Details

Autor(en) / Beteiligte

• 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
• Buscail, Louis
• Moutardier, Vincent
• Gilabert, Marine
• Iovanna, Juan
• Dusetti, Nelson

Titel

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

Ist Teil von

• EBioMedicine, 2020-07, Vol.57, p.102858-102858, Article 102858

Ort / Verlag

Netherlands: Elsevier B.V

Erscheinungsjahr

2020

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.