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Endocrine Abstracts (2019) 63 P452 | DOI: 10.1530/endoabs.63.P452

1Cochin Institute – Inserm U1016 - Cnrs UMR8104 - University Paris Descartes, Paris, France; 2Sorbonne University, Inserm, UMS Pass, Plateforme Post-génomique de la Pitié-Salpêtrière, P3S, F-75013, Paris, France; 3Department of Medical Oncology, Cochin Hospital, Paris Descartes University, CARPEM, AP-HP, Paris, France; 4European Hospital Georges Pompidou, HEGP - Paris Cardiovascular Research Center, PARCC - Inserm U970, Paris, France; 5Medizinische Klinik und Poliklinik IV, Ludwig Maximilian University Munich, Munich, Germany; 6Department of Endocrinology, Center for Rare Adrenal Diseases, Cochin Hospital, AP-HP, Paris, France; 7Klinik für Endokrinologie, Diabetologie und Klinische Ernährung, UniversitätsSpital Zürich, Zurich, Switzerland.


The prolonged exposure to an excess of circulating cortisol (Cushing’s syndrome) causes various complications. An accurate and early diagnosis is critical for effective surgical management and optimal prognosis. However, the current diagnostic approach based on hormonal assays can be complex and requires multiple tests. The identification of novel, specific and easily measurable biomarkers of hypercortisolism may help to improve the diagnosis and to evaluate the complications. As already shown in several diseases/disorders, stress-associated epigenetic markers can be measured at whole blood level by analyzing the leukocyte DNA methylation profile.

Objective: To analyze the whole blood methylome in patients with Cushing’s syndrome before and after hypercortisolism cure to identify specific features related to DNA methylation.

Methods: Methylome analysis was performed on leucocyte DNA from paired blood samples of 32 patients with confirmed hypercortisolism. Samples were obtained before (‘pre’) and several months after (‘post’) the cure. Methylome data were generated by the Infinium®MethylationEPICBeadChip assay (Illumina) and pre-processed by the minfi package (version 1.24.0) developed for the R software (version 3.4.4), in order to obtain the methylation data for the entire set of CpGs (n=~850000). To eliminate aberrant values, the minfi ‘cpgCollapse’ function was used to group physically adjacent CpG loci into clusters (n=~400000), estimating a single methylation value per cluster. Data analysis was performed on R by both unsupervised and supervised approaches.

Results: Unsupervised clustering of samples, based on the most variable features, showed a distribution of the 32 samples in pairs, each corresponding to an individual. Twenty-four out of the 32 patients showed a signature of hypercortisolism, corresponding to a group of features differentially methylated in the pre- compared to the post-cure samples. A supervised comparison performed on a training sub-cohort (n=12 patients, 24 samples) identified the most discriminating features of hypercortisolism, further used to classify each sample: 10/11 classified as ‘pre’ were indeed pre-cure samples, 9/9 classified as ‘post’ were indeed post-cure samples, 4/24 samples were classified as undetermined. These same features tested on a validation sub-cohort (n=20 patients, 40 samples) allowed to properly classify 13/13 pre-cure samples and 13/13 post-cure samples, with 14/40 samples classified as undetermined.

Conclusions: Whole blood methylome analysis allows to detect a specific epigenetic signature of hypercortisolism, with a high rate of discrimination in patients with Cushing’s syndrome. This approach promises to be powerful to explore the genomic regions involved and to identify specific biomarkers of hypercortisolism-related complications.

Volume 63

21st European Congress of Endocrinology

Lyon, France
18 May 2019 - 21 May 2019

European Society of Endocrinology 

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