Consensus Statement on next-generation-sequencing-based diagnostic testing of hereditary phaeochromocytomas and paragangliomas

Journal name:
Nature Reviews Endocrinology
Volume:
13,
Pages:
233–247
Year published:
DOI:
doi:10.1038/nrendo.2016.185
Published online

Abstract

Phaeochromocytomas and paragangliomas (PPGLs) are neural-crest-derived tumours of the sympathetic or parasympathetic nervous system that are often inherited and are genetically heterogeneous. Genetic testing is recommended for patients with these tumours and for family members of patients with hereditary forms of PPGLs. Due to the large number of susceptibility genes implicated in the diagnosis of inherited PPGLs, next-generation sequencing (NGS) technology is ideally suited for carrying out genetic screening of these individuals. This Consensus Statement, formulated by a study group comprised of experts in the field, proposes specific recommendations for the use of diagnostic NGS in hereditary PPGLs. In brief, the study group recommends target gene panels for screening of germ line DNA, technical adaptations to address different modes of disease transmission, orthogonal validation of NGS findings, standardized classification of variant pathogenicity and uniform reporting of the findings. The use of supplementary assays, to aid in the interpretation of the results, and sequencing of tumour DNA, for identification of somatic mutations, is encouraged. In addition, the study group launches an initiative to develop a gene-centric curated database of PPGL variants, with annual re-evaluation of variants of unknown significance by an expert group for purposes of reclassification and clinical guidance.

At a glance

Figures

  1. Framework for variant interpretation of phaeochromocytomas and/or paragangliomas (PPGLs) susceptibility genes into five classes based on the likelihood of pathogenicity.
    Figure 1: Framework for variant interpretation of phaeochromocytomas and/or paragangliomas (PPGLs) susceptibility genes into five classes based on the likelihood of pathogenicity.

    The classification relies on multiple criteria listed in each box and further detailed in the text. AF, allele frequency; TSG, tumour suppressor gene. *See Table 5 for additional details.

  2. Proposed workflow for re-evaluation of genetic variants detected in phaeochromocytomas and/or paragangliomas (PPGLs).
    Figure 2: Proposed workflow for re-evaluation of genetic variants detected in phaeochromocytomas and/or paragangliomas (PPGLs).

    An annual review of variants might lead to re-classification based on new research and/or clinical evidence, with an effect on clinical follow up and screening of at-risk family members. Class 1 (not pathogenic), class 2 (likely not pathogenic), class 3 (variant of unknown significance), class 4 (likely pathogenic), class 5 (pathogenic).

Introduction

Phaeochromocytomas and paragangliomas (PPGLs) are catecholamine-secreting, neural-crest-derived tumours of the adrenal medulla and extra-adrenal sympathetic nervous system, respectively1, 2. Paragangliomas can also arise from the parasympathetic nervous system; these tumours are usually located in the head and neck and typically do not secrete catecholamines3. Approximately 50% of PPGLs are caused by a single driver germ line mutation, which means that these tumours are the most highly heritable tumours in humans1. Due to this high heritability, genetic testing has been recommended in all patients with PPGLs independent of a clear family history4. Another striking characteristic of PPGLs is their genetic heterogeneity. Over 15 different susceptibility genes have been implicated in familial cases; however, the susceptibility gene has not been identified in all cases, which indicates that this number will continue to grow in the near future1, 2 (Table 1). As a result of this large number of driver genes, genetic diagnosis of PPGLs by traditional technologies, including PCR-based amplification followed by Sanger sequencing and multiplex ligation-dependent probe amplification (MLPA) for larger gene disruptions, is becoming impractical as they are laborious, costly and time consuming.

Table 1: Genes involved in PPGL pathogenesis

Genetic testing algorithms based on clinical features (that is, tumour localization, malignancy and syndromic characteristics), biochemical profile (that is, types of catecholamines secreted by the tumour) or immunohistochemistry pattern have been developed to aid prioritizing genetic testing of a single or a few PPGLs susceptibility genes5, 6, 7, 8, 9. Although this approach is helpful for patients in whom a pathogenic driver mutation is identified promptly, it can be cumbersome when this quick identification does not happen, as the analysis must be extended to the remaining susceptibility genes. Notably, when variants of unknown significance (VUS; variants for which the pathogenicity is not clear) are found in the initial test, expanded screening is required in an effort to identify a more plausible causative mutation10.

The technology that has become widely known as next-generation sequencing (NGS) was first introduced in 2005 (Ref. 11). Using novel methods of sequencing by ligation or synthesis, NGS platforms enhanced the capability of genetic testing by many orders of magnitude. In the first decade of its use, NGS methodology was improved to increase throughput, accuracy and speed, while simultaneously reducing costs and experimental complexity. Currently available NGS platforms are powerful and flexible, and can be adapted easily to the analysis of a single gene region in thousands of samples, or for sequencing the entire genome of a single patient. The implementation of NGS has been a paradigm shift in genetics research and is now considered the gold standard for genetic diagnosis12, 13. NGS has also been widely embraced by the fields of cancer and hereditary diseases. Therefore, inherited neoplasia, a group to which PPGLs belong, represent a particularly relevant class of disorders where the use of NGS for diagnostic purposes deserves special focus.

Recognizing the need to develop standards for broad implementation of NGS as a methodology for clinical diagnosis of hereditary PPGL, a Study Group comprising international experts from the Pheochromocytoma and paraganglioma RESearch Support Organization (PRESSOR, R.A.T., P.L.M.D., A.-P.G.-R., N.B., M.R., A.C., D.E.B., T.D., R. C.-B., J.P.B., C.M.T., J.W., O.G., H.F., E.M., M.M., T.E., G.O.) and the PPGL working group of the European Network for the Study of Adrenal Tumors (ENS@T, A.-P.G.-R., N.B., M.R., A.C., J.P.B., C.M.T., J.W., O.G., H.F., E.M., M.M., T.E., G.O.) was formed to spearhead discussions on the application of NGS for diagnostic genetic testing in PPGLs (NGSnPPGL).

Methods

The NGSnPPGL Study Group was comprised of 18 experts in PPGLs from ten separate institutions representing eight countries and included both clinicians who provide genetic counselling for their patients and basic researchers who design and perform the diagnostic tests. All participants have adopted, and reported on, NGS-based technologies in their research and/or clinical practice14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26. Discussions took place via conference calls, e-mail communications and file exchanges and one plenary session (at the 14th ENS@T Scientific Meeting on November 20th, 2015, Munich, Germany). In these multiple exchanges, current practices and literature evidence were critically reviewed and a set of recommendations was developed to guide broad implementation of this methodology for the diagnosis of hereditary PPGLs. During these encounters, pertinent technical, ethical and reporting issues were addressed. This Consensus Statement summarizes the outcome of these discussions.

While many of the topics included in this Consensus Statement are common to other hereditary conditions and/or cancers, aspects unique to PPGLs were considered when making recommendations (Box 1). As in many other fields, guidelines for NGS-based testing are continually evolving and the recommendations set out here will be subject to change as our knowledge advances. Therefore, the current guidelines are based on the evidence available in 2016.

