Using Transcript Data for Clonal Tumor Phylogenies
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)
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