subjects with PDAC may have been exposed to 1 therapy). Definitions of eligible CLDN18.2 expression levels will be based on retrospective analysis of data from Phase 1 in association with clinical efficacy. A Simon 2-stage design will be used to enroll ≤57 subjects in Group A and ≤22 subjects in Group C. Group B will be exploratory and will enroll ≤10 subjects with an opportunity of cohort enrichment based on clinical efficacy data. Citation Format: Benjamin Schlechter, Ecaterina Dumbrava, Samuel Saibil, Martin Gutierez, Syma Iqbal, Davendra Sohal, Maria Apostolopoulou, Kara Moss, Deyaa Adib, Daniel Olson. A phase 1/2 study investigating the safety and efficacy of autologous TAC T cells in subjects with unresectable, locally advanced or metastatic claudin 18.2+ solid tumors [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C028.

Bile Microbiome Signatures Associated with Pancreatic Ductal Adenocarcinoma Compared to Benign Disease: A UK Pilot Study

Pancreatic ductal adenocarcinoma (PDAC) has a very poor survival. The intra-tumoural microbiome can influence pancreatic tumourigenesis and chemoresistance and, therefore, patient survival. The role played by bile microbiota in PDAC is unknown. We aimed to define bile microbiome signatures that can effectively distinguish malignant from benign tumours in patients presenting with obstructive jaundice caused by benign and malignant pancreaticobiliary disease. Prospective bile samples were obtained from 31 patients who underwent either Endoscopic Retrograde Cholangiopancreatography (ERCP) or Percutaneous Transhepatic Cholangiogram (PTC). Variable regions (V3–V4) of the 16S rRNA genes of microorganisms present in the samples were amplified by Polymerase Chain Reaction (PCR) and sequenced. The cohort consisted of 12 PDAC, 10 choledocholithiasis, seven gallstone pancreatitis and two primary sclerosing cholangitis patients. Using the 16S rRNA method, we identified a total of 135 genera from 29 individuals (12 PDAC and 17 benign). The bile microbial beta diversity significantly differed between patients with PDAC vs. benign disease (Permanova p = 0.0173). The separation of PDAC from benign samples is clearly seen through unsupervised clustering of Aitchison distance. We found three genera to be of significantly lower abundance among PDAC samples vs. benign, adjusting for false discovery rate (FDR). These were Escherichia (FDR = 0.002) and two unclassified genera, one from Proteobacteria (FDR = 0.002) and one from Enterobacteriaceae (FDR = 0.011). In the same samples, the genus Streptococcus (FDR = 0.033) was found to be of increased abundance in the PDAC group. We show that patients with obstructive jaundice caused by PDAC have an altered microbiome composition in the bile compared to those with benign disease. These bile-based microbes could be developed into potential diagnostic and prognostic biomarkers for PDAC and warrant further investigation.

Mechanisms Underlying the Therapeutic Effects of Banzhilian- Baihuasheshecao for Treating Pancreatic Ductal Adenocarcinoma Based on Bioinformatics Strategy

Introduction:: Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related deaths. Banzhilian (BZL) and Baihuasheshecao (BHSSC) are classical Chinese herbs used in tumor therapy. However, the underlying mechanisms of BZL-BHSSC in treating PDAC have not been identified. Combining network pharmacology with single-cell RNA sequencing (scRNAseq), this study systematically explored the potential mechanisms of BZL-BHSSC in the treatment of PDAC. Methods:: The bioactive ingredients of BZL-BHSSC were screened from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database, while the PDAC-related datasets were obtained from the Gene Expression Omnibus (GEO) database. Based on the dataset GSE62452, we adopted differential expression analysis and weighted gene co-expression network analysis (WGCNA) to screen the signature genes of PDAC. To reveal the cell types of the pharmacological targets of BZL-BHSSC against PDAC, we performed scRNA-seq analysis and principal component analysis (PCA) on the dataset GSE111672. Molecular docking and immunohistochemical staining were used to validate our initial results. Results:: We obtained 29 bioactive ingredients from BZL-BHSSC and screened 210 signature genes of PDAC. Using network pharmacology, we identified 7 key therapeutic targets CDK1, MYC, CCNB1, TOP2A, CLDN4, NUF2, and MET, revealing that baicalein, quercetin, and luteolin are core components for the efficacy of BZL-BHSSC. The main signaling pathways involved in therapy were the PI3K-AKT signaling pathway and the p53 signaling pathway. The molecular docking results verified the strong binding activity (binding energy &gt; -7 kJ/mol) between active ingredients and targets. The scRNA-seq results informed that cells from 3 PDAC samples could aggregate into 19 clusters and 3 cell types. The target genes were almost concentrated on the immune cells. Immunoinfiltration analysis suggested that the expression of Macrophages M0 and Dendritic cells activated was significantly upregulated in the PDAC group (p&lt;0.001), while the opposite was true for B cells naïve and T cells CD8 expression (p&lt;0.05). Conclusion:: We concluded that BZL-BHSSC can improve the overall survival prognosis of PDAC patients by interfering with the signature genes of PDAC through direct and indirect pathways and improving immunity. Our study provides a basis for subsequent studies. other: No

