Whole blood transcriptomic signature of Cushing's syndrome.

OBJECTIVE: Cushing's syndrome is characterized by high morbidity and mortality with high interindividual variability. Easily measurable biomarkers, in addition to the hormone assays currently used for diagnosis, could reflect the individual biological impact of glucocorticoids. The aim of this study is to identify such biomarkers through the analysis of whole blood transcriptome. DESIGN: Whole blood transcriptome was evaluated in 57 samples from patients with overt Cushing's syndrome, mild Cushing's syndrome, eucortisolism, and adrenal insufficiency. Samples were randomly split into a training cohort to set up a Cushing's transcriptomic signature and a validation cohort to assess this signature. METHODS: Total RNA was obtained from whole blood samples and sequenced on a NovaSeq 6000 System (Illumina). Both unsupervised (principal component analysis) and supervised (Limma) methods were used to explore the transcriptome profile. Ridge regression was used to build a Cushing's transcriptome predictor. RESULTS: The transcriptomic profile discriminated samples with overt Cushing's syndrome. Genes mostly associated with overt Cushing's syndrome were enriched in pathways related to immunity, particularly neutrophil activation. A prediction model of 1500 genes built on the training cohort demonstrated its discriminating value in the validation cohort (accuracy .82) and remained significant in a multivariate model including the neutrophil proportion (P = .002). Expression of FKBP5, a single gene both overexpressed in Cushing's syndrome and implied in the glucocorticoid receptor signaling, could also predict Cushing's syndrome (accuracy .76). CONCLUSIONS: Whole blood transcriptome reflects the circulating levels of glucocorticoids. FKBP5 expression could be a nonhormonal marker of Cushing's syndrome.

Whole blood transcriptome signature predicts severe forms of COVID-19: Results from the COVIDeF cohort study.

COVID-19 is associated with heterogeneous outcome. Early identification of a severe progression of the disease is essential to properly manage the patients and improve their outcome. Biomarkers reflecting an increased inflammatory response, as well as individual features including advanced age, male gender, and pre-existing comorbidities, are risk factors of severe COVID-19. Yet, these features show limited accuracy for outcome prediction. The aim was to evaluate the prognostic value of whole blood transcriptome at an early stage of the disease. Blood transcriptome of patients with mild pneumonia was profiled. Patients with subsequent severe COVID-19 were compared to those with favourable outcome, and a molecular predictor based on gene expression was built. Unsupervised classification discriminated patients who would later develop a COVID-19-related severe pneumonia. The corresponding gene expression signature reflected the immune response to the viral infection dominated by a prominent type I interferon, with IFI27 among the most over-expressed genes. A 48-genes transcriptome signature predicting the risk of severe COVID-19 was built on a training cohort, then validated on an external independent cohort, showing an accuracy of 81% for predicting severe outcome. These results identify an early transcriptome signature of severe COVID-19 pneumonia, with a possible relevance to improve COVID-19 patient management.

Whole blood methylome-derived features to discriminate endocrine hypertension.

BACKGROUND: Arterial hypertension represents a worldwide health burden and a major risk factor for cardiovascular morbidity and mortality. Hypertension can be primary (primary hypertension, PHT), or secondary to endocrine disorders (endocrine hypertension, EHT), such as Cushing's syndrome (CS), primary aldosteronism (PA), and pheochromocytoma/paraganglioma (PPGL). Diagnosis of EHT is currently based on hormone assays. Efficient detection remains challenging, but is crucial to properly orientate patients for diagnostic confirmation and specific treatment. More accurate biomarkers would help in the diagnostic pathway. We hypothesized that each type of endocrine hypertension could be associated with a specific blood DNA methylation signature, which could be used for disease discrimination. To identify such markers, we aimed at exploring the methylome profiles in a cohort of 255 patients with hypertension, either PHT (n = 42) or EHT (n = 213), and at identifying specific discriminating signatures using machine learning approaches. RESULTS: Unsupervised classification of samples showed discrimination of PHT from EHT. CS patients clustered separately from all other patients, whereas PA and PPGL showed an overall overlap. Global methylation was decreased in the CS group compared to PHT. Supervised comparison with PHT identified differentially methylated CpG sites for each type of endocrine hypertension, showing a diffuse genomic location. Among the most differentially methylated genes, FKBP5 was identified in the CS group. Using four different machine learning methods-Lasso (Least Absolute Shrinkage and Selection Operator), Logistic Regression, Random Forest, and Support Vector Machine-predictive models for each type of endocrine hypertension were built on training cohorts (80% of samples for each hypertension type) and estimated on validation cohorts (20% of samples for each hypertension type). Balanced accuracies ranged from 0.55 to 0.74 for predicting EHT, 0.85 to 0.95 for predicting CS, 0.66 to 0.88 for predicting PA, and 0.70 to 0.83 for predicting PPGL. CONCLUSIONS: The blood DNA methylome can discriminate endocrine hypertension, with methylation signatures for each type of endocrine disorder.

