TP53 mutations in functional corticotroph tumors are linked to invasion and worse clinical outcome

Corticotroph macroadenomas are rare but difficult to manage intracranial neoplasms. Mutations in the two Cushing’s disease mutational hotspots USP8 and USP48 are less frequent in corticotroph macroadenomas and invasive tumors. There is evidence that TP53 mutations are not as rare as previously thought in these tumors. The aim of this study was to determine the prevalence of TP53 mutations in corticotroph tumors, with emphasis on macroadenomas, and their possible association with clinical and tumor characteristics. To this end, the entire TP53 coding region was sequenced in 86 functional corticotroph tumors (61 USP8 wild type; 66 macroadenomas) and the clinical characteristics of patients with TP53 mutant tumors were compared with TP53/USP8 wild type and USP8 mutant tumors. We found pathogenic TP53 variants in 9 corticotroph tumors (all macroadenomas and USP8 wild type). TP53 mutant tumors represented 14% of all functional corticotroph macroadenomas and 24% of all invasive tumors, were significantly larger and invasive, and had higher Ki67 indices and Knosp grades compared to wild type tumors. Patients with TP53 mutant tumors had undergone more therapeutic interventions, including radiation and bilateral adrenalectomy. In conclusion, pathogenic TP53 variants are more frequent than expected, representing a relevant amount of functional corticotroph macroadenomas and invasive tumors. TP53 mutations associated with more aggressive tumor features and difficult to manage disease

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

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

Machine learning for classification of hypertension subtypes using multi-omics: a multi-centre, retrospective, data-driven study

Background: Arterial hypertension is a major cardiovascular risk factor. Identification of secondary hypertension in its various forms is key to preventing and targeting treatment of cardiovascular complications. Simplified diagnostic tests are urgently required to distinguish primary and secondary hypertension to address the current underdiagnosis of the latter. Methods: This study uses Machine Learning (ML) to classify subtypes of endocrine hypertension (EHT) in a large cohort of hypertensive patients using multidimensional omics analysis of plasma and urine samples. We measured 409 multi-omics (MOmics) features including plasma miRNAs (PmiRNA: 173), plasma catechol O-methylated metabolites (PMetas: 4), plasma steroids (PSteroids: 16), urinary steroid metabolites (USteroids: 27), and plasma small metabolites (PSmallMB: 189) in primary hypertension (PHT) patients, EHT patients with either primary aldosteronism (PA), pheochromocytoma/functional paraganglioma (PPGL) or Cushing syndrome (CS) and normotensive volunteers (NV). Biomarker discovery involved selection of disease combination, outlier handling, feature reduction, 8 ML classifiers, class balancing and consideration of different age- and sex-based scenarios. Classifications were evaluated using balanced accuracy, sensitivity, specificity, AUC, F1, and Kappa score. Findings: Complete clinical and biological datasets were generated from 307 subjects (PA=113, PPGL=88, CS=41 and PHT=112). The random forest classifier provided ∼92% balanced accuracy (∼11% improvement on the best mono-omics classifier), with 96% specificity and 0.95 AUC to distinguish one of the four conditions in multi-class ALL-ALL comparisons (PPGL vs PA vs CS vs PHT) on an unseen test set, using 57 MOmics features. For discrimination of EHT (PA + PPGL + CS) vs PHT, the simple logistic classifier achieved 0.96 AUC with 90% sensitivity, and ∼86% specificity, using 37 MOmics features. One PmiRNA (hsa-miR-15a-5p) and two PSmallMB (C9 and PC ae C38:1) features were found to be most discriminating for all disease combinations. Overall, the MOmics-based classifiers were able to provide better classification performance in comparison to mono-omics classifiers. Interpretation: We have developed a ML pipeline to distinguish different EHT subtypes from PHT using multi-omics data. This innovative approach to stratification is an advancement towards the development of a diagnostic tool for EHT patients, significantly increasing testing throughput and accelerating administration of appropriate treatment. Funding: European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 633983, Clinical Research Priority Program of the University of Zurich for the CRPP HYRENE (to Z.E. and F.B.), and Deutsche Forschungsgemeinschaft (CRC/Transregio 205/1)

ENDOCRINE TUMOURS: The genomics of adrenocortical tumors.

