Inhibition of glucuronidation in pancreatic cancer improves gemcitabine anticancer activity

Pancreatic ductal adenocarcinoma (PDAC) treatmentis focused on two regimens. The polychemotherapy, FOLFIRINOX (folinic acid, fluorouracil, irinotecan, oxali-platin), is used in patients with good health conditions, while gemcitabine, as monotherapy, in patients withpoor health conditions. Gemcitabine resistance-associated pathways have been targeted to sensitize cancercells, but the results were disappointing. Using a transcrip-tomic bioinformatics analysis combined with biologicalvalidation, we showed that glucuronidation was associated with the gemcitabine resistance in PDAC, and its inhibition could switch tumors from resistant to sensitive.To unravel the biological drivers of gemcitabineresponse in PDAC, we determined the transcriptomic dissimilarity between two preclinical models with definedgemcitabine sensitivity.Fil: Fraunhoffer Navarro, Nicolas Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Centro de Estudios Farmacológicos y Botánicos. Universidad de Buenos Aires. Facultad de Medicina. Centro de Estudios Farmacológicos y Botánicos; ArgentinaFil: Meilerman Abuelafia, Analía. Inserm; FranciaFil: Chanez, Brice. Inserm; FranciaFil: Bigonnet, Martin. Inserm; FranciaFil: Gayet, Odile. Inserm; FranciaFil: Roques, Julie. Inserm; FranciaFil: Chuluyan, Hector Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Centro de Estudios Farmacológicos y Botánicos. Universidad de Buenos Aires. Facultad de Medicina. Centro de Estudios Farmacológicos y Botánicos; ArgentinaFil: Dusetti, Nelson. Inserm; FranciaFil: Iovanna, Juan Lucio. Inserm; Franci

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.Fil: Fraunhoffer Navarro, Nicolas Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Centro de Estudios Farmacológicos y Botánicos. Universidad de Buenos Aires. Facultad de Medicina. Centro de Estudios Farmacológicos y Botánicos; Argentina. Inserm; FranciaFil: Meilerman Abuelafia, Miriam Analia. Inserm; FranciaFil: Bigonnet, Martin. Inserm; FranciaFil: Gayet, Odile. Inserm; FranciaFil: Roque, Julie. Inserm; FranciaFil: Telle, Emmanuel. Inserm; FranciaFil: Santofimia-Castaño, Patricia. Inserm; FranciaFil: Borrello, Maria Teresa. Inserm; FranciaFil: Chuluyan, Hector Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Centro de Estudios Farmacológicos y Botánicos. Universidad de Buenos Aires. Facultad de Medicina. Centro de Estudios Farmacológicos y Botánicos; ArgentinaFil: Dusetti, Nelson. Inserm; FranciaFil: Iovanna, Juan Lucio. Inserm; Franci

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 De-sign: 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 in-fluences their global chemosensitivity. Each preclinical model is imperfect but complementary, sug-gesting 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 applica-bility 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.Fil: Hoare, Owen. Centre National de la Recherche Scientifique; FranciaFil: Fraunhoffer Navarro, Nicolas Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Centro de Estudios Farmacológicos y Botánicos. Universidad de Buenos Aires. Facultad de Medicina. Centro de Estudios Farmacológicos y Botánicos; ArgentinaFil: Elkaoutari, Abdessamad. Centre National de la Recherche Scientifique; FranciaFil: Gayet, Odile. Centre National de la Recherche Scientifique; FranciaFil: Bigonnet, Martin. Centre National de la Recherche Scientifique; FranciaFil: Roques, Julie. Centre National de la Recherche Scientifique; FranciaFil: Nicolle, Rémy. No especifíca;Fil: McGuckin, Colin. Cell Therapy Research Institute; FranciaFil: Forraz, Nico. Cell Therapy Research Institute; FranciaFil: Sohier, Emilie. Le Centre Régional de Lutte Contre Le Cancer Léon Bérard; FranciaFil: Tonon, Laurie. Le Centre Régional de Lutte Contre Le Cancer Léon Bérard; FranciaFil: Wajda, Pauline. Le Centre Régional de Lutte Contre Le Cancer Léon Bérard; FranciaFil: Boyault, Sandrine. Le Centre Régional de Lutte Contre Le Cancer Léon Bérard; FranciaFil: Attignon, Valéry. Le Centre Régional de Lutte Contre Le Cancer Léon Bérard; FranciaFil: Tabone, Luciana Belen. Le Centre Régional de Lutte Contre Le Cancer Léon Bérard; FranciaFil: Barbier, Sandrine. No especifíca;Fil: Mignard, Caroline. No especifíca;Fil: Duchamp, Olivier. No especifíca;Fil: Iovanna, Juan. Centre National de la Recherche Scientifique; FranciaFil: Dusetti, Nelson J.. Centre National de la Recherche Scientifique; Franci

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

Abstract 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

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

International audiencePancreatic 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

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

International audiencePancreatic 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

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

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

International audienceThe 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

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

International audienceBackground: 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

Pancreatic ductal adenocarcinoma ubiquitination profiling reveals specific prognostic and theranostic markers

International audiencePancreatic ductal adenocarcinoma (PDAC) has been widely studied at multiomics level. However, little isknown 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 PDA