Refinement of the critical region for MCKD1 by detection of transcontinental haplotype sharing

Refinement of the critical region for MCKD1 by detection of transcontinental haplotype sharing.BackgroundAutosomal-dominant medullary cystic kidney disease type 1 (MCKD1) [OMIM 174000] is a hereditary nephropathy that leads to renal salt wasting and end-stage renal failure at a median age of 62 years. In a Welsh MCKD1 kindred we have recently demonstrated linkage to the MCKD1 locus on chromosome 1q23.1 and refined the critical MCKD1 region to <3.3Mb.MethodsIn order to refine the candidate gene region for MCKD1, high-resolution haplotype analysis in three large kindreds with MCKD1 was performed.ResultsWe report here on high-resolution haplotype analysis in this Welsh kindred, as well as in the Arizona kindred, which was used for the first definition of MCKD as a disease entity, and in a kindred from the Dutch/German border. We detected extensive haplotype sharing among all affected individuals of all three kindreds. Scrutinization of the genealogy of the Arizona kindred revealed an origin from Germany in the 17th century, thereby providing historical data for haplotype sharing by descent at the MCKD1 locus.ConclusionUnder the hypothesis of haplotype sharing by descent, we refined the critical genetic interval to <650kb, thus enabling candidate gene analysis

Responses to systemic therapy in metastatic pheochromocytoma/paraganglioma: a retrospective multicenter cohort study

OBJECTIVE The therapeutic options for metastatic pheochromocytomas/paragangliomas (mPPGLs) include chemotherapy with cyclophosphamide/vincristine/dacarbazine (CVD), temozolomide monotherapy, radionuclide therapies, and tyrosine kinase inhibitors such as sunitinib. The objective of this multicenter retrospective study was to evaluate and compare the responses of mPPGLs including those with pathogenic variants in succinate dehydrogenase subunit B (SDHB), to different systemic treatments. DESIGN This is a retrospective analysis of treatment responses of mPPGL patients (n = 74) to systemic therapies. METHODS Patients with mPPGLs treated at 6 specialized national centers were selected based on participation in the ENSAT registry. Survival until detected progression (SDP) and disease-control rates (DCRs) at 3 months were evaluated based on imaging reports. RESULTS For the group of patients with progressive disease at baseline (83.8% of 74 patients), the DCR with first-line CVD chemotherapy was 75.0% (n = 4, SDP 11 months; SDHB [n = 1]: DCR 100%, SDP 30 months), with somatostatin peptide receptor-based radionuclide therapy (PPRT) 85.7% (n = 21, SDP 17 months; SDHB [n = 10]: DCR 100%, SDP 14 months), with 131I-meta-iodobenzylguanidine (131I-MIBG) 82.6% (n = 23, SDP 43 months; SDHB [n = 4]: DCR 100%, SDP 24 months), with sunitinib 100% (n = 7, SDP 18 months; SDHB [n = 3]: DCR 100%, SDP 18 months), and with somatostatin analogs 100% (n = 4, SDP not reached). The DCR with temozolomide as second-line therapy was 60.0% (n = 5, SDP 10 months; SDHB [n = 4]: DCR 75%, SDP 10 months). CONCLUSIONS We demonstrate in a real-life clinical setting that all current therapies show reasonable efficacy in preventing disease progression, and this is equally true for patients with germline SDHB mutations

Metastatic Pheochromocytoma and Paraganglioma: Somatostatin Receptor 2 Expression, Genetics, and Therapeutic Responses

CONTEXT: Pheochromocytomas and paragangliomas (PPGLs) with pathogenic mutations in the succinate dehydrogenase subunit B (SDHB) are associated with a high metastatic risk. Somatostatin receptor 2 (SSTR2)-dependent imaging is the most sensitive imaging modality for SDHB-related PPGLs, suggesting that SSTR2 expression is a significant cell surface therapeutic biomarker of such tumors. OBJECTIVE: Exploration of the relationship between SSTR2 immunoreactivity and SDHB immunoreactivity, mutational status, and clinical behavior of PPGLs. Evaluation of SSTR-based therapies in metastatic PPGLs. METHODS: Retrospective analysis of a multicenter cohort of PPGLs at 6 specialized Endocrine Tumor Centers in Germany, The Netherlands, and Switzerland. Patients with PPGLs participating in the ENSAT registry were included. Clinical data were extracted from medical records, and immunohistochemistry (IHC) for SDHB and SSTR2 was performed in patients with available tumor tissue. Immunoreactivity of SSTR2 was investigated using Volante scores. The main outcome measure was the association of SSTR2 IHC positivity with genetic and clinical-pathological features of PPGLs. RESULTS: Of 202 patients with PPGLs, 50% were SSTR2 positive. SSTR2 positivity was significantly associated with SDHB- and SDHx-related PPGLs, with the strongest SSTR2 staining intensity in SDHB-related PPGLs (P = .01). Moreover, SSTR2 expression was significantly associated with metastatic disease independent of SDHB/SDHx mutation status (P < .001). In metastatic PPGLs, the disease control rate with first-line SSTR-based radionuclide therapy was 67% (n = 22, n = 11 SDHx), and with first-line "cold" somatostatin analogs 100% (n = 6, n = 3 SDHx). CONCLUSION: SSTR2 expression was independently associated with SDHB/SDHx mutations and metastatic disease. We confirm a high disease control rate of somatostatin receptor-based therapies in metastatic PPGLs

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

Metabolomics, machine learning and immunohistochemistry to predict succinate dehydrogenase mutational status in phaeochromocytomas and paragangliomas

Phaeochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumours with a hereditary background inover one-third of patients. Mutations in succinate dehydrogenase (SDH) genes increase the risk for PPGLs and severalother tumours. Mutations in subunit B (SDHB) in particular are a risk factor for metastatic disease, further highlight-ing the importance of identifying SDHx mutations for patient management. Genetic variants of unknown signi-cance, where implications for the patient and family members are unclear, are a problem for interpretation. Forsuch cases, reliable methods for evaluating protein functionality are required. Immunohistochemistry for SDHB(SDHB-IHC) is the method of choice but does not assess functionality at the enzymatic level. Liquid chromatogra-phy–mass spectrometry-based measurements of metabolite precursors and products of enzymatic reactions providean alternative method. Here, we compare SDHB-IHC with metabolite proling in 189 tumours from 187 PPGLpatients. Besides evaluating succinate:fumarate ratios (SFRs), machine learning algorithms were developed to estab-lish predictive models for interpreting metabolite data. Metabolite proling showed higher diagnostic specicitycompared to SDHB-IHC (99.2% versus 92.5%, p = 0.021), whereas sensitivity was comparable. Application of machine learning algorithms to metabolite proles improved predictive ability over that of the SFR, in particular forhard-to-interpret cases of head and neck paragangliomas (AUC 0.9821 versus 0.9613, p = 0.044). Importantly, thecombination of metabolite proling with SDHB-IHC has complementary utility, as SDHB-IHC correctly classied allbut one of the false negatives from metabolite proling strategies, while metabolite proling correctly classied allbut one of the false negatives/positives from SDHB-IHC. From 186 tumours with conrmed status of SDHx variantpathogenicity, the combination of the two methods resulted in 185 correct predictions, highlighting the benets ofboth strategies for patient management

Identification of Gemin5 as a Novel 7-Methylguanosine Cap-Binding Protein

A unique attribute of RNA molecules synthesized by RNA polymerase II is the presence of a 7-methylguanosine (m(7)G) cap structure added co-transcriptionally to the 5' end. Through its association with trans-acting effector proteins, the m(7)G cap participates in multiple aspects of RNA metabolism including localization, translation and decay. However, at present relatively few eukaryotic proteins have been identified as factors capable of direct association with m(7)G.Employing an unbiased proteomic approach, we identified gemin5, a component of the survival of motor neuron (SMN) complex, as a factor capable of direct and specific interaction with the m(7)G cap. Gemin5 was readily purified by cap-affinity chromatography in contrast to other SMN complex proteins. Investigating the underlying basis for this observation, we found that purified gemin5 associates with m(7)G-linked sepharose in the absence of detectable eIF4E, and specifically crosslinks to radiolabeled cap structure after UV irradiation. Deletion analysis revealed that an intact set of WD repeat domains located in the N-terminal half of gemin5 are required for cap-binding. Moreover, using structural modeling and site-directed mutagenesis, we identified two proximal aromatic residues located within the WD repeat region that significantly impact m(7)G association.This study rigorously identifies gemin5 as a novel cap-binding protein and describes an unprecedented role for WD repeat domains in m(7)G recognition. The findings presented here will facilitate understanding of gemin5's role in the metabolism of non-coding snRNAs and perhaps other RNA pol II transcripts

Comprehensive Pan-Genomic Characterization of Adrenocortical Carcinoma

SummaryWe describe a comprehensive genomic characterization of adrenocortical carcinoma (ACC). Using this dataset, we expand the catalogue of known ACC driver genes to include PRKAR1A, RPL22, TERF2, CCNE1, and NF1. Genome wide DNA copy-number analysis revealed frequent occurrence of massive DNA loss followed by whole-genome doubling (WGD), which was associated with aggressive clinical course, suggesting WGD is a hallmark of disease progression. Corroborating this hypothesis were increased TERT expression, decreased telomere length, and activation of cell-cycle programs. Integrated subtype analysis identified three ACC subtypes with distinct clinical outcome and molecular alterations which could be captured by a 68-CpG probe DNA-methylation signature, proposing a strategy for clinical stratification of patients based on molecular markers

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)

Medullary thyroid cancer with ectopic Cushing's syndrome: A multicentre case series

OBJECTIVE Ectopic Cushing's syndrome (ECS) induced by medullary thyroid cancer (MTC) is rare, and data on clinical characteristics, treatment and outcome are limited. DESIGN Retrospective cohort study in three German and one Swiss referral centres. PATIENTS Eleven patients with MTC and occurrence of ECS and 22 matched MTC patients without ECS were included. MEASUREMENTS The primary endpoint of this study was the overall survival (OS) in MTC patients with ECS versus 1:2 matched MTC patients without ECS. RESULTS The median age at diagnosis of ECS was 59 years (range: 35-81) and the median time between initial diagnosis of MTC and diagnosis of ECS was 29 months (range: 0-193). Median serum morning cortisol was 49 µg/dl (range: 17-141, normal range: 6.2-18). Eight (73%) patients received treatment for~ECS. Treatment of ECS consisted of bilateral adrenalectomy (BADX) in four (36%) patients and adrenostatic treatment in eight (73%) patients. One patient received treatment with multityrosine kinase inhibitor (MKI) to control hypercortisolism. All patients experienced complete resolution of symptoms of Cushing's syndrome and biochemical control of hypercortisolism. Patients with ECS showed a shorter median OS of 87 months (95% confidence interval 95{\%} CI: 64-111) than matched controls (190 months, 95{\%} CI: 95-285). Of the nine deaths, four were related to progressive disease (PD). Four patients showed PD as well as complications and comorbidities of hypercortisolism before death. CONCLUSION This study shows that ECS occurs in advanced stage MTC and is associated with a poor prognosis. Adrenostatic treatment and BADX were effective systemic treatment options in patients with MTC and ECS to control their hypercortisolism. MKI treatment achieved complete remission of hypercortisolism and sustained tumour control in one treated case