Basal-like and classical cells coexist in pancreatic cancer revealed by single-cell analysis on biopsy-derived pancreatic cancer organoids from the classical subtype.

Pancreatic ductal adenocarcinoma (PDAC) is composed of stromal, immune, and cancerous epithelial cells. Transcriptomic analysis of the epithelial compartment allows classification into different phenotypic subtypes as classical and basal-like. However, little is known about the intra-tumor heterogeneity particularly in the epithelial compartment. Growing evidences suggest that this phenotypic segregation is not so precise and different cancerous cell types may coexist in a single tumor. To test this hypothesis, we performed single-cell transcriptomic analyses using combinational barcoding exclusively on epithelial cells from six different classical PDAC patients obtained by Endoscopic Ultrasound (EUS) with Fine Needle Aspiration (FNA). To purify the epithelial compartment, PDAC were grown as biopsy-derived pancreatic cancer organoids. Single-cell transcriptomic analysis allowed the identification of four main cell clusters present in different proportions in all tumors. Remarkably, although all these tumors were classified as classical, one cluster present in all corresponded to a basal-like phenotype. These results reveal an unanticipated high heterogeneity of pancreatic cancers and demonstrate that basal-like cells, which have a highly aggressive phenotype, are more widespread than expected.

PML hyposumoylation is responsible for the resistance of pancreatic cancer.

The dismal prognosis of pancreatic ductal adenocarcinoma (PDAC) is mainly due to its rapidly acquired resistance to all conventional treatments. Despite drug-specific mechanisms of resistance, none explains how these cells resist the stress induced by any kind of anticancer treatment. Activation of stress-response pathways relies on the post-translational modifications (PTMs) of involved proteins. Among all PTMs, those mediated by the ubiquitin family of proteins play a central role. Our aim was to identify alterations of ubiquitination, neddylation, and sumoylation associated with the multiresistant phenotype and demonstrate their implications in the survival of PDAC cells undergoing treatment. This approach pointed at an alteration of promyelocytic leukemia (PML) protein sumoylation associated with both gemcitabine and oxaliplatin resistance. We could show that this alteration of PML sumoylation is part of a general mechanism of drug resistance, which in addition involves the abnormal activation of NF-κB and cAMP response element binding pathways. Importantly, using patient-derived tumors and cell lines, we identified a correlation between the levels of PML expression and sumoylation and the sensitivity of tumors to anticancer treatments.-Swayden, M., Alzeeb, G., Masoud, R., Berthois, Y., Audebert, S., Camoin, L., Hannouche, L., Vachon, H., Gayet, O., Bigonnet, M., Roques, J., Silvy, F., Carrier, A., Dusetti, N., Iovanna, J. L., Soubeyran, P. PML hyposumoylation is responsible for the resistance of pancreatic cancer.

Pancreatic ductal adenocarcinoma ubiquitination profiling reveals specific prognostic and theranostic markers.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) has been widely studied at multiomics level. However, little is known about its specific ubiquitination, a major post-translational modification (PTM). As PTMs regulate the final function of any gene, we decided to establish the ubiquitination profiles of 60 PDAC. METHODS: We used specific proteomic tools to establish the ubiquitin dependent proteome (ubiquitinome) of frozen PDXs (Patients' derived xenographs). Then, we performed bioinformatics analysis to identify the possible associations of these ubiquitination profiles with tumour phenotype, patient survival and resistance to chemotherapies. Finally, we used proximity ligation assays (PLA) to detect and quantify the ubiquitination level of one identified marker. FINDINGS: We identified 38 ubiquitination site profiles correlating with the transcriptomic phenotype of tumours and four had notable prognostic capabilities. Seventeen ubiquitination profiles displayed potential theranostic marker for gemcitabine, seven for 5-FU, six for oxaliplatin and thirteen for irinotecan. Using PLA, we confirmed the use of one ubiquitination profile as a drug-response marker, directly on paraffin embedded tissues, supporting the possible application of these biomarkers in the clinical setting. INTERPRETATION: These findings bring new and important insights on the relationship between ubiquitination levels of proteins and different molecular and clinical features of PDAC patients. Markers identified in this study could have a potential application in clinical settings to help to predict response to chemotherapies thereby allowing the personalization of treatments. FUNDING: Fondation ARC (PJA 20181208270 and PGA 12021010002840_3562); INCa; Canceropôle PACA; DGOS; Amidex Foundation; Fondation de France; and INSERM.

