Although improvements have been made in the management of pancreatic adenocarcinoma (PDAC) during the past 20 years, the prognosis of this deadly disease remains poor with an overall 5-year survival under 10%. Treatment with FOLFIRINOX, a combined regimen of 5-fluorouracil, irinotecan (SN-38) and oxaliplatin, is nonetheless associated with an excellent initial tumour response and its use has allowed numerous patients to go through surgery while their tumour was initially considered unresectable. These discrepancies between initial tumour response and very low long-term survival are the consequences of rapidly acquired chemoresistance and represent a major therapeutic frontier. To our knowledge, a model of resistance to the combined three drugs has never been described due to the difficulty of modelling the FOLFIRINOX protocol both in vitro and in vivo. Patient-derived tumour organoids (PDO) are the missing link that has long been lacking in the wide range of epithelial cancer models between 2D adherent cultures and in vivo xenografts. In this work we sought to set up a model of PDO with resistance to FOLFIRINOX regimen that we could compare to the paired naive PDO.

We first extrapolated physiological concentrations of the three drugs using previous pharmacodynamics studies and bi-compartmental elimination models of oxaliplatin and SN-38. We then treated PaTa-1818x naive PDAC organoids with six cycles of 72 h-FOLFIRINOX treatment followed by 96 h interruption. Thereafter, we systematically compared treated organoids to PaTa-1818x naive organoids in terms of growth, proliferation, viability and expression of genes involved in cancer stemness and aggressiveness.

We reproductively obtained resistant organoids FoxR that significantly showed less sensitivity to FOLFORINOX treatment than the PaTa-1818x naive organoids from which they were derived. Our resistant model is representative of the sequential steps of chemoresistance observed in patients in terms of growth arrest (proliferation blockade), residual disease (cell quiescence/dormancy) and relapse.

To our knowledge, this is the first genuine in vitro model of resistance to the three drugs in combined therapy. This new PDO model will be a great asset for the discovery of acquired chemoresistance mechanisms, knowledge that is mandatory before offering new therapeutic strategies for pancreatic cancer.

Patient-derived in vivo models of human cancer have become a reality, yet their turnaround time is inadequate for clinical applications. Therefore, tailored ex vivo models that faithfully recapitulate in vivo tumour biology are urgently needed. These may especially benefit the management of pancreatic ductal adenocarcinoma (PDAC), where therapy failure has been ascribed to its high cancer stem cell (CSC) content and high density of stromal cells and extracellular matrix (ECM). To date, these features are only partially reproduced ex vivo using organoid and sphere cultures. We have now developed a more comprehensive and highly tuneable ex vivo model of PDAC based on the 3D co-assembly of peptide amphiphiles (PAs) with custom ECM components (PA-ECM). These cultures maintain patient-specific transcriptional profiles and exhibit CSC functionality, including strong in vivo tumourigenicity. User-defined modification of the system enables control over niche-dependent phenotypes such as epithelial-to-mesenchymal transition and matrix deposition. Indeed, proteomic analysis of these cultures reveals improved matrisome recapitulation compared to organoids. Most importantly, patient-specific in vivo drug responses are better reproduced in self-assembled cultures than in other models. These findings support the use of tuneable self-assembling platforms in cancer research and pave the way for future precision medicine approaches.

Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of death among cancers, it is characterized by poor prognosis and strong chemoresistance. In the PDAC microenvironment, stromal cells release different extracellular components, including CXCL12. The CXCL12 is a chemokine promoting the communication between tumour and stromal cells. Six different splicing isoforms of CXCL12 are known (α, β, γ, δ, ε, θ) but their role in PDAC has not yet been characterized.

To investigate the specific role of α, β, and γ CXCL12 isoforms in PDAC onset.

We used hTERT-HPNE E6/E7/KRasG12D (Human Pancreatic Nestin-Expressing) cell line as a pancreatic pre-tumour model and exposed it to the α, β, and γ CXCL12 isoforms. The altered expression profiles were assessed by microarray analyses and confirmed by Real-Time polymerase chain reaction. The functional enrichment analyses have been performed by Enrichr tool to highlight Gene Ontology enriched terms. In addition, wound healing assays have been carried out to assess the phenotypic changes, in terms of migration ability, induced by the α, β, and γ CXCL12 isoforms.

Microarray analysis of hTERT-HPNE cells treated with the three different CXCL12 isoforms highlighted that the expression of only a few genes was altered. Moreover, the α and β isoforms showed an alteration in expression of different genes, whereas γ isoform affected the expression of genes also common with α and β isoforms. The β isoform altered the expression of genes mainly involved in cell cycle regulation. In addition, all isoforms affected the expression of genes associated to cell migration, adhesion and cytoskeleton. In vitro cell migration assay confirmed that CXCL12 enhanced the migration ability of hTERT-HPNE cells. Among the CXCL12 splicing isoforms, the γ isoform showed higher induction of migration than α and β isoforms.

Our data suggests an involvement and different roles of CXCL12 isoforms in PDAC onset. However, more investigations are needed to confirm these preliminary observations.