Box 1: Features unique to PPGLs

Hereditary Mendelian diseases are caused by one driver mutation inherited in an autosomal dominant or recessive manner. This feature is relevant because the finding of a single unquestionably pathogenic mutation will define the proband's diagnosis and should trigger testing of the specific mutation in at-risk family members. Approximately 50% of phaeochromocytomas and paragangliomas (PPGLs), a rate higher than any other human neoplasia, are caused by an autosomal dominantly inherited mutation detectable in the germ line1, 2.

Mosaic transmission, in addition to classic germ line transmission, of PPGLs can also occur. The EPAS1 gene was found to be somatically mutated in PPGLs and in patients who had an association between these tumours and polycythaemia and/or, rarely, duodenal somatostatinomas19, 75, 76, 77. Further studies have demonstrated that these mutations can be mosaic, and are occasionally detected in non-tumorous tissue at a low frequency78. Detection of these low-representation alleles requires the use of highly sensitive techniques such as NGS. Therefore, the Study Group suggests inclusion of EPAS1 in the group of genes mutated at the germ line level.

The extent to which mosaicism occurs in PPGLs has not been systematically examined across all known susceptibility genes. Both NF1 and VHL, which are established PPGL susceptibility genes, have been detected as mosaic, post-zygotic mutations in neurofibromatosis type 1 or von Hippel–Lindau syndrome; however, this finding has not been described in the specific setting of PPGLs79, 80. In 2015, mosaic mutations leading to a syndrome involving PPGL and giant cell tumours of bone were reported in association with the H3F3A gene14. Therefore, mosaic transmission might occur more frequently in PPGLs than hitherto appreciated.

General ethical considerations

Specific written informed consent must be obtained from all patients following standards for diagnostic genetic testing established by certified and accredited diagnostic laboratories of individual countries27, 28. Special consideration is required when whole-exome sequencing (WES), whole-genome sequencing (WGS) or extended gene panels (that is, not limited to PPGL susceptibility genes) are used. In these circumstances, patients should indicate if they wish to be informed of incidental findings. These findings represent significant genetic variants in a specific group of genes (unrelated to PPGLs) that are implicated in disorders that require medical action, such as those specified in recommendations by the American College of Medical Genetics and Genomics (ACMG)29.

Samples

The sample requirements for NGS are similar to those currently adopted for clinical diagnosis of PPGLs using Sanger sequencing. For the analysis of germ line variants in PPGLs susceptibility genes, laboratories should request blood (fresh (collected <7 days ago) or frozen) or a frozen leukocyte pellet. When a blood sample is not available, laboratories can accept buccal cells either as a cheek swab or in saliva obtained with specific collection kits containing preservatives30. DNA extraction and quality assessment should follow standard procedures established for conventional genetic testing29, 31, 32.

For the analysis of somatic variants in PPGLs susceptibility genes, laboratories should require fresh frozen tumour fragments (50 mg of frozen tissue provides a sufficient amount of high-molecular weight DNA for sequencing). Alternatively, formalin-fixed paraffin embedded (FFPE) sections or other fixed tumour material (for example, maintained in alcohol) might also be acceptable; however, the quality of DNA from these materials is variable and can be suboptimal33, 34. DNA from the tumour should be processed and assessed for quality according to standard protocols29, 31, 32. Over the past few years, protocols for DNA, and even RNA, isolation from FFPE samples have considerably improved, and technical adaptations for handling potential artefacts generated from these materials have yielded increasingly reliable sequencing data, which has expanded the use of FFPE in clinical settings35. Tumour tissue, when available, can provide valuable information that will aid interpretation of results from germ line samples. For example, identification of loss of heterozygosity (LOH) in a region where a potential pathogenic germ line mutation of a tumour suppressor gene is detected supports and reinforces the likelihood of pathogenicity. In patients with hereditary PPGLs, the tumour DNA is used exclusively for the purposes of supplementing the diagnostic value of germ line variants of unclear pathogenic status (see additional details in a subsequent section). To this end, the tumour sample could be analysed by either Sanger sequencing or specific targeted sequencing (single gene or exon); therefore, the tumour sample (frozen or FFPE) should be of sufficient amount and quality to provide reliable genotype results.

NGS-based platform and processing

After considering costs, turnaround time, autonomy of individual laboratories, assay flexibility, scalability, bioinformatics needs, data storage and data interpretation, a consensus was achieved by the Study Group that targeted NGS is currently the favoured method for genetic diagnosis of PPGLs. Specific recommendations for implementing this method are as follows.

Approach. Amplicon-based targeted sequencing was the approach preferred by the Study Group, as this approach has been adopted and successfully optimized by the majority of the group members in their own laboratories. However, no objections were raised regarding the use of the hybridization-captured NGS method.

Depth coverage. The minimum recommended sequence depth coverage was 100x for each sample from blood or saliva. Higher coverages (200x or higher) might be required for detection of mosaic variants in blood or saliva.

In-laboratory validation. Sensitivity and specificity of the developed NGS assay should be established by individual groups based on data obtained from a set of samples carrying known mutations (identified by Sanger sequencing). This positive control group should include samples spanning a comprehensive set of mutations (point mutations and indels) and origins (germ line, mosaic and somatic). The Study Group also suggested that samples positive for rare mutations, which might not be available to every laboratory in their positive control set, could be shared among multiple laboratories to enable the development of more uniform and comprehensive 'calibration sets'. Distribution of such DNA materials in an anonymized manner would be subject to sample availability, approval of the institutions' ethics committees and material transfer agreement arrangements. Importantly, it is recommended that a set of normal reference samples of matching ethnic background is also sequenced using the same NGS platform to determine false positive rates of the assay and to establish the frequency of common and private or population-specific polymorphisms.

Limitations. Special attention should be given to limitations of NGS methods for sequencing and detection of variants in specific regions of the genome, including homopolymer repeats, indels, AT-rich regions and GC-rich regions36. Some NGS techniques, such as Ion Torrent (Life Technologies/ThermoFisher, Waltham, Massachusetts, USA), rely on single-nucleotide additions and can have a high error rate for indel detection (1%)37. Illumina platforms have high sensitivity (0.1%); however, false-positive errors have also been reported37, 38. AT-rich regions and GC-rich regions are well known to be problematic in conventional PCR and Sanger sequencing38. These areas can also be challenging for capture by target and WES probes and, therefore, tend to be underrepresented by NGS. If regions of low coverage are noticed, complementary assays should be designed using a different method (for example, Sanger sequencing) to achieve the desired minimal coverage of the target region. Off-target sequencing (unwanted regions) might occur in genomic regions with low sequence complexity, which can be removed by filtering during sequencing analysis.

Confirmation. Given the reasons outlined in the previous section, the detection of a variation or mutation in a new sample should be confirmed using an orthogonal method, such as Sanger sequencing, real-time PCR genotyping or a distinct NGS-based assay. As in conventional genetic testing, whenever possible, confirmation of the NGS-identified variant in a separate aliquot of the patient's DNA (ideally obtained from an independent blood or saliva sample) is highly recommended. However, the Study Group recognizes that this practice is not universally adopted by diagnostic laboratories.

