Identification of glucocorticoid-related molecular signature by whole blood methylome analysis.

OBJECTIVE: Cushing's syndrome represents a state of excessive glucocorticoids related to glucocorticoid treatments or to endogenous hypercortisolism. Cushing's syndrome is associated with high morbidity, with significant inter-individual variability. Likewise, adrenal insufficiency is a life-threatening condition of cortisol deprivation. Currently, hormone assays contribute to identify Cushing's syndrome or adrenal insufficiency. However, no biomarker directly quantifies the biological glucocorticoid action. The aim of this study was to identify such markers. DESIGN: We evaluated whole blood DNA methylome in 94 samples obtained from patients with different glucocorticoid states (Cushing's syndrome, eucortisolism, adrenal insufficiency). We used an independent cohort of 91 samples for validation. METHODS: Leukocyte DNA was obtained from whole blood samples. Methylome was determined using the Illumina methylation chip array (~850 000 CpG sites). Both unsupervised (principal component analysis) and supervised (Limma) methods were used to explore methylome profiles. A Lasso-penalized regression was used to select optimal discriminating features. RESULTS: Whole blood methylation profile was able to discriminate samples by their glucocorticoid status: glucocorticoid excess was associated with DNA hypomethylation, recovering within months after Cushing's syndrome correction. In Cushing's syndrome, an enrichment in hypomethylated CpG sites was observed in the region of FKBP5 gene locus. A methylation predictor of glucocorticoid excess was built on a training cohort and validated on two independent cohorts. Potential CpG sites associated with the risk for specific complications, such as glucocorticoid-related hypertension or osteoporosis, were identified, needing now to be confirmed on independent cohorts. CONCLUSIONS: Whole blood DNA methylome is dynamically impacted by glucocorticoids. This biomarker could contribute to better assessment of glucocorticoid action beyond hormone assays.

Genomic classification of benign adrenocortical lesions.

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.

Intratumor heterogeneity of prognostic DNA-based molecular markers in adrenocortical carcinoma.

BACKGROUND: The prognosis of adrenocortical carcinoma (ACC) is heterogeneous. Genomic studies have identified ACC subgroups characterized by specific molecular alterations, including features measured at DNA level (somatic mutations, chromosome alterations, DNA methylation), which are closely associated with outcome. The aim of this study was to evaluate intratumor heterogeneity of prognostic molecular markers at the DNA level. METHODS: Two different tissue samples (primary tumor, local recurrence or metastasis) were analyzed in 26 patients who underwent surgery for primary or recurrent ACC. DNA-related biomarkers with prognostic role were investigated in frozen and paraffin-embedded samples. Somatic mutations of p53/Rb and Wnt/ß-catenin pathways were assessed using next-generation sequencing (n = 26), chromosome alteration profiles were determined using SNP arrays (n = 14) and methylation profiles were determined using four-gene bisulfite pyrosequencing (n = 12). RESULTS: Somatic mutations for ZNRF3, TP53, CTNN1B and CDKN2A were found in 7, 6, 6 and 4 patients, respectively, with intratumor heterogeneity in 8/26 patients (31%). Chromosome alteration profiles were 'Noisy' (numerous and anarchic alterations) in 8/14 and 'Chromosomal' (extended patterns of loss of heterozygosity) in 5/14 of the study samples. For these profiles, no intratumor heterogeneity was observed. Methylation profiles were hypermethylated in 5/12 and non-hypermethylated in 7/12 of the study samples. Intratumor heterogeneity of methylation profiles was observed in 2/12 patients (17%). CONCLUSIONS: Intratumor heterogeneity impacts DNA-related molecular markers. While somatic mutation can differ, prognostic DNA methylation and chromosome alteration profile seem rather stable and might be more robust for the prognostic assessment.

Pangenomic Classification of Pituitary Neuroendocrine Tumors.

Pituitary neuroendocrine tumors (PitNETs) are common, with five main histological subtypes: lactotroph, somatotroph, and thyrotroph (POU1F1/PIT1 lineage); corticotroph (TBX19/TPIT lineage); and gonadotroph (NR5A1/SF1 lineage). We report a comprehensive pangenomic classification of PitNETs. PitNETs from POU1F1/PIT1 lineage showed an epigenetic signature of diffuse DNA hypomethylation, with transposable elements expression and chromosomal instability (except for GNAS-mutated somatotrophs). In TPIT lineage, corticotrophs were divided into three classes: the USP8-mutated with overt secretion, the USP8-wild-type with increased invasiveness and increased epithelial-mesenchymal transition, and the large silent tumors with gonadotroph transdifferentiation. Unexpected expression of gonadotroph markers was also found in GNAS-wild-type somatotrophs (SF1 expression), challenging the current definition of SF1/gonadotroph lineage. This classification improves our understanding and affects the clinical stratification of patients with PitNETs.

