Balázs Sarkadi1,2, Istvan Liko1,3, Kinga Németh1,2, Henriett Butz1,3,4, Otto Darvasi1, Attila Patócs1,3, 4
1 HAS-SE “Lendulet” Hereditary Endocrine Tumors Research Group, Hungarian Academy of Sciences and Semmelweis University, Budapest, Hungary
2 2nd Department of Medicine, Semmelweis University, Budapest, Hungary
3 Bionics Innovation Center, Budapest, Hungary
4 Department of Laboratory Medicine, Semmelweis University, Budapest, Hungary
Germline mutations in 17 genes can be identified in 40% of the patients with pheochromocytoma/paraganglioma (Pheo/PGL). Phenotype-driven genetic screening is recommended, however, when the probability of a disease-associated germline mutation exceeds 10% whole-gene panel testing is suggested. Due to the increasing demands, the cost-effective next-generation sequencing (NGS) based methods are emerging as routine diagnostic tools.
OBJECTIVE: To develop an endocrine onco-panel for Pheo/PGL genetic screening (PPGLpanel) and to compare it’s efficiency and sensitivity to whole-exome sequencing (WES) for mutation detection in Pheo/PGL associated genes.
METHODS: PPGLpanel covers 15 Pheo/PGL genes. Its efficacy was tested on 48 samples obtained from patients with various endocrine tumours. Previous genetic analysis was performed with Sanger sequencing. WES was performed on 20 samples with Illumina’s Rapid Capture Exome kit (4 samples), Agilent SureSelect Biotinylated RNA Library kit (4 samples) or with BGI 59Mb exome kit and Complete Genomics workflow (12 samples).
RESULTS: Target capture of Pheo/PGL genes differs markedly between platforms. Mean coverage of the 15 genes with PPGLpanel was higher (348,3±60,8 reads) compared to WES (87,9±10,1; 102,5±8,1; 146,5±27,6 reads; Agilent, BGI, Nextera kit, respectively). Significant difference was observed between the total number of reads mapping to known variants (52±9,1 vs. 301±169,7; p=0.043) and in the deviation from the optimal read number ratio used for determination of heterozygosity (8,9±3,6 vs. 3,9±2,2) (WES vs. PPGLpanel). Bioinformatical settings for variant
filtering were established to reach the best analytical performance. Using
samples with known genetic alterations, bioinformatical workflow was optimized and all
known genetic variations were identified.
CONCLUSION: WES is efficient in detection of germline mutations in Pheo/PGL–associated genes, however, targeted sequencing may be more cost-effective and validation of the entire workflow is easier. Careful selection of NGS method is required for clinical genetic analysis, because library preparation, sequencing platforms and bioinformatical settings significantly affect the results.
Doctoral School: Clinical Medicine
Program: Hormonal Regulations
Supervisor: Attila Patócs