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- Author: Anne Jouinot x
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Department of Diabetology, Assistance Publique Hôpitaux de Paris, Hôpital Cochin, Paris, France
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Department of Gastroenterology and Digestive Oncology, Assistance Publique Hôpitaux de Paris, Hôpital Cochin, Paris, France
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Department of Endocrinology, Center for Rare Adrenal Diseases, Assistance Publique Hôpitaux de Paris, Hôpital Cochin, Paris, France
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Department of Digestive Surgery, Assistance Publique Hôpitaux de Paris, Hôpital Cochin, Paris, France
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Department of Digestive Surgery, Assistance Publique Hôpitaux de Paris, Hôpital Cochin, Paris, France
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Department of Diabetology, Assistance Publique Hôpitaux de Paris, Hôpital Cochin, Paris, France
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Department of Endocrinology, Center for Rare Adrenal Diseases, Assistance Publique Hôpitaux de Paris, Hôpital Cochin, Paris, France
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Department of Pathology, Assistance Publique Hôpitaux de Paris, Hôpital Cochin, Paris, France
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Department of Gastroenterology and Digestive Oncology, Assistance Publique Hôpitaux de Paris, Hôpital Cochin, Paris, France
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Department of Endocrinology, Center for Rare Adrenal Diseases, Assistance Publique Hôpitaux de Paris, Hôpital Cochin, Paris, France
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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.
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INSERM UMR745, Biological and Pharmaceutical Sciences University, Université Paris Descartes, Sorbonne Paris Cité, Paris, France
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Department of Medical Oncology, Hôpital Cochin, Assistance Publique – Hôpitaux de Paris, Paris, France
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Department of Endocrinology, Cochin Hospital, Assistance Publique – Hôpitaux de Paris, Paris, France
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Department of Endocrinology, Cochin Hospital, Assistance Publique – Hôpitaux de Paris, Paris, France
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Department of Endocrinology, Cochin Hospital, Assistance Publique – Hôpitaux de Paris, Paris, France
Reference Center for Rare Adrenal Diseases, Reference Center for Rare Adrenal Cancer Network COMETE, Hôpital Cochin, AssistancePublique – Hôpitaux de Paris, Paris, France
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Department of Endocrinology, Cochin Hospital, Assistance Publique – Hôpitaux de Paris, Paris, France
Reference Center for Rare Adrenal Diseases, Reference Center for Rare Adrenal Cancer Network COMETE, Hôpital Cochin, AssistancePublique – Hôpitaux de Paris, Paris, France
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Department of Pathology, AP-HP, Hôpital Pitié-Salpétrière, Pierre et Marie Curie Université, Paris, France
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Department of Endocrinology, Center for Rare Adrenal Diseases, AP-HP, Hôpital Cochin, Paris, France
Department of Hormonology, AP-HP, Hôpital Cochin, Paris, France
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Department of Hormonology, AP-HP, Hôpital Cochin, Paris, France
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Department of Endocrinology, Center for Rare Adrenal Diseases, AP-HP, Hôpital Cochin, Paris, France
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Department of Endocrinology, Center for Rare Adrenal Diseases, AP-HP, Hôpital Cochin, Paris, France
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Department of Endocrinology, Center for Rare Adrenal Diseases, AP-HP, Hôpital Cochin, Paris, France
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Department of Endocrinology, Center for Rare Adrenal Diseases, AP-HP, Hôpital Cochin, Paris, France
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Benign adrenal tumors cover a spectrum of lesions with distinct morphology and steroid secretion. Current classification is empirical. Beyond a few driver mutations, pathophysiology is not well understood. Here, a pangenomic characterization of benign adrenocortical tumors is proposed, aiming at unbiased classification and new pathophysiological insights. Benign adrenocortical tumors (n = 146) were analyzed by transcriptome, methylome, miRNome, chromosomal alterations and mutational status, using expression arrays, methylation arrays, miRNA sequencing, SNP arrays, and exome or targeted next-generation sequencing respectively. Pathological and hormonal data were collected for all tumors. Pangenomic analysis identifies four distinct molecular categories: (1) tumors responsible for overt Cushing, gathering distinct tumor types, sharing a common cAMP/PKA pathway activation by distinct mechanisms; (2) adenomas with mild autonomous cortisol excess and non-functioning adenomas, associated with beta-catenin mutations; (3) primary macronodular hyperplasia with ARMC5 mutations, showing an ovarian expression signature; (4) aldosterone-producing adrenocortical adenomas, apart from other benign tumors. Epigenetic alterations and steroidogenesis seem associated, including CpG island hypomethylation in tumors with no or mild cortisol secretion, miRNA patterns defining specific molecular groups, and direct regulation of steroidogenic enzyme expression by methylation. Chromosomal alterations and somatic mutations are subclonal, found in less than 2/3 of cells. New pathophysiological insights, including distinct molecular signatures supporting the difference between mild autonomous cortisol excess and overt Cushing, ARMC5 implication into the adreno-gonadal differentiation faith, and the subclonal nature of driver alterations in benign tumors, will orient future research. This first genomic classification provides a large amount of data as a starting point.
