Related Experiment Video For CGH
Updated: Jun 5, 2026

Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants
Published on: February 21, 2015
Copy Number Analysis in Congenital Nevi: Concordance and Diagnostic Limitations of aCGH, sWGS, and Methylation
Anton Karelin1, Ines B Brecht2, Michaela Pogoda1,3
1Institute of Medical Genetics and Applied Genomics, Eberhard Karls University of Tübingen, Tübingen, Germany.
Abstract:
Distinguishing benign proliferative nodules (PNs) from melanoma arising within congenital melanocytic nevi remains a major diagnostic challenge. Copy number alteration (CNA) analysis is widely used to support classification, but current criteria were developed using array comparative genomic hybridization (aCGH). The performance of alternative platforms such as shallow whole-genome sequencing (sWGS) and methylation sequencing is poorly defined in this context. The objective of this study is to compare CNA profiles obtained from aCGH, sWGS, and methylation sequencing in atypical nodules arising within congenital nevi, and to explore their clinical context based on available follow-up data. Sixteen samples from 14 patients were retrospectively analyzed using all three platforms. CNAs were cataloged, concordance across methods was quantified using the Jaccard index, and molecular classifications were compared. Clinical follow-up was reviewed to provide clinical context. aCGH detected 39 CNAs, sWGS 60, and methylation sequencing 66. Concordance was highest between sWGS and methylation (mean Jaccard 0.67), followed by aCGH versus sWGS (0.64) and aCGH versus methylation (0.49). Cases with high aneuploidy demonstrated strong cross-platform agreement, whereas low-burden lesions exhibited greater variability between methods. Divergent molecular classifications were observed in six cases. While all methods reliably detect broad chromosomal changes, sWGS and methylation sequencing identify many additional focal CNAs that may not align with CGH-based diagnostic criteria. These findings highlight the need for platform-adapted thresholds and validation in larger outcome-linked cohorts.

