Ready to clone: CNV detection and breakpoint fine-mapping in breast and ovarian cancer susceptibility genes by high-resolution array CGH
Purpose Detection of predisposing copy number variants (CNV) in 330 families affected with hereditary breast and ovarian cancer (HBOC). Methods In order to complement mutation detection with Illumina’s TruSight Cancer panel, we designed a customized high-resolution 8 × 60k array for CGH (aCGH) that...
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Veröffentlicht in: | Breast cancer research and treatment 2016-10, Vol.159 (3), p.585-590 |
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Sprache: | eng |
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Zusammenfassung: | Purpose
Detection of predisposing copy number variants (CNV) in 330 families affected with hereditary breast and ovarian cancer (HBOC).
Methods
In order to complement mutation detection with Illumina’s TruSight Cancer panel, we designed a customized high-resolution 8 × 60k array for CGH (aCGH) that covers all 94 genes from the panel.
Results
Copy number variants with immediate clinical relevance were detected in 12 families (3.6%). Besides 3 known CNVs in
CHEK2
,
RAD51C
, and
BRCA1
, we identified 3 novel pathogenic CNVs in
BRCA1
(deletion of exons 4–13, deletion of exons 12–18) and
ATM
(deletion exons 57–63) plus an intragenic duplication of BRCA2 (exons 3–11) and an intronic
BRCA1
variant with unknown pathogenicity. The precision of high-resolution aCGH enabled straight forward breakpoint amplification of a BRCA1 deletion which subsequently allowed for fast and economic CNV verification in family members of the index patient. Furthermore, we used our aCGH data to validate an algorithm that was able to detect all identified copy number changes from next-generation sequencing (NGS) data.
Conclusions
Copy number detection is a mandatory analysis in HBOC families at least if no predisposing mutations were found by sequencing. Currently, high-resolution array CGH is our first choice of method of analysis due to unmatched detection precision. Although it seems possible to detect CNV from sequencing data, there currently is no satisfying tool to do so in a routine diagnostic setting. |
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ISSN: | 0167-6806 1573-7217 |
DOI: | 10.1007/s10549-016-3956-z |