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.

Somatotroph Tumors and the Epigenetic Status of the GNAS Locus.

Forty percent of somatotroph tumors harbor recurrent activating GNAS mutations, historically called the gsp oncogene. In gsp-negative somatotroph tumors, GNAS expression itself is highly variable; those with GNAS overexpression most resemble phenotypically those carrying the gsp oncogene. GNAS is monoallelically expressed in the normal pituitary due to methylation-based imprinting. We hypothesize that changes in GNAS imprinting of gsp-negative tumors affect GNAS expression levels and tumorigenesis. We characterized the GNAS locus in two independent somatotroph tumor cohorts: one of 23 tumors previously published (PMID: 31883967) and classified by pan-genomic analysis, and a second with 82 tumors. Multi-omics analysis of the first cohort identified a significant difference between gsp-negative and gsp-positive tumors in the methylation index at the known differentially methylated region (DMR) of the GNAS A/B transcript promoter, which was confirmed in the larger series of 82 tumors. GNAS allelic expression was analyzed using a polymorphic Fok1 cleavage site in 32 heterozygous gsp-negative tumors. GNAS expression was significantly reduced in the 14 tumors with relaxed GNAS imprinting and biallelic expression, compared to 18 tumors with monoallelic expression. Tumors with relaxed GNAS imprinting showed significantly lower SSTR2 and AIP expression levels. Altered A/B DMR methylation was found exclusively in gsp-negative somatotroph tumors. 43% of gsp-negative tumors showed GNAS imprinting relaxation, which correlated with lower GNAS, SSTR2 and AIP expression, indicating lower sensitivity to somatostatin analogues and potentially aggressive behavior.

Whole blood transcriptomic signature of Cushing's syndrome.

OBJECTIVE: Cushing's syndrome is characterized by high morbidity and mortality with high inter-individual 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. Rigde 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 0.82) and remained significant in a multivariate model including the neutrophil proportion (p=0.002). Expression of FKBP5, a single gene both overexpressed in Cushing's syndrome and implied in the glucocorticoid receptor signalling, could also predict Cushing's syndrome (accuracy 0.76). CONCLUSIONS: Whole blood transcriptome reflects the circulating levels of glucocorticoids. FKBP5 expression could be a non-hormonal marker of Cushing's syndrome.

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.