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Project description:ObjectivePancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a 5-year survival of less than 5%. Transcriptomic analysis has identified two clinically relevant molecular subtypes of PDAC: classical and basal-like. The classical subtype is characterised by a more favourable prognosis and better response to chemotherapy than the basal-like subtype. The classical subtype also expresses higher levels of lineage specifiers that regulate endodermal differentiation, including the nuclear receptor hepatocyte nuclear factor 4 α (HNF4α). The objective of this study is to evaluate the role of HNF4α, SIX4 and SIX1 in regulating the growth and molecular subtype of PDAC.DesignWe manipulate the expression of HNF4α, SIX4 and SIX1 in multiple in vitro and in vivo PDAC models. We determine the consequences of manipulating these genes on PDAC growth, differentiation and molecular subtype using functional assays, gene expression analysis and cross-species comparisons with human datasets.ResultsWe show that HNF4α restrains tumour growth and drives tumour cells toward an epithelial identity. Gene expression analysis of murine models and human tumours shows that HNF4α activates expression of genes associated with the classical subtype. HNF4α also directly represses SIX4 and SIX1, two mesodermal/neuronal lineage specifiers expressed in the basal-like subtype. Finally, SIX4 and SIX1 drive proliferation and regulate differentiation in HNF4α-negative PDAC.ConclusionOur data show that HNF4α regulates the growth and molecular subtype of PDAC by multiple mechanisms, including activation of the classical gene expression programme and repression of SIX4 and SIX1, which may represent novel dependencies of the basal-like subtype.
| S-EPMC7945295 | biostudies-literature
Project description:Reciprocal Regulation of Pancreatic Ductal Adenocarcinoma Growth and Molecular Subtype by HNF4a and SIX1/4
| PRJNA575905 | ENA
Project description:Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer-related death and is one of the most difficult-to-treat cancers. Surgical resection and adjuvant therapy have limited effects on the overall survival of PDAC patients. PDAC exhibits an immunosuppressive microenvironment, the immune response predicts survival, and activation of immune system has the potential to produce an efficacious PDAC therapy. However, chimeric antigen receptor T (CAR-T) cell immunotherapy and immune checkpoint blockade (ICB), which have produced unprecedented clinical benefits in a variety of different cancers, produce promising results in only some highly selected patients with PDAC. This lack of efficacy may be because existing immunotherapies mainly target the interactions between cancer cells and immune cells. However, PDAC is characterized by an abundant tumor stroma that includes a heterogeneous mixture of immune cells, fibroblasts, endothelial cells, neurons and some molecular events. Immune cells engage in extensive and dynamic crosstalk with stromal components in the tumor tissue in addition to tumor cells, which subsequently impacts tumor suppression or promotion to a large extent. Therefore, exploration of the interactions between the stroma and immune cells may offer new therapeutic opportunities for PDAC. In this review, we discuss how infiltrating immune cells influence PDAC development and explore the contributions of complex components to the immune landscape of tumor tissue. The roles of stromal constituents in immune modulation are emphasized. We also predict potential therapeutic strategies to target signals in the immune network in the abundant stromal microenvironment of PDAC.
| S-EPMC6909567 | biostudies-literature
Project description:To bridge the translational gap between recent discoveries of distinct molecular phenotypes of pancreatic cancer and tangible improvements in patient outcome, there is an urgent need to develop strategies and tools informing and improving the clinical decision process. Radiomics and machine learning approaches can offer non-invasive whole tumor analytics for clinical imaging data-based classification. The retrospective study assessed baseline computed tomography (CT) from 207 patients with proven pancreatic ductal adenocarcinoma (PDAC). Following expert level manual annotation, Pyradiomics was used for the extraction of 1474 radiomic features. The molecular tumor subtype was defined by immunohistochemical staining for KRT81 and HNF1a as quasi-mesenchymal (QM) vs. non-quasi-mesenchymal (non-QM). A Random Forest machine learning algorithm was developed to predict the molecular subtype from the radiomic features. The algorithm was then applied to an independent cohort of histopathologically unclassifiable tumors with distinct clinical outcomes. The classification algorithm achieved a sensitivity, specificity and ROC-AUC (area under the receiver operating characteristic curve) of 0.84 ± 0.05, 0.92 ± 0.01 and 0.93 ± 0.01, respectively. The median overall survival for predicted QM and non-QM tumors was 16.1 and 20.9 months, respectively, log-rank-test p = 0.02, harzard ratio (HR) 1.59. The application of the algorithm to histopathologically unclassifiable tumors revealed two groups with significantly different survival (8.9 and 39.8 months, log-rank-test p < 0.001, HR 4.33). The machine learning-based analysis of preoperative (CT) imaging allows the prediction of molecular PDAC subtypes highly relevant for patient survival, allowing advanced pre-operative patient stratification for precision medicine applications.
