Biology subjects

Gayet, O.

Publications and source records attributed to Gayet, O..

4 recordsLinked to original sources

bioRxiv2021

Metabolomic profiling of pancreatic adenocarcinoma reveals fundamental clinical features

In this study, we perform the metabolomics profiling of 77 PDAC patient-derived tumor xenografts (PDTX) to investigate the relationship of metabolic profiles with overall survival (OS) in PDAC patients, tumor phenotypes and resistance to five anticancer drugs (gemcitabine, oxaliplatin, docetaxel, SN-38 and 5-Fluorouracil). We identified a metabolic signature that was able to predict the clinical outcome of PDAC patients (p < 0.001, HR=2.68 [95% CI: 1.5-4.9]). The correlation analysis showed that this metabolomic signature was significantly correlated with the PDAC molecular gradient (PAMG) (R = 0.44 and p < 0.001) indicating significant association to the transcriptomic phenotypes of tumors. Resistance score established, based on growth rate inhibition metrics using 35 PDTX-derived primary cells, allowed to identify several metabolites related to drug resistance which was globally accompanied by accumulation of several diacy-phospholipids and decrease in lysophospholipids. Interestingly, targeting glycerophospholipid synthesis improved sensitivity to the three tested cytotoxic drugs indicating that interfering with metabolism could be a promising therapeutic strategy to overcome the challenging resistance of PDAC. Short abstractTargeting metabolism of cancer cells gives a precious opportunity to overcome challenges related to the high mortality and chemoresistance in PDAC. Metabolic profiling of PDAC patient-derived tumor xenografts used in this study allowed highlighting the strong link between metabolism and both clinical outcome of the patients and chemoresistance. Metabolic signature was able to discriminate between good and bad prognosis groups of patients based on their level of key metabolites. Identification of key metabolic markers associated to chemoresistance allowed to improve sensitivity to anticancer drugs. These results provide new insights to help to predict patient survival and elaborate new combinatory therapies against chemoresistance in PDAC patients attesting of the important clinical value of this work.

cancer biology↗

bioRxiv2020

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

BACKGROUNDA significant gap in pancreatic ductal adenocarcinoma (PDAC) patients care is the lack of molecular parameters characterizing tumors and allowing a personalized treatment. The goal of this study was to examine whole PDAC transcriptomic profiles to define a signature that would predict aggressiveness and treatment responsiveness better than done until now. METHODS AND PATIENTSTumors were obtained from 76 consecutive resectable (n=40) or unresectable (n=36) tumors. PDAC were transplanted in mice to produce patient-drived xenografts (PDX). PDX were classified according to their histology into five groups, from highly undifferentiated to well differentiated. This classification resulted strongly associated with tumors aggressiveness. 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 tumors; ii/ 60 advanced tumors obtained by diagnostic EUS-guided biopsy needle flushing and iii/ on 28 biopsies from mFOLFIRINOX treated metastatic tumors. RESULTSA 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 tumors (e.g. 308 consecutive resected PDAC, HR=0.321 95% CI [0.207;0.5] and 60 locally-advanced or metastatic PDAC, HR=0.308 95% CI [0.113;0.836]). The PAMG signature is also associated with progression under mFOLFIRINOX treatment (Pearson correlation to tumor response: -0.67, p-value < 0.001). CONCLUSIONWe identified a transcriptomic signature (PAMG) that, unlike all other stratification schemas already proposed, classifies PDAC along a continuous gradient. It can be performed on formalin-fixed paraffin-embedded samples and EUS-guided biopsies showing a strong prognostic value and predicting mFOLFIRINOX responsiveness. We think that PAMG could unify all PDAC preexisting classifications inducing a shift in the actual paradigm of binary classifications towards a better characterization in a gradient. Trial RegistrationThe PaCaOmics study is registered at www.clinicaltrials.gov with registration number NCT01692873. The validation BACAP study is registered at www.clinicaltrials.gov with registration number NCT02818829.

cancer biology↗

bioRxiv2020

Basal-like and Classical cells coexistence in pancreatic cancer revealed by single cell analysis

Pancreatic ductal adenocarcinoma (PDAC) is composed of stromal, immune and epithelial cells. Transcriptomic analysis of the epithelial compartment allows a binary classification into mainly two phenotypic subtypes, classical and basal-like. However, little is known about the intra-tumor heterogeneity of the epithelial component. Growing evidences suggest that this two side phenotypic segregation is not so clear and that both could coexist in a single tumor. In order to elucidate this hypothesis, we performed single-cell transcriptomic analyses using combinational barcoding on epithelial cells from 6 different classical PDAC obtained by Endoscopic Ultrasound (EUS) with Fine Needle Aspiration (FNA). In order to purify the epithelial compartment, PDAC were grown as Biopsy Derived Pancreatic Cancer Organoids. Single cell transcriptomic analysis allowed the identification of 4 main cell clusters present in different proportions in all tumors. Remarkably, although these tumors were classified as Classical, one of the clusters corresponded to a basal-like. These results depict the unanticipated high heterogeneity of pancreatic cancers and demonstrated that basal-like cells with a high aggressive phenotype are more widespread than expected.

cancer biology↗

bioRxiv2018

Inactivation of NUPR1 promotes cell death by coupling ER-stress responses with necrosis

Genetic inhibition of NUPR1 induces tumor growth arrest. Inactivation of NUPR1 expression in pancreatic cancer cells results in lower ATP production, higher consumption of glucose with a significant switch from OXPHOS to glycolysis followed by necrotic cell death. Importantly, induction of necrosis is independent of the caspase activity. We demonstrated that NUPR1 inactivation triggers a massive release of Ca2+ from the endoplasmic reticulum (ER) to the cytosol and a strong increase in ROS production by mitochondria with a concomitant relocalization of mitochondria to the vicinity of the ER. In addition, transcriptomic analysis of NUPR1-deficient cells shows the induction of an ER stress which is associated to a decrease in the expression of some ER stress response-associated genes. Indeed, during ER stress induced by the treatment with thapsigargin, brefeldin A or tunicamycin, an increase in the mitochondrial malfunction with higher induction of necrosis was observed in NUPR1-defficent cells. Finally, activation of NUPR1 during acute pancreatitis protects acinar cells of necrosis in mice. Altogether, these data enable us to describe a model in which inactivation of NUPR1 in pancreatic cancer cells results in an ER stress that induces a mitochondrial malfunction, a deficient ATP production and, as consequence, the cell death by necrosis.\n\nHighlightsNUPR1 expression promotes pancreatic cancer development and progression\n\nNUPR1-depletion is a promising therapeutic strategy to be used for treating cancers\n\nNUPR1-depletion induces ER stress, mitochondrial malfunction and a significant switch from OXPHOS to glycolysis followed by necrotic cell death\n\nInactivation of NUPR1 antagonizes cell growth by coupling a defective ER-stress response and a caspase-independent necrosis.

cell biology↗