Multi-omics data integration and modeling unravels new mechanisms for pancreatic cancer and improves prognostic prediction.

Pancreatic ductal adenocarcinoma (PDAC), has recently been found to be a heterogeneous disease, although the extension of its diversity remains to be fully understood. Here, we harmonize transcriptomic profiles derived from both PDAC epithelial and microenvironment cells to develop a Master Regulators (MR)-Gradient model that allows important inferences on transcriptional networks, epigenomic states, and metabolomics pathways that underlies this disease heterogeneity. This gradient model was generated by applying a blind source separation based on independent components analysis and robust principal component analyses (RPCA), following regulatory network inference. The result of these analyses reveals that PDAC prognosis strongly associates with the tumor epithelial cell phenotype and the immunological component. These studies were complemented by integration of methylome and metabolome datasets generated from patient-derived xenograft (PDX), together experimental measurements of metabolites, immunofluorescence microscopy, and western blot. At the metabolic level, PDAC favorable phenotype showed a positive correlation with enzymes implicated in complex lipid biosynthesis. In contrast, the unfavorable phenotype displayed an augmented OXPHOS independent metabolism centered on the Warburg effect and glutaminolysis. Epigenetically, we find that a global hypermethylation profile associates with the worst prognosis. Lastly, we report that, two antagonistic histone code writers, SUV39H1/SUV39H2 (H3K9Me3) and KAT2B (H3K9Ac) were identified key deregulated pathways in PDAC. Our analysis suggests that the PDAC phenotype, as it relates to prognosis, is determined by a complex interaction of transcriptomic, epigenomic, and metabolic features. Furthermore, we demonstrated that PDAC prognosis could be modulated through epigenetics.

Sympathetic axonal sprouting induces changes in macrophage populations and protects against pancreatic cancer.

Neuronal nerve processes in the tumor microenvironment were highlighted recently. However, the origin of intra-tumoral nerves remains poorly known, in part because of technical difficulties in tracing nerve fibers via conventional histological preparations. Here, we employ three-dimensional (3D) imaging of cleared tissues for a comprehensive analysis of sympathetic innervation in a murine model of pancreatic ductal adenocarcinoma (PDAC). Our results support two independent, but coexisting, mechanisms: passive engulfment of pre-existing sympathetic nerves within tumors plus an active, localized sprouting of axon terminals into non-neoplastic lesions and tumor periphery. Ablation of the innervating sympathetic nerves increases tumor growth and spread. This effect is explained by the observation that sympathectomy increases intratumoral CD163+ macrophage numbers, which contribute to the worse outcome. Altogether, our findings provide insights into the mechanisms by which the sympathetic nervous system exerts cancer-protective properties in a mouse model of PDAC.