Transcriptome in paraffin samples for the diagnosis and prognosis of adrenocortical carcinoma.

Design: Molecular classification is important for the diagnosis and prognosis of adrenocortical tumors (ACT). Transcriptome profiles separate adrenocortical adenomas 'C2' from carcinomas, and identify two groups of carcinomas 'C1A' and 'C1B', of poor and better prognosis respectively. However, many ACT cannot be profiled because of improper or absent freezing procedures, a mandatory requirement so far. The main aim was to determine transcriptome profiles on formalin-fixed paraffin-embedded (FFPE) samples, using the new 3'-end RNA-sequencing technology. A secondary aim was to demonstrate the ability of this technique to explore large FFPE archives, by focusing on the rare oncocytic ACT variants. Methods: We included 131 ACT: a training cohort from Cochin hospital and an independent validation cohort from Wuerzburg hospital. The 3' transcriptome was generated from FFPE samples using QuantSeq (Lexogen, Vienna, Austria) and NextSeq500 (Illumina, San Diego, CA, USA). Results: In the training cohort, unsupervised clustering identified three groups: 'C1A' aggressive carcinomas (n = 28, 29%), 'C1B' more indolent carcinomas (n = 28, 29%), and 'C2' adenomas (n = 39, 41%). The prognostic value of FFPE transcriptome was confirmed in the validation cohort (5-year OS: 26% in 'C1A' (n = 26) and 100% in 'C1B' (n = 10), P = 0.003). FFPE transcriptome was an independent prognostic factor in a multivariable model including tumor stage and Ki-67 (OS HR: 7.5, P = 0.01). Oncocytic ACT (n = 19) did not form any specific cluster. Oncocytic carcinomas (n = 6) and oncocytic ACT of uncertain malignant potential (n = 4) were all in 'C1B'. Conclusions: The 3' RNA-sequencing represents a convenient solution for determining ACT molecular class from FFPE samples. This technique should facilitate routine use and large retrospective studies.

KDM1A inactivation causes hereditary food-dependent Cushing syndrome.

PURPOSE: This study aimed to investigate the genetic cause of food-dependent Cushing syndrome (FDCS) observed in patients with primary bilateral macronodular adrenal hyperplasia (PBMAH) and adrenal ectopic expression of the glucose-dependent insulinotropic polypeptide receptor. Germline ARMC5 alterations have been reported in about 25% of PBMAH index cases but are absent in patients with FDCS. METHODS: A multiomics analysis of PBMAH tissues from 36 patients treated by adrenalectomy was performed (RNA sequencing, single-nucleotide variant array, methylome, miRNome, exome sequencing). RESULTS: The integrative analysis revealed 3 molecular groups with different clinical features, namely G1, comprising 16 patients with ARMC5 inactivating variants; G2, comprising 6 patients with FDCS with glucose-dependent insulinotropic polypeptide receptor ectopic expression; and G3, comprising 14 patients with a less severe phenotype. Exome sequencing revealed germline truncating variants of KDM1A in 5 G2 patients, constantly associated with a somatic loss of the KDM1A wild-type allele on 1p, leading to a loss of KDM1A expression both at messenger RNA and protein levels (P = 1.2 × 10-12 and P < .01, respectively). Subsequently, KDM1A pathogenic variants were identified in 4 of 4 additional index cases with FDCS. CONCLUSION: KDM1A inactivation explains about 90% of FDCS PBMAH. Genetic screening for ARMC5 and KDM1A can now be offered for most PBMAH operated patients and their families, opening the way to earlier diagnosis and improved management.

