Targeted massively parallel sequencing panel to diagnose genetic endocrine disorders in a tertiary hospital

Abstract Objectives To analyze the efficiency of a multigenic targeted massively parallel sequencing panel related to endocrine disorders for molecular diagnosis of patients assisted in a tertiary hospital involved in the training of medical faculty. Material and methods Retrospective analysis of the clinical diagnosis and genotype obtained from 272 patients in the Endocrine unit of a tertiary hospital was performed using a custom panel designed with 653 genes, most of them already associated with the phenotype (OMIM) and some candidate genes that englobes developmental, metabolic and adrenal diseases. The enriched DNA libraries were sequenced in NextSeq 500. Variants found were then classified according to ACMG/AMP criteria, with Varsome and InterVar. Results Three runs were performed; the mean coverage depth of the targeted regions in panel sequencing data was 249×, with at least 96.3% of the sequenced bases being covered more than 20-fold. The authors identified 66 LP/P variants (24%) and 27 VUS (10%). Considering the solved cases, 49 have developmental diseases, 12 have metabolic and 5 have adrenal diseases. Conclusion The application of a multigenic panel aids the training of medical faculty in an academic hospital by showing the picture of the molecular pathways behind each disorder. This may be particularly helpful in developmental disease cases. A precise genetic etiology provides an improvement in understanding the disease, guides decisions about prevention or treatment, and allows genetic counseling.

Performance of mutation pathogenicity prediction tools on missense variants associated with 46, XY differences of sex development

OBJECTIVES: Single nucleotide variants (SNVs) are the most common type of genetic variation among humans. High-throughput sequencing methods have recently characterized millions of SNVs in several thousand individuals from various populations, most of which are benign polymorphisms. Identifying rare disease-causing SNVs remains challenging, and often requires functional in vitro studies. Prioritizing the most likely pathogenic SNVs is of utmost importance, and several computational methods have been developed for this purpose. However, these methods are based on different assumptions, and often produce discordant results. The aim of the present study was to evaluate the performance of 11 widely used pathogenicity prediction tools, which are freely available for identifying known pathogenic SNVs: Fathmn, Mutation Assessor, Protein Analysis Through Evolutionary Relationships (Phanter), Sorting Intolerant From Tolerant (SIFT), Mutation Taster, Polymorphism Phenotyping v2 (Polyphen-2), Align Grantham Variation Grantham Deviation (Align-GVGD), CAAD, Provean, SNPs&GO, and MutPred. METHODS: We analyzed 40 functionally proven pathogenic SNVs in four different genes associated with differences in sex development (DSD): 17β-hydroxysteroid dehydrogenase 3 (HSD17B3), steroidogenic factor 1 (NR5A1), androgen receptor (AR), and luteinizing hormone/chorionic gonadotropin receptor (LHCGR). To evaluate the false discovery rate of each tool, we analyzed 36 frequent (MAF>0.01) benign SNVs found in the same four DSD genes. The quality of the predictions was analyzed using six parameters: accuracy, precision, negative predictive value (NPV), sensitivity, specificity, and Matthews correlation coefficient (MCC). Overall performance was assessed using a receiver operating characteristic (ROC) curve. RESULTS: Our study found that none of the tools were 100% precise in identifying pathogenic SNVs. The highest specificity, precision, and accuracy were observed for Mutation Assessor, MutPred, SNP, and GO. They also presented the best statistical results based on the ROC curve statistical analysis. Of the 11 tools evaluated, 6 (Mutation Assessor, Phanter, SIFT, Mutation Taster, Polyphen-2, and CAAD) exhibited sensitivity >0.90, but they exhibited lower specificity (0.42-0.67). Performance, based on MCC, ranged from poor (Fathmn=0.04) to reasonably good (MutPred=0.66). CONCLUSION: Computational algorithms are important tools for SNV analysis, but their correlation with functional studies not consistent. In the present analysis, the best performing tools (based on accuracy, precision, and specificity) were Mutation Assessor, MutPred, and SNPs&GO, which presented the best concordance with functional studies.

