Back

Intraoperative copy number profiling from ultra-low coverage long-read sequencing for molecular tumor assessment

Wang, G.; Kubelt, C.; Smicius, R.; Zidane, K.; Rohrandt, C.; Brändl, B.; Wong, D.; Steiger, M.; Lum, A.; Evers, M.; Schmidt, N. O.; Pröscholdt, M.; Riemenschneider, M. J.; Kretzmer, H.; Synowitz, M.; Yip, S.; Vingron, M.; Müller, F.-J.

2026-07-23 oncology
10.64898/2026.07.21.26358307 medRxiv
Show abstract

Copy number variations (CNVs) can serve as important clinical biomarkers for tumor classification and stratification. However, the utility of these CNV biomarkers for intraoperative tumor assessment within the timeframe of neurosurgical procedures has remained elusive due to the protracted duration of conventional CNV characterization methods. Here, we introduce CNVisor, a statistical framework for reliable and robust CNV detection from long-read sequencing, even under ultra-low coverage. Applied to neurosurgical tumor specimens, the proposed method enabled genome-wide CNV profiling and identified clinically relevant CNVs using roughly 60,000 reads within 20 minutes of sequencing. Integrating CNVisor with methylation-based classifiers can further reduce turnaround time and increase the accuracy of glioma subtype stratification. Together, these findings establish real-time CNV profiling using ultra-low coverage nanopore sequencing as a feasible strategy for intraoperative, genomics-informed assessment of CNS tumors.

Matching journals

The top 11 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.