Crucial parameters for precise copy number variation detection in formalin-fixed paraffin-embedded solid cancer...

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Copy number variations (CNVs) are critical biomarkers for cancer diagnostics and prognostics.
• Formalin-fixed paraffin-embedded (FFPE) samples are widely used but present challenges for CNV detection.
• Ultra-low-pass whole-genome sequencing (ULP-WGS) is an emerging, cost-effective alternative to array-based methods.

Purpose of the Study:

• To evaluate the performance of open-source CNV detection tools using ULP-WGS data from FFPE samples.
• To compare ULP-WGS-based CNV detection with traditional SNP array methods.
• To identify optimal parameters for accurate CNV detection in challenging FFPE samples.

Main Methods:

• Comparative analysis of three CNV callers: CNVpytor, ichorCNA, and WisecondorX.
• Utilized ULP-WGS data generated from FFPE samples.
• Performance benchmarked against SNP array data.

Main Results:

• ichorCNA and WisecondorX demonstrated high accuracy, matching true positive rates with fewer false positives than SNP arrays.
• ichorCNA and WisecondorX exhibited the most similar CNV detection patterns.
• Neoplastic cell content, sequencing coverage, and bin size significantly influenced CNV detection accuracy.

Conclusions:

• ULP-WGS is a viable and robust alternative to SNP arrays for CNV detection in FFPE samples.
• WisecondorX is identified as the most suitable tool for clinical implementation due to its performance and comparable detection patterns.
• Optimization of pre-analytical and analytical parameters is crucial for maximizing CNV detection accuracy with ULP-WGS.