Clinical Spectrum of USP8 Pathogenic Variants in Cushing's Disease

Cushing's disease (CD) is a life-threatening condition with a challenging diagnostic process and scarce treatment options. CD is caused by usually benign adrenocorticotrophic hormone (ACTH)-secreting pituitary neuroendocrine tumors (PitNETs), known as corticotropinomas. These tumors are predominantly of sporadic origin, and usually derive from the monoclonal expansion of a mutated cell. Somatic activating variants located within a hotspot of the USP8 gene are present in 11-62% of corticotropinomas, making USP8 the most frequent genetic driver of corticotroph neoplasia. In contrast, other somatic defects such as those affecting the glucocorticoid receptor gene (NR3C1), the BRAF oncogene, the deubiquitinase-encoding gene USP48, and TP53 are infrequent. Moreover, patients with familial tumor syndromes, such as multiple endocrine neoplasia, familial isolated pituitary adenoma, and DICER1 rarely develop corticotropinomas. One of the main molecular alterations in USP8-driven tumors is an overactivation of the epidermal growth factor receptor (EGFR) signaling pathway, which induces ACTH production. Hotspot USP8 variants lead to persistent EGFR overexpression, thereby perpetuating the hyper-synthesis of ACTH. More importantly, they condition a characteristic transcriptomic signature that might be useful for the clinical prognosis of patients with CD. Nevertheless, the clinical phenotype associated with USP8 variants is less well defined. Hereby we discuss the current knowledge on the molecular pathogenesis and clinical picture associated with USP8 hotspot variants. We focus on the potential significance of the USP8 mutational status for the design of tailored clinical strategies in CD.

Technological and computational approaches to detect somatic mosaicism in epilepsy

Lesional epilepsy is a common and severe disease commonly associated with malformations of cortical development, including focal cortical dysplasia and hemimegalencephaly. Recent advances in sequencing and variant calling technologies have identified several genetic causes, including both short/single nucleotide and structural somatic variation. In this review, we aim to provide a comprehensive overview of the methodological advancements in this field while highlighting the unresolved technological and computational challenges that persist, including ultra-low variant allele fractions in bulk tissue, low availability of paired control samples, spatial variability of mutational burden within the lesion, and the issue of false-positive calls and validation procedures. Information from genetic testing in focal epilepsy may be integrated into clinical care to inform histopathological diagnosis, postoperative prognosis, and candidate precision therapies.

Deep DNA sequencing of MGMT, TP53 and AGT in Mexican astrocytoma patients identifies an excess of genetic variants in women and a predictive biomarker

PURPOSE

Astrocytomas are a type of malignant brain tumor with an unfavorable clinical course. The impact of AGT and MGMT somatic variants in the prognosis of astrocytoma is unknown, and it is controversial for TP53. Moreover, there is a lack of knowledge regarding the molecular characteristics of astrocytomas in Mexican patients.

METHODS

We studied 48 Mexican patients, men and women, with astrocytoma (discovery cohort). We performed DNA deep sequencing in tumor samples, targeting AGT, MGMT and TP53, and we studied MGMT gene promoter methylation status. Then we compared our findings to a cohort which included data from patients with astrocytoma from The Cancer Genome Atlas (validation cohort).

RESULTS

In the discovery cohort, we found a higher number of somatic variants in AGT and MGMT than in the validation cohort (10.4% vs < 1%, p < 0.001), and, in both cohorts, we observed only women carried variants AGT variants. We also found that the presence of either MGMT variant or promoter methylation was associated to better survival and response to chemotherapy, and, in conjunction with TP53 variants, to progression-free survival.

CONCLUSIONS

The occurrence of AGT variants only in women expands our knowledge about the molecular differences in astrocytoma between men and women. The increased prevalence of AGT and MGMT variants in the discovery cohort also points towards possible distinctions in the molecular landscape of astrocytoma among populations. Our findings warrant further study.

Postzygotic mutations and where to find them - Recent advances and future implications in the field of non-neoplastic somatic mosaicism

The technological progress of massively parallel sequencing (MPS) has triggered a remarkable development in the research on postzygotic mutations. Although the overwhelming majority of studies in the field focus on oncogenesis, non-neoplastic diseases are attracting more and more attention. The aim of this review was to summarize some of the most recent findings in the field of somatic mosaicism in diseases other than neoplastic events. We discuss the abundance and role of postzygotic mutations, with a special emphasis on disorders which occur only in a mosaic form (obligatory mosaic diseases; OMDs). Based on the list of OMDs compiled from the published literature and three databases (OMIM, Orphanet and MosaicBase), we demonstrate the prevalence of cancer-related genes across OMDs and suggest other sources to further explore OMDs and OMD-related genes. Additionally, we comment on some practical aspects related to mosaic diseases, such as approaches to tissue sampling, the MPS coverage required to detect variants at a very low frequency, as well as on bioinformatic and molecular tools dedicated to detect somatic mutations in MPS data.