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.

Prognostic and Monitoring Value of Circulating Tumor Cells in Adrenocortical Carcinoma: A Preliminary Monocentric Study.

Adrenocortical carcinoma (ACC), a rare and aggressive neoplasia, presents poor prognosis when metastatic at diagnosis and limited therapies are available. Specific and sensitive markers for early diagnosis and a monitoring system of therapy and tumor evolution are urgently needed. The liquid biopsy represents a source of tumor material within a minimally invasive blood draw that allows the recovery of circulating tumor cells (CTCs). CTCs have been recently shown to be detectable in ACC. In the present paper, we evaluated the prognostic value of CTCs obtained by size-filtration in a small pilot cohort of 19 ACC patients. We found CTCs in 68% of pre-surgery and in 38% of post-surgery blood samples. In addition, CTC clusters (CTMs) and cancer associated macrophages (CAMLs) were detectable in some ACC patients. The median number of CTCs significantly decreased after the mass removal. Finally, stratifying patients in high and low pre-surgery CTC number groups, assuming the 75th percentile CTC value as cut-off, CTCs significantly predicted patients' overall survival (log rank = 0.005), also in a multivariate analysis adjusted for age and tumor stage. In conclusion, though preliminary and performed in a small cohort of patients, our study suggests that CTC number may represent a promising marker for prognosis and disease monitoring in ACC.

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.

The Adipose Stem Cell as a Novel Metabolic Actor in Adrenocortical Carcinoma Progression: Evidence from an In Vitro Tumor Microenvironment Crosstalk Model.

Metabolic interplay between the tumor microenvironment and cancer cells is a potential target for novel anti-cancer approaches. Among stromal components, adipocytes and adipose precursors have been shown to actively participate in tumor progression in several solid malignancies. In adrenocortical carcinoma (ACC), a rare endocrine neoplasia with a poor prognosis, cancer cells often infiltrate the fat mass surrounding the adrenal organ, enabling possible crosstalk with the adipose cells. Here, by using an in vitro co-culture system, we show that the interaction between adipose-derived stem cells (ASCs) and the adrenocortical cancer cell line H295R leads to metabolic and functional reprogramming of both cell types: cancer cells limit differentiation and increase proliferation of ASCs, which in turn support tumor growth and invasion. This effect associates with a shift from the paracrine cancer-promoting IGF2 axis towards an ASC-associated leptin axis, along with a shift in the SDF-1 axis towards CXCR7 expression in H295R cells. In conclusion, our findings suggest that adipose precursors, as pivotal components of the ACC microenvironment, promote cancer cell reprogramming and invasion, opening new perspectives for the development of more effective therapeutic approaches.

Genomics of benign adrenocortical tumors.

Benign adrenocortical adenomas and hyperplasia are relatively common and include a spectrum of distinct entities, which diagnosis depends on the macroscopic aspect and the secretion profile. Recent advances in genomics have proposed high-throughput molecular characterization of adrenal tumors, thereby improving our knowledge on the pathophysiology and tumorigenesis of these tumors. Genomic (exome and chromosome alteration profiles), epigenomic (micro-RNAs expression and methylation profiles) and transcriptomic (gene expression profiles) studies highlighted the major roles of intracellular calcium signaling in aldosterone-producing adenomas (APA), of protein kinase A (PKA)/cAMP pathway in cortisol-producing tumors, and of Wnt/beta-catenin pathway in non-secreting tumors. Exome sequencing revealed new major drivers in all tumor types, including KCNJ5, ATP1A1, ATP2B3, CACNA1D and CACNA1H mutations in APA, PRKACA mutations in cortisol-producing adenomas (CPA) and ARMC5 mutations in primary macronodular adrenocortical hyperplasia (PMAH). The clinical impact of these findings is just starting to evolve. The identification of genetic syndromes, such as germline ARMC5 mutations in PMAH, has allowed genetic counseling. Key molecular alterations could serve as a basis for the development of targeted medical treatments for benign adrenal tumors. The recent developments in genomics, including single-cell technologies, and in proteomics and metabolomics will probably offer new perspectives for characterizing benign adrenal tumorigenesis.

