SuperFreq: Integrated mutation detection and clonal tracking in cancer.

Area of Science:

• Oncology
• Bioinformatics
• Genomics

Background:

• Analyzing multiple cancer samples reveals disease evolution and clonal dynamics.
• Existing clonal tracking tools are often fragmented and require matched normal samples, limiting their application.
• Understanding clonal expansion and contraction is key to identifying cancer-initiating and progression-driving mutations.

Purpose of the Study:

• To develop SuperFreq, an integrated cancer exome sequencing analysis pipeline for somatic single nucleotide variant (SNV) and copy number alteration (CNA) identification and clonal tracking.
• To create a tool that does not require a matched normal sample, enhancing its applicability.
• To improve the accuracy of clonal tracking by cross-checking variant calls across multiple patient samples.

Main Methods:

• SuperFreq integrates SNV and CNA identification with clonal tracking for cancer exome sequencing data.
• The pipeline utilizes unrelated controls instead of matched normal samples.
• Cross-checking variant calls between multiple patient samples enhances somatic variant identification and CNA resolution.

Main Results:

• SuperFreq demonstrated high accuracy in identifying clones (93% of those with ≥50% cellular fraction) and assigning mutations.
• The pipeline maintained strong performance even when run without a matched normal sample.
• Analysis of 304 TCGA samples across 33 cancer types validated the quality of SNV and CNA calls.

Conclusions:

• SuperFreq offers a versatile and robust solution for analyzing cancer exome sequencing data, including clonal tracking.
• The ability to perform analysis without a matched normal sample significantly broadens its utility in various research settings.
• SuperFreq's application to leukaemia diagnosis and relapse samples highlights its practical value in studying cancer progression.