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Open AccessArticle

Performance of In Silico Prediction Tools for the Detection of Germline Copy Number Variations in Cancer Predisposition Genes in 4208 Female Index Patients with Familial Breast and Ovarian Cancer

1
Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Medical Faculty, University Hospital Cologne, 50931 Cologne, Germany
2
Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland
*
Author to whom correspondence should be addressed.
Current address: Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA.
Cancers 2021, 13(1), 118; https://doi.org/10.3390/cancers13010118
Received: 20 November 2020 / Revised: 17 December 2020 / Accepted: 22 December 2020 / Published: 1 January 2021
(This article belongs to the Special Issue Genetic Variants Associated with Breast and Ovarian Cancer Risk)
The identification of germline copy number variants (CNVs) by targeted nextgeneration sequencing frequently relies on in silico prediction tools with unknown sensitivities. We investigated the performances of four in silico CNV prediction tools in 17 cancer predisposition genes in a large series of 4208 female index patients with familial breast and/or ovarian cancer. We identified 77 CNVs in 76 out of 4208 patients; six CNVs were missed by at least one of the prediction tools. Experimental verification of in silico predicted CNVs is required due to high frequencies of false positive predictions. For female index patients with familial breast and/or ovarian cancer, CNV detection should not be restricted to BRCA1/2 due to the relevant proportion of CNVs in further cancer predisposition genes.
The identification of germline copy number variants (CNVs) by targeted next-generation sequencing (NGS) frequently relies on in silico CNV prediction tools with unknown sensitivities. We investigated the performances of four in silico CNV prediction tools, including one commercial (Sophia Genetics DDM) and three non-commercial tools (ExomeDepth, GATK gCNV, panelcn.MOPS) in 17 cancer predisposition genes in 4208 female index patients with familial breast and/or ovarian cancer (BC/OC). CNV predictions were verified via multiplex ligation-dependent probe amplification. We identified 77 CNVs in 76 out of 4208 patients (1.81%); 33 CNVs were identified in genes other than BRCA1/2, mostly in ATM, CHEK2, and RAD51C and less frequently in BARD1, MLH1, MSH2, PALB2, PMS2, RAD51D, and TP53. The Sophia Genetics DDM software showed the highest sensitivity; six CNVs were missed by at least one of the non-commercial tools. The positive predictive values ranged from 5.9% (74/1249) for panelcn.MOPS to 79.1% (72/91) for ExomeDepth. Verification of in silico predicted CNVs is required due to high frequencies of false positive predictions, particularly affecting target regions at the extremes of the GC content or target length distributions. CNV detection should not be restricted to BRCA1/2 due to the relevant proportion of CNVs in further BC/OC predisposition genes. View Full-Text
Keywords: breast/ovarian cancer susceptibility genes; HBOC; CNV; multigene panel sequencing breast/ovarian cancer susceptibility genes; HBOC; CNV; multigene panel sequencing
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MDPI and ACS Style

Lepkes, L.; Kayali, M.; Blümcke, B.; Weber, J.; Suszynska, M.; Schmidt, S.; Borde, J.; Klonowska, K.; Wappenschmidt, B.; Hauke, J.; Kozlowski, P.; Schmutzler, R.K.; Hahnen, E.; Ernst, C. Performance of In Silico Prediction Tools for the Detection of Germline Copy Number Variations in Cancer Predisposition Genes in 4208 Female Index Patients with Familial Breast and Ovarian Cancer. Cancers 2021, 13, 118. https://doi.org/10.3390/cancers13010118

AMA Style

Lepkes L, Kayali M, Blümcke B, Weber J, Suszynska M, Schmidt S, Borde J, Klonowska K, Wappenschmidt B, Hauke J, Kozlowski P, Schmutzler RK, Hahnen E, Ernst C. Performance of In Silico Prediction Tools for the Detection of Germline Copy Number Variations in Cancer Predisposition Genes in 4208 Female Index Patients with Familial Breast and Ovarian Cancer. Cancers. 2021; 13(1):118. https://doi.org/10.3390/cancers13010118

Chicago/Turabian Style

Lepkes, Louisa; Kayali, Mohamad; Blümcke, Britta; Weber, Jonas; Suszynska, Malwina; Schmidt, Sandra; Borde, Julika; Klonowska, Katarzyna; Wappenschmidt, Barbara; Hauke, Jan; Kozlowski, Piotr; Schmutzler, Rita K.; Hahnen, Eric; Ernst, Corinna. 2021. "Performance of In Silico Prediction Tools for the Detection of Germline Copy Number Variations in Cancer Predisposition Genes in 4208 Female Index Patients with Familial Breast and Ovarian Cancer" Cancers 13, no. 1: 118. https://doi.org/10.3390/cancers13010118

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