A highly accurate platform for clone-specific mutation discovery enables the study of active mutational processes

Area of Science:

• Genomics
• Cancer Research
• Molecular Biology

Background:

• Bulk whole genome sequencing (WGS) has limitations in detecting recent tumor evolution due to depth constraints.
• Analyzing individual cells or clones is crucial for understanding current mutational processes but is hindered by false positive mutations.
• Existing data pooling methods for mutation confidence reduce the temporal resolution of detected events.

Purpose of the Study:

• To develop a robust whole genome sequencing (WGS) and analysis pipeline (DigiPico/MutLX) to accurately identify mutations in low-input DNA.
• To overcome the challenge of excessive false positive mutations in single-cell or low-input DNA sequencing.
• To enable the detection of recent and clone-specific mutational events for predicting tumor evolution.

Main Methods:

• Development of a novel WGS and analysis pipeline named DigiPico/MutLX.
• Application of the pipeline to picogram quantities of DNA from individual cancer cells.
• Validation of the pipeline's accuracy in identifying true positive mutations while minimizing false positives.

Main Results:

• The DigiPico/MutLX pipeline effectively eliminates false positive mutations from low-input DNA sequencing.
• The method successfully identified a hyper-mutation (kataegis) event in approximately 30 cancer cells from recurrent ovarian carcinoma.
• This hyper-mutation event was previously undetectable using bulk WGS data.

Conclusions:

• DigiPico/MutLX provides a powerful framework for accurate identification of clone-specific variants from low-input DNA.
• The pipeline enables the discovery of previously undetectable mutational processes, such as kataegis, in cancer evolution.
• This advancement offers unprecedented accuracy for studying tumor evolution at the single-cell level.