Clonal phylogenies inferred from bulk, single cell, and spatial transcriptomic analysis of epithelial cancers
2025

Using Transcript Data for Clonal Tumor Phylogenies

Sample size: 30 publication Evidence: high

Author Information

Author(s): Erickson Andrew, Figiel Sandy, Rajakumar Timothy, Rao Srinivasa, Yin Wencheng, Doultsinos Dimitrios, Magnussen Anette, Singh Reema, Poulose Ninu, Bryant Richard J., Cussenot Olivier, Hamdy Freddie C., Woodcock Dan, Mills Ian G., Lamb Alastair D.

Primary Institution: Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom

Hypothesis

Can transcript-based tumor phylogenies accurately reflect DNA-based tumor phylogenies?

Conclusion

Transcript-based inferred phylogenies recapitulate conventional genomic phylogenies.

Supporting Evidence

  • Inferred SNV phylogenies accurately recapitulate DNA phylogenies with an entanglement of 0.097.
  • Similar results were observed in iCNV and CNV based phylogenies with an entanglement of 0.11.
  • Analysis of published prostate cancer DNA phylogenies demonstrated phylogenetic concordance with inferred CNV, SNV, and transcript-based phylogenies.
  • A comparison of pseudo-bulked spatial transcriptomic data to adjacent sections with WGS data showed recapitulation of ground truth with an entanglement of 0.35.

Takeaway

This study shows that we can use RNA data to understand how tumors evolve, similar to how we use DNA data.

Methodology

The study involved in-silico comparisons of inferred and directly resolved single-nucleotide and copy number variant status from single cancer cells across different cell lines.

Potential Biases

Potential biases may arise from the use of different sequencing technologies and the absence of control references for RNA sequencing.

Limitations

The accuracy of transcript-based phylogenies can be affected by the design and resolution of sequencing technologies and the lack of well-annotated references.

Participant Demographics

The study analyzed data from prostate cancer patients, specifically focusing on single cancer cells and tumor specimens.

Digital Object Identifier (DOI)

10.1371/journal.pone.0316475

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