Standards for the classification of pathogenicity of somatic variants in cancer (oncogenicity): Joint recommendations of Clinical Genome Resource (ClinGen), Cancer Genomics Consortium (CGC), and Variant Interpretation for Cancer Consortium (VICC)

PURPOSE

Several professional societies have published guidelines for the clinical interpretation of somatic variants, which specifically address diagnostic, prognostic, and therapeutic implications. Although these guidelines for the clinical interpretation of variants include data types that may be used to determine the oncogenicity of a variant (eg, population frequency, functional, and in silico data or somatic frequency), they do not provide a direct, systematic, and comprehensive set of standards and rules to classify the oncogenicity of a somatic variant. This insufficient guidance leads to inconsistent classification of rare somatic variants in cancer, generates variability in their clinical interpretation, and, importantly, affects patient care. Therefore, it is essential to address this unmet need.

METHODS

Clinical Genome Resource (ClinGen) Somatic Cancer Clinical Domain Working Group and ClinGen Germline/Somatic Variant Subcommittee, the Cancer Genomics Consortium, and the Variant Interpretation for Cancer Consortium used a consensus approach to develop a standard operating procedure (SOP) for the classification of oncogenicity of somatic variants.

RESULTS

This comprehensive SOP has been developed to improve consistency in somatic variant classification and has been validated on 94 somatic variants in 10 common cancer-related genes.

CONCLUSION

The comprehensive SOP is now available for classification of oncogenicity of somatic variants.

Standardisation of pathogenicity classification for somatic alterations in solid tumours and haematologic malignancies

BACKGROUND

The difficulty in interpreting somatic alterations is correlated with the increase in sequencing panel size. To correctly guide the clinical management of patients with cancer, there needs to be accurate classification of pathogenicity followed by actionability assessment. Here, we describe a specific detailed workflow for the classification of the pathogenicity of somatic variants in cancer into five categories: benign, likely benign, unknown significance, likely pathogenic and pathogenic.

METHODS

Classification is obtained by combining a set of eight relevant criteria in favour of either a pathogenic or a benign effect (pathogenic stand-alone, pathogenic very strong, pathogenic strong, pathogenic moderate, pathogenic supporting, benign supporting, benign strong and benign stand-alone).

RESULTS

Our guide is concordant with the ACMG/AMP 2015 guidelines for germline variants. Interpretation of somatic variants requires considering specific criteria, such as the disease and therapeutic context, co-occurring genomic events in the tumour when available and the use of cancer-specific variant databases. In addition, the gene role in tumorigenesis (oncogene or tumour suppressor gene) also needs to be taken into consideration.

CONCLUSION

Our classification could contribute to homogenize best practices on somatic variant pathogenicity interpretation and improve interpretation consistency both within and between laboratories.

Feature selection algorithm based on dual correlation filters for cancer-associated somatic variants

Background

Since the development of sequencing technology, an enormous amount of genetic information has been generated, and human cancer analysis using this information is drawing attention. As the effects of variants on human cancer become known, it is important to find cancer-associated variants among countless variants.

Results

We propose a new filter-based feature selection method applicable for extracting cancer-associated somatic variants considering correlations of data. Both variants associated with the activation and deactivation of cancer’s characteristics are analyzed using dual correlation filters. The multiobjective optimization is utilized to consider two types of variants simultaneously without redundancy. To overcome high computational complexity problem, we calculate the correlation-based weight to select significant variants instead of directly searching for the optimal subset of variants. The proposed algorithm is applied to the identification of melanoma metastasis or breast cancer stage, and the classification results of the proposed method are compared with those of conventional single correlation filter-based method.

Conclusions

We verified that the proposed dual correlation filter-based method can extract cancer-associated variants related to the characteristics of human cancer.