A glycosyltransferase gene signature to detect pancreatic ductal adenocarcinoma patients with poor prognosis.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is characterized by an important heterogeneity, reflected by different clinical outcomes and chemoresistance. During carcinogenesis, tumor cells display aberrant glycosylated structures, synthetized by deregulated glycosyltransferases, supporting the tumor progression. In this study, we aimed to determine whether PDAC could be stratified through their glycosyltransferase expression profiles better than the current binary classification (basal-like and classical) in order to improve detection of patients with poor prognosis. METHODS: Bioinformatic analysis of 169 glycosyltransferase RNA sequencing data were performed for 74 patient-derived xenografts (PDX) of resected and unresectable tumors. The Australian cohort of International Cancer Genome Consortium and the microarray dataset from Puleo patient's cohort were used as independent validation datasets. FINDINGS: New PDAC stratification based on glycosyltransferase expression profile allowed to distinguish different groups of patients with distinct clinical outcome (p-value = 0.007). A combination of 19 glycosyltransferases differentially expressed in PDX defined a glyco-signature, whose prognostic value was validated on datasets including resected whole tumor tissues. The glyco-signature was able to discriminate three clusters of PDAC patients on the validation cohorts, two clusters displaying a short overall survival compared to one cluster having a better prognosis. Both poor prognostic clusters having different glyco-profiles in Puleo patient's cohort were correlated with stroma activated or desmoplastic subtypes corresponding to distinct microenvironment features (p-value < 0.0001). Besides, differential expression and enrichment analyses revealed deregulated functional pathways specific to different clusters. INTERPRETATION: This study identifies a glyco-signature relevant for a prognostic use, potentially applicable to resected and unresectable PDAC. Furthermore, it provides new potential therapeutic targets. FUNDING: This work was supported by INCa (Grants number 2018-078 and 2018-079), Fondation ARC (Grant number ARCPJA32020070002326), Cancéropôle PACA, DGOS (labelization SIRIC, Grant number 6038), Amidex Foundation and Ligue Nationale Contre le Cancer and by institutional fundings from INSERM and the Aix-Marseille Université.

Squamousness gain defines pancreatic ductal adenocarcinoma hepatic metastases phenotype, and gemcitabine response.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a dismal disease with a survival rate of less than 7%, mainly due to the hepatic metastatic spread. Despite the importance of understanding PDAC metastases, central questions remain concerning their biology and chemosensitivity. Moreover, the transcriptomic divergence between primary tumor (PT) and hepatic metastases (HM) has been poorly studied and without a clear dissection of the confounding tumoral-surrounding tissue. METHODS: Here, to unravel key biological features not biased by the surrounding tissue, we implemented a blind source separation based on independent component analysis, ProDenICA, on a treatment-naïve cohort of PDAC paired samples and a cohort of 305 resectable patients. In addition, a time-lapse experiment was performed to assess the gemcitabine chemosensitivity profile between the PT and HM. RESULTS: We identified HM's specific transcriptomic characteristics related to the upregulation of cell cycle checkpoint, mitochondria activity, and extracellular matrix reorganization, which could be associated with metastatic niche adaptation mechanisms. Furthermore, squamous lineage emerged as a key feature linked with a downregulation in the epithelial-to-mesenchymal program that can stratifies PDAC HM independent of the classical/basal-like spectrum. Remarkably, we also demonstrated that gemcitabine response is influenced by the squamous profile, being the HM more refractory to the treatment than the PT. CONCLUSIONS: These results pointed out divergent HM aspects compared to PT and allowed their stratification through the squamous lineage. Moreover, we unravel a clinical actionable squamous signature that predicts the gemcitabine response.

Exploring the Complementarity of Pancreatic Ductal Adenocarcinoma Preclinical Models.

Purpose: Compare pancreatic ductal adenocarcinoma (PDAC), preclinical models, by their transcriptome and drug response landscapes to evaluate their complementarity. Experimental Design: Three paired PDAC preclinical models-patient-derived xenografts (PDX), xenograft-derived pancreatic organoids (XDPO) and xenograft-derived primary cell cultures (XDPCC)-were derived from 20 patients and analyzed at the transcriptomic and chemosensitivity level. Transcriptomic characterization was performed using the basal-like/classical subtyping and the PDAC molecular gradient (PAMG). Chemosensitivity for gemcitabine, irinotecan, 5-fluorouracil and oxaliplatin was established and the associated biological pathways were determined using independent component analysis (ICA) on the transcriptome of each model. The selection criteria used to identify the different components was the chemosensitivity score (CSS) found for each drug in each model. Results: PDX was the most dispersed model whereas XDPO and XDPCC were mainly classical and basal-like, respectively. Chemosensitivity scoring determines that PDX and XDPO display a positive correlation for three out of four drugs tested, whereas PDX and XDPCC did not correlate. No match was observed for each tumor chemosensitivity in the different models. Finally, pathway analysis shows a significant association between PDX and XDPO for the chemosensitivity-associated pathways and PDX and XDPCC for the chemoresistance-associated pathways. Conclusions: Each PDAC preclinical model possesses a unique basal-like/classical transcriptomic phenotype that strongly influences their global chemosensitivity. Each preclinical model is imperfect but complementary, suggesting that a more representative approach of the clinical reality could be obtained by combining them. Translational Relevance: The identification of molecular signatures that underpin drug sensitivity to chemotherapy in PDAC remains clinically challenging. Importantly, the vast majority of studies using preclinical in vivo and in vitro models fail when transferred to patients in a clinical setting despite initially promising results. This study presents for the first time a comparison between three preclinical models directly derived from the same patients. We show that their applicability to preclinical studies should be considered with a complementary focus, avoiding tumor-based direct extrapolations, which might generate misleading conclusions and consequently the overlook of clinically relevant features.