Tumor-initiating cells (TICs) or cancer stem cells are believed to be responsible for gastrointestinal tumor initiation, progression, metastasis, and drug resistance. It is hypothesized that gastrointestinal TICs (giTICs) might originate from cell-cell fusion. Here, we systemically evaluate the evidence that supports or opposes the hypothesis of giTIC generation from cell-cell fusion both in vitro and in vivo. We review giTICs that are capable of initiating tumors in vivo with 5000 or fewer in vivo fused cells. Under this restriction, there is currently little evidence demonstrating that giTICs originate from cell-cell fusion in vivo. However, there are many reports showing that tumor generation in vitro occurs with more than 5000 fused cells. In addition, the mechanisms of giTIC generation via cell-cell fusion are poorly understood, and thus, we propose its potential mechanisms of action. We suggest that future research should focus on giTIC origination from cell-cell fusion in vivo, isolation or enrichment of giTICs that have tumor-initiating capabilities with 5000 or less in vivo fused cells, and further clarification of the underlying mechanisms. Our review of the current advances in our understanding of giTIC origination from cell-cell fusion may have significant implications for the understanding of carcinogenesis and future cancer therapeutic strategies targeting giTICs.

High mortality and low survival rates for pancreatic ductal adenocarcinoma (PDAC) mainly result from the delay in diagnosis and treatment. Therefore, there is an urgent need to identify early PDAC biomarkers and new therapeutic targets. In this study, we applied a commonly used systems biology approach, the weighted gene co-expression network analysis (WGCNA), on lncRNA expression data. Eleven lncRNAs, namely A2M-AS1, DLEU2, LINC01133, LINC00675, MIR155HG, SLC25A25-AS1, LINC01857, LOC642852 (LINC00205), ITGB2-AS1, TSPOAP1-AS1 and PSMB8-AS1 have been identified and validated on an independent PDAC expression dataset. Furthermore, we characterized them by functional and pathway enrichment analysis and identified which lncRNAs showed differential expression, differential promoter methylation levels and copy number alterations between normal and PDAC samples. Finally, we also performed a survival analysis and identified A2M-AS1, LINC01133, LINC00205 and TSPOAP1-AS1 as prognostic biomarkers for PDAC. Interestingly, although only a few cancer-associated lncRNAs have been functionally characterized, LINC00675 and LINC01133 lncRNAs have already been demonstrated to be involved in PDAC development and progression. Therefore, our results provide new potential diagnostic/prognostic biomarkers and therapeutic targets for PDAC that deserve to be further investigated. Moreover, these lncRNAs may improve the understanding about molecular pathogenesis of PDAC.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with a dismal prognosis which is, among others, due to a lack of suitable biomarkers and therapeutic targets. Previously, basic gene expression analysis methods have been used for their identification, but recently new algorithms have been developed allowing more comprehensive data analyses. Among them, weighted gene co-expression network analysis (WGCNA) has already been applied to several cancer types with promising results.

We applied WGCNA to miRNA expression data from PDAC patients. Specifically, we processed microarray-based expression data of 2555 miRNAs in serum from 100 PDAC patients and 150 healthy subjects. We identified network modules of co-expressed miRNAs in the healthy subject dataset and verified their preservation in the PDAC dataset. In the non-preserved modules, we selected key miRNAs and carried out functional enrichment analyses of their experimentally known target genes. Finally, we tested their prognostic significance using overall survival analyses.

Through WGCNA we identified several miRNAs that discriminate healthy subjects from PDAC patients and that, therefore, may play critical roles in PDAC development. At a functional level, we found that they regulate p53, FoxO and ErbB associated cellular signalling pathways, as well as cell cycle progression and various genes known to be involved in PDAC development. Some miRNAs were also found to serve as novel prognostic biomarkers, whereas others have previously already been proposed as such, thereby validating the WGCNA approach. In addition, we found that these novel data may explain at least some of our previous PDAC gene expression analysis results.

We identified several miRNAs critical for PDAC development using WGCNA. These miRNAs may serve as biomarkers for PDAC diagnosis/prognosis and patient stratification, and as putative novel therapeutic targets.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy. Up till now, the patient's prognosis remains poor which, among others, is due to the paucity of reliable early diagnostic biomarkers. In the past, candidate diagnostic biomarkers and therapeutic targets have been delineated from genes that were found to be differentially expressed in normal versus tumour samples. Recently, new systems biology approaches have been developed to analyse gene expression data, which may yield new biomarkers. As of yet, the weighted gene co-expression network analysis (WGCNA) tool has not been applied to PDAC microarray-based gene expression data.

PDAC microarray-based gene expression datasets, listed in the Gene Expression Omnibus (GEO) database, were analysed. After pre-processing of the data, we built two final datasets, Normal and PDAC, encompassing 104 and 129 patient samples, respectively. Next, we constructed a weighted gene co-expression network and identified modules of co-expressed genes distinguishing normal from disease conditions. Functional annotations of the genes in these modules were carried out to highlight PDAC-associated molecular pathways and common regulatory mechanisms. Finally, overall survival analyses were carried out to assess the suitability of the genes identified as prognostic biomarkers.

Using WGCNA, we identified several key genes that may play important roles in PDAC. These genes are mainly related to either endoplasmic reticulum, mitochondrion or membrane functions, exhibit transferase or hydrolase activities and are involved in biological processes such as lipid metabolism or transmembrane transport. As a validation of the applied method, we found that some of the identified key genes (CEACAM1, MCU, VDAC1, CYCS, C15ORF52, TMEM51, LARP1 and ERLIN2) have previously been reported by others as potential PDAC biomarkers. Using overall survival analyses, we found that several of the newly identified genes may serve as biomarkers to stratify PDAC patients into low- and high-risk groups.

Using this new systems biology approach, we identified several genes that appear to be critical to PDAC development. As such, they may represent potential diagnostic biomarkers as well as therapeutic targets with clinical utility.