Abstract
Background:
Pheochromocytoma and paraganglioma (PPGL) are rare neuroendocrine tumors with a high rate of germline predisposition. Although multigene panel testing (MGPT) using next-generation sequencing (NGS) is widely adopted globally, its clinical application in Japan remains limited. Methods: We developed a custom amplicon-based NGS panel targeting 12 established PPGL susceptibility genes. Germline analysis was performed in 23 Japanese patients with confirmed PPGL to evaluate sequencing quality and variant detection.
Results:
Sequencing quality was consistently high (Q30 > 96%, mapping rate >99%, on-target rate >80%), with nearly all exons (148/149) achieving >1,000× coverage. Pathogenic or likely pathogenic variants were found in 21.7% (5/23), including SDHB, VHL, and RET. In addition, variants of uncertain significance (VUS) were found in 17.4% (4/23), including novel missense variants in FH, SDHA, and MAX.
Conclusions:
This study demonstrates the feasibility and clinical utility of amplicon-based MGPT for PPGL in a real-world Japanese setting and highlights the importance of ongoing VUS reclassification to improve clinical interpretation. The findings support its diagnostic value, reflect underlying clinical demand, and contributed to its non-insured clinical adoption at certified laboratories in Japan.
Introduction
Pheochromocytoma and paraganglioma (PPGL) are rare neuroendocrine tumors with a well-established genetic basis. Large-scale studies have shown that 20–40% of PPGL cases harbor heterozygous pathogenic variants in germline susceptibility genes (Fishbein et al., 2017; Jiang et al., 2020; Lenders et al., 2014). Germline genetic testing links molecular subtypes—characterized by distinct biochemical profiles, imaging features, and metastatic potential—to precision medicine strategies in the management of PPGL and also enables genetic counseling and surveillance of at-risk relatives (Nölting et al., 2022).
The 2014 Endocrine Society guideline recommends sequential testing based on clinical features such as syndromic presentation, metastatic behavior, tumor location, and biochemical phenotype (Lenders et al., 2014). However, with the advent of next-generation sequencing (NGS), multigene panel testing (MGPT) has emerged as a comprehensive, time-efficient, and cost-effective approach. A 2017 expert consensus statement provided detailed guidance on the clinical application of MGPT in hereditary PPGL (Toledo et al., 2017), and subsequent studies from the United States have supported its diagnostic and clinical utility (Horton et al., 2022).
Despite these global developments, the clinical adoption of MGPT remains limited in Japan due to insufficient insurance coverage and infrastructure. Our previous Sanger-based study demonstrated a comparable prevalence of germline pathogenic variants in Western populations, with recurrent detection of a shared SDHB variant among unrelated individuals, suggesting ethnic-specific patterns (Yonamine et al., 2021). To address the limitations of Sanger sequencing, we developed a custom amplicon-based NGS panel targeting 12 PPGL susceptibility genes. This study evaluates its diagnostic performance and explores the germline variant landscape in Japanese PPGL patients.
Materials and Methods
Subjects
This study included Japanese probands with clinically and pathologically confirmed pheochromocytoma or paraganglioma (PPGL) who were referred to the University of Tsukuba Hospital for genetic testing between July 2021 and November 2022. Peripheral blood samples and clinical data (sex, age at diagnosis, tumor location, and presence of metastasis) were collected. A total of 23 patients (11 female, 12 male) underwent testing, including 12 cases of pheochromocytoma and 11 of paraganglioma. The mean age at diagnosis was 44.3 years (range: 18–73). The study was conducted in accordance with the Declaration of Helsinki and Japanese guidelines for human genome research. Written informed consent was obtained from all participants, and genetic counseling was provided before and after testing. The protocol was approved by the Institutional Review Board of the University of Tsukuba Hospital (approval #H28–134; March 31, 2021).
Next-generation sequencing and bioinformatic workflow
Genomic DNA was extracted from 10 mL of ethylenediaminetetraacetic acid-treated peripheral blood using QIAGEN DNA extraction kits (QIAGEN, Aarhus C, Denmark). A custom amplicon-based NGS panel targeting 12 PPGL-associated genes was designed using the AmpliSeq for Illumina Custom Panel platform (Illumina, San Diego, CA). The panel included 253 predesigned primer pairs to amplify 149 coding exons and flanking intronic regions (29.3 kb in total) by multiplex polymerase chain reaction. Library preparation was performed using the AmpliSeq for Illumina Library PLUS kit, and sequencing was conducted on the Illumina MiSeq platform according to the manufacturer’s instructions. Sequences were aligned to the GRCh38/hg38 human reference genome. Transcript reference sequences (RefSeq) used for alignment, corresponding to the 12 target genes (FH, MAX, NF1, RET, SDHA, SDHAF2, SDHB, SDHC, SDHD, TMEM127, VHL, and MEN1), are listed in Supplementary Table S2.
