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  1. Home
  2. Inferring Active Mutational Processes In Cancer Using Single Cell Sequencing And Evolutionary Constraints.
  1. Home
  2. Inferring Active Mutational Processes In Cancer Using Single Cell Sequencing And Evolutionary Constraints.

Related Experiment Video

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
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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

View abstract on PubMed

Summary
This summary is machine-generated.

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.

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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.