| S-EPMC7141256 | biostudies-literature
Project description:The aberrant expression of squamous lineage markers in pancreatic ductal adenocarcinoma (PDA) has been correlated with poor clinical outcomes. However, the functional role of this putative transdifferentiation event in PDA pathogenesis remains unclear. Here, we show that expression of the transcription factor TP63 (?Np63) is sufficient to install and sustain the enhancer landscape and transcriptional signature of the squamous lineage in human PDA cells. We also demonstrate that TP63-driven enhancer reprogramming promotes aggressive tumor phenotypes, including enhanced cell motility and invasion, and an accelerated growth of primary PDA tumors and metastases in vivo. This process ultimately leads to a powerful addiction of squamous PDA cells to continuous TP63 expression. Our study demonstrates the functional significance of squamous transdifferentiation in PDA and reveals TP63-based reprogramming as an experimental tool for investigating mechanisms and vulnerabilities linked to this aberrant cell fate transition.
| S-EPMC6296757 | biostudies-literature
Project description:ObjectivesIQ motif containing GTPase-activating protein 1 (IQGAP1) acts as a scaffold for aberrant mitogen-activated protein kinase (MAPK) signaling driven by KRAS mutations in pancreatic ductal adenocarcinoma (PDAC). We determined the role of IQGAP1 in clonogenic growth and metastasis in PDAC.MethodsWe inhibited IQGAP1 expression using shRNA and assessed clonogenic growth, cell migration, and MAPK signaling in vitro and tumor initiation and metastasis in vivo. The efficacy of a peptide mimicking the IQGAP1 WW domain that binds and inhibits ERK1/2 was determined in vitro and in vivo.ResultsIQGAP1 loss inhibited clonogenic growth and migration of KRAS-dependent PDAC cells by disrupting MAPK signaling. In mice, IQGAP1 knockdown decreased tumor-initiating cell frequency and metastasis. WW peptide treatment inhibited clonogenic growth and in vivo tumor growth.ConclusionsPancreatic ductal adenocarcinoma clonogenic growth, metastasis, and tumor initiation are dependent on MAPK signaling via IQGAP1. Treatment with a WW peptide disrupts IQGAP1 function and represents a novel targeting strategy for PDAC.
| S-EPMC6293988 | biostudies-literature
Project description:Pancreatic ductal adenocarcinoma (PDAC) is a malignancy characterized by a poor prognosis and high mortality rate. Genetic mutations and altered molecular pathways serve as targets in precise therapy. Using next-generation sequencing (NGS), these aberrant alterations can be identified and used to develop strategies that will selectively kill cancerous cells in patients with PDAC. The realization of targeted therapies in patients with PDAC may be summarized by three approaches. First, because oncogenes play a pivotal role in tumorigenesis, inhibition of dysregulated oncogenes is a promising method (Table 3). Numerous researchers are developing strategies to target oncogenes, such as KRAS, NRG1, and NTRK and related molecules, although most of the results are unsatisfactory. Accordingly, emerging strategies are being developed to target these oncogenes, including simultaneously inhibiting multiple molecules or pathways, modification of mutant residues by small molecules, and RNA interference. Second, researchers have attempted to reactivate inactivated tumour suppressors or modulate related molecules. TP53, CDKN2A and SMAD4 are three major tumour suppressors involved in PDAC. Advances have been achieved in clinical and preclinical trials of therapies targeting these three genes, and further investigations are warranted. The TGF-β-SMAD4 signalling pathway plays a dual role in PDAC tumorigenesis and participates in mediating tumour-stroma crosstalk and modulating the tumour microenvironment (TME); thus, molecular subtyping of pancreatic cancer according to the SMAD4 mutation status may be a promising precision oncology technique. Finally, genes such as KDM6A and BRCA have vital roles in maintaining the structural stability and physiological functions of normal chromosomes and are deficient in some patients with PDAC, thus serving as potential targets for correcting these deficiencies and precisely killing these aberrant tumour cells. Recent clinical trials, such as the POLO (Pancreas Cancer Olaparib Ongoing) trial, have reported encouraging outcomes. In addition to genetic event-guided treatment, immunotherapies such as chimeric antigen receptor T cells (CAR-T), antibody-drug conjugates, and immune checkpoint inhibitors also exhibit the potential to target tumours precisely, although the clinical value of immunotherapies as treatments for PDAC is still limited. In this review, we focus on recent preclinical and clinical advances in therapies targeting aberrant genes and pathways and predict the future trend of precision oncology for PDAC.