Bioinformatic analysis and variant interpretation
Raw data processing, including quality control, alignment, and variant calling for single nucleotide variants and small insertions/deletions (indels), was performed using CLC Genomics Workbench (QIAGEN, Aarhus C, Denmark) and Seqkit (Shen et al., 2016). Aligned sequences were exported as binary alignment/map files for quality control using the Picard Toolkit (Broad Institute, http://broadinstitute.github.io/picard). Variant annotation was performed using CLC Genomics Workbench (QIAGEN, Aarhus C, Denmark), SnpEff (Cingolani et al., 2012), and Vcfanno (Pedersen et al., 2016). Identified variants were filtered based on allele frequencies using population databases, including jMorp (ToMMo 38KJPN), gnomAD v4.0, and dbSNP build 156. ClinVar was used as the clinical variant database to aid in variant classification. Interpretation of sequence variations was performed according to the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) guidelines (Li et al., 2017). Missense variant pathogenicity was assessed using the in silico variant meta-predictor REVEL (Ioannidis et al., 2016); a score above 0.5 was used for supporting pathogenic variants (ACMG/AMP codes: PP3), based on previously reported criteria (Alirezaie et al., 2018). Variants classified as benign or likely benign were excluded using predefined criteria: minor allele frequency >1% in population databases. Synonymous variants without predicted splice site effects and those located in untranslated regions (UTRs) were also excluded unless supported by additional evidence of pathogenicity. Uniform resource locators and access information for all databases and tools are provided in Supplementary Table S1.
Results
NGS panel performance
Sequencing and alignment metrics for the 23 samples are summarized in Table 1. The total number of sequenced bases ranged from 236 million to 438 million. Q30 scores were consistently high: 96.9–98.2% for Read 1 and 93.8–97.3% for Read 2. All samples exhibited mapping rates >99%, and on-target rates ranged from 81.3% to 93.7%, indicating efficient enrichment of target regions.
Summary of Sequencing and Mapping Quality Metrics Across 23 Samples
Percentage of bases with a Phred quality score ≥30.
Number of bases successfully mapped to the panel’s intended amplicon regions.
SD, Standard deviation.
Coverage depth per exon is detailed in Supplementary Table S3. Of 149 exons across the 12 target genes, 148 achieved an average coverage >1,000×, including 90 exons with >10,000× depth. These results confirm the high coverage and uniformity of the panel.
Summary of clinical presentation and germline findings
The clinical and genetic features of individuals with pathogenic, likely pathogenic, or VUS variants are shown in Table 2. Full data for all 23 probands are provided in Supplementary Table S4. The mean age at diagnosis was 42.1 ± 14.8 years (range: 18–75). Most underwent testing within three years of diagnosis, except for case #18 (tested 19 years later due to recurrence) and case #10 (tested 8 years later for familial screening).
Genetic and Clinical Profiles of PPGL Patients with Identified Germline Variants
AA, amino acid; ATPGL, abdominal and thoracic paraganglioma; HNPGL, head and neck paraganglioma; PCC, pheochromocytoma; PPGL, pheochromocytoma and paraganglioma; TNM, Tumor–Node–Metastasis; VUS, variant of uncertain significance.
Pathogenic or likely pathogenic variants were identified in five patients (21.7%): SDHB (n = 3), VHL (n = 1), and RET (n = 1). Two unrelated individuals with abdominal or thoracic PGL harbored the SDHB nonsense variant c.470del (p.Leu157Ter). Another patient with head and neck PGL and metastasis carried SDHB c.137G>T (p.Arg46Leu). A pathogenic VHL variant (c.470C>T) was detected in a 19-year-old with bilateral PCC.
Variants of uncertain significance (VUS) were identified in SDHA (c.740T>C, p.Ile247Thr) in a case of thoracoabdominal PGL (#5), FH (c.465A>T, p.Glu155Asp) in unilateral PCC (#8), and MAX (c.295G>T, p.Val99Phe) in bilateral PCC (#19). These variants have not been reported in ClinVar and were absent from population databases, including gnomAD and jMorp. In silico prediction tools (e.g., REVEL) suggested potential deleterious effects, but the evidence was insufficient for pathogenic classification. Clinically, these patients presented with apparently sporadic PPGL and did not exhibit additional syndromic features. Based on ACMG/AMP criteria, these three novel missense variants were classified as VUS.