Evidencing a Pancreatic Ductal Adenocarcinoma Subpopulation Sensitive to the Proteasome Inhibitor Carfilzomib.

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) is a lethal cancer with a survival rate less than 5%. Multiple chemotherapeutic drugs have been tested to improve patient prognosis; however, the clinical efficacy of these treatments is low. One of the most controversial family of drugs are the proteasome inhibitors, which have displayed promising effects in preclinical studies, but low clinical performance. Here, we unravel a specific transcriptomic signature that discriminates a subgroup of patients sensitive to the proteasome inhibitor carfilzomib. EXPERIMENTAL DESIGN: First, we identified a subpopulation of PDAC-derived primary cells cultures (PDPCC) sensitive to the proteasome inhibitor carfilzomib. Then, we selected a transcriptomic signature that predicts carfilzomib chemosensitivity using independent component analysis on the transcriptome of PDPCC. Finally, we validated the signature in an independent cohort of PDAC biopsy-derived pancreatic organoids. RESULTS: Sensitive phenotype was characterized by a high expression of genes related with a cornified/squamous pathway and a downregulation of epithelial-mesenchymal transition genes. Interestingly, carfilzomib-sensitive transcriptomic profile did not show any association with the proteasome activity but strongly correlates with ATF4 and CHOP expression, which are key markers of the unfolded protein response and critical to trigger the cell death program. Concordantly, sensitive phenotype showed a high level of the de novo RNA and protein synthesis compared with the resistant one and, most important, cell death induced by carfilzomib is dependent of the translational activity. CONCLUSIONS: We demonstrate the existence of a carfilzomib-sensitive PDAC subgroup with a specific transcriptomic phenotype that could explain the biological reason for this responsiveness.

Establishment of a pancreatic adenocarcinoma molecular gradient (PAMG) that predicts the clinical outcome of pancreatic cancer.

BACKGROUND: A significant gap in pancreatic ductal adenocarcinoma (PDAC) patient's care is the lack of molecular parameters characterizing tumours and allowing a personalized treatment. METHODS: Patient-derived xenografts (PDX) were obtained from 76 consecutive PDAC and classified according to their histology into five groups. A PDAC molecular gradient (PAMG) was constructed from PDX transcriptomes recapitulating the five histological groups along a continuous gradient. The prognostic and predictive value for PMAG was evaluated in: i/ two independent series (n = 598) of resected tumours; ii/ 60 advanced tumours obtained by diagnostic EUS-guided biopsy needle flushing and iii/ on 28 biopsies from mFOLFIRINOX treated metastatic tumours. FINDINGS: A unique transcriptomic signature (PAGM) was generated with significant and independent prognostic value. PAMG significantly improves the characterization of PDAC heterogeneity compared to non-overlapping classifications as validated in 4 independent series of tumours (e.g. 308 consecutive resected PDAC, uHR=0.321 95% CI [0.207-0.5] and 60 locally-advanced or metastatic PDAC, uHR=0.308 95% CI [0.113-0.836]). The PAMG signature is also associated with progression under mFOLFIRINOX treatment (Pearson correlation to tumour response: -0.67, p-value < 0.001). INTERPRETATION: PAMG unify all PDAC pre-existing classifications inducing a shift in the actual paradigm of binary classifications towards a better characterization in a gradient. FUNDING: Project funding was provided by INCa (Grants number 2018-078 and 2018-079, BACAP BCB INCa_6294), Canceropole PACA, DGOS (labellisation SIRIC), Amidex Foundation, Fondation de France, INSERM and Ligue Contre le Cancer.