The last decade witnessed the emergence of genomics, a set of high-throughput molecular measurements in biological samples. These pan-genomic and agnostic approaches have revolutionized the molecular biology and genetics of malignant and benign tumors. These techniques have been applied successfully to adrenocortical tumors. Exome sequencing identified new major drivers in all tumor types, including KCNJ5, ATP1A1, ATP2B3 and CACNA1D mutations in aldosterone producing adenomas (APA), PRKACA mutations in cortisol producing adenomas (CPA), ARMC5 mutations in primary bilateral macronodular adrenocortical hyperplasia (PBMAH), and ZNRF3 mutations in adrenocortical carcinomas (ACC). Moreover, the various genomic approaches -including exome sequencing, transcriptome, miRNome, genome, and methylome-, converge into a single molecular classification of adrenocortical tumors. Especially for ACC, two main molecular groups have emerged, showing major differences in outcomes. These ACC groups differ by their gene expression profiles, but also by recurrent mutations and specific DNA hypermethylation patterns in the subgroup of poor outcome. The clinical impact of these findings is just starting. The main altered signaling pathways now become therapeutic targets. The molecular groups of diseases individualize robust subtypes within diseases such as APA, CPA, PBMAH and ACC. A revised nosology of adrenocortical tumors should impact the clinical research. Obvious consequences also include genetic counseling for the new genetic diseases such as ARMC5 mutations in PBMAH, and a better prognostication of ACC based on targeted measurements of a few discriminant molecular alterations. Identifying the main molecular groups of adrenocortical tumors by extensively gathering the molecular variations is a significant step forward towards precision medicine

Implementing precision antimicrobial therapy for the treatment of bovine respiratory disease: Current limitations and perspectives

The therapeutic efficacy of an early treatment protocol with an infection-stage adjusted fluoroquinolone regimen was evaluated in a field study on young bulls (YBs) presenting signs of bovine respiratory disease (BRD). A total of 195 YB (Charolais, Limousin, and Rouge-des-Prés breeds) from 6 farms implementing or not prophylactic antimicrobial treatments (PROPHY or absence) were randomly assigned to 1 of 2 experiment groups based on time of detection of BRD and first-line marbofloxacin regimen, early adjusted dose [Early 2 (E2)] or late standard dose [Late 10 (L10)]. Each YB was administered orally a reticulo-rumen bolus, allowing continuous monitoring of ruminal temperature. In the E2 group, YB presenting early signs of BRD, i.e., an increase in ruminal temperature over 40.2°C and persisting more than 12 h, confirmed by a clinical examination showing no or mild signs of BRD, were given 2 mg/kg of marbofloxacin. In the L10 group, YBs presenting moderate or severe signs of BRD at visual inspection, confirmed at clinical examination, were given 10 mg/kg of marbofloxacin. If needed, YBs were given a relapse treatment. The YBs were followed for 30 days. The proportions of first and relapse treatments were calculated, as well as the therapeutic efficacy at day 10. In the E2 group, the first-line treatments' proportion was significantly higher (P < 0.05), while the relapse treatments' proportion tended to be higher (P = 0.08), than in the L10 group. Evolution of clinical scores (CSs) of diseased YB was followed for 10 days. In both groups, CS and rectal temperature decreased significantly 24 h after treatment (P < 0.05). Treatment incidences (TI) representing antimicrobial consumption assessed on used daily doses (UDD) were calculated. Antimicrobial consumption of marbofloxacin and relapse treatments were not significantly different between the groups. These values were strongly influenced by the recourse to a prophylactic antimicrobial treatment, accounting for more than 90% of the antimicrobial amount in the herds implementing prophylaxis. The higher number of treatments in the groups treated on the basis of ruminal temperature monitoring, the accuracy of the detection method, and the necessary conditions to implement precision antimicrobial therapy in the field are discussed in this article

Investigation of Al/CuO multilayered thermite ignition

International audienceThe ignition of Al/CuO multilayered material is studied experimentally to explore the effect of heating surface area, layering and film thickness on the ignition characteristics and reaction performances. After the description of the micro-initiator devices and ignition conditions, we show that the heating surface area has to be properly calibrated to optimize the nanothermite ignition performances. We demonstrated experimentally that a heating surface area of 0.25 mm 2 is sufficient to ignite a multilayered thermite film of 1.6 mm wide by a few cm long, with a success rate of 100%. A new analytical and phenomenological ignition model based on atomic diffusion across layers and thermal exchange is also proposed. This model considers that CuO first decomposes into Cu 2 O, the oxygen diffusing across Cu 2 O and Al 2 O 3 layers before reaching the Al layer where it reacts to form Al 2 O 3. The theoretical results in terms of ignition response times confirm the experimental observation. The increase of the heating surface area leads to an increase of the ignition response time and ignition power threshold (go/no go condition). We also evidence that for any heating surface area, the ignition time rapidly decreases when the electrical power density increases until an asymptotic value, named minimum response ignition time, which is a characteristic of the multilayered thermite itself. At the stoichiometric ratio (Al thickness is half of CuO one), the minimum ignition response time can be easily tuned from 59 µs to 418 ms by tuning the heating surface area. The minimum ignition response time increases when the bilayer thickness increases. Not only this work gives a set of micro-initiator design rules to obtain the best ignition conditions and reaction performances but it also details a reliable and robust MEMS process to fabricate igniters and it brings new understanding of phenomena governing the ignition process of Al/CuO multilayers