Increased Levels of miR-15b-5p and miR-20b-5p in Pancreatic Ductal Adenocarcinoma with Hepatic Metastases.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive and lethal forms of cancer. The symptoms appear in advanced stages, and diagnostic and prognostic tests for the early detection of PDAC and disease evolution are not available. The dysregulation of microRNAs (miRNAs) has been associated with cancer development and progression, and some miRNAs have been reported to promote specific metastasis. In this study we aimed to identify the miRNAs dysregulated in PDAC tumoral tissues and a subset of miRNAs associated with tumoral characteristics, mainly metastasis presence and site. For this, the expression of 84 miRNAs was evaluated by qPCR in 30 tumoral tissues and 16 samples of non-tumoral pancreatic tissues. The comparison revealed 32 dysregulated miRNAs (19 upregulated and 13 downregulated) in the PDAC group. Reactome pathway over-representation analysis revealed that these miRNAs are involved in several biological pathways, including "ESR-mediated signaling", "PIP3 activates AKT signaling", and "Regulation of PTEN", among others. Moreover, our study identified an upregulation of miR-15b-5p and miR-20b-5p in the tumoral tissues of patients with hepatic metastasis, outlining these miRNAs as potential markers for hepatic metastasis. No significant difference in miRNA expression was observed in relation to anatomic location, lymphovascular invasion, lung metastasis, and the presence of diabetes.

Development and evaluation of 5FUCore, OxaliCore, and IrinoCore transcriptomic signatures for predicting response to individual drugs in the FOLFIRINOX regimen: Towards toxicity reduction and personalized chemotherapy in PDAC.

4141 Background: Recently, we have successfully developed a transcriptomic signature that predicts the response to gemcitabine treatment in patients with localized pancreatic ductal adenocarcinoma (PDAC) (PMID: 36178036) and also in those with metastatic PDAC (PMID: 36496056). This was achieved by using an approach that combined several preclinical PDAC models. The FOLFIRINOX regimen (FFX) consisting of 5-Fluorouracil (5FU), Oxaliplatin, and Irinotecan is the more effective treatment option, but the high toxicity limits its use. In this study, we developed and analyzed three different transcriptomic signatures for the drugs composing FFX separately and evaluated their clinical utility. Methods: We combined the transcriptomic profile of 14 patients derived xenograft (PDX) and 31 patients derived cell cultures (PDC) with their corresponding chemotherapy response profiles. We developed predictive signatures for 5FU ( 5FUCore), Oxaliplatin ( OxaliCore), and Irinotecan ( IrinoCore), as previously reported for the GemCore signature (PMID: 36178036). They were then validated in three independent cohorts of patients: COMPASS (n = 94), Linehan (n = 28), and a new cohort of 87 advanced PDAC patients. Results: Three ICA components that reflect the chemotherapy response profiles for 5FU, oxaliplatin, and irinotecan were evaluated in the COMPASS cohort to determine their ability to identify sensitive patients in the FFX arm. Patients predicted as sensitive to each drug separately had a median overall survival (OS) of 77.6 (95% CI, 67.9-NR, P =0.02), 78.7 (95% CI, 57.6-NR, P= 0.002), and 70.4 (95% CI, 57.6-NR, P= 0.02) months for 5FU, oxaliplatin, and irinotecan, respectively. Notably, the model that integrated the group of sensitive patients to two or three drugs had the highest median OS in the COMPASS and Linehan cohorts with a HR of 0.25 (95% CI, 0.11-0.57; P &lt; 0.001) and 0.11 (95% CI, 0.02-0.70; P= 0.019), respectively. Importantly, they also show a better objective response than the other groups ( P= 0.009). In the advanced PDAC group, the signatures showed a strong ability to discriminate sensitive patients in the FFX arm with a median OS of 20.1 (95% CI, 2.3-NR, P= 0.006), 13.64 (95% CI, 9.57-NR, P= 0.003), and 15.87 (95% CI, 2.9-NR, P= 0.002) months for 5FU, oxaliplatin, and irinotecan, respectively. Finally, we evaluated the best combination of two drugs in the pooled group. Patients sensitive to both 5FU and oxaliplatin showed the best response with 60% of patients achieving partial response. Conclusions: The signatures developed provide a tool for predicting responsiveness to the individual therapeutic drugs of the FFX regimen. They may ultimately lead to the development of a personalized strategy for combined chemotherapy regimens, increasing efficacy and reducing toxicity in PDAC.

