Copy Number Variations (CNVs) Account for 10.8% of Pathogenic Variants in Patients Referred for Hereditary Cancer Testing

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Agiannitopoulos K., Pepe G., Tsaousis G. N., Potska K., Bouzarelou D., Katseli A., ...Daha Fazla

Cancer genomics & proteomics, cilt.20, sa.5, ss.448-455, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 20 Sayı: 5
  • Basım Tarihi: 2023
  • Doi Numarası: 10.21873/cgp.20396
  • Dergi Adı: Cancer genomics & proteomics
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, Biotechnology Research Abstracts, Chemical Abstracts Core, EMBASE, MEDLINE
  • Sayfa Sayıları: ss.448-455
  • Anahtar Kelimeler: CNVs, computational CNV analysis, digital MLPA, Hereditary cancer, MLPA
  • İstanbul Üniversitesi Adresli: Evet

Özet

BACKGROUND/AIM: Germline copy number variation (CNV) is a type of genetic variant that predisposes significantly to inherited cancers. Today, next-generation sequencing (NGS) technologies have contributed to multi gene panel analysis in clinical practice. MATERIALS AND METHODS: A total of 2,163 patients were screened for cancer susceptibility, using a solution-based capture method. A panel of 52 genes was used for targeted NGS. The capture-based approach enables computational analysis of CNVs from NGS data. We studied the performance of the CNV module of the commercial software suite SeqPilot (JSI Medical Systems) and of the non-commercial tool panelcn.MOPS. Additionally, we tested the performance of digital multiplex ligation-dependent probe amplification (digitalMLPA). RESULTS: Pathogenic/likely pathogenic variants (P/LP) were identified in 464 samples (21.5%). CNV accounts for 10.8% (50/464) of pathogenic variants, referring to deletion/duplication of one or more exons of a gene. In patients with breast and ovarian cancer, CNVs accounted for 10.2% and 6.8% of pathogenic variants, respectively. In colorectal cancer patients, CNV accounted for 28.6% of pathogenic/likely pathogenic variants. CONCLUSION: In silico CNV detection tools provide a viable and cost-effective method to identify CNVs from NGS experiments. CNVs constitute a substantial percentage of P/LP variants, since they represent up to one of every ten P/LP findings identified by NGS multigene analysis; therefore, their evaluation is highly recommended to improve the diagnostic yield of hereditary cancer analysis.