Evaluating the Use of Single-cell and Bulk Sequences in Phylogenetic Analyses of Multi-tumour Evolution

Abstract

Studying the phylogenetic reconstruction of somatic evolution can be challenging due to constraints in resources, the influence of various biological factors, and technical limitations. Many methods concentrate on the analysis of single-cell sequencing data. This approach, while generally more accurate than using bulk-sequencing data, can be limiting due to its computational complexity and potential technical artefacts. We integrate two simulation software tools to facilitate the modelling of evolutionary histories between multiple metastatic sites within a patient. The first tool is used to generate phylogenetic trees, providing a framework that represents the true evolutionary histories both at a multi-tumour and individual cell level. Subsequently, the second tool generates single-cell tumour sequences based on the true cell-cell trees. By combining these two software tools, cells are assigned to specific tumours, and therefore simulated under a structured population. Our study evaluates the viability of pooling single-cell data into consensus sequences by comparing their accuracy in reconstructing the multi-tumour tree against pseudo-bulk data. This approach addresses the challenge of obtaining multi-tumour level evolutionary histories from single-cell data. We aim to provide guidance for researchers when choosing their preferred sequencing analysis method or for those looking to trace multi-tumour evolution. Under various biological conditions, we simulate single-cell tumour sequences with a predefined multi-tumour tree and its corresponding cell-cell tree replicates. We construct consensus sequences, pooling cell sequences based on shared tumour lineage. For comparison, we construct pseudobulk data. Calculations of tree distance between initial trees against reconstructed trees show that consensus sequences do not perform as well as a pseudobulk dataset. We explore the process of reconstructing the true multi-tumour tree by integrating existing data, compiled as sets of replicates. First, we perform tree reconstruction using a species estimation method on single-cell data. Additionally, we explore supertree construction as well as the use of concatenated sequences, leveraging pooled data. Through this analysis, we find that using single-cell data directly or utilising pseudobulk data for reconstructing multi-tumour evolution yields the best results.