Fascin-1 Is a Novel Prognostic Biomarker Associated With Tumor Invasiveness in Adrenocortical Carcinoma.

CONTEXT: Novel tumor markers are urgently needed to better stratify adrenocortical cancer (ACC) patients and improve therapies for this aggressive neoplasm. OBJECTIVE: To assess the diagnostic and prognostic value of the actin-bundling protein fascin-1 (FSCN1) in adrenocortical tumors. DESIGN, SETTING AND PARTICIPANTS: A local series of 37 malignant/37 benign adrenocortical tumors at Careggi University Hospital and two independent validation ACC cohorts (Cochin, TCGA) from the European Network for the Study of Adrenal Tumors were studied. MAIN OUTCOME MEASURES: FSCN1 expression was quantified by immunohistochemistry, Western blot and quantitative RT-PCR in ACC specimens; overall and disease-free survival associated with FSCN1 expression were assessed by Kaplan-Meier analysis and compared with that of Ki67 labeling index and tumor stage. RESULTS: Despite the low diagnostic power, in the Florence ACC series, FSCN1 immunohistochemical detection appeared as an independent prognostic factor, also refining results obtained with staging and Ki67 labeling index. The robust prognostic power of FSCN1 levels was further confirmed in two independent ACC cohorts. A positive correlation was found between FSCN1 and steroidogenic factor-1 (SF-1), with a substantially higher expression of both factors in ACCs at advanced stages and with at least one of the three Weiss score parameters associated with invasiveness. Moreover, we demonstrated FSCN1 role in promoting cell invasion in a human ACC cell line only in the case of increased SF-1 dosage. CONCLUSIONS: These findings show that FSCN1 is a novel independent prognostic marker in ACC and may serve as a potential therapeutic target to block tumor spread.

Human fetal adrenal cells retain age-related stem- and endocrine-differentiation potential in culture.

The adrenal gland is a multiendocrine organ with a steroidogenic mesenchymal cortex and an inner catecholamine-producing medulla of neuroendocrine origin. After embryonic development, this plastic organ undergoes a functional postnatal remodeling. Elucidating these complex processes is pivotal for understanding the early bases of functional endocrine disorders and tumors affecting the mature gland. We developed an in vitro human adrenal cell model derived from fetal adrenal specimens at different gestational ages, consisting of neuroendocrine and cortical components and expressing the zona and functional markers of the original fetal organ. These cortical and neuroendocrine progenitor cells retain in vitro an intrinsic gestational-age-related differentiation and functional program. In vitro these cells spontaneously form 3-dimensional structure organoids with a structure similar to the fetal gland. The organoids show morphofunctional features and adrenal steroidogenic factor, steroid acute regulatory, cytochrome-P450-17A1, dosage-sensitive, sex-reversal, adrenal hypoplasia-critical region on chromosome X protein , NOTCH1, and nephroblastoma overexpressed/cysteine-rich protein 61/connective tissue growth factor/nephroblastoma overexpressed gene-3; stem (BMI1, nestin); and chromaffin (chromogranin A, tyrosine hydroxylase) markers similar to those of the populations of origin. This in vitro human adrenal system represents a unique but preliminar model for investigating the pathophysiological processes underlying physiologic adrenal remodeling and pathologic alterations involved in organ hypo- and hyperplasia and cancer.-Poli, G., Sarchielli, E., Guasti, D., Benvenuti, S., Ballerini, L., Mazzanti, B., Armignacco, R., Cantini, G., Lulli, M., Chortis, V., Arlt, W., Romagnoli, P., Vannelli, G. B., Mannelli, M., Luconi, M. Human fetal adrenal cells retain age-related stem- and endocrine-differentiation potential in culture.

New insights in the clinical and translational relevance of miR483-5p in adrenocortical cancer.