Whole-exome sequencing. The Study Group chose WES as the preferred method for investigational genetic analysis for PPGLs, with the research purpose of discovering the primary mutation when none is found among the PPGLs susceptibility genes. WES coverage can vary greatly but a mean coverage of 50x or higher was recommended for identification of germ line variants. With decreasing costs of NGS methodology, the ability to sequence at progressively higher depth without added budgetary burden makes this coverage goal easily attainable.

Quality control. Efficient capture of exons and adjacent regions, quality of sequencing and error rates are influenced by the reagents and kits used in library preparation and exome capture, as well as by the chemicals and equipment used for sequencing38. For laboratories that adopt commercial NGS services or institutional core facilities for processing their samples, it is critical to ensure that every step of the protocol is performed following strict quality control standards, using reliable reagents and sequencers with low error rates. The Study Group recommends establishing a bioinformatics pipeline in which at least two algorithms are used for sequence alignment with the goal of enhancing both the sensitivity and specificity of sequence calls12.

Other considerations. Low-coverage WES is not suitable for clinical sequencing. WES should be considered for patients with PPGLs who have no germ line mutations in the genes analysed by targeted NGS, also referred to as a 'negative PPGL'. However, before labelling a sample 'negative' it is imperative to establish a comprehensive analysis of all known PPGLs susceptibility genes. This analysis should not only include sequence evaluation of coding regions and exon–intron boundaries of target genes, but also large indels or gene rearrangements. These grosser defects, which have been reported in the VHL, SDH and MAX genes39, 40, 41, 42, might not be identifiable by WES performed at average depth of coverage. Instead, other methods that are well-established for detection of these genetic lesions, such as MLPA, quantitative multiplex PCR or other genome-wide copy number analysis assays, can be performed43, 44. Alternatively, targeted NGS panels can be designed to optimize detection of larger deletions, insertions or rearrangements, as reported in hereditary breast cancer diagnostic panels45. Finally, the existence of epimutations, such as those detected in the promoter of the SDHC gene should also be considered in cases where no mutations are detected46, 47. Specific attention to the mode of inheritance and the existence of mosaicism are briefly discussed in Box 1.

Targeted NGS PPGLs gene panels

With the important advances in our understanding of the genetics of PPGLs that have occurred in the past decade, a large number of genes have been implicated in susceptibility to PPGLs, which are also known as 'driver' genes (Table 1). Some of these driver genes are only mutated at the germ line level, while others can be mutated either at the germ line or somatic level. A third group of driver genes are only mutated somatically1, 2, 48. The relative frequency of overall mutations and specific germ line and/or somatic events for each of these genes varies (Table 1). Although accumulated evidence regarding the role of some of these susceptibility genes is fairly extensive, as expected, the discoveries from the past few years have not yet been fully validated clinically, genetically or functionally.

An extensive discussion on the requirements for determining a bona fide 'driver status' of the PPGLs susceptibility genes is beyond the scope of this Consensus Statement. Therefore, to harmonize the current evidence available for each gene we have applied general concepts of tumour predisposition genes49 and the 'review status' established by ClinVar, the public archive of reports of the relationships among human variations and phenotypes curated by the National Center for Biotechnology Information (NCBI). ClinVar uses a five-level rank of evidence to establish variant pathogenicity that was suggested by the American College of Medical Genetics and Genomics50, 51 (Table 2). In this Consensus Statement, we adopted a modified version of ClinVar's 'gold star' scale to create three PPGLs panel types based on the current evidence of involvement of these genes in PPGLs susceptibility at the germ line (the basic premise for hereditary PPGLs screening) and somatic level. On the basis of the current literature, we propose the development of three sets of gene panels for the diagnosis of PPGLs (Table 3). Table 4 lists the genes that belong to each panel type, and summarizes the current level of evidence of their pathogenic driver status. Importantly, as our knowledge of the genetics of PPGLs evolves, re-evaluation of this list and reclassification of susceptibility genes will be warranted.

Table 2: Modified ClinVar review status adapted for this Consensus Statement on PPGLs driver genes
Table 3: Target panel versions based on ClinVar gold star variant evidence level
Table 4: Gene panels of PPGLs based on current evidence

Basic panel. The basic panel includes genes with the highest level of evidence for their involvement in the pathogenesis of PPGLs and that are mutated at the germ line level. These genes have been extensively validated in the literature and are predominantly associated with familial disease or syndromic features.

Extended panel. The extended panel includes all 'basic panel' genes, along with other candidate susceptibility genes that are mutated at the germ line level and are found at a low frequency (<1% of hereditary PPGLs) but that have been proven to be functionally relevant. This panel also includes genes that can contain mutations with mosaic transmission and that might occasionally also be detected in non-tumour tissue, including blood or saliva (for example, EPAS1, also known as HIF2A).

Comprehensive panel. The comprehensive panel includes all 'extended panel' genes, genes found to be exclusively mutated at the somatic level and recently identified genes mutated at the germ line and/or somatic levels for which the evidence is still limited due to the low number of events. The comprehensive panel can be used for blood, saliva and tumour tissue analysis.

The target area

The suggested targeted panels should encompass coding exons and intron boundaries of the targeted genes. At this stage, the Study Group opted to exclude deep intronic, promoter and intergenic regions from the panel design, as the pathogenic relevance of variants detected in these genomic areas is of unclear diagnostic value. In addition, special caution should be taken when designing primers that target areas of homology to pseudogenes or other partially homologous sequences, which can confound variant interpretation. Furthermore, the inclusion of just the hotspot exons of two PPGLs oncogenes, EPAS1 (exons 9 and 12) and RET (exons 8, 10, 11 and 13–16), instead of the entire coding region, was favoured for gene panels given the highly selective mutation distribution of these oncogenes2, 15, 43, 52. However, these settings might need to be re-evaluated as the field evolves53.

Other sequencing methods

WGS is the most comprehensive method for mutation analysis, as it can be used to assess nearly all types of genetic disruptions (including large deletions and insertions) of the entire coding and noncoding regions of PPGLs susceptibility genes without amplification bias introduced by PCR. The main barriers for using WGS in clinical diagnostics are the high cost, the need for expert bioinformatics support to perform the analysis and the necessity for a multidisciplinary expert group to help with variant interpretation (see subsequent section on variant reporting). However, the technological advances in the past few years indicate that soon some of these issues will no longer be impediments. The sequencing of the entire human genome for less than US$1,000 is finally possible with the launch of new sequencers focused on population-scale and production-scale genomics, such as Illumina X10 (Ref. 54). In addition, automated bioinformatics analyses with cloud-based shared free software have been developed and continue to be implemented by many leading institutions in the field of advanced genomics and biocomputing55. These shared spaces will enable the analysis of whole genomes without the requirement for individual institutional acquisition of super computers or rental of computer clusters.