Correlation between molecular features and genetic subtypes of Glioblastoma: critical analysis in 109 cases / Correlação entre as características moleculares e os subtipos genéticos dos glioblastomas: análise crítica de 109 casos

OBJECTIVE: Glioblastoma, the most common and lethal brain tumor, is also one of the most defying forms of malignancies in terms of treatment. Integrated genomic analysis has searched deeper into the molecular architecture of GBM, revealing a new sub-classification and promising precision in the care for patients with specific alterations. METHOD: Here, we present the classification of a Brazilian glioblastoma cohort into its main molecular subtypes. Using a high-throughput DNA sequencing procedure, we have classified this cohort into proneural, classical and mesenchymal sub-types. Next, we tested the possible use of the overexpression of the EGFR and CHI3L1 genes, detected through immunohistochemistry, for the identification of the classical and mesenchymal subtypes, respectively. RESULTS: Our results demonstrate that genetic identification of the glioblastoma subtypes is not possible using single targeted mutations alone, particularly in the case of the Mesenchymal subtype. We also show that it is not possible to single out the mesenchymal cases through CHI3L1 expression. CONCLUSION: Our data indicate that the Mesenchymal subtype, the most malignant of the glioblastomas, needs further and more thorough research to be ensure adequate identification.

OBJETIVO: O glioblastoma (GBM), o tumor cerebral mais comum e mais letal, é também um dos tipos de tumores de mais difícil tratamento. Análises genômicas integradas têm contribuído para um melhor entendimento da arquitetura molecular dos GBMs, revelando uma nova subclassificação com a promessa de precisão no tratamento de pacientes com alterações específicas. Neste estudo, nós apresentamos a classificação de uma casuística brasileira de GBMs dentro dos principais subtipos do tumor. MÉTODO: Usando sequenciamento de DNA em larga escala, foi possível classificar os tumores em proneural, clássico e mesenquimal. Em seguida, testamos o possível uso da hiperexpressão de EGFR e CHI3L1 para a identificação dos subtipos clássico e mesenquimal, respectivamente. RESULTADOS: Nossos resultados deixam claro que a identificação genética dos subtipos moleculares de GBM não é possível utilizando-se apenas um único tipo de mutação, em particular nos casos de GBMs mesenquimais. Da mesma forma, não é possível distinguir os casos mesenquimais apenas com a expressão de CHI3L1. CONCLUSÃO: Nossos dados indicam que o subtipo mesenquimal, o mais maligno dos GBMs, necessita de uma análise mais aprofundada para sua identificação.

New evidences on the regulation of SF-1 expression by POD1/TCF21 in adrenocortical tumor cells

OBJECTIVES: Transcription Factor 21 represses steroidogenic factor 1, a nuclear receptor required for gonadal development, sex determination and the regulation of adrenogonadal steroidogenesis. The aim of this study was to investigate whether silencing or overexpression of the gene Transcription Factor 21 could modulate the gene and protein expression of steroidogenic factor 1 in adrenocortical tumors. METHODS: We analyzed the gene expression of steroidogenic factor 1 using qPCR after silencing endogenous Transcription Factor 21 in pediatric adrenal adenoma-T7 cells through small interfering RNA. In addition, using overexpression of Transcription Factor 21 in human adrenocortical carcinoma cells, we analyzed the protein expression of steroidogenic factor 1 using Western blotting. RESULTS: Transcription Factor 21 knockdown increased the mRNA expression of steroidogenic factor 1 by 5.97-fold in pediatric adrenal adenoma-T7 cells. Additionally, Transcription Factor 21 overexpression inhibited the protein expression of steroidogenic factor 1 by 0.41-fold and 0.64-fold in two different adult adrenocortical carcinoma cell cultures, H295R and T36, respectively. CONCLUSIONS: Transcription Factor 21 is downregulated in adrenocortical carcinoma cells. Taken together, these findings support the hypothesis that Transcription Factor 21 is a regulator of steroidogenic factor 1 and is a tumor suppressor gene in pediatric and adult adrenocortical tumors.