Discussion
This short report presents the first Japan-based study to evaluate the analytical performance and clinical utility of a disease-specific NGS panel for germline PPGL testing. We adopted an amplicon-based approach for its flexibility in target selection, ease of custom design, and streamlined library preparation (Jennings et al., 2017). Sequencing quality was consistently high, with Q30 scores >96%, mapping rates >99%, and on-target rates >80%, indicating low error rates and efficient target enrichment. These metrics support the technical reliability of the workflow and its suitability for accurate variant detection using limited input material. Coverage was robust across the panel, with 148 of 149 exons exceeding 1,000× and the remaining (VHL exon 2) still above the 100× detection threshold (see Supplementary Table S3) (Jennings et al., 2017). Minor variability likely reflects inherent differences in amplification efficiency, underscoring the robustness of the amplicon-based design.
The selected 12-gene panel was based on international consensus statements (Toledo et al., 2017) and encompasses the major PPGL-related molecular pathways: pseudohypoxia (SDHx, VHL, FH) and kinase signaling (RET, NF1, MAX, TMEM127). This gene set is consistent with recent large-scale U.S. studies (Horton et al., 2022), ensuring relevance and comparability. Genes such as EPAS1, MDH2, and SLC25A11 were not incorporated, given their extremely low prevalence of germline pathogenic variants with uncertain penetrance in PPGL and the fact that they are not listed as core PPGL driver genes in the NCCN guidelines (National Comprehensive Cancer Network, 2025). Pathogenic or likely pathogenic variants were identified in 5 of 23 patients (21.7%). This detection rate is lower than that of our previous Sanger-based study in 370 Japanese cases (32.4%) (Yonamine et al., 2021), likely reflecting the smaller sample size, reduced inclusion of syndromic or familial cases, and the technical limitations of amplicon-based methods in detecting structural variants (Tattini et al., 2015). Nonetheless, identifying actionable variants in over one-fifth of patients reinforces its diagnostic value. VUS were identified in three patients: FH c.465A>T (p.Glu155Asp), SDHA c.740T>C (p.Ile247Thr), and MAX c.295G>T (p.Val99Phe). All three variants were absent from both population and clinical databases and lacked functional evidence. In the present study, their interpretation relied on in silico prediction algorithms, although functional validation and family segregation analyses will be indispensable to confirm pathogenicity and enable reclassification. The identification of VUS remains a significant challenge in comprehensive MGPT, requiring systematic reevaluation through population-specific databases, functional studies, and family segregation analysis (Zhang et al., 2024).
Limitations include the small cohort size, exclusion of other rare susceptibility genes, lack of structural variant detection, and the inability to pursue functional or segregation analyses for cases with VUS. Notably, amplicon-based NGS cannot reliably detect structural variants, particularly large deletions and duplications that are known to occur in PPGL susceptibility genes. Integration of complementary approaches such as multiplex ligation-dependent probe amplification or dedicated copy number variation analysis will therefore be essential in future clinical diagnostic workflows. Despite these limitations, our study still addressed a growing clinical demand and contributed to the introduction of quality-assured, non-insured clinical MGPT for PPGL at certified clinical laboratories in Japan as of May 2023, thereby expanding nationwide access.
In conclusion, our findings validate the technical performance and clinical relevance of amplicon-based MGPT and have contributed to facilitating broader access to germline testing for PPGL in Japan. Future studies with larger cohorts, expanded gene panels, and longitudinal follow-up are warranted to enhance diagnostic yield and clarify genotype—phenotype correlations in the Japanese PPGL population.
Footnotes
Acknowledgments
The authors are grateful to all the patients who participated and to the collaborating research facilities for providing samples and partially covering the NGS analysis costs.
Authors’ Contributions
Conceptualization, M.Y. and K.T.; methodology, M.Y., R.K., N.I., and K.T.; formal analysis and investigation, M.Y., R.K., N.I., and Y.A.; resources, R.K. and N.I.; data curation, M.Y.; writing—original draft preparation, M.Y.; writing—review and editing, R.K. and K.T.; visualization, M.Y.; supervision, K.T.; project administration, M.Y. and Y.A.; funding acquisition, K.T. All authors have read and agreed to the published version of the article.
Author Disclosure Statement
No competing financial interests exist.
Funding Information
No funding was provided by governmental agencies, public research grants, or nonprofit organizations.
References
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