Pancreatic Cancer Organoids for Determining Sensitivity to Bromodomain and Extra-Terminal Inhibitors (BETi).

Pancreatic ductal adenocarcinoma (PDAC) is a heterogeneous disease, therefore stratification of patients is essential to predict their responses to therapies and to choose the best treatment. PDAC-derived organoids were produced from PDTX and Endoscopic Ultrasound-Guided Fine-Needle Aspiration (EUS-FNA) biopsies. A signature based on 16 genes targets of the c-MYC oncogene was applied to classify samples into two sub-groups with distinctive phenotypes named MYC-high and MYC-low. The analysis of 9 PDTXs and the corresponding derived organoids revealed that this signature which was previously designed from PDTX is transferable to the organoid model. Primary organoids from 24 PDAC patients were treated with NHWD-870 or JQ1, two inhibitors of c-MYC transcription. Notably, the comparison of their effect between the two sub-groups showed that both compounds are more efficient in MYC-high than in MYC-low samples, being NHWD-870 the more potent treatment. In conclusion, this study shows that the molecular signatures could be applied to organoids obtained directly from PDAC patients to predict the treatment response and could help to take the more appropriate therapeutic decision for each patient in a clinical timeframe.

E2F signature is predictive for the pancreatic adenocarcinoma clinical outcome and sensitivity to E2F inhibitors, but not for the response to cytotoxic-based treatments.

The main goal of this study was to find out strategies of clinical relevance to classify patients with a pancreatic ductal adenocarcinoma (PDAC) for individualized treatments. In the present study a set of 55 patient-derived xenografts (PDX) were obtained and their transcriptome were analyzed by using an Affymetrix approach. A supervised bioinformatics-based analysis let us to classify these PDX in two main groups named E2F-highly dependent and E2F-lowly dependent. Afterwards their characterization by using a Kaplan-Meier analysis demonstrated that E2F high patients survived significantly less than E2F low patients (9.5 months vs. 16.8 months; p = 0.0066). Then we tried to establish if E2F transcriptional target levels were associated to the response to cytotoxic treatments by comparing the IC50 values of E2F high and E2F low cells after gemcitabine, 5-fluorouracil, oxaliplatin, docetaxel or irinotecan treatment, and no association was found. Then we identified an E2F inhibitor compound, named ly101-4B, and we observed that E2F-higly dependent cells were more sensitive to its treatment (IC50 of 19.4 ± 1.8 µM vs. 44.1 ± 4.4 µM; p = 0.0061). In conclusion, in this work we describe an E2F target expression-based classification that could be predictive for patient outcome, but more important, for the sensitivity of tumors to the E2F inhibitors as a treatment. Finally, we can assume that phenotypic characterization, essentially by an RNA expression analysis of the PDAC, can help to predict their clinical outcome and their response to some treatments when are rationally selected.

Distinct epigenetic landscapes underlie the pathobiology of pancreatic cancer subtypes.

Recent studies have offered ample insight into genome-wide expression patterns to define pancreatic ductal adenocarcinoma (PDAC) subtypes, although there remains a lack of knowledge regarding the underlying epigenomics of PDAC. Here we perform multi-parametric integrative analyses of chromatin immunoprecipitation-sequencing (ChIP-seq) on multiple histone modifications, RNA-sequencing (RNA-seq), and DNA methylation to define epigenomic landscapes for PDAC subtypes, which can predict their relative aggressiveness and survival. Moreover, we describe the state of promoters, enhancers, super-enhancers, euchromatic, and heterochromatic regions for each subtype. Further analyses indicate that the distinct epigenomic landscapes are regulated by different membrane-to-nucleus pathways. Inactivation of a basal-specific super-enhancer associated pathway reveals the existence of plasticity between subtypes. Thus, our study provides new insight into the epigenetic landscapes associated with the heterogeneity of PDAC, thereby increasing our mechanistic understanding of this disease, as well as offering potential new markers and therapeutic targets.

