Related Experiment Video
Updated: May 23, 2025
07:49
Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
Published on: April 18, 2025
89
Inferring active mutational processes in cancer using single cell sequencing and evolutionary constraints
Gryte Satas1,2, Matthew A Myers1,2, Andrew McPherson1,2
1Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Biorxiv : the Preprint Server for Biology
|March 10, 2025
Summary
Ultra-low-coverage single-cell whole-genome sequencing (scWGS) can distinguish active from historical cancer mutational processes. This method reveals dynamic mutational patterns linked to tumor evolution and therapeutic resistance.
Area of Science:
- Genomics
- Cancer Biology
- Evolutionary Biology
Background:
- Cancer's natural history is shaped by ongoing mutagenesis, creating genetic diversity.
- Distinguishing active from historical mutational processes is crucial for understanding tumor evolution, presentation, and therapeutic resistance.
- Bulk sequencing typically captures only historical mutational signatures, limiting insights into dynamic processes.
Purpose of the Study:
- To investigate if ultra-low-coverage single-cell whole-genome sequencing (scWGS) can differentiate between historical and active mutational processes in cancer.
- To develop a method for robustly analyzing single nucleotide variants (SNVs) in sparse scWGS data.
- To uncover temporal and spatial patterns of mutagenesis in various cancer types.
Main Methods:
- Introduced ArtiCull, a novel method to identify and remove SNV artifacts in scWGS data by utilizing evolutionary constraints.
- Applied ArtiCull to analyze scWGS data from pancreatic ductal adenocarcinoma (PDAC), triple-negative breast cancer (TNBC), and high-grade serous ovarian cancer (HGSOC).
- Examined mutation patterns in therapy-treated and untreated cancer models to identify active mutational signatures.
Main Results:
- Demonstrated that scWGS data, despite sparsity, contains valuable information on dynamic mutational processes.
- Observed a temporal increase in mismatch repair deficiency (MMRd) in PDAC.
- Identified therapy-induced mutagenesis and APOBEC3 inactivation in cisplatin-treated TNBC xenografts.
- Revealed distinct APOBEC3 mutagenesis patterns and late tumor-wide activation in HGSOC.
- Detected clone-specific increases in SBS17 activity associated with recurrence in HGSOC.
Conclusions:
- Ultra-low-coverage scWGS is a powerful tool for studying active mutational processes in cancer.
- This approach can provide insights into ongoing clonal evolution and mechanisms of therapeutic resistance.
- Findings highlight the potential of scWGS for dissecting dynamic mutagenic landscapes in diverse cancers.
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