Genome Sequencing of Undiagnosed European Patients Suspected of Hereditary Cancer: Diagnostic Yield and Identification of Candidate Causative Variants

PURPOSE – Hereditary cancers represent 5%-10% of all cancers, typically characterized by familial aggregation, early onset, and/or multiple primary tumors. Isolated cases with extreme early-onset or multiple unrelated cancers are rare and frequently underdiagnosed. This study aimed to improve genetic diagnostic yield in unresolved patients with strong clinical suspicion of hereditary cancer. Inclusion criteria were (1) ≥4 primary tumors in different organs (or ≥3 if two are rare), (2) adult-type cancers at ≤25 years, or (3) profuse gastrointestinal adenomatous polyposis before age 50 years or profuse polyposis of unknown type before age 30 years.METHODS – Germline DNA from 98 patients selected through ERN-GENTURIS underwent short-read whole-genome sequencing (WGS). Ninety had received negative results from standard genetic testing, whereas eight had not been tested. Variant analysis was performed using the RD-Connect GPAP and Solve-RD frameworks.RESULTS – Pathogenic or likely pathogenic variants in phenotype-related high-penetrance genes were found in 6% of patients, including APC and TP53 mosaicisms and germline variants in TP53, BAP1, BARD1, and MBD4 (homozygous). Three patients carried pathogenic variants in hereditary cancer genes unrelated to their phenotype, and a heterozygous ERCC3 pathogenic variant was detected in a young patient with breast cancer. Suggestive variants of uncertain significance were identified, including a chromosome 3 inversion affecting VHL and MLH1 in a patient with renal and gastric cancers. Potentially regulatory noncoding variants in phenotype-associated genes were observed in 15% of patients (22 variants).CONCLUSION – Comprehensive resequencing of patients suspected of hereditary cancer increased the diagnostic yield by 6%. Detected pathogenic variants involved phenotype-related genes not previously analyzed or missed because of mosaicism. WGS further enables identification of novel gene associations and structural, deep intronic, or regulatory variants.