Gene expression profiling of patient-derived pancreatic cancer xenografts predicts sensitivity to the BET bromodomain inhibitor JQ1: implications for individualized medicine efforts.

c-MYC controls more than 15% of genes responsible for proliferation, differentiation, and cellular metabolism in pancreatic as well as other cancers making this transcription factor a prime target for treating patients. The transcriptome of 55 patient-derived xenografts show that 30% of them share an exacerbated expression profile of MYC transcriptional targets (MYC-high). This cohort is characterized by a high level of Ki67 staining, a lower differentiation state, and a shorter survival time compared to the MYC-low subgroup. To define classifier expression signature, we selected a group of 10 MYC target transcripts which expression is increased in the MYC-high group and six transcripts increased in the MYC-low group. We validated the ability of these markers panel to identify MYC-high patient-derived xenografts from both: discovery and validation cohorts as well as primary cell cultures from the same patients. We then showed that cells from MYC-high patients are more sensitive to JQ1 treatment compared to MYC-low cells, in monolayer, 3D cultured spheroids and in vivo xenografted tumors, due to cell cycle arrest followed by apoptosis. Therefore, these results provide new markers and potentially novel therapeutic modalities for distinct subgroups of pancreatic tumors and may find application to the future management of these patients within the setting of individualized medicine clinics.

Pancreatic Adenocarcinoma Therapeutic Targets Revealed by Tumor-Stroma Cross-Talk Analyses in Patient-Derived Xenografts.

Preclinical models based on patient-derived xenografts have remarkable specificity in distinguishing transformed human tumor cells from non-transformed murine stromal cells computationally. We obtained 29 pancreatic ductal adenocarcinoma (PDAC) xenografts from either resectable or non-resectable patients (surgery and endoscopic ultrasound-guided fine-needle aspirate, respectively). Extensive multiomic profiling revealed two subtypes with distinct clinical outcomes. These subtypes uncovered specific alterations in DNA methylation and transcription as well as in signaling pathways involved in tumor-stromal cross-talk. The analysis of these pathways indicates therapeutic opportunities for targeting both compartments and their interactions. In particular, we show that inhibiting NPC1L1 with Ezetimibe, a clinically available drug, might be an efficient approach for treating pancreatic cancers. These findings uncover the complex and diverse interplay between PDAC tumors and the stroma and demonstrate the pivotal role of xenografts for drug discovery and relevance to PDAC.

A pancreatic ductal adenocarcinoma subpopulation is sensitive to FK866, an inhibitor of NAMPT.

Treating pancreatic cancer is extremely challenging due to multiple factors, including chemoresistance and poor disease prognosis. Chemoresistance can be explained by: the presence of a dense stromal barrier leading to a lower vascularized condition, therefore limiting drug delivery; the huge intra-tumoral heterogeneity; and the status of epithelial-to-mesenchymal transition. These factors are highly variable between patients making it difficult to predict responses to chemotherapy. Nicotinamide phosphoribosyl transferase (NAMPT) is the main enzyme responsible for recycling cytosolic NAD+ in hypoxic conditions. FK866 is a noncompetitive specific inhibitor of NAMPT, which has proven anti-tumoral effects, although a clinical advantage has still not been demonstrated. Here, we tested the effect of FK866 on pancreatic cancer-derived primary cell cultures (PCCs), both alone and in combination with three different drugs typically used against this cancer: gemcitabine, 5-Fluorouracil (5FU) and oxaliplatin. The aims of this study were to evaluate the benefit of drug combinations, define groups of sensitivity, and identify a potential biomarker for predicting treatment sensitivity. We performed cell viability tests in the presence of either FK866 alone or in combination with the drugs above-mentioned. We confirmed both inter- and intra-tumoral heterogeneity. Interestingly, only the in vitro effect of gemcitabine was influenced by the addition of FK866. We also found that NAMPT mRNA expression levels can predict the sensitivity of cells to FK866. Overall, our results suggest that patients with tumors sensitive to FK866 can be identified using NAMPT mRNA levels as a biomarker and could therefore benefit from a co-treatment of gemcitabine plus FK866.