A model for gene deregulation detection using expression data

In tumoral cells, gene regulation mechanisms are severely altered, and these modifications in the regulations may be characteristic of different subtypes of cancer. However, these alterations do not necessarily induce differential expressions between the subtypes. To answer this question, we propose a statistical methodology to identify the misregulated genes given a reference network and gene expression data. Our model is based on a regulatory process in which all genes are allowed to be deregulated. We derive an EM algorithm where the hidden variables correspond to the status (under/over/normally expressed) of the genes and where the E-step is solved thanks to a message passing algorithm. Our procedure provides posterior probabilities of deregulation in a given sample for each gene. We assess the performance of our method by numerical experiments on simulations and on a bladder cancer data set

DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification

Quantifcation of tumor heterogeneity is essential to better understand cancer progression and to adapt therapeutic treatments to patient specifcities. Bioinformatic tools to assess the diferent cell populations from single-omic datasets as bulk transcriptome or methylome samples have been recently developed, including reference-based and reference-free methods. Improved methods using multi-omic datasets are yet to be developed in the future and the community would need systematic tools to perform a comparative evaluation of these algorithms on controlled data

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

Identification et modélisation du comportement viscoélastique linéaire et non linéaire du tissu cérébral en situation d'impacts

Ce travail a pour objectif de contribuer au développement du modèle par éléments finis de la tête humaine de l'ULP. Il intègre une étude du comportement viscoélastique linéaire et non linéaire du tissu cérébral dont la connaissance reste actuellement incomplète et contrastée. Les propriétés matérielles en cisaillement du cerveau sont déterminées aux petites déformations sur une gamme de fréquences inédite (de 0.1 à plus de 6000 Hz) qui inclut les fréquences associées aux chocs automobiles et balistiques non pénétrant. La robustesse du protocole opératoire et la fiabilité des résultats expérimentaux sont confirmées par l'utilisation de deux outils de mesure différents et par l'analyse d'un certain nombre de facteurs pouvant influer sur l'objectivité des mesures. L'étude s'accompagne également d'une analyse de l'anisotropie pour une région particulière du cerveau (la couronne radiaire), et des différences inter-espèces et régionales. Le comportement du cerveau aux grandes déformations est appréhendé au travers d'essais de relaxation effectués sur une plage de déformations allant de 0.1% à 50%. Il ressort que l'augmentation du niveau de déformation influe sur l'amplitude des modules mesurés mais non sur leurs temps de relaxation. Les résultats aux petites déformations aboutissent à une modélisation phénoménologique du comportement linéaire du cerveau par un modèle de Maxwell généralisé à cinq modes. Le comportement non linéaire est modélisé par une loi hyperélastique de Ogden dans sa phase caoutchoutique puis par une extension proposée de cette loi (loi visco-hyperélastique) qui tient compte des effets dissipatifs observés sur l'intervalle de temps considéré. Une confrontation des lois de comportement linéaire et non linéaire du cerveau est enfin réalisée au cours d'une simulation numérique d'un choc de référence et d'un choc balistique. Il ressort que la mise en évidence de l'apport des différentes lois nécessite d'autres critères de validation des modèles numériques.The aim of this work is to contribute to the development of the ULP human head Finite Element model. This study concerns the linear and nonlinear viscoelastic brain tissue behaviour of which the knowledge remains currently incomplete and contrasted. The small shear strains brain properties are determined on new frequency range (from 0.1 to more than 6000 Hz) which includes frequencies associated with traffic road accidents and non penetrating ballistic impacts. The robustness of the protocol and the reliability of the experimental results are confirmed by the use of two different testing devices and by the analysis of several factors which could affect measurements objectivity. The study is also accompanied by an analysis of the anisotropy for a particular area of the brain (the corona radiate), and inter-species and regional differences. The large strain brain behaviour is characterized by shear relaxation tests between 0.1% and 50% strain. The results show the increase of the strain level affect the modulus magnitude but not their relaxation times. The brain linear behaviour is modelled by a phenomenological five-mode Maxwell model. The brain rubberlike behaviour is modelled by an Ogden hyperelastic law. This law is extended to take account of the observed dissipative effects on all time range (visco-hyperelastic law). Finally, a comparison of these brain linear and nonlinear constitutive laws is realised from numerical simulations of a reference and a ballistic impact. The conclusion is that the pertinence of the different laws contribution requires other numerical model validation criteria.STRASBOURG-Sc. et Techniques (674822102) / SudocSudocFranceF

Cancer-associated fibroblasts: Accomplices in the tumor immune evasion

International audienceCancer-associated fibroblasts (CAFs) are prominent cells within the tumor microenvironment, by communicating with other cells within the tumor and by secreting the extracellular matrix components. The discovery of the immunogenic role of CAFs has made their study particularly attractive due to the potential applications in the field of cancer immunotherapy. Indeed, CAFs are highly involved in tumor immune evasion by physically impeding the immune system and interacting with both myeloid and lymphoid cells. However, CAFs do not represent a single cell entity but are divided into several subtypes with different functions that may be antagonistic. Considering that CAFs are orchestrators of the tumor microenvironment and modulate immune cells, targeting their functions may be a promising strategy. In this review, we provide an overview of (i) the mechanisms involved in immune regulation by CAFs and (ii) the therapeutic applications of CAFs modulation to improve the antitumor immune response and the effcacy of immunotherapy

PPAPDC1B and WHSC1L1 Are Common Drivers of the 8p11-12 Amplicon, Not Only in Breast Tumors But Also in Pancreatic Adenocarcinomas and Lung Tumors

International audienceAmplification of the 8p11-12 chromosomal region is a common genetic event in many epithelial cancers. In breast cancer, several genes within this region have been shown to display oncogenic activity. Among these genes, the enzyme-encoding genes, PPAPDC1B and WHSC1L1, have been identified as potential therapeutic targets. We investigated whether PPAPDC1B and WHSC1L1 acted as general driver genes, thereby serving as therapeutic targets in other tumors with 8p11-12 amplification. By using publicly available genomic data from a panel of 883 cell lines derived from different cancers, we identified the cell lines presenting amplification of both WHSC1L1 and PPAPDC1B. In particular, we focused on cell lines derived from lung cancer and pancreatic adenocarcinoma and found a correlation between the amplification of PPAPDC1B and WHSC1L1 with their overexpression. Loss-of-function studies based on the use of siRNA and shRNA demonstrated that PPAPDC1B and WHSC1L1 played a major role in regulating the survival of pancreatic adenocarcinoma and small-cell lung cancer-derived cell lines, both in anchorage-dependent and anchorage-independent conditions, displaying amplification and overexpression of these genes. We also demonstrated that PPAPDC1B and WHSC1L1 regulated xenograft growth in these cell lines. Finally, quantitative RT-PCR experiments after PPAPDC1B and WHSC1L1 knockdown revealed exclusive PPAPDC1B and WHSC1L1 gene targets in small-cell lung cancer and pancreatic adenocarcinoma-derived cell lines compared with breast cancer

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