The evolution of tumour phylogenetics: principles and practice

Abstract

Rapid advances in high-throughput sequencing and a growing realization of the importance of evolutionary theory to cancer genomics have led to a proliferation of phylogenetic studies of tumour progression. These studies have yielded not only new insights but also a plethora of experimental approaches, sometimes reaching conflicting or poorly supported conclusions. Here, we consider this body of work in light of the key computational principles underpinning phylogenetic inference, with the goal of providing practical guidance on the design and analysis of scientifically rigorous tumour phylogeny studies. We survey the range of methods and tools available to the researcher, their key applications, and the various unsolved problems, closing with a perspective on the prospects and broader implications of this field.

Figure 1. Classification of tumour phylogeny methods by study design

a | Cross-sectional tumour phylogeny methods model distinct tumours (coloured circles) sampled from multiple patients as though they are species. These methods infer phylogenies (also known as oncogenetic trees) in which tumours are grouped approximately into subtypes, with tree edges corresponding to common recurring mutations that identify a subtype. b | Regional bulk tumour phylogeny methods are applied to bulk genomic samples from a single patient, typically subregions of a tumour or distinct tumour sites (coloured circles). Trees provide a coarse model of the major cell lineages developing over the course of progression in the single patient. c | Single-cell tumour phylogeny methods build phylogenetic trees using variations between single cells (coloured circles) in one or more tumour sites. Trees group cells into major clonal subgroups and infer shared ancestry and mutation events at the level of single clones.

Figure 2. Some challenges in synchronizing data, models and algorithms when applying tumour phylogenetics to a scientific question

An illustration of a hypothetical scenario described in the main text, in which we seek to infer a phylogenetic history of copy number variant (CNV) events in the progression of a single tumour. Each tree shows the potential evolution of genomic copy number profiles for a set of observed clones (blue lines) and computationally inferred intermediate states (red lines) for a single tumour. a | The hypothetical ‘true’ tree describing the evolution of a set of clones from a diploid root via a series of CNVs: gain or loss of copy number in a localized region, as well as whole-genome duplication, leading to a doubled copy number genome-wide. b | Incorrect inference due to the use of a model designed for single-base changes, leading to a substantially incorrect phylogeny involving various biologically ‘impossible’ evolutionary events, such as partial (non-integer) gain, loss, or whole-genome duplications, leading to fractional copy numbers. c | Improved but still inaccurate inference after correcting to an evolutionary model cognizant of the type of variation occurring with CNVs; this eliminates impossible events and leads to a more accurate tree topology, but still fails to identify the correct tree because the analysis is using an algorithm that identifies biologically plausible but still sub-optimal phylogenies for this kind of evolutionary model. d | Still inaccurate inference after changing to a more sophisticated model and algorithm that are well suited to CNV evolution but make it impractical to use single-cell sequence data; this forces a change to a bulk genomic data type, leading to inadequate sampling of extant clones to capture the rapid mutation process typical of CNV-driven evolution and observed in the true tree.