| S-EPMC7532113 | biostudies-literature
Project description:The incidence and death rate of pancreatic ductal adenocarcinoma (PDAC) have increased in recent years, therefore the identification of novel targets for treatment is extremely important. Interactions between cancer and stromal cells are critically involved in tumour formation and development of metastasis. Here we report that PDAC cells secrete BAG3, which binds and activates macrophages, inducing their activation and the secretion of PDAC supporting factors. We also identify IFITM-2 as a BAG3 receptor and show that it signals through PI3K and the p38 MAPK pathways. Finally, we show that the use of an anti-BAG3 antibody results in reduced tumour growth and prevents metastasis formation in three different mouse models. In conclusion, we identify a paracrine loop involved in PDAC growth and metastatic spreading, and show that an anti-BAG3 antibody has therapeutic potential.
| S-EPMC4659838 | biostudies-other
Project description:BACKGROUND We aimed to identify pivotal genes and pathways involved in pancreatic ductal adenocarcinoma (PDAC), and explore possible molecular markers for the early diagnosis of the disease. MATERIAL AND METHODS The array data of GSE74629, including 34 PDAC samples and 16 healthy samples, was downloaded from GEO (Gene Expression Omnibus) database. Then, the DEGs (differentially expressed genes) in PDAC samples were compared with healthy samples using limma (linear models for microarray). Gene functional interaction networks were analyzed with Cytoscape and ReactomeFIViz. PPI networks were constructed with Cytoscape software. In addition, PPI (protein-protein interaction) network clustering modules were analyzed with ClusterONE, and the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analyses for modules were performed. RESULTS A total of 630 upregulated and 1,002 downregulated DEGs were identified in PDAC samples compared with healthy samples. Some ribosomal protein genes with higher average correlation in module 0 were enriched in the ribosome pathway. NUP107 (nucleoporin 107 kDa) and NUP160 (nucleoporin 160 kDa) were enriched in module 3. HNRNPU (heterogeneous nuclear ribonucleoprotein U) with higher average correlation in module 8 was enriched in the spliceosome pathway. The ribosome pathway and the spliceosome pathway were significantly enriched in cluster 1 and cluster 2, respectively. CONCLUSIONS Ribosomal protein genes Nup170, Nup160, and HNRNPU, and the ribosome pathway as well as the spliceosome pathway may play important roles in PDAC progression. In addition, ribosomal protein genes Nup170, Nup160, and HNRNPU may be used as possible molecular markers for the early diagnosis of the disease.
| S-EPMC5928849 | biostudies-literature
Project description:Background:Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest solid tumors. The rapid progression of PDAC results in an advanced stage of patients when diagnosed. However, the dynamic molecular mechanism underlying PDAC progression remains far from clear. Methods:The microarray GSE62165 containing PDAC staging samples was obtained from Gene Expression Omnibus and the differentially expressed genes (DEGs) between normal tissue and PDAC of different stages were profiled using R software, respectively. The software program Short Time-series Expression Miner was applied to cluster, compare, and visualize gene expression differences between PDAC stages. Then, function annotation and pathway enrichment of DEGs were conducted by Database for Annotation Visualization and Integrated Discovery. Further, the Cytoscape plugin DyNetViewer was applied to construct the dynamic protein-protein interaction networks and to analyze different topological variation of nodes and clusters over time. The phosphosite markers of stage-specific protein kinases were predicted by PhosphoSitePlus database. Moreover, survival analysis of candidate genes and pathways was performed by Kaplan-Meier plotter. Finally, candidate genes were validated by immunohistochemistry in PDAC tissues. Results:Compared with normal tissues, the total DEGs number for each PDAC stage were 994 (stage I), 967 (stage IIa), 965 (stage IIb), 1027 (stage III), 925 (stage IV), respectively. The stage-course gene expression analysis showed that 30 distinct expressional models were clustered. Kyoto Encyclopedia of Genes and Genomes analysis indicated that the up-regulated DEGs were commonly enriched in five fundamental pathways throughout five stages, including pathways in cancer, small cell lung cancer, ECM-receptor interaction, amoebiasis, focal adhesion. Except for amoebiasis, these pathways were associated with poor PDAC overall survival. Meanwhile, LAMA3, LAMB3, LAMC2, COL4A1 and FN1 were commonly shared by these five pathways and were unfavorable factors for prognosis. Furthermore, by constructing the stage-course dynamic protein interaction network, 45 functional molecular modules and 19 nodes were identified as featured regulators for all PDAC stages, among which the collagen family and integrins were considered as two main regulators for facilitating aggressive progression. Additionally, the clinical relevance analysis suggested that the stage IV featured nodes MLF1IP and ITGB4 were significantly correlated with shorter overall survival. Moreover, 15 stage-specific protein kinases were identified from the dynamic network and CHEK1 was particularly activated at stage IV. Experimental validation showed that MLF1IP, LAMA3 and LAMB3 were progressively increased from tumor initiation to progression. Conclusions:Our study provided a view for a better understanding of the dynamic landscape of molecular interaction networks during PDAC progression and offered potential targets for therapeutic intervention.
| S-EPMC6303882 | biostudies-literature