137P - PHANTOM: Bootstrap inference of single-cell tumour phylogenies by integrating sequencing read counts

Background

Recent single-cell DNA sequencing (scDNA-seq) technologies have enabled the parallel investigation of thousands of individual cells. This is required for accurately reconstructing tumour evolution, during which cancer cells acquire a multitude of different genetic alterations. Although the evolutionary analysis of scDNA-seq datasets is complex due to their unique combination of errors and missing data, several methods have been developed to infer single-cell tumour phylogenies by integrating estimates of the false positive and false negative error rates. This integration relies on the assumption that errors are uniformly distributed both within and across cells. However, this assumption does not always hold; error rates depend on sequencing coverage, which is not constant within or across cells in a sequencing experiment due to, e.g., copy-number alterations and the replication status of a cell, limiting the accuracy of existing methods.

Methods

To address this challenge, we developed PHANTOM, a novel single-cell phylogenetic method that integrates raw sequencing read counts into a statistical framework to robustly correct the errors and missing data. PHANTOM includes bootstrapping to correct for high error frequency genomic positions and a fast probabilistic heuristic based on hypothesis testing to distinguish the remaining errors from truly observed genotypes.

Results

We demonstrate the improved accuracy and robustness of our method compared to existing approaches across several simulation settings. To demonstrate its impact, we applied our method to scDNA-seq datasets comprising >25,000 cells from 31 patients with triple-negative breast cancer or high-grade serous ovarian cancer, as well as targeted scDNA-seq from a cohort of 123 patients with acute myeloid leukaemia, revealing more accurate phylogenies consistent with copy-number alterations.

Conclusions

We developed PHANTOM, a novel single-cell phylogenetic method that accurately and robustly infers single-cell tumour phylogenies. We applied PHANTOM to scDNA-seq cancer datasets, revealing its potential to investigate tumour evolution at unprecedented resolution.

Editorial acknowledgement

Clinical trial identification

Legal entity responsible for the study

The authors.

Funding

Cancer Research UK.

Disclosure

All authors have declared no conflicts of interest.