Publications
EBioMedicineJul 2020 |
57
102858
DOI:
10.1016/j.ebiom.2020.102858

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

Nicolle, Rémy; Blum, Yuna; Duconseil, Pauline; Vanbrugghe, Charles; Brandone, Nicolas; Poizat, Flora; Roques, Julie; Bigonnet, Martin; Gayet, Odile; Rubis, Marion; Elarouci, Nabila; Armenoult, Lucile; Ayadi, Mira; de Reyniès, Aurélien; Giovannini, Marc; Grandval, Philippe; Garcia, Stephane; Canivet, Cindy; Cros, Jérôme; Bournet, Barbara; Moutardier, Vincent; Gilabert, Marine; Iovanna, Juan; Dusetti, Nelson; Buscail, Louis; ,
Product Used
NGS
Abstract
A significant gap in pancreatic ductal adenocarcinoma (PDAC) patient's care is the lack of molecular parameters characterizing tumours and allowing a personalized treatment. 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. 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). PAMG unify all PDAC pre-existing classifications inducing a shift in the actual paradigm of binary classifications towards a better characterization in a gradient. 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.
Product Used
NGS

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