Fully automated magnetic resonance imaging-based radiomics analysis for differentiating pancreatic adenosquamous carcinoma from pancreatic ductal adenocarcinoma.

To develop and validate an automated magnetic resonance imaging (MRI)-based model to preoperatively differentiate pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC). This retrospective study included patients with surgically resected, histopathologically confirmed PASC or PDAC who underwent MRI between January 2011 and December 2020. According to time of treatment, they were divided into training and validation sets. Automated deep-learning-based artificial intelligence was used for pancreatic tumor segmentation. Linear discriminant analysis was performed with conventional MRI and radiomic features to develop clinical, radiomics, and mixed models in the training set. The models' performances were determined from their discrimination and clinical utility. Kaplan-Meier and log-rank tests were used for survival analysis. Overall, 389 and 123 patients with PDAC (age, 61.37 ± 9.47years; 251 men) and PASC (age, 61.99 ± 9.82years; 78 men) were included, respectively; they were split into the training (n = 358) and validation (n = 154) sets. The mixed model showed good performance in the training and validation sets (area under the curve: 0.94 and 0.96, respectively). The sensitivity, specificity, and accuracy were 76.74%, 93.38%, and 89.39% for the training set, respectively, and 67.57%, 97.44%, and 90.26% for the validation set, respectively. The mixed model outperformed the clinical (p = 0.001) and radiomics (p = 0.04) models in the validation set. Log-rank test revealed significantly longer survival in the predicted PDAC group than in the predicted PASC group (p = 0.003), according to the mixed model. Our mixed model, which combined MRI and radiomic features, can be used to differentiate PASC from PDAC.

Pancreatic Mass Characterization Using IVIM-DKI MRI and Machine Learning-Based Multi-Parametric Texture Analysis.

Non-invasive characterization of pancreatic masses aids in the management of pancreatic lesions. Intravoxel incoherent motion-diffusion kurtosis imaging (IVIM-DKI) and machine learning-based texture analysis was used to differentiate pancreatic masses such as pancreatic ductal adenocarcinoma (PDAC), pancreatic neuroendocrine tumor (pNET), solid pseudopapillary epithelial neoplasm (SPEN), and mass-forming chronic pancreatitis (MFCP). A total of forty-eight biopsy-proven patients with pancreatic masses were recruited and classified into pNET (n = 13), MFCP (n = 6), SPEN (n = 4), and PDAC (n = 25) groups. All patients were scanned for IVIM-DKI sequences acquired with 14 b-values (0 to 2500 s/mm2) on a 1.5T MRI. An IVIM-DKI model with a 3D total variation (TV) penalty function was implemented to estimate the precise IVIM-DKI parametric maps. Texture analysis (TA) of the apparent diffusion coefficient (ADC) and IVIM-DKI parametric map was performed and reduced using the chi-square test. These features were fed to an artificial neural network (ANN) for characterization of pancreatic mass subtypes and validated by 5-fold cross-validation. Receiver operator characteristics (ROC) analyses were used to compute the area under curve (AUC). Perfusion fraction (f) was significantly higher (p &lt; 0.05) in pNET than PDAC. The f showed better diagnostic performance for PDAC vs. MFCP with AUC:0.77. Both pseudo-diffusion coefficient (D*) and f for PDAC vs. pNET showed an AUC of 0.73. ADC and diffusion coefficient (D) showed good diagnostic performance for pNET vs. MFCP with AUC: 0.79 and 0.76, respectively. In the TA of PDAC vs. non-PDAC, f and combined IVIM-DKI parameters showed high accuracy ≥ 84.3% and AUC ≥ 0.84. Mean f and combined IVIM-DKI parameters estimated that the IVIM-DKI model with TV texture features has the potential to be helpful in characterizing pancreatic masses.

