Phaeochromocytomas and paragangliomas (PPGLs) are tumours of the adrenal medulla and
extra-adrenal sympathetic nervous system, respectively, that often secrete catecholamines(1).
The tumours are derived from the neural crest and approximately 50% are caused by a germline
variant. Because of the strong heritability, genetic testing is recommended in all patients
with PPGLs (2-5). Most PPGLs are non-malignant (defined as non-metastatic) tumours and
respond well to surgical treatment. Approximately 15% of PPGLs are metastatic and incurable
by currently available therapies(1). The latest WHO Blue Book classifies all PPGLs as
possibly malignant due to their unpredictable behaviour(6).
PPGLs are very heterogeneous tumours predisposed by more than 20 distinct genes(1). In two
thirds of the tumours a germline or somatic variant can be identified and thus PPGLs have
been divided into distinct biological subgroups on the basis of these variants(1). A
consensus statement from 2017 stated the necessity for a targeted gene panel (Next-Generation
Sequencing, NGS) in all PPGL patients and testing of tumour DNA if possible to identify
potential future targets for drug therapies(2). Furthermore, epigenetic changes in PPGLs and
other endocrine tumours have been shown to be associated with the risk of metastatic
disease(7-11) and future research should be focused on understanding both epigenetic and
genetic changes in PPGLs as this will open opportunities for targeted molecular therapies and
personalized medicine(11,12). It has been demonstrated that DNA methylation-based tumour
classification is a valuable asset for clinical decision making(13).
Many histopathological predictive risk factors for poor prognosis have been proposed and
scoring systems have been developed to identify those PPGLs that have a metastatic potential.
The PASS (Pheochromocytoma of the Adrenal Gland Scaled Score) and GAPP (Grading system for
Adrenal Phaeochromocytoma and Paraganglioma) scores are the most well-known (1,14-18). In a
recent meta-analysis the algorithms were evaluated and it was concluded that both algorithms
had a low positive predictive value but a high negative predictive value for PPGLs which
indicated that the models were relevant for ruling out metastatic potential for both tumour
types rather than identifying cases with an increased risk of disseminated disease. This
could most probably be attributed to subjective assessment of criteria involved in these
scoring systems. The authors concluded that the inclusion of NGS data and an overall
molecular approach is needed to accurately pinpoint cases at risk for future metastases(19).
Furthermore, there is a need for an algorithm based on objective measurements to ensure
objectivity in the pathological diagnosis of PPGLs, similar to algorithms for adrenocortical
carcinomas like the Helsinki Score and the Reticulin Algorithm(19-21). To this end
computer-assisted techniques have shown promising results(11,20).
Correctly identifying PPGLs with a future risk of metastatic disease is a clinical challenge
which impacts not only the physicians but also the patients and their family members. Today's
clinical guidelines are based on 'rule all in' methods which have a costly impact on the
healthcare resources and the quality of life of the patients. An algorithm based on
histopathological, genetic, epigenetic, and clinical variables may be helpful in stratifying
patients into risk categories with different needs for clinical follow-up.
Pilot Study: Patients followed at Copenhagen University Hospital, Copenhagen have been part
of a pilot study on the effect of genetic screening of family members with SDHX variants on
the clinical presentation of cases. Variants of the SDHX (SDHA, -AF2, -B, -C, -D) genes are a
frequent cause of familial PPGLs. The investigators found distinct differences in the
clinical and histopathological characteristics between genetic variants in SDHB. Family
screening for SDHB variants resulted in earlier detection of tumours in two families.
Patients with SDHA, SDHC and SDHD variants also had severe phaenotypes, underlining the
necessity for a broad genetic screening of the proband. The study corroborated previous
findings of poor prognostic markers and found that the genetic variants and clinical
phenotype are linked and therefore useful in the decision of clinical follow-up. This study
has been presented as master thesis at the University of Copenhagen 2019 (Grade A) and the
manuscript has been published(22). The study corroborates the feasibility of the current
project.
Process of the study. Part 1: Description of the genotype-phaenotype association in a nation-wide cohort of
approximately 400 patients with PPGLs or germ-line genetic variants predisposing to PPGLs
using clinical data, biochemical variables, tumour characteristics and imaging results. This
national cohort will provide characterization of patients making it possible to identify a
metastatic and non-metastatic clinical course, respectively.
Part 2: Identification of novel prognostic biomarkers using formalin-fixed paraffin-embedded
tumor tissue samples for genetic testing (NGS for somatic variants), DNA methylation, PASS
scoring, and immunohistochemical markers. Twenty patients with a poor prognosis (proof of
metastases) and 20 patients with a good prognosis (non-metastatic) will be included from the
Capital Region of Denmark. The results will be used to develop a new algorithm for clinical
prognosis.
Part 3: Validation of the new algorithm in a second subcohort of patients with tumor tissue
specimens selected at random from the national cohort. Tumor specimens will be subjected to
investigation of the same novel prognostic biomarkers identified in part 2. Results will be
entered into the new algorithm to test whether the clinical course can be accurately
predicted.
Dissemination Positive, negative or inconclusive results will be published in peer-reviewed
international journals and presented at scientific meetings and in relevant patient fora. The
study group members are affiliated to ENDO-ERN (European Reference Network on Rare Endocrine
Conditions), a European collaboration of highly specialized centres treating rare endocrine
conditions through which results can be disseminated (https://endo-ern.eu/).
To ensure completeness and transparency in the reporting of results the investigators will
use the STROBE reporting guidelines relevant to cohort studies.
Patient involvement:
Patients with lived experiences contribute additional expertise and give valuable, novel
insights and thus improve the effectiveness of the study. Furthermore, the patient
perspective is essential in the presentation of the results. Relevant patients with the
disease in question from our outpatient clinic will be invited to participate in the Steering
Committee.
Safety and ethical considerations Treatment of personal data: General Data Protection
Regulation will be strictly adhered to. Approval by the Ethics Committee for the Capital
Region has been granted (H-20065699, 19th of February 2021).
Database: The database will be established under RedCap, which is accessible nation-wide and
managed centrally by the data protection unit of The Capital Region.
Statistics: Based on previous studies which have developed similar algorithms (20,21) the
investigators will need approximately 200 patients to achieve statistical power in relation
to validation of the algorithm in the larger cohort. Study population characteristics will be
presented using descriptive statistics. Group differences will be tested primarily using
non-parametric tests (Mann-Whitney, Wilcoxon) or t-test. Associations between the genotype
and phaenotype will be analysed by ANOVA (analysis of variance) and regression analysis
controlling for relevant confounders, i.e. age and sex, where appropriate. The sensitivity
and specificity of the algorithm to predict poor or good outcome will be determined using
multiple regression analyses, Chi2 and ROC (Receiver Operating Characteristics). The
investigators will seek help from professional statisticians at Copenhagen University at
Statistical Advisory Service for PhD-students and students at the Faculty of Health Sciences.
