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Confounding factors in assessing the enriched expression of somatic mutant allele in bulk tumor samples

Biorxiv : the Preprint Server for Biology +

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June 12, 2025

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June 12, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Allele-specific expression (ASE) analysis can be misleading due to tumor purity. This study introduces a model to correct for confounding factors, improving somatic mutation detection using RNA sequencing data.

Area Of Science

  • Genomics
  • Cancer Biology
  • Bioinformatics

Background

  • Allele-specific expression (ASE) analysis, often used to study somatic mutations, relies on mutant allele enrichment in RNA versus DNA.
  • This method can be confounded by differences in gene expression between tumor and normal cells present in bulk samples.

Purpose Of The Study

  • To develop a model to account for confounding factors in mutation-based ASE analysis.
  • To assess the utility of RNA sequencing for novel somatic mutation detection.

Main Methods

  • Modeled mutant allele expression incorporating tumor/normal expression differences, allele dosage, tumor purity, and nonsense-mediated decay (NMD).
  • Validated the model using somatic insertions/deletions (indels) from The Cancer Genome Atlas (TCGA) RNA-Seq data.
  • Performed de novo somatic indel calling using TCGA RNA-Seq data.

Main Results

  • The developed model demonstrated that mutant allele enrichment can occur without true ASE, influenced by tumor purity and NMD.
  • Empirical validation using TCGA data showed a three-fold higher enrichment in driver genes compared to non-drivers.
  • De novo indel calling using RNA-Seq increased the TCGA driver indel repertoire by approximately 14%, particularly in samples with low tumor purity.

Conclusions

  • Gene expression differences and tumor purity significantly confound mutation-based ASE analyses.
  • RNA sequencing data can be effectively utilized to identify and complement DNA-based somatic mutation detection, especially for driver mutations.
  • This approach enhances the repertoire of identified driver mutations, particularly in challenging low-purity samples.