A whole germline BRCA2 gene deletion: How to learn from CNV in silico analysis

  • Giovanni Luca Scaglione
  • , Paola Concolino
  • , Maria De Bonis
  • , Elisa De Paolis
  • , Angelo Minucci
  • , Maria Gabriella Ferrandina
  • , Giovanni Scambia
  • , Ettore Capoluongo*
  • *Autore corrispondente per questo lavoro

Risultato della ricerca: Contributo in rivistaArticolo

9 Citazioni (Scopus)

Abstract

BRCA1/2 screening in Hereditary Breast and Ovarian Syndrome (HBOC) is an essential step for effective patients’ management. Next-Generation Sequencing (NGS) can rapidly provide high throughput and reliable information about the qualitative and quantitative status of tumor-associated genes. Straightforwardly, bioinformatics methods play a key role in molecular diagnostics pipelines. BRCA1/2 genes were evaluated with our NGS workflow, coupled with Multiplex Amplicon Quantification (MAQ) and Multiplex Ligation-dependent Probe Amplification (MLPA) assays. Variant calling was performed on Amplicon Suite, while Copy Number Variant (CNV) prediction by in house and commercial CNV tools, before confirmatory MAQ/MLPA testing. The germline profile of BRCA genes revealed a unique HBOC pattern. Although variant calling analysis pinpointed heterozygote and homozygote polymorphisms on BRCA1 and BRCA2, respectively, the CNV predicted by our script suggested two conflicting interpretations: BRCA1 duplication and/or BRCA2 deletion. Our commercial software reported a BRCA1 duplication, in contrast with variant calling results. Finally, the MAQ/MLPA assays assessed a whole BRCA2 copy loss. In silico CNV analysis is a time and cost-saving procedure to powerfully identify possible Large Rearrangements using robust and efficient NGS pipelines. Our layout shows as bioinformatics algorithms alone cannot completely and correctly identify whole BRCA1/2 deletions/duplications. In particular, the complete deletion of an entire gene, like in our case, cannot be solved without alternative strategies as MLPA/MAQ. These findings support the crucial role of bioinformatics in deciphering pitfalls within NGS data analysis.
Lingua originaleInglese
pagine (da-a)961-N/A
RivistaInternational Journal of Molecular Sciences
Volume19
Numero di pubblicazione4
DOI
Stato di pubblicazionePubblicato - 2018

All Science Journal Classification (ASJC) codes

  • Catalisi
  • Biologia Molecolare
  • Spettroscopia
  • Informatica Applicata
  • Chimica Fisica e Teorica
  • Chimica Organica
  • Chimica Inorganica

Keywords

  • BRCA1/2
  • CNV
  • Catalysis
  • Computer Science Applications1707 Computer Vision and Pattern Recognition
  • Data analysis
  • HBOC
  • Inorganic Chemistry
  • MLPA
  • Molecular Biology
  • NGS
  • Organic Chemistry
  • Physical and Theoretical Chemistry
  • Spectroscopy

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