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  • Author: Jean Guibourdenche x
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Françoise Galland, Ludovic Lacroix, Patrick Saulnier, Philippe Dessen, Geri Meduri, Michèle Bernier, Stéphane Gaillard, Jean Guibourdenche, Thierry Fournier, Danièle Evain-Brion, Jean Michel Bidart, and Philippe Chanson

Non-functioning pituitary adenomas (NFPAs) may be locally invasive. Markers of invasiveness are needed to guide patient management and particularly the use of adjuvant radiotherapy. To examine whether invasive NFPAs display a specific gene expression profile relative to non-invasive tumors, we selected 40 NFPAs (38 of the gonadotroph type) and classified them as invasive (n=22) or non-invasive (n=18) on the basis of magnetic resonance imaging and surgical findings. We then performed pangenomic analysis with the 44k Agilent human whole genome expression oligonucleotide microarray in order to identify genes with differential expression between invasive and non-invasive NFPAs. Candidate genes were then tested in qRT-PCR. Prediction class analysis showed that the expression of 346 genes differed between invasive and non-invasive NFPAs (P<0.001), of which 233 genes were up-regulated and 113 genes were down-regulated in invasive tumors. On the basis of Ingenuity networks and the degree of up- or down-regulation in invasive versus non-invasive tumors, 35 genes were selected for expression quantification by qRT-PCR. Overexpression of only four genes was confirmed, namely IGFBP5 (P=0.02), MYO5A (P=0.04), FLT3 (P=0.01), and NFE2L1 (P=0.02). At the protein level, only myosin 5A (MYO5A) immunostaining was stronger in invasive than in non-invasive NFPAs. Molecular signature allows to differentiate ‘grossly’ invasive from non-invasive NFPAs. The product of one of these genes, MYO5A, may be a useful marker of tumor invasiveness.

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Simon Faillot, Thomas Foulonneau, Mario Néou, Stéphanie Espiard, Simon Garinet, Anna Vaczlavik, Anne Jouinot, Windy Rondof, Amandine Septier, Ludivine Drougat, Karine Hécale-Perlemoine, Bruno Ragazzon, Marthe Rizk-Rabin, Mathilde Sibony, Fidéline Bonnet-Serrano, Jean Guibourdenche, Rosella Libé, Lionel Groussin, Bertrand Dousset, Aurélien de Reyniès, Jérôme Bertherat, and Guillaume Assié

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.