Intraoperative copy number profiling from ultra-low coverage long-read sequencing for molecular tumor assessment
TL;DR
CNVisor is introduced, a statistical framework for reliable and robust CNV detection from long-read sequencing, even under ultra-low coverage, and established real-time CNV profiling using ultra-low coverage nanopore sequencing as a feasible strategy for intraoperative, genomics-informed assessment of CNS tumors.
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.