ABEMUS: platform-specific and data-informed detection of somatic SNVs in cfDNA

Area of Science:

• Genomics
• Cancer Research
• Bioinformatics

Background:

• Liquid biopsies offer non-invasive monitoring of cancer treatment response and tumor dynamics using blood tests.
• Next-generation sequencing of cell-free DNA (cfDNA) can identify somatic single-nucleotide variants (SNVs).
• Low circulating tumor DNA (ctDNA) fractions, sequencing noise, and tumor heterogeneity challenge accurate SNV detection.

Purpose of the Study:

• To present ABEMUS, a computational methodology for detecting and quantifying somatic SNVs in cfDNA.
• To evaluate ABEMUS's performance across different sequencing platforms and compare it with existing SNV callers.
• To demonstrate ABEMUS's reliability in identifying low-frequency SNVs in samples with low ctDNA levels.

Main Methods:

• Developed ABEMUS, a computational method integrating platform-specific genetic information and empirical signals.
• Tested ABEMUS on data from various sequencing platforms with different error profiles.
• Compared ABEMUS performance against other SNV callers using synthetic and real cancer patient data.

Main Results:

• ABEMUS demonstrated superior performance in detecting somatic SNVs across various conditions.
• The method proved reliable for calling low variant allele frequencies in plasma samples with low ctDNA levels.
• ABEMUS effectively handles distinct sequencing error properties from different platforms.

Conclusions:

• ABEMUS is a robust and reliable tool for SNV detection in cfDNA from liquid biopsies.
• The methodology enhances the analysis of low ctDNA samples, crucial for non-invasive cancer monitoring.
• ABEMUS offers a cross-platform solution for identifying somatic SNVs, improving cancer research and clinical applications.