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Quantifying the seed sensitivity of cancer subclonal reconstruction algorithms
Philippa L Steinberg1,2,3, Lydia Y Liu1,2,3,4,5, Anna Neiman-Golden1,2,3
1Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
Biorxiv : the Preprint Server for Biology
|February 19, 2024
Summary
The initializing seed significantly impacts subclonal reconstruction (SRC) results, affecting cancer subclone estimates. Reporting and randomizing seeds are crucial for reproducible bioinformatics research.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Intra-tumoural heterogeneity (ITH) is a key challenge in cancer prognosis and treatment.
- Subclonal reconstruction (SRC) algorithms estimate ITH by identifying cancer subclones from bulk DNA sequencing data.
- Probabilistic SRC algorithms require initialization with a random seed, the impact of which is largely unstudied.
Approach:
- Benchmarked the seed sensitivity of three probabilistic SRC algorithms (PyClone-VI, DPClust, PhyloWGS).
- Utilized fourteen whole-genome sequences from head and neck squamous cell carcinoma.
- Evaluated nine SRC pipelines across 1470 subclonal reconstructions (single- and multi-region).
Key Points:
- All evaluated SRC algorithms demonstrated substantial seed sensitivity, with varying subclone estimates for identical input data.
- Subclone estimates differed across SRC pipelines, but seed variability was consistent within each algorithm.
- No single seed consistently identified the most frequent number of subclones across all patients for any algorithm.
Conclusions:
- Seed sensitivity introduces significant variability in quantifying ITH using probabilistic SRC algorithms.
- Recommends reporting and randomizing seed choices in publications to enhance reproducibility.
- Suggests considering seed sensitivity in future SRC algorithm benchmarking and bioinformatics tool development.
