Diedisheim Marc, Dermine Solène, Jouinot Anne, Septier Amandine, Gaujoux Sébastien, Dousset Bertrand, Cadiot Guillaume, Larger Etienne, Bertherat Jérôme, Scharfmann Raphael, Terris Benoit, Coriat Romain, Assié Guillaume
Université de Paris, Institut Cochin, Inserm U1016, CNRS UMR8104, F-75014, Paris, France.
Department of Diabetology, Assistance Publique Hôpitaux de Paris, Hôpital Cochin, Paris, France.
Endocr Relat Cancer. 2021 Jun 21;28(8):563-571. doi: 10.1530/ERC-21-0051.
Duodenopancreatic neuroendocrine tumors (DPNETs) aggressiveness is heterogeneous. Tumor grade and extension are commonly used for prognostic determination. Yet, grade classes are empirically defined, with regular updates changing the definition of classes. Genomic screening may provide more objective classes and reflect tumor biology. The aim of this study was to provide a transcriptome classification of DPNETs. We included 66 DPNETs, covering the entire clinical spectrum of the disease in terms of secretion, grade, and stage. Three distinct molecular groups were identified, associated with distinct outcomes (log-rank P < 0.01): (i) better-outcome DPNETs with pancreatic beta-cell signature. This group was mainly composed of well-differentiated, grade 1 insulinomas; (ii) poor-outcome DPNETs with pancreatic alpha-cell and hepatic signature. This group included all neuroendocrine carcinomas and grade 3 DPNETs, but also some grade 1 and grade 2 DPNETs and (iii) intermediate-outcome DPNETs with pancreatic exocrine and progenitor signature. This group included grade 1 and grade 2 DPNETs, with some insulinomas. Fibrinogen gene FGA expression was one of the topmost expressed liver genes. FGA expression was associated with disease-free survival (HR = 1.13, P = 0.005) and could be validated on two independent cohorts. This original pathophysiologic insight provides new prognostic classification perspectives.
十二指肠胰腺神经内分泌肿瘤(DPNETs)的侵袭性具有异质性。肿瘤分级和范围通常用于预后判定。然而,分级类别是根据经验定义的,且定期更新会改变类别的定义。基因组筛查可能会提供更客观的类别并反映肿瘤生物学特性。本研究的目的是提供DPNETs的转录组分类。我们纳入了66例DPNETs,涵盖了该疾病在分泌、分级和分期方面的整个临床谱。识别出三个不同的分子组,与不同的预后相关(对数秩检验P < 0.01):(i)具有胰腺β细胞特征的预后较好的DPNETs。该组主要由高分化的1级胰岛素瘤组成;(ii)具有胰腺α细胞和肝脏特征的预后较差的DPNETs。该组包括所有神经内分泌癌和3级DPNETs,但也有一些1级和2级DPNETs;(iii)具有胰腺外分泌和祖细胞特征的预后中等的DPNETs。该组包括1级和2级DPNETs,以及一些胰岛素瘤。纤维蛋白原基因FGA表达是表达最丰富的肝脏基因之一。FGA表达与无病生存期相关(HR = 1.13,P = 0.005),并且可以在两个独立队列中得到验证。这一独特的病理生理学见解提供了新的预后分类视角。