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Theissen, J.

Publications and source records attributed to Theissen, J..

2 recordsLinked to original sources

Quantitative and sensitive neuroblastoma minimal residual disease detection using extrachromosomal DNA (ecDNA) breakpoints

Sensitive detection of minimal residual disease (MRD) remains a major unmet need in high-risk neuroblastoma. MYCN amplification, a hallmark of high-risk disease, typically occurs on extrachromosomal DNA (ecDNA), but the potential of ecDNA-associated genomic rearrangements for individualized MRD monitoring has not been fully exploited. Here, we applied neuroblastoma-specific hybrid capture-based panel sequencing to identify patient-unique breakpoints within MYCN amplicons, and used Circle-seq and Nanopore sequencing to resolve the extrachromosomal amplicon structure in representative samples. Analysis of 8 neuroblastoma cell lines and 22 primary tumors identified 69 tumor-specific breakpoints. Those selected for assay development were validated by breakpoint-specific PCR and Sanger sequencing. Breakpoints detected in primary tumors remained detectable at relapse, supporting their stability as MRD markers. Breakpoint-specific real-time quantitative PCR and droplet digital PCR detected these junctions in bone marrow aspirates with high specificity and reached sensitivities down to a tumor DNA fraction of 10^-6. We applied this approach to 53 serial bone marrow aspirates from 14 patients with high-risk neuroblastoma to monitor MRD dynamics, resolving treatment response and molecular persistence. In six samples, breakpoint-positive DNA was detected in bone marrow that was negative by conventional cytology and immunocytology, highlighting the added value of molecular monitoring. Together, these findings establish ecDNA breakpoint-based detection as a strategy for MRD assessment in neuroblastoma, that is, in principle, applicable to any ecDNA-amplified oncogene.

cancer biology↗

Accurate Prediction of ecDNA in Interphase Cancer Cells using Deep Neural Networks

Oncogene amplification is a key driver of cancer pathogenesis and is often mediated by extrachromosomal DNA (ecDNA). EcDNA amplifications are associated with increased pathogenicity of cancer and poorer outcomes for patients. EcDNA can be detected accurately using fluorescence in situ hybridization (FISH) when cells are arrested in metaphase. However, the majority of cancer cells are non-mitotic and must be analyzed in interphase, where it is difficult to discern extrachromosomal amplifications from chromosomal amplifications. Thus, there is a need for methods that accurately predict oncogene amplification status from interphase cells. Here, we present interSeg, a deep learning-based tool to cytogenetically determine the amplification status as EC-amp, HSR-amp, or not amplified from interphase FISH images. We trained and validated interSeg on 652 images (40,446 nuclei). Tests on 215 cultured cell and tissue model images (9,733 nuclei) showed 89% and 97% accuracy at the nuclear and sample levels, respectively. The neuroblastoma patient tissue hold-out set (67 samples and 1,937 nuclei) also revealed 97% accuracy at the sample level in detecting the presence of focal amplification. In experimentally and computationally mixed images, interSeg accurately predicted the level of heterogeneity. The results showcase interSeg as an important method for analyzing oncogene amplifications.

cancer biology↗