Adrenocortical cancer (ACC) is a rare aggressive malignancy. Recent ACC integrated genomics analysis contributed to redefine the risk groups on molecular basis, including tumor microRNAs (miRs), detectable also in the bloodstream. We developed a quantitative real-time (RT) assay for the measurement of miR483 and miR483-5p absolute levels in plasma samples. miR483/miR483-5p levels were evaluated in plasma samples of 27 patients with ACC before surgery and at follow-up. Statistically significant differences in miR483-5p and miR483 levels were found between stage 1/2 and stage 3/4 ACCs in pre-surgery and post-surgery samples. ROC curve analysis of miR483-5p levels gave a prediction of the clinical stage (accuracy 0.917±0.084), with the best cut-off value of 0.221 ng/ml, prognosticating overall and recurrence-free survival. In a multivariate Cox analysis (HR 16.2, 95%CI[1.39-188.6, P<0.026]), miR483-5p was the only variable that significantly predicted recurrence, but not overall survival. In addition, miR483 and miR483-5p levels correlated with the number of circulating tumor cells (CTCs) detected in the same blood samples, independently of the timing of sampling. In conclusion, we demonstrated that miR483-5p absolute plasma levels in ACC patients are powerful molecular markers that may help in the follow-up of patients after surgery and chemotherapy, and contribute to more accurately classify and predict tumor progression.

Metformin as a new anti-cancer drug in adrenocortical carcinoma.

Adrenocortical carcinoma (ACC) is a rare heterogeneous malignancy with poor prognosis. Since radical surgery is the only available treatment, more specific and effective drugs are urgently required. The anti-diabetic drug metformin has been associated with a decreased cancer prevalence and mortality in several solid tumors, prompting its possible use for ACC treatment.This paper evaluates the in vitro and in vivo anti-cancer effects of metformin using the ACC cell model H295R.Metformin treatment significantly reduces cell viability and proliferation in a dose- and time-dependent manner and associates with a significant inhibition of ERK1/2 and mTOR phosphorylation/activation, as well as with stimulation of AMPK activity. Metformin also triggers the apoptotic pathway, shown by the decreased expression of Bcl-2 and HSP27, HSP60 and HSP70, and enhanced membrane exposure of annexin V, resulting in activation of caspase-3 apoptotic effector. Metformin interferes with the proliferative autocrine loop of IGF2/IGF-1R, which supports adrenal cancer growth. Finally, in the ACC xenograft mouse model, obtained by subcutaneous injection of H295R cells, metformin intraperitoneal administration inhibits tumor growth, confirmed by the significant reduction of Ki67%.Our data suggest that metformin inhibits H295R cell growth both in vitro and in vivo. Further preclinical studies are necessary to validate the potential anti-cancer effect of metformin in patients affected by ACC.

2D-DIGE proteomic analysis identifies new potential therapeutic targets for adrenocortical carcinoma.

Adrenocortical carcinoma (ACC) is a rare aggressive tumor with poor prognosis when metastatic at diagnosis. The tumor biology is still mostly unclear, justifying the limited specificity and efficacy of the anti-cancer drugs currently available. This study reports the first proteomic analysis of ACC by using two-dimensional-differential-in-gel-electrophoresis (2D-DIGE) to evaluate a differential protein expression profile between adrenocortical carcinoma and normal adrenal. Mass spectrometry, associated with 2D-DIGE analysis of carcinomas and normal adrenals, identified 22 proteins in 27 differentially expressed 2D spots, mostly overexpressed in ACC. Gene ontology analysis revealed that most of the proteins concurs towards a metabolic shift, called the Warburg effect, in adrenocortical cancer. The differential expression was validated by Western blot for Aldehyde-dehydrogenase-6-A1,Transferrin, Fascin-1,Lamin A/C,Adenylate-cyclase-associated-protein-1 and Ferredoxin-reductase. Moreover, immunohistochemistry performed on paraffin-embedded ACC and normal adrenal specimens confirmed marked positive staining for all 6 proteins diffusely expressed by neoplastic cells, compared with normal adrenal cortex.In conclusion, our preliminary findings reveal a different proteomic profile in adrenocortical carcinoma compared with normal adrenal cortex characterized by overexpression of mainly metabolic enzymes, thus suggesting the Warburg effect also occurs in ACC. These proteins may represent promising novel ACC biomarkers and potential therapeutic targets if validated in larger cohorts of patients.