Another NGS method that generates information on nucleotide variation is the sequencing of the entire collection of mRNA molecules (RNA-seq), which yields both expression profile and mutational status. Using RNA-seq could be considered when a fresh frozen tumour sample is available. A possible limitation of this technique is the difficulty in identifying mutations that lead to decreased or absent transcription or very unstable mRNA of the target gene. RNA-seq has been performed in only a small number of PPGLs14, 26. Although not tested for all known driver genes for PPGLs, at least in one report, truncating germ line mutations in SDHB and SDHD and a missense germ line mutation in VHL were promptly detected with evidence of LOH, and later validated by Sanger sequencing14, which supports the efficacy of RNA-based sequencing for screening of susceptibility mutations. RNA-seq might also enable detection of fusion transcripts that cannot be identified by WES26. However, given the added technical and material source challenges, the Study Group recommends that DNA is the preferred source of material for mutation screening of PPGLs susceptibility genes in routine practice.

Somatic variants

Studies published over the past 5 years have demonstrated that somatic mutations frequently occur in PPGLs (Table 1). These mutations are only detected in the tumour DNA and not in germ line DNA, and, therefore, they do not have implications for heritability of the disease. Detection of a PPGL-related somatic mutation in the tumour of a germ line 'negative PPGL' suggests sporadic disease, and, consequently, averts the need for screening the patient's relatives. Moreover, identification of a somatic mutation provides insights into tumour biology, and might guide targeted therapies, especially in patients with metastatic disease (when treatment options are limited)56, 57, 58, 59. The Study Group recommends that the analysis of somatic mutations in PPGLs be carried out whenever possible. Genes targeted at the somatic level are indicated in Table 1. These genes should be included in panels in which both germ line and tumour analyses are performed. The number of clinical trials available for patients with non-operable and/or metastatic PPGLs is currently small (ClinicalTrials.gov; search for active PPGL trials), so expanding our knowledge of potential targets can have a broad effect on therapeutic choices for these patients (see Supplementary information S1 (table)).

Additional clinical trials based on therapies that target specific molecular findings are expected to be developed in the near future. Moreover, new discoveries focused on personalized drug therapies for PPGLs are anticipated to guide the development of more research trials. In this context, the NGSnPPGL Study Group recommends the analysis of somatic mutations in all metastatic PPGLs, as mutations in 'druggable' genes might be identified, which could help guide therapeutic choices and/or select patients for genetics-based clinical trials.

Data reporting

A written report of the genetic test results has to be clear, concise, understandable by non-experts and in full compliance with general recommendations for reporting results of diagnostic genetic testing60, 61. The results report should include administrative information, such as name and full contact details of the laboratory performing the analysis, date, name and address of the referring physician and signature of the laboratory specialist who validated and interpreted the results. Patient identification should include patient name (or unique identifier, as in the case of some referral diagnostic laboratories), date of birth, sex and, ideally, ethnicity. Sample details, such as tested material type, date of sample collection and arrival at the laboratory and unique sample identification number should also be part of the report.

Other sample information, including histological confirmation of PPGLs diagnosis, tumour location, tumour number, occurrence of metastasis, age at first diagnosis, hormonal phenotype, family pedigree with clinical information and personal and/or familial history of other diseases or clinical manifestations consistent with syndromic forms of PPGLs or any familial history of other associated tumours or diseases can add valuable information to the interpretation of the results. In addition, results from immunohistochemistry analysis of tumour sections, if available, can also be relevant.

A summary of technical information should be included, encompassing a list of targeted genes, the sequencing platform used, any appropriate kits (for example, library generation and capture), the mean sequence coverage achieved for the target genes (including areas of low coverage, if any) and the bioinformatics pipeline (alignment and annotation software used). The test results, their interpretation and technical limitations (that is, whether the test is capable, or not, of detecting specific variants such as mosaic mutations or copy number changes) must be specified. Importantly, test results and variant interpretation should be consistent across laboratories to avoid variability that could affect clinical decision-making.

The results report should mention whether the findings were confirmed by another method, and whether Sanger sequencing was used to fill gaps of poorly covered regions. These reports should also state whether MLPA or other methods have been performed to detect large rearrangements. Examples of effective NGS-based reports are included in the reference list60, 61.

Variant classification

The International Agency for Research on Cancer (IARC)62 has classified genetic variants into five categories (class 5: 'pathogenic'; class 4: 'likely pathogenic'; class 3: 'VUS'; class 2: 'likely not pathogenic'; and class 1: 'not pathogenic'), and this system has been adopted by most laboratories61, 63. The five-category system is the most comprehensive classification system for molecular geneticists and research experts; however, the Study Group recognizes that a simplified classification in three categories only ('pathogenic', 'VUS' and 'benign') can be considered for reports to physicians and for genetic counselling purposes, as this distinction is usually adequate for clinical decision-making.

The objective of gathering the information detailed in this section is to integrate all available evidence of pathogenicity or non-pathogenicity from the different criteria listed here to reach a conclusion on the status of the detected variants.

For variants identified in genes associated with PPGLs, the Study Group proposes a simplified framework (Fig. 1) that should provide an objective and reproducible classification for the majority of identified variants. Recognizing that some variants will have more complex requirements for classification, the Study Group recommends following the rules proposed by Richard and colleagues, which combine multiple criteria for classifying variants63. Variant classification requires the combination of several criteria, which are described in the following paragraphs63, 64.

Figure 1: Framework for variant interpretation of phaeochromocytomas and/or paragangliomas (PPGLs) susceptibility genes into five classes based on the likelihood of pathogenicity.
Framework for variant interpretation of phaeochromocytomas and/or paragangliomas (PPGLs) susceptibility genes into five classes based on the likelihood of pathogenicity.

The classification relies on multiple criteria listed in each box and further detailed in the text. AF, allele frequency; TSG, tumour suppressor gene. *See Table 5 for additional details.

Criterion 1: the type of mutation. Whether or not it is likely to result in a null variant should be considered. For example, nonsense, frameshift or canonical splice sites (positions ±1 or ±2 bp) that result in frameshift of the coding sequence, mutations that affect the initiation codon, or those that lead to single exon or multi-exon deletion are generally considered null mutations, although this concept is proving to have some notable exceptions65.

Criterion 2: the variant frequency in large population databases. These databases include dbSNP, Exome Aggregation Consortium (ExAC), Exome Variant Server and 1000 Genomes. Of note, these databases do not include only healthy individuals and, therefore, can contain some pathogenic variants. This feature is particularly relevant in the case of low-penetrance alleles, such as SDHB mutations, and their association with malignant PPGLs66. Thus, the simple presence of a VUS in any of these databases does not invalidate their potential pathogenic role in PPGLs. In addition, these databases are not independent datasets, so some redundancy exists. Local, population-specific reference datasets (such as the Spanish National database67), if existent, can be informative as they help uncover population-based VUS frequency bias.

Criterion 3: the variant description in disease or gene-specific databases. These databases include ClinVar, the Human Gene Mutation Database, Leiden Open source Variation Database and in-house databases. These sources provide various levels of annotation of variants, including in many cases the clinical context in which they were described. Of note, not all variants from these databases have been systematically curated or are described with the most updated nomenclature. Therefore, pathogenicity of variants cannot be assumed solely by its presence in any of these databases (please see additional details later in the article).

Criterion 4: previous reporting of the variant in the literature. Variants reported as pathogenic or possibly pathogenic with limited evidence in a single patient, or a small number of patients, must be carefully evaluated before being considered a PPGL-predisposing mutation, as they could be rare or private polymorphisms.

