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A Bayesian framework to study tumor subclone-specific expression by combining bulk DNA and single-cell RNA sequencing

Yi Qiao1, Xiaomeng Huang1, Philip J Moos2

  • 1Eccles Institute of Human Genetics, University of Utah, Salt Lake City, Utah 84112, USA.

Genome Research
|January 9, 2024
PubMed
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This study introduces scBayes, a novel framework linking tumor genetic subclones to gene expression patterns. It enables detailed analysis of cancer evolution and treatment resistance by integrating bulk DNA and single-cell RNA sequencing data.

Area of Science:

  • Genomics
  • Cancer Biology
  • Bioinformatics

Background:

  • Tumor genetic and gene expression heterogeneity drives cancer evolution and treatment resistance.
  • Current methods like whole-genome sequencing (WGS) and single-cell RNA sequencing (scRNA-seq) study heterogeneity separately.
  • A gap exists in integrating genomic subclonal structure with single-cell transcriptomic data from the same tumor.

Purpose of the Study:

  • To develop a computational framework, scBayes, for integrating bulk DNA sequencing and scRNA-seq data.
  • To link genetically defined tumor subclones with their corresponding gene expression profiles.
  • To enable robust analysis of cancer subclonal evolution, treatment response, and spatial heterogeneity.

Main Methods:

  • Developed scBayes, a Bayesian probabilistic framework.

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  • Inferred tumor subclonal structure from bulk DNA sequencing data.
  • Assigned cell identities to subclones using scRNA-seq data.

    • Main Results:

    • scBayes successfully links genomic subclonality to transcriptomic heterogeneity.
    • Validated performance using simulated, in silico, and real cancer data.
    • Demonstrated improved capabilities over existing methods for analyzing cancer subclonal expression.

    Conclusions:

    • scBayes provides a powerful tool for dissecting cancer heterogeneity.
    • Enables deeper insights into cancer progression, treatment response, and relapse.
    • Applicable across various single-cell sequencing technologies (e.g., Smart-seq, 10x Genomics).