Value of Molecular Classification for Prognostic Assessment of Adrenocortical Carcinoma.

IMPORTANCE: The risk stratification of adrenocortical carcinoma (ACC) based on tumor proliferation index and stage is limited. Adjuvant therapy after surgery is recommended for most patients. Pan-genomic studies have identified distinct molecular groups closely associated with outcome. OBJECTIVE: To compare the molecular classification for prognostic assessment of ACC with other known prognostic factors. DESIGN, SETTING, AND PARTICIPANTS: In this retrospective biomarker analysis, ACC tumor samples from 368 patients who had undergone surgical tumor removal were collected from March 1, 2005, to September 30, 2015 (144 in the training cohort and 224 in the validation cohort) at 21 referral centers with a median follow-up of 35 months (interquartile range, 18-74 months). Data were analyzed from March 2016 to March 2018. EXPOSURES: Meta-analysis of pan-genomic studies (transcriptome, methylome, chromosome alteration, and mutational profiles) was performed on the training cohort. Targeted biomarker analysis, including targeted gene expression (BUB1B and PINK1), targeted methylation (PAX5, GSTP1, PYCARD, and PAX6), and targeted next-generation sequencing, was performed on the training and validation cohorts. MAIN OUTCOMES AND MEASURES: Disease-free survival. Cox proportional hazards regression and C indexes were used to assess the prognostic value of each model. RESULTS: Of the 368 patients (mean [SD] age, 49 [16] years), 144 were in the training cohort (100 [69.4%] female) and 224 were in the validation cohort (142 [63.4%] female). In the training cohort, pan-genomic measures classified ACC into 3 molecular groups (A1, A2, and A3-B), with 5-year survival of 9% for group A1, 45% for group A2, and 82% for group A3-B (log-rank P < .001). Molecular class was an independent prognostic factor of recurrence in stage I to III ACC after complete surgery (hazard ratio, 55.91; 95% CI, 8.55-365.40; P < .001). The combination of European Network for the Study of Adrenal Tumors (ENSAT) stage, tumor proliferation index, and molecular class provided the most discriminant prognostic model (C index, 0.88). In the validation cohort, the molecular classification, determined by targeted biomarker measures, was confirmed as an independent prognostic factor of recurrence (hazard ratio, 5.96 [95% CI, 1.81-19.58], P = .003 for the targeted classifier combining expression, methylation, and chromosome alterations; and 2.61 [95% CI, 1.31-5.19], P = .006 for the targeted classifier combining methylation, chromosome alterations, and mutational profile). The prognostic value of the molecular markers was limited for patients with stage IV ACC. CONCLUSIONS AND RELEVANCE: The findings suggest that in localized ACC, targeted classifiers may be used as independent markers of recurrence. The determination of molecular class may improve individual prognostic assessment and thus may spare unnecessary adjuvant treatment.

Calling Chromosome Alterations, DNA Methylation Statuses, and Mutations in Tumors by Simple Targeted Next-Generation Sequencing: A Solution for Transferring Integrated Pangenomic Studies into Routine Practice?

Pangenomic studies identified distinct molecular classes for many cancers, with major clinical applications. However, routine use requires cost-effective assays. We assessed whether targeted next-generation sequencing (NGS) could call chromosomal alterations and DNA methylation status. A training set of 77 tumors and a validation set of 449 (43 tumor types) were analyzed by targeted NGS and single-nucleotide polymorphism (SNP) arrays. Thirty-two tumors were analyzed by NGS after bisulfite conversion, and compared to methylation array or methylation-specific multiplex ligation-dependent probe amplification. Considering allelic ratios, correlation was strong between targeted NGS and SNP arrays (r = 0.88). In contrast, considering DNA copy number, for variations of one DNA copy, correlation was weaker between read counts and SNP array (r = 0.49). Thus, we generated TARGOMICs, optimized for detecting chromosome alterations by combining allelic ratios and read counts generated by targeted NGS. Sensitivity for calling normal, lost, and gained chromosomes was 89%, 72%, and 31%, respectively. Specificity was 81%, 93%, and 98%, respectively. These results were confirmed in the validation set. Finally, TARGOMICs could efficiently align and compute proportions of methylated cytosines from bisulfite-converted DNA from targeted NGS. In conclusion, beyond calling mutations, targeted NGS efficiently calls chromosome alterations and methylation status in tumors. A single run and minor design/protocol adaptations are sufficient. Optimizing targeted NGS should expand translation of genomics to clinical routine.

Loss-of-function mutations in the CABLES1 gene are a novel cause of Cushing's disease.