Criterion 5: whether the variant was previously functionally evaluated. This analysis should determine whether the variant is pathogenic or not. Importantly, establishing the functional effect of a variation must involve rigorous standards. For example, certain variations in downstream effectors might not necessarily reflect pathogenicity and should not be used as the sole criterion to classify a variant68, 69.

Criterion 6: in silico predictions. These predictions focus on the pathogenicity of missense or splice site variants. The Study Group recommends using multiple prediction software packages (including SIFT, PolyPhen2 and MutationTaster for missense mutations or MaxEntScan, Splice Site Finder Like and NNSplice for splice prediction), all of which are freely available, or commercial software integrating multiple sources such as Alamut Visual. The results generated by these prediction programmes can help interpretation, but should not be taken as the sole or final determinant of pathogenicity of a variant.

Criterion 7: co-occurrence of pathogenic variants. The co-occurrence can either be in the same gene or in another susceptibility gene. On the basis of the general premise of mutual exclusivity of driver mutations in PPGLs1, the detection, in the same individual, of a known pathogenic variant either in the same gene or in other PPGL genes essentially excludes pathogenicity of the unknown variant.

Criterion 8: analysis of co-segregation of the disease in families. The presence of the same variant in other affected relatives supports pathogenicity of the variant. However, more importantly, the lack of co-segregation (for example, at least one affected relative without the variant) excludes its role as the susceptibility mutation in that family. Absence of the candidate variant in unaffected relatives can also be helpful to ascribe a role for a candidate variant in families.

Criterion 9: concordance with phenotype. Concordance with phenotype is especially relevant in susceptibility genes for established syndromes, such as neurofibromatosis type 1, von Hippel–Lindau disease or multiple endocrine neoplasia type 2, for which other clinical manifestations generally coexist with PPGL (Box 2). Given the diversity of PPGL susceptibility genes and associated subphenotypes, the Study Group recommends that the referring physicians provide detailed phenotypic information to assist the laboratory in analysing and interpreting the results of testing. This information could help to prioritize variants for further consideration61. An example of this situation is the RET gene, in which the identification of an unquestionable pathogenic mutation has clear clinical implications (for example, thyroidectomy to prevent the development of the associated, highly penetrant medullary carcinoma of the thyroid). By contrast, detection of RET VUS should not trigger indiscriminate screening of relatives69.

Box 2: PPGLs-associated syndromes, other associations and shared susceptibility

Phaeochromocytomas and paragangliomas (PPGLs) have been reported as part of well-established hereditary syndromes, including multiple endocrine neoplasia type 2A and 2B (RET), von Hippel–Lindau syndrome (VHL), neurofibromatosis type 1 (NF1) and familial paraganglioma syndromes, type 1 (SDHD), 2 (SDHAF2), 3 (SDHC), 4 (SDHB) and 5 (SDHA)1, 2, 48.

Gastrointestinal stromal tumours can associate with paragangliomas due to germ line SDH mutations (Stratakis–Carney dyad)81, 82. EPAS1 mosaic mutations have been detected in patients with multiple paragangliomas, duodenal somatostatinomas and polycythaemia80, 83. Pituitary adenomas were reported in patients with PPGLs in familial settings in which an SDH mutation was detected84, 85. Germ line FH mutations have been described in patients with phaeochromocytomas and uterine leiomyomas14, 20, 24, and, in the past year histone gene mutations (H3F3A) were found in a new syndrome of multiple paragangliomas and giant cell tumour of bone, which implicates chromatin remodelling defects in PPGL tumorigenesis and susceptibility14.

The growing link between susceptibility to renal carcinomas and PPGLs is worth highlighting. An increasing number of genes can be responsible for development of both tumour types individually or in association, which suggests a closer connection than previously appreciated86, 87, 88, 89. In addition to VHL, SDH90, 91, FH92 and TMEM127 (Refs 93,94), genes can also be mutated in renal carcinomas either with or without co-occurrence of PPGL. The MET gene, previously known to cause hereditary papillary renal cancer95, was also found to be mutated in PPGLs14, 96. Finally, somatic mutations in chromatin remodelling genes are recurrently detected in renal carcinomas and PPGLs14, 96, 97, 98.

Further associations between PPGLs and other conditions might be detected in the future. Their rare occurrence makes it challenging to establish a causative link, but those infrequent associations can offer invaluable insights into the biology of these tumours and possible paths to their development.

Variant reporting

Reports should provide a list of variants with clinical interest only. Gene name, zygosity status, cDNA nomenclature and protein nomenclature must be clearly defined and follow Human Genome Organization (HUGO) criteria70 (see later).

The Study Group recommends that variants classified as not pathogenic (class 1) or likely not pathogenic (class 2) should not be reported, as the report of a common SNP or silent variant can generate anxiety for patients and relatives. In addition, reports should clearly distinguish known pathogenic (class 5) or likely pathogenic (class 4) variants from VUS (class 3).

Whether and how to report VUS can be laboratory-dependent but physicians should be aware of the policies about reporting VUS61, 62. When multiple variants that might be clinically important are identified (for example, class 4 variants), they should be prioritized according to their relevance to the patient's phenotype. For example, in a patient with a syndromic clinical presentation suggesting von Hippel–Lindau disease, if NGS identifies a VUS in the VHL gene and a second VUS in another gene associated with PPGL, the VHL variant is more likely than the second variant to be disease-causing and, therefore, should be emphasized. A comprehensive review of interpretation of VUS of the RET gene has discussed this challenge69.

Nomenclature

The gene names used in reports should adhere to the approved HUGO Gene Nomenclature61. The reference nucleotide and protein sequence accession number (and version number) should be indicated.

The Human Genome Variation Society (HGVS) nomenclature is currently the standard worldwide and is recommended for variant reporting71. Unambiguous naming of the variants is critical for the patient's medical records as well as for the pre-symptomatic genetic testing that could be offered to the patient's relatives. Indeed, screening for the mutation in a family generally comprises Sanger sequencing that exclusively targets the mutation identified in the proband. Misleading mutation nomenclature could lead to the amplification and sequencing of a different gene region and return a false negative result in relatives. Correct nomenclature is also critical for unambiguous registration of the data in human variation databases and for accurate searches for a previous description of any identified variant.

Interpretation

The interpretation section of the report should clearly state whether any identified variant is likely to be responsible for the patient's PPGL development and should also include the evidence that supports the variant classification. The report should list additional studies, if available, that could be performed to assist in further clarifying the variant classification. Supplemental material (such as the frozen tumour sample, FFPE tumour block or slides and/or RNA samples) required for these additional studies should be requested from the referring physician. Similarly, the participation of family members for segregation analysis should be requested, if appropriate. Finally, the report should mention whether a pre-symptomatic genetic test can be offered to first-degree relatives or not.

VUS and tools for classification

The interpretation of VUS is challenging and in general these variants should not be used for clinical management of patients and families. VUS can be classified based on multiple parameters (described in a previous section)69.

