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

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          Abstract

          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.

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          Most cited references14

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          Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma

          Pancreatic ductal adenocarcinoma (PDAC) remains a lethal disease with a 5-year survival of 4%. A key hallmark of PDAC is extensive stromal involvement, which makes capturing precise tumor-specific molecular information difficult. Here, we have overcome this problem by applying blind source separation to a diverse collection of PDAC gene expression microarray data, which includes primary, metastatic, and normal samples. By digitally separating tumor, stroma, and normal gene expression, we have identified and validated two tumor-specific subtypes including a “basal-like” subtype which has worse outcome, and is molecularly similar to basal tumors in bladder and breast cancer. Furthermore, we define “normal” and “activated” stromal subtypes which are independently prognostic. Our results provide new insight into the molecular composition of PDAC which may be used to tailor therapies or provide decision support in a clinical setting where the choice and timing of therapies is critical.
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            Genomics-Driven Precision Medicine for Advanced Pancreatic Cancer: Early Results from the COMPASS Trial

            Purpose: To perform real-time whole genome sequencing (WGS) and RNA sequencing (RNASeq) of advanced pancreatic ductal adenocarcinoma (PDAC) to identify predictive mutational and transcriptional features for better treatment selection.Experimental Design: Patients with advanced PDAC were prospectively recruited prior to first-line combination chemotherapy. Fresh tumor tissue was acquired by image-guided percutaneous core biopsy for WGS and RNASeq. Laser capture microdissection was performed for all cases. Primary endpoint was feasibility to report WGS results prior to first disease assessment CT scan at 8 weeks. The main secondary endpoint was discovery of patient subsets with predictive mutational and transcriptional signatures.Results: Sixty-three patients underwent a tumor biopsy between December 2015 and June 2017. WGS and RNASeq were successful in 62 (98%) and 60 (95%), respectively. Genomic results were reported at a median of 35 days (range, 19-52 days) from biopsy, meeting the primary feasibility endpoint. Objective responses to first-line chemotherapy were significantly better in patients with the classical PDAC RNA subtype compared with those with the basal-like subtype (P = 0.004). The best progression-free survival was observed in those with classical subtype treated with m-FOLFIRINOX. GATA6 expression in tumor measured by RNA in situ hybridization was found to be a robust surrogate biomarker for differentiating classical and basal-like PDAC subtypes. Potentially actionable genetic alterations were found in 30% of patients.Conclusions: Prospective genomic profiling of advanced PDAC is feasible, and our early data indicate that chemotherapy response differs among patients with different genomic/transcriptomic subtypes. Clin Cancer Res; 24(6); 1344-54. ©2017 AACR.
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              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.
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                Author and article information

                Journal
                EBioMedicine
                EBioMedicine
                EBioMedicine
                Elsevier
                2352-3964
                03 July 2020
                July 2020
                03 July 2020
                : 57
                : 102858
                Affiliations
                [a ]Programme Cartes d'Identité des Tumeurs (CIT), Ligue Nationale Contre le Cancer, Paris, France
                [b ]Centre de Recherche en Cancérologie de Marseille, CRCM, Inserm, CNRS, Institut Paoli-Calmettes, Aix-Marseille Université, Marseille, France
                [c ]Institut Paoli-Calmettes, Marseille, France
                [d ]Hôpital Nord, Marseille, France
                [e ]Hôpital de la Timone, Marseille, France
                [f ]Department of Gastroenterology and Pancreatology, CHU - Rangueil and University of Toulouse, Toulouse, France
                [g ]Department of Digestive Oncology, Beaujon Hospital, Paris 7 University, APHP, Clichy, France
                Author notes
                [# ]Corresponding authors.
                [⁎]

                Denotes authors with equal contribution.

                [+]

                Denotes co-corresponding authors.

                Article
                S2352-3964(20)30233-4 102858
                10.1016/j.ebiom.2020.102858
                7334821
                32629389
                a2c91578-c686-4a2b-9539-fba45c259fd5
                © 2020 The Author(s)

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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                Categories
                Research paper

                pancreatic cancer,transcriptomic signature,chemosensitivity prediction,prognostic,translational medicine,precision medicine

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