Clinical Features and Computed Tomography Radiomics-Based Model for Predicting Pancreatic Ductal Adenocarcinoma and Focal Mass-Forming Pancreatitis.

Objective: To establish a predictive model distinguishing focal mass-forming pancreatitis (FMFP) from pancreatic ductal adenocarcinoma (PDAC) based on computed tomography (CT) radiomics and clinical data. Methods: A total of 78 FMFP patients (FMFP group) and 120 PDAC patients (PDAC group) who were admitted to Xiangyang No.1 People's Hospital and Xiangyang Central Hospital from February 2012 to May 2021 and were pathologically diagnosed were included in this study, and were input to set up the training set and test set at a ratio of 7:3. The 3Dslicer software was used to extract the radiomic features and radiomic scores (Radscores) of the 2 groups, and the clinical data (age, gender, etc), CT imaging features (lesion location, size, enhancement degree, vascular wrapping, etc) and CT radiomic features of the 2 groups were compared. Logistic regression was used to screen the independent risk factors of the 2 groups, and multiple prediction models (clinical imaging model, radiomics model, and combined model) were established. Then the receiver operating characteristic (ROC) analysis and decision curve analysis (DCA) were conducted to compare the prediction performance and net benefit of the models. Results: The multivariate logistic regression results indicated that dilation of the main pancreatic duct, vascular wrapping, Radscore1 and Radscore2 were independent influencing factors for distinguishing FMFP from PDAC. In the training set, the combined model showed the best predictive performance (area under the ROC curve [AUC] 0.857, 95% CI [0.787-0.910]), significantly higher than the clinical imaging model (AUC 0.650, 95% CI [0.565-0.729]) and the radiomics model (AUC 0.812, 95% CI [0.759-0.890]). DCA confirmed that the combined model had the highest net benefit. These results were further validated by the test set. Conclusion: The combined model based on clinical-CT radiomics data can effectively identify FMFP and PDAC, providing a reference for clinical decision-making.

Influences of calorie restriction and lipopolysaccharide therapy on inflammation, cytokine response, and cell proliferation in pancreatic adenocarcinoma mouse model.

The study aimed to investigate the effects of lipopolysaccharide (LPS) alone and in combination with calorie restriction (CR) on the pancreatic tissues in C57BL/6 mice modeled with pancreatic ductal adenocarcinoma (PDAC). Forty male C57BL/6 mice (10-13 weeks old) were divided into five groups; LPS,LPS + CR, PDAC, PDAC + LPS, and PDAC + LPS + CR. Nuclear factor kappa B (NF-κβ), interleukin-6 (IL-6), and c-Jun N-terminal kinases (JNK) mRNA expression levels were measured in pancreatic tissues. NF-κβ, IL-6, JNK, and proliferating cell nuclear antigen(PCNA) peptide levels were determined by immunohistochemistry. Oxidative stress markers and antioxidant enzyme activities were determined spectrophotometrically. TH1/TH2 cytokine measurements were determined by a flow cytometer. It was detected that the number of PCNA immune + cells in the PDAC + LPS + CR group was significantly lower than in the PDAC and PDAC + LPS groups (p < 0.01, p < 0.05 respectively). PDAC + LPS + CR group's plasma interferon-gamma(IFN-γ), IL-6, IL-2, tumor necrosis factor-alpha, IL-3, and IL-4 levels were found to be significantly lower than the PDAC group (p < 0.01, p < 0.001, p < 0.01, p < 0.05, p < 0.01, and p < 0.05 respectively). According to our findings, the combination of low-dose LPS and 40% CR was found to be more effective in PDAC model mice.