Additional tests and functional studies, listed in Table 5, can be performed to assess the pathogenicity of variants. These tests require different types of biological material and have distinct degrees of complexity and accuracy. When a VUS is identified in a PPGL tumour suppressor gene, the demonstration of tumour LOH, either by deletion or additional somatic mutation, is a strong argument supporting its pathogenicity, according to the Knudson 'two-hit' hypothesis72. However, it is important to note that the accuracy of the LOH analysis might be dependent on the marker or primer that was used. In addition, the frequency of somatic deletion of the region of interest can vary considerably (that is, chromosome 1p LOH, which spans the SDHB gene locus, is frequent in PPGLs but chromosome 5p LOH, which comprises the SDHA locus, is not). Thus, results of LOH analyses have to be interpreted with caution.

Table 5: Supplementary and/or functional tests available for PPGL genes

Molecular geneticists and researchers of the Study Group agreed on sharing data and protocols of functional assays previously developed in their respective laboratories or institutions, as well as on providing specialized technical assistance for newly identified variations that can aid in their classification. As distinct assays have been developed and optimized by individual laboratories, sharing of these protocols and controls will occur on a case-by-case basis, according to the specific variant and gene involved.

An international PPGL variant database

Public gene-specific databases already exist for various genes that predispose to PPGLs (Box 3). The Study Group emphasizes the importance of multi-institutional, internationally shared efforts to compile resources of genomic and clinical data, as well as publicly accessible deposition of novel variants (for example, ClinVar and Decipher). In the interest of economy of scale and technology development, the Study Group recommends adopting the existing gene-centred database integrated in the framework of the Leiden Open-source Variation Database (LOVD) system73 rather than creating a disease-specific database. Accurate classification of variants requires databases specifically curated by a panel of PPGLs experts who span the range of expertise for each gene and associated functional studies. As recommended by the IARC, a consensus opinion on variant pathogenicity validated by a panel of experts should be established before making the report available62, 74.

To that end, the Study Group launched an initiative to establish gene-oriented groups of experts (including both basic researchers and clinicians) from multiple institutions worldwide to submit and review variants. During the 14th ENS@T Scientific Meeting on November 20th, 2015, Munich, Germany, the Study Group launched the first pilot of the PPGL Database Project, which focused on the SDHB gene. The objectives of this project are, firstly, to collect standardized genomic and clinical data for each submitted SDHB variant. Secondly, to review manually each variant and combine multiple lines of evidence for classification. Requests for additional information or supplementary functional analyses will be made when necessary. Thirdly, to develop standardized, transparent and consensus criteria for variant classification. Fourthly, to conclusively assign each variant to one of the existing classes (1–5; Fig. 1). Fifthly, to update and re-evaluate the variant list annually (see subsequent section).

Re-evaluation of VUS

This re-evaluation process will take place during ENS@T or PRESSOR face-to-face meetings or conference calls. These meetings will also address other details pertinent to the structure and configuration of the LOVD databases. A summary of the proposed database algorithm is shown in Fig. 1.

During the annual update of the PPGL Database Project, undefined variants (also known as class 2 or class 3 variants, which are not usable for predictive testing in relatives) could be assigned to the pathogenic or disease-causing mutation group (usable for predictive testing), on the basis of new evidence from the literature or functional assays. In that situation, information about variant re-classification will be disseminated to all Study Group participants. These professionals, in turn, should advise the referring physicians of the new classification status, when applicable. A summary of a suggested flow-chart for genetic testing is depicted in Fig. 2. More rarely, change in status of class 1 or class 5 mutations will also trigger a similar process.

Figure 2: Proposed workflow for re-evaluation of genetic variants detected in phaeochromocytomas and/or paragangliomas (PPGLs).
Proposed workflow for re-evaluation of genetic variants detected in phaeochromocytomas and/or paragangliomas (PPGLs).

An annual review of variants might lead to re-classification based on new research and/or clinical evidence, with an effect on clinical follow up and screening of at-risk family members. Class 1 (not pathogenic), class 2 (likely not pathogenic), class 3 (variant of unknown significance), class 4 (likely pathogenic), class 5 (pathogenic).

Conclusions

Advances in sequencing technologies within the past few years led a majority of genetics laboratories to adopt NGS as the new gold standard for routine diagnosis. NGS is especially pertinent for diseases with broad genetic heterogeneity, as is the case with hereditary PPGLs. Considering the technological challenges inherent to NGS methodology, PPGL experts emphasize the need for specific recommendations. The main objective of this Consensus Statement is to support worldwide good laboratory practices and quality standards for clinical application of NGS for PPGLs diagnosis, taking into account technical, interpretational and reporting aspects of genetic variants.

In brief, the Study Group recommends using a validated targeted gene panel for clinical genetic diagnosis of hereditary PPGLs. At present, WES or WGS should be adopted preferentially for research purposes, although these strategies will probably be incorporated as diagnostic tools in the future once they become more affordable. Blood or buccal (saliva) DNA is an adequate biological material for germ line diagnosis, but analysis of tumour DNA can aid interpretation of germ line variants and is also of interest to detect somatic mutations that could be targeted therapeutically. The Study Group acknowledges technical limitations of NGS and the need for orthogonal validation of findings. In addition, the Study Group highlights the importance of fostering collaborations to achieve consensus on VUS classification, the development and application of functional assays to aid in interpretation of findings, and, finally, implementation of curated variant databases. As NGS technologies are still evolving, these guidelines are subject to change and will be updated when necessary. Re-evaluation of these guidelines will require ongoing communication among the experts in the PPGL field.

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Acknowledgements

The authors are grateful to Joakim Krona (Linköping University, Sweden) for his contributions to this discussion and earlier NGS research in PPGLs; to Peter Söderkvist for his critical involvement in implementing NGS-based studies of PPGLs in Linköping University, Sweden; and to all colleagues who have advanced our understanding of the genetics of PPGLs through their research. P.L.M.D. is a recipient of awards from the Cancer Prevention and Research Institute of Texas (CPRIT) Individual Investigator Grants RP101202 and RP57154, the Department of Defense CDMRP W81XWH-12-1-0508, the Voelcker Fund and from the National Institutes of Health (NIH)'s National Center for Research Resources and the National Center for Advancing Translational Sciences, through Grant 8UL1TR000149. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. A.-P.G.-R. thanks INSERM and the French National Cancer Institute (INCA) for their financial support to the French registry for SDH-related paraganglioma (PGL.R) and the Groupe des Tumeurs Endocrines, réseau des laboratoires (TENgen) network, respectively. Her research team is supported by Direction Générale de l'Offre de Soins (DGOS), by INCA (INCA-DGOS_8663) and by the European Union's Horizon 2020 research and innovation programme (#633983). R.A.T. was a recipient of a research fellowship from the Brazilian National Council for Scientific and Technological Development (CNPq). This work was supported in part by a salary grant to N.B. from Cancer Research for PErsonalized Medicine (CARPEM). E.R.M. is a receipt of an ERC Advanced Researcher Award.

Author information

Affiliations

  1. Division of Hematology and Medical Oncology, Department of Medicine, Cancer Therapy and Research Center, University of Texas Health Science Center at San Antonio (UTHSCSA), 7703 Floyd Curl Drive, MC7880, San Antonio, Texas 78229, USA.

