Germline mutations of renal tumor predisposition genes in early-onset patients
INTRODUCTION
Inherited susceptibility to renal tumors has been associated with an array of RCC predisposing genes, but most screening has been limited patients with a strong family history of RCC. Next generation sequencing (NGS-) based multi-gene panel analysis provides an economic, efficient, and adaptable tool for investigating the frequency of germline pathogenic mutation on a wider scale. This study investigated the frequency of germline pathogenic mutations of renal tumor predisposition genes in sporadic, early-onset RCC.
METHODS
An NGS-based array for 23 known and potential RCC predisposition genes (VHL, TSC1, TSC2, PTEN, MET, FH, SDHB, SDHC, SDHD, FLCN, BAP1, MITF, HNF1B, PBRM1,BRCA1, BRCA2, KDM5C, KDM6A, NF2, SETD2, CDKN2A, TCEB1, TP53) was used to perform germline mutation analysis on 190 unrelated Chinese patients who presented with renal tumors at under 45 years old. Variants detected were filtrated for pathogenicity and frequencies were calculated and correlated with clinical features.
RESULTS
Eighteen of 190 patients (9.5%) had germline pathogenic mutations in 10 out of 23 selected RCC predisposition genes. Twelve patients had alterations in known RCC predisposition genes (6.3%), including 3 germline BAP1 mutations. While, 6 patients had mutations in potential RCC predisposition genes, such as BRCA1/2. Carrier status was significantly associated with second-degree relative tumor history (p
CONCLUSION
In early-onset patients, multi-gene panel testing identified pathogenic germline mutations in known and potential RCC predisposition genes. This emphasizes the importance of screening these early-onset patients, irrelevant of family history, and provides valuable epidemiological information. Germline mutation screening for RCC susceptibility represents an achievable aspect of personalized medicine that can improve patient outcomes.
Funding: National Natural Science Foundation of China (Grant No. 81370073) and Shanghai Rising Star Program (Grant No. 16QA1401100)