Focal pancreatic parenchyma atrophy is a harbinger of pancreatic cancer and a clue to the intraductal spreading subtype

Background/ObjectivesRadiological evidence of focal pancreatic parenchymal atrophy (FPPA) may presage early pancreatic ductal adenocarcinoma (PDAC) development. We aimed to clarify the incidence of FPPA and the clinicopathological features of PDAC with FPPA before diagnosis. MethodsData on endoscopic ultrasound-guided fine-needle biopsies and surgical samples from 170 patients with pancreatic cancer histologically diagnosed between 2014 and 2019 were extracted from the pathology database of Komagome Hospital and Juntendo University hospital and retrospectively evaluated together with 51 patients without PDAC. ResultsFPPA was identified in 47/170 (28%) patients before PDAC diagnosis and in 2/51 (4%) patients in the control group (P < 0.01). The median duration from FPPA detection to diagnosis was 35 (interquartile range [IQR]:16–63) months. In 24/47 (51%) patients with FPPA, the atrophic area resolved.The lesion was in the head and body/tail in 7/40 and 67/56 of the patients with (n = 47) and without FPPA (n = 123), respectively (P < 0.001). Histopathologically confirmed non-invasive lesions in the main pancreatic duct and a positive surgical margin in the resected specimens occurred in 53% vs. 21% (P = 0.078) and 29% vs. 3% (P = 0.001) of the groups, respectively. The PDAC patients with FPPA accompanied by a malignant pancreatic resection margin had high-grade pancreatic intraepithelial neoplasia. ConclusionsFPPA occurred in 28% of the PDAC group at 35 months prediagnosis. The FPPA area resolved before PDAC onset. Benchmarking previous images of the pancreas with the focus on FPPA may enable prediction of PDAC. PDAC with FPPA involves widespread high-grade pancreatic intraepithelial neoplasia requiring a wide surgical margin for surgical excision.

Comparison of Multiple Radiomics Models for Identifying Histological Grade of Pancreatic Ductal Adenocarcinoma Preoperatively Based on Multiphasic Contrast-Enhanced Computed Tomography: A Two-Center Study in Southwest China.

Background: We designed and validated the value of multiple radiomics models for diagnosing histological grade of pancreatic ductal adenocarcinoma (PDAC), holding a promise of assisting in precision medicine and providing clinical therapeutic strategies. Methods: 198 PDAC patients receiving surgical resection and pathological confirmation were enrolled and classified as 117 low-grade PDAC and 81 high-grade PDAC group. An external validation group was used to assess models’ performance. Available radiomics features were selected using GBDT algorithm on the basis of the arterial and venous phases, respectively. Five different machine learning models were built including k-nearest neighbour, logistic regression, naive bayes model, support vector machine, and random forest using ten times tenfold cross-validation. Multivariable logistic regression analysis was applied to establish clinical model and combined model. The models’ performance was assessed according to its predictive performance, calibration curves, and decision curves. A nomogram was established for visualization. Survival analysis was conducted for stratifying the overall survival prior to treatment. Results: In the training group, the RF model demonstrated the optimal predictive ability and robustness with an AUC of 0.943; the SVM model achieved the secondary performance, followed by Bayes model. In the external validation group, these three models (Bayes, RF, SVM) also achieved the top three predictive ability. A clinical model was built by selected clinical features with an AUC of 0.728, and combined model was established by an RF model and a clinical model with an AUC of 0.961. The log-rank test revealed that the low-grade group survived longer than the high-grade group. Conclusions: The multiphasic CECT radiomics models offered an accurate and noninvasive perspective to differentiate histological grade in PDAC and advantages of machine learning models including RF, SVM and Bayes were more remarkable.