    • Rodrigo A. Toledo &
    • Patricia L. M. Dahia
  2. Spanish National Cancer Research Centre, CNIO, Calle de Melchor Fernández Almagro, 3, 28029, Madrid, Spain.

    • Rodrigo A. Toledo
  3. Assistance Publique Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Service de Génétique; Université Paris Descartes, Sorbonne Paris Cité, Faculté de Médecine, 20 Rue Leblanc, 75015 Paris, France.

    • Nelly Burnichon &
    • Anne-Paule Gimenez-Roqueplo
  4. INSERM, UMR970, Paris Cardiovascular Research Center (PARCC), 56 Rue Leblanc, 75015, Paris, France.

    • Nelly Burnichon &
    • Anne-Paule Gimenez-Roqueplo
  5. Hereditary Endocrine Cancer Group, Spanish National Cancer Research Centre (CNIO) and ISCIII Center for Biomedical Research on Rare Diseases (CIBERER), Calle de Melchor Fernández Almagro, 3, 28029, Madrid, Spain.

    • Alberto Cascon &
    • Mercedes Robledo
  6. Cancer Genetics Unit, Kolling Institute, Royal North Shore Hospital, St Leonards, University of Sydney, Reserve Road, St Leonards, Sydney, New South Wales 2065, Australia.

    • Diana E. Benn,
    • Trish Dwight &
    • Roderick Clifton-Bligh
  7. Department of Human Genetics, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, Netherlands.

    • Jean-Pierre Bayley
  8. Department of Clinical and Experimental Medicine, Linköping University, 58183 Linköping, Sweden.

    • Jenny Welander
  9. Department of Clinical Genetics, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, Netherlands.

    • Carli M. Tops
  10. Department of Medical Genetics, University of Cambridge, Cambridge and NIHR Cambridge Biomedical Research Centre, Hills Road, Cambridge, CB2 0QQ, UK.

    • Helen Firth &
    • Eamonn R. Maher
  11. Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, University of Florence, Viale GB Morgagni 50, 50134, Florence, Italy.

    • Tonino Ercolino &
    • Massimo Mannelli
  12. Familial Cancer Clinic, Veneto Institute of Oncology, IRCCS, Via Gattamelata, 64 Padova, Veneto 35128, Padova, Italy.

    • Giuseppe Opocher
  13. Department of Surgery, Region Östergötland, Linköping University, 581 83 Linköping, Sweden.

    • Oliver Gimm

Consortia

  1. The NGS in PPGL (NGSnPPGL) Study Group

Contributions

R.A.T., N.B., A.-P.G.R. and P.L.M.D. researched data for the article, made substantial contributions to discussion of the content, wrote the article and reviewed and/or edited the manuscript before submission. A.C., D.E.B., J.-P. B., J.W. C.M.T., H.F., T.D., T.E., M.M., G.O., R.C.-B., O.G., E.R.M. and M.R. contributed to discussion of the content and reviewed and/or edited the manuscript before submission. A.-P.G.R. and P.L.M.D. are senior authors with equal contribution and R.A.T and N.B. are first authors with equal contribution.

Competing interests statement

The authors declare no competing interests.

Corresponding authors

Correspondence to:

Author details

  • The NGS in PPGL (NGSnPPGL) Study Group

  • Rodrigo A. Toledo

    Rodrigo A. Toledo, MSc, PhD, graduated in Biosciences and Biomedical Sciences at the University of São Paulo, Brazil, where he later received his Masters and Doctorate degrees in 2007 and 2010, respectively. He completed his postdoctoral work at The University of Texas Health Science Center at San Antonio, USA, in Dr Patricia Dahia's laboratory, before joining the Clinical Research Program at the Spanish National Cancer Research Centre (CNIO) in Madrid, Spain. Dr Toledo was directly involved in the establishment of the genetic screening programme for multiple endocrine neoplasias at the largest public hospital in South America, Hospital das Clínicas, São Paulo, Brazil. Dr Toledo has identified and characterized functionally pathogenic mutations in new susceptibility genes of hereditary endocrine neoplasias using whole-exome sequencing.

  • Nelly Burnichon

    Nelly Burnichon is an associate professor in the Molecular Genetics Department of the Georges Pompidou European Hospital (HEGP), Paris Descartes University in Paris, France. She received her PharmD training in the Lyon 1 University, France, was trained as a PhD in the Paris-Cardiovascular Research Centre of HEGP in Paris, France, and as a postdoctoral fellow in the University of California San Francisco Helen Diller Cancer Center, USA. She focuses on genetics and genomics of endocrine tumours and kidney cancer. She is currently a member of the COMETE and ENS@T networks dedicated to adrenal tumours.

  • Alberto Cascon

    Alberto Cascon obtained his BSc and PhD in Biology from the University of León, Spain. In 2001, he was awarded a Postdoctoral Fellowship by the Madrid City Council and took up a position with the Hereditary Endocrine Cancer Group at the Spanish National Cancer Research Centre (CNIO) in Madrid, Spain. In 2004, he was awarded a Fellowship by the Spanish Department of Health, and became a Staff Scientist in the same group at the CNIO. Since 2001, he has been investigating the genetics of phaeochromocytomas and paragangliomas, a subject on which he has focused 40 of his more than 70 published manuscripts.

  • Diana E. Benn

    Diana Benn is a Senior Hospital Scientist, Kolling Institute, Royal North Shore Hospital and Senior Research Fellow (Honourary), Sydney Medical School, University of Sydney, Australia, where she completed her PhD in 2003 on the genetics of phaeochromocytomas and/or paragangliomas. Since then, she has established accredited diagnostic genetic testing, published 22 articles in this field and continues to study genetic factors influencing the development of these tumours in families.

  • Jean-Pierre Bayley

    Jean-Pierre Bayley obtained his PhD in molecular immunology and genetics at the Department of Rheumatology, Leiden University Medical Center (LUMC), Netherlands. In 2002, he joined the lab of Prof Peter Devilee (Human Genetics, LUMC) and has focused on the genetics and biology of paragangliomas for over a decade. His current research focus is on developing cell and mouse models of paragangliomas and understanding the early genetic events underlying SDHB, SDHD and SDHAF2 tumorigenesis.

  • Jenny Welander

    Jenny Welander, MSc, PhD, is a bioinformatician and molecular biologist at the Linköping University Hospital, Sweden. She graduated from Linköping University with a thesis focusing on genetic alterations in phaeochromocytomas and/or paragangliomas. Her primary research interest is the development of diagnostic tools based on next-generation sequencing technologies.

  • Carli M. Tops

    Carli Tops is a Clinical Laboratory Geneticist (focus on Molecular Genetics) accredited by the Netherlands Society of Human Genetics (since 01 April 2002) and the European Board of Medical Genetics (since April 2015). Since 2008, she has been responsible for molecular diagnostic germ line tests of paragangliomas and other cancer-related genes at the Laboratory for Diagnostic Genome Analysis (NEN-EN-ISO 15189:2012 Accredited), Leiden University Medical Center, Netherlands. She is experienced in variant classification as a member of the Variant Interpretation Committee for MisMatchRepair genes (Lynch syndrome, MLH1, MSH2, MSH6, PMS2) and curator of the MUTYH gene specific databases.