Korean Society for Genetic Diagnostics Guidelines for Validation of Next-Generation Sequencing-Based Somatic Variant Detection in Hematologic Malignancies

Next-generation sequencing (NGS) is currently used in the clinical setting for targeted therapies and diagnosis of hematologic malignancies. Accurate detection of somatic variants is challenging because of tumor purity, heterogeneity, and the complexity of genetic alterations, with various issues ranging from high detection design to test implementation. This article presents guidelines developed through consensus among a panel of experts from the Korean Society for Genetic Diagnostics. They are based on experiences with the validation processes of NGS-based somatic panels for hematologic malignancies, with reference to previous international recommendations. These guidelines describe basic parameters with emphasis on the design of a validation protocol for NGS-based somatic panels to be used in practice. In addition, they suggest thresholds of key metrics, including minimum coverage, mean coverage with uniformity index, and minimum variant allele frequency, for the initial diagnosis of hematologic malignancies.

Dissecting the genetic basis of focal cortical dysplasia: a large cohort study

Genetic malformations of cortical development (MCDs), such as mild MCDs (mMCD), focal cortical dysplasia (FCD), and hemimegalencephaly (HME), are major causes of severe pediatric refractory epilepsies subjected to neurosurgery. FCD2 are characterized by neuropathological hallmarks that include enlarged dysmorphic neurons (DNs) and balloon cells (BCs). Here, we provide a comprehensive assessment of the contribution of germline and somatic variants in a large cohort of surgical MCD cases. We enrolled in a monocentric study 80 children with drug-resistant epilepsy and a postsurgical neuropathological diagnosis of mMCD, FCD1, FCD2, or HME. We performed targeted gene sequencing ( ≥ 2000X read depth) on matched blood-brain samples to search for low-allele frequency variants in mTOR pathway and FCD genes. We were able to elucidate 29% of mMCD/FCD1 patients and 63% of FCD2/HME patients. Somatic loss-of-function variants in the N-glycosylation pathway-associated SLC35A2 gene were found in mMCD/FCD1 cases. Somatic gain-of-function variants in MTOR and its activators (AKT3, PIK3CA, RHEB), as well as germline, somatic and two-hit loss-of-function variants in its repressors (DEPDC5, TSC1, TSC2) were found exclusively in FCD2/HME cases. We show that panel-negative FCD2 cases display strong pS6-immunostaining, stressing that all FCD2 are mTORopathies. Analysis of microdissected cells demonstrated that DNs and BCs carry the pathogenic variants. We further observed a correlation between the density of pathological cells and the variant-detection likelihood. Single-cell microdissection followed by sequencing of enriched pools of DNs unveiled a somatic second-hit loss-of-heterozygosity in a DEPDC5 germline case. In conclusion, this study indicates that mMCD/FCD1 and FCD2/HME are two distinct genetic entities: while all FCD2/HME are mosaic mTORopathies, mMCD/FCD1 are not caused by mTOR-pathway-hyperactivating variants, and ~ 30% of the cases are related to glycosylation defects. We provide a framework for efficient genetic testing in FCD/HME, linking neuropathology to genetic findings and emphasizing the usefulness of molecular evaluation in the pediatric epileptic neurosurgical population.

Prevalence and characteristics of likely-somatic variants in cancer susceptibility genes among individuals who had hereditary pan-cancer panel testing

Next-generation sequencing (NGS) hereditary pan-cancer panel testing can identify somatic variants, which exhibit lower allele frequencies than do germline variants and may confound hereditary cancer predisposition testing. This analysis examined the prevalence and characteristics of likely-somatic variants among 348,543 individuals tested using a clinical NGS hereditary pan-cancer panel. Variants showing allele frequencies between 10% and 30% were interpreted as likely somatic and identified in 753 (0.22%) individuals. They were most frequent in TP53, CHEK2 and ATM, commonly as C-to-T transitions. Among individuals who carried a likely-somatic variant and reported no personal cancer history, 54.2% (78/144) carried a variant in TP53, CHEK2 or ATM. With a reported cancer history, this percentage increased to 81.1% (494/609), predominantly in CHEK2 and TP53. Their presence was associated with age (OR=3.1, 95% CI 2.5, 3.7; p<0.001) and personal history of cancer (OR=3.3, 95% CI 2.7, 4.0; p<0.001), particularly ovarian cancer. Germline ATM pathogenic variant carriers showed significant enrichment of likely-somatic variants (OR=2.8, 95% CI 1.6, 4.9; p = 0.005), regardless of cancer status. The appearance of likely-somatic variants is consistent with clonal hematopoiesis, possibly influenced by cancer treatment. These findings highlight the precision required of diagnostic laboratories to deliver accurate germline testing results.