The CABLES1 cell cycle regulator participates in the adrenal-pituitary negative feedback, and its expression is reduced in corticotropinomas, pituitary tumors with a largely unexplained genetic basis. We investigated the presence of CABLES1 mutations/copy number variations (CNVs) and their associated clinical, histopathological and molecular features in patients with Cushing's disease (CD). Samples from 146 pediatric (118 germline DNA only/28 germline and tumor DNA) and 35 adult (tumor DNA) CD patients were screened for CABLES1 mutations. CNVs were assessed in 116 pediatric CD patients (87 germline DNA only/29 germline and tumor DNA). Four potentially pathogenic missense variants in CABLES1 were identified, two in young adults (c.532G > A, p.E178K and c.718C > T, p.L240F) and two in children (c.935G > A, p.G312D and c.1388A > G, and p.D463G) with CD; no CNVs were found. The four variants affected residues within or close to the predicted cyclin-dependent kinase-3 (CDK3)-binding region of the CABLES1 protein and impaired its ability to block cell growth in a mouse corticotropinoma cell line (AtT20/D16v-F2). The four patients had macroadenomas. We provide evidence for a role of CABLES1 as a novel pituitary tumor-predisposing gene. Its function might link two of the main molecular mechanisms altered in corticotropinomas: the cyclin-dependent kinase/cyclin group of cell cycle regulators and the epidermal growth factor receptor signaling pathway. Further studies are needed to assess the prevalence of CABLES1 mutations among patients with other types of pituitary adenomas and to elucidate the pituitary-specific functions of this gene.

DNA Methylation Is an Independent Prognostic Marker of Survival in Adrenocortical Cancer.

CONTEXT: Adrenocortical cancer (ACC) is an aggressive tumor with a heterogeneous outcome. Prognostic stratification is difficult even based on tumor stage and Ki67. Recently integrated genomics studies have demonstrated that CpG islands hypermethylation is correlated with poor survival. OBJECTIVE: The goal of this study was to confirm the prognostic value of CpG islands methylation on an independent cohort. DESIGN: Methylation was measured by methylation-specific multiplex ligation-dependent probe amplification (MS-MLPA). SETTING: MS-MLPA was performed in a training cohort of 50 patients with ACC to identify the best set of probes correlating with disease-free survival (DFS) and overall survival (OS). These outcomes were validated in an independent cohort from 21 ENSAT centers. PATIENTS: The validation cohort included 203 patients (64% women, median age 50 years, 80% localized tumors). MAIN OUTCOME MEASURES: DFS and OS. RESULTS: In the training cohort, mean methylation of 4 genes (PAX5, GSTP1, PYCARD, PAX6) was the strongest methylation marker. In the validation cohort, methylation was a significant prognostic factor of DFS (P < 0.0001) and OS (P < 0.0001). Methylation, Ki67, and ENSAT stage were combined in multivariate models. For DFS, methylation (P = 0.0005) and stage (P < 0.0001) but not Ki67 (P = 0.19) remained highly significant. For OS, methylation (P = 0.0006), stage (P < 0.0001), and Ki67 (P = 0.024) were independent prognostic factors. CONCLUSIONS: Tumor DNA methylation emerges as an independent prognostic factor in ACC. MS-MLPA is readily compatible with clinical routine and should enhance our ability for prognostication and precision medicine.

Comparative analyses of Legionella species identifies genetic features of strains causing Legionnaires' disease.

BACKGROUND: The genus Legionella comprises over 60 species. However, L. pneumophila and L. longbeachae alone cause over 95% of Legionnaires' disease. To identify the genetic bases underlying the different capacities to cause disease we sequenced and compared the genomes of L. micdadei, L. hackeliae and L. fallonii (LLAP10), which are all rarely isolated from humans. RESULTS: We show that these Legionella species possess different virulence capacities in amoeba and macrophages, correlating with their occurrence in humans. Our comparative analysis of 11 Legionella genomes belonging to five species reveals highly heterogeneous genome content with over 60% representing species-specific genes; these comprise a complete prophage in L. micdadei, the first ever identified in a Legionella genome. Mobile elements are abundant in Legionella genomes; many encode type IV secretion systems for conjugative transfer, pointing to their importance for adaptation of the genus. The Dot/Icm secretion system is conserved, although the core set of substrates is small, as only 24 out of over 300 described Dot/Icm effector genes are present in all Legionella species. We also identified new eukaryotic motifs including thaumatin, synaptobrevin or clathrin/coatomer adaptine like domains. CONCLUSIONS: Legionella genomes are highly dynamic due to a large mobilome mainly comprising type IV secretion systems, while a minority of core substrates is shared among the diverse species. Eukaryotic like proteins and motifs remain a hallmark of the genus Legionella. Key factors such as proteins involved in oxygen binding, iron storage, host membrane transport and certain Dot/Icm substrates are specific features of disease-related strains.