  • Helen Firth

    Helen Firth, DM, FRCP, is a Consultant Clinical Geneticist at Cambridge University Hospitals, an Honourary Faculty Member of the Wellcome Trust Sanger Institute, and a Bye-Fellow of Newnham College, Cambridge, UK. Her main research interests are in mapping the clinical genome and the matching of rare genomic variants to empower discovery and diagnosis in rare disease. In 2004, she initiated the DECIPHER project (http://decipher.sanger.ac.uk), which enables clinicians and scientists around the world to share information about rare genomic variants to facilitate diagnosis and help to elucidate the role of genes with unknown function.

  • Trish Dwight

    Trish Dwight (Research Fellow, Kolling Institute, Royal North Shore Hospital and University of Sydney, Australia) is currently focused on the application of massively parallel sequencing to identify genomic anomalies associated with endocrine tumours, in particular phaeochromocytomas and/or paragangliomas, as well as investigating the functional effects of these anomalies, with the aim of improving clinical management for affected patients. Her PhD (University of Sydney) and postdoctoral (Karolinska Institutet, Sweden) work focused on the genetic basis of endocrine tumours, following which she spent time as a scientific communicator within the pharmaceutical industry.

  • Tonino Ercolino

    Tonino Ercolino, PhD, is a biologist specialized in genetics and molecular biology. He has been working for many years in the field of diabetes mellitus, having been research fellow at Joslin Diabetes Center of Boston, USA, for 2 years (2001–2003). For the past 12 years, he has been mainly working in the cancer field. He is interested in the study of the molecular biology and genetic mechanisms of the susceptibility genes involved in tumours of the paraganglial system, such as phaeochromocytomas and head and neck paragangliomas, always looking for new diagnostic approaches, especially with regard to the new sequencing techniques. He is involved in several national and international collaborations.

  • Massimo Mannelli

    Massimo Mannelli is a Full Professor of Endocrinology and Senior Lecturer in Endocrinology at the Postgraduate School of Endocrinology, University of Florence, Italy. He was the coordinator of the Scientific Committee of the Italian Society of Endocrinology (2003–2005), Director of the Postgraduate School of Endocrinology and Metabolism (2009–2015) and coordinator of the Pheochromocytoma/Paraganglioma working group of the Italian Society of Endocrinology (SIE, 2010–2015). He is a founding member of ENS@T (European Network for the Study of Adrenal Tumors). He is member of the SIE, the European Society of Endocrinology, the Endocrine Society and the European NeuroEndocrine Association. He is a reviewer for many international journals in the field of endocrinology and hypertension. He has published more than 50 book chapters and more than 200 original papers in peer-reviewed international journals with a total impact factor greater than 1,000.

  • Giuseppe Opocher

    Giuseppe Opocher is an Associate Professor of Endocrinology at the University of Padova, Italy, and is the Scientific Director of the Veneto Institute of Oncology, IRCCS, Italy, and past Director of the Familial Cancer Clinic and onco-endocrinology of the Veneto Institute of Oncology, Italy. His main research interests and achievements are the characterization of predictors of SDHB, SDHC and SDHD mutations; the identification and characterization of a founder effect for a mutation predisposing to SDHD paraganglioma syndrome type 1 and the study of the natural history of paraganglioma type 1; and the discovery and characterization of other phaeochromocytoma susceptibility genes.

  • Roderick Clifton-Bligh

    Roderick Clifton-Bligh is a clinician scientist with research programmes in endocrine neoplasia and metabolic bone diseases. Following his PhD in thyroid diseases at the University of Cambridge, UK, he completed his fellowship in Endocrinology before joining the staff at Royal North Shore Hospital in Sydney, Australia, where he is now Head of Department.

  • Oliver Gimm

    Oliver Gimm is Professor of surgery and the deputy head of the Department of Clinical and Experimental Medicine at Linköping University in Sweden. He received his surgical training in Germany where he became interested in endocrine diseases. His research training was intensified during a 3-year stay in the USA, where he became familiar with molecular genetics. Since 2007, Oliver Gimm has worked at the University Hospital in Linköping. His research on the thyroid gland, parathyroid glands, adrenal gland and neuroendocrine tumours is both preclinically and clinically oriented. Currently, Oliver Gimm is the chairman of the Division of Endocrine Surgery, Section of Surgery, Faculty of Medicine and Health Sciences, Linköping University.

  • Eamonn R. Maher

    Eamonn Maher is currently Professor of Medical Genetics and Genomic Medicine and Head of the Department of Medical Genetics at the University of Cambridge, UK. After graduating in medicine he trained in internal medicine and medical genetics in London and Cambridge, UK. Before his current post, he was Professor of Medical Genetics and Head of the Centre of Rare Diseases and Personalised Medicine at the University of Birmingham, UK. His current clinical and research interests relate to genomic approaches to inherited disease. He has a longstanding interest in the genetics of renal cell carcinoma and phaeochromocytomas and participated in the identification of the VHL and SDHB genes.

  • Mercedes Robledo

    Mercedes Robledo obtained her PhD in Biology at the Autónoma University in Madrid, Spain. From 1996 onwards she has been working on cancer susceptibility as a staff member in the Genetics Service of the FJD, and since 2000 in the the Spanish National Cancer Research Centre (CNIO), where she is the head of the Hereditary Endocrine Cancer Group. She is interested in understanding the genetic bases of endocrine tumours and identifying new susceptibility genes as well as predictive diagnostic and prognostic markers of utility in the clinical setting. She was co-chair of the Pheochromocytoma–Paraganglioma Research Support Organization (PRESSOR) and is currently a member of the Steering Committee of ENS@T (European Network for the Study of Adrenal Tumors).

  • Anne-Paule Gimenez-Roqueplo

    Anne-Paule Gimenez-Roqueplo (MD, specialized in Endocrinology, PhD in Genetics) is Full Professor in Genetics (Paris Descartes University, France). She is the Director of the team 13 (INSERM U970) at PARCC@HEGP in Paris entitled 'Pheochromocytomas and Paragangliomas: From Genetics to Molecular Targeted Therapies'. She is the past chairman of the Pheochromocytoma–Paraganglioma Research Support Organization (PRESSOR), the Head of the PPGL working group of the European Network for the Study of Adrenal Tumors (ENS@T) and member of the ENS@T steering committee. She coordinates the National French registry for SDH-related paraganglioma (PGL.R). She has published 130 papers (H-index 39).

  • Patricia L. M. Dahia

    Patricia L. M. Dahia, MD, PhD, is a Professor of Medicine in the Division of Hematology and Medical Oncology and Cancer Therapy and Research Center at The University of Texas Health Science Center at San Antonio (UTHSCSA), USA. Her research interests are the study of phaeochromocytomas and paragangliomas as model systems to study genetic heterogeneity in cancer and the intersection between oncogenic and metabolic pathways. She has been involved in the identification of multiple cancer susceptibility genes. She is a founding member of the Pheochromocytoma–Paraganglioma Research Support Organization (PRESSOR).

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