Somatic mutation panels: Time to clear their names

With improvements in DNA sequencing technologies and the consequent reduction in costs, next generation sequencing is being utilized increasingly in panel-based testing to perform molecular profiling of tumors. Such tumor-based panels are often referred to as 'somatic' panels, but this term is misleading and should not be used, since not all DNA variants within a tumor are somatic in nature. Every cell in a person's body contains that person's germline DNA, including tumor cells. Moreover, tumor samples are invariably contaminated with blood, a tissue that can contain somatic mutations itself in a process now called clonal hematopoiesis. Differentiating between germline variants or tumor-associated somatic mutations versus clonal hematopoiesis can be challenging. In this review, we address how to interpret the results of somatic mutation panels, how to differentiate between germline and truly somatic events, and discuss the importance of this distinction.

Understanding human DNA variants affecting pre-mRNA splicing in the NGS era

Pre-mRNA splicing, an essential step in eukaryotic gene expression, relies on recognition of short sequences on the primary transcript intron ends and takes place along transcription by RNA polymerase II. Exonic and intronic auxiliary elements may modify the strength of exon definition and intron recognition. Splicing DNA variants (SV) have been associated with human genetic diseases at canonical intron sites, as well as exonic substitutions putatively classified as nonsense, missense or synonymous variants. Their effects on mRNA may be modulated by cryptic splice sites associated to the SV allele, comprehending exon skipping or shortening, and partial or complete intron retention. As splicing mRNA outputs result from combinatorial effects of both intrinsic and extrinsic factors, in vitro functional assays supported by computational analyses are recommended to assist SV pathogenicity assessment for human Mendelian inheritance diseases. The increasing use of next-generating sequencing (NGS) targeting full genomic gene sequence has raised awareness of the relevance of deep intronic SV in genetic diseases and inclusion of pseudo-exons into mRNA. Finally, we take advantage of recent advances in sequencing and computational technologies to analyze alternative splicing in cancer. We explore the Catalog of Somatic Mutations in Cancer (COSMIC) to describe the proportion of splice-site mutations in cis and trans regulatory elements. Genomic data from large cohorts of different cancer types are increasingly available, in addition to repositories of normal and somatic genetic variations. These are likely to bring new insights to understanding the genetic control of alternative splicing by mapping splicing quantitative trait loci in tumors.

SNooPer: a machine learning-based method for somatic variant identification from low-pass next-generation sequencing

Background

Next-generation sequencing (NGS) allows unbiased, in-depth interrogation of cancer genomes. Many somatic variant callers have been developed yet accurate ascertainment of somatic variants remains a considerable challenge as evidenced by the varying mutation call rates and low concordance among callers. Statistical model-based algorithms that are currently available perform well under ideal scenarios, such as high sequencing depth, homogeneous tumor samples, high somatic variant allele frequency (VAF), but show limited performance with sub-optimal data such as low-pass whole-exome/genome sequencing data. While the goal of any cancer sequencing project is to identify a relevant, and limited, set of somatic variants for further sequence/functional validation, the inherently complex nature of cancer genomes combined with technical issues directly related to sequencing and alignment can affect either the specificity and/or sensitivity of most callers.

Results

For these reasons, we developed SNooPer, a versatile machine learning approach that uses Random Forest classification models to accurately call somatic variants in low-depth sequencing data. SNooPer uses a subset of variant positions from the sequencing output for which the class, true variation or sequencing error, is known to train the data-specific model. Here, using a real dataset of 40 childhood acute lymphoblastic leukemia patients, we show how the SNooPer algorithm is not affected by low coverage or low VAFs, and can be used to reduce overall sequencing costs while maintaining high specificity and sensitivity to somatic variant calling. When compared to three benchmarked somatic callers, SNooPer demonstrated the best overall performance.

Conclusions

While the goal of any cancer sequencing project is to identify a relevant, and limited, set of somatic variants for further sequence/functional validation, the inherently complex nature of cancer genomes combined with technical issues directly related to sequencing and alignment can affect either the specificity and/or sensitivity of most callers. The flexibility of SNooPer’s random forest protects against technical bias and systematic errors, and is appealing in that it does not rely on user-defined parameters. The code and user guide can be downloaded at https://sourceforge.net/projects/snooper/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3281-2) contains supplementary material, which is available to authorized users.