The evolution of tumour phylogenetics: principles and practice
Russell Schwartz
1
and Alejandro A. Schäffer
2
1
Department of Biological Sciences and Computational Biology Department, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15217, USA.
2
Computational Biology Branch, National Center for Biotechnology Information, National Institutes
of Health, Bethesda, Maryland 20892, USA.
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.
Cancer is a genetic disease characterized by a progressive accumulation of genomic
aberrations that are sometimes augmented by predisposing germline mutations
1
. In the
1970s, Nowell
2
and others proposed that this accumulation of mutations is guided by
evolutionary principles via a process of diversification and selection for mutations that
promote tumour cell proliferation and survival. The idea that evolutionary mechanisms
underlie cancer progression has become a guiding principle in understanding,
predicting, and controlling cancer progression
3
, metastasis
4
, and therapeutic responses
5,6
.
Models of tumour evolution have incorporated advanced evolutionary theory
7–9
and
complex evolutionary mechanisms that have been revealed by modern genomic
technologies
10,11
. The application of evolutionary principles to cancers has blossomed into a
field in its own right, with a rich foundation of theory and methods for interpreting tumour
evolution
12,13
. Here, we survey one influential thread: the use of phylogenetics — that is,
evolutionary tree building — to understand tumour progression.
Although evolutionary theory has proven to be powerful for understanding cancer
progression, evolutionary processes are quite different in cancers versus in species
14
in ways
Correspondence to R.S. russells@andrew.cmu.edu.
Competing interests statement
The authors declare competing interests: see Web version for details.
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that are important to phylogenetic inference. These differences manifest in at least four
areas: first, the types of aberration that commonly arise; second, the rates of mutation; third,
the extent and intensity of selection; and fourth, the typically high heterogeneity of tumour
cell subclones. One frequent feature of cancer evolution is hypermutability
15
, often
associated with types of mutation that are rare in species evolution. Hypermutability
phenotypes include chromosome instability (CIN) phenotypes that are characteristic of p53
dysfunction
16
, microsatellite instability (MIN)
17
, and elevated point mutation phenotypes,
such as those arising from dysregulation of the APOBEC family of deaminase proteins
17,18
.
Some variant types, such as copy number variants (CNVs), may be induced by multiple
mechanisms — including breakage–fusion–bridge (BFB) cycles, missegregation of
chromosomes, and genome doubling — each producing distinct scales and locations of
aberrations
19–22
. Other tumour-specific mutational mechanisms include the following:
kataegis
23
, in which single nucleotide variants (SNVs) occur at a high rate in a small
chromosomal region; chromothripsis
24
, in which a single chromosome shatters and
reassembles in a seemingly random manner; and chromoplexy
25
, a complex structural
variation characterized by chains of BFB-induced chromosome rearrangements occurring in
successive mitoses.
Likewise, patterns of elevated SNV accumulation can differ widely by tissue of origin or
from patient to patient. Alexandrov
et al.
26
characterized dozens of ‘mutation signatures’
defining the nucleotide biases exhibited in subsets of cancers, some with known
environmental triggers
27
, others attributable to specific sources of somatic
hypermutability
18
, and some of unknown cause. Mechanisms of hypermutability may vary
by tumour and over time in ways that are not observed in species evolution
21,28–31
.
Treatment creates another complication, as chemotherapy or radiation therapy can
themselves cause double-strand breaks in the DNA
32
or other forms of hypermutation
33,34
,
inducing new mutation signatures
26,30
. Conversely, prophylactic therapies can suppress
hypermutability
35
.
The predominant mechanisms of selection in cancers also differ from those in species
evolution. Most studies of tumour evolution have assumed selection for mutations that
promote survival, proliferation, or other phenotypic hallmarks of cancer
36
. Selection, like
diversification, can be dynamic, as cell populations adapt to or change their
microenvironment
11
. However, recent work has suggested that selection often plays only a
minor part in tumour evolution, in contrast to its role in Darwinian evolution of species. The
repeated observation of substantial intra-tumour heterogeneity
21,37–44
runs counter
to the idea that only the fittest subclones survive. Some recent studies have suggested that
some tumours evolve by effectively neutral processes without selection, at least
pretreatment
45–47
. It has been suggested that strong versus weak selection might be
reconciled by a ‘punctuated equilibrium’ model
9
, in which long periods of slow mutation
under weak selection are interrupted by short bursts of rapid evolution under strong
selection, although this model cannot explain the evidence for a lack of selection in some
tumours
48
.
Therapy must also be considered when modelling selection
49,50
. In contrast to the
disagreement about whether tumour evolution is non-Darwinian at the pretreatment stage,
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there is general agreement that treatment leads to selection which can alter the dominant
clones
10,14,34,51
. Single-agent treatment can lead to relapse
49,52
by selecting for non-
responsive clones
29,53
. Durable targeted therapies may require the identification of driver
mutations in all tumour subclones and the design of patient-specific drug
combinations
8,11,54,55
.
High heterogeneity is another characteristic feature of tumour evolution. Higher intra-
tumour heterogeneity has been associated with poorer prognosis
8,56–58
and linked with the
ability of the tumour to resist immune surveillance and therapy
3,59,60
. Progression,
metastasis, and therapeutic resistance frequently proceed from clones that were rare at
earlier progression stages
41,43,49,61
. Interactions among distinct clones may also drive
tumour progression, for example through tumour self-seeding
4,62
and cooperation
between clones
63,64
.
This Review examines one important direction in which evolutionary models are shaping
cancer research: the use of phylogenetic methods in interpreting genomic data from cancers.
We specifically seek to provide guidance to the users of phylogenetic methods in cancer
research and to those critically reading about those uses, especially those lacking formal
training in phylogenetics. To accomplish that, we give a short overview of the field, we
review past uses of tumour phylogenetics, and we explain some relevant principles of
phylogenetic inference. We conclude with speculation about the challenges and
opportunities for realizing the potential of phylogenetics in cancer research.
Overview of tumour phylogenetics
The recognition that cancer is an evolutionary phenomenon led to the insight that
computational methods for reconstructing evolutionary processes — that is, phylogenetics
— might prove valuable for making sense of tumour progression processes. Tsao
et al.
were
among the first to suggest that variations in microsatellite markers could be used to infer a
tree model of the evolution of tumour cells
65
. The idea was subsequently put into practice
for bulk comparative genomic hybridization (CGH) data by Desper
et al.
66
. After
percolating for a decade within a specialist community of evolutionary and computational
biologists, this type of analysis has exploded to become a new field known as tumour
phylogenetics, which aims to reconstruct tumour evolution from genomic variations. In
almost all cases, the goal of such work is to produce evolutionary trees, potentially allowing
for uncertainty among the space of possible trees explaining a data set
21,67,68
.
Within that basic framework, tumour phylogenetics encompasses diverse methods. This
diversity includes various data types, referring both to the basic study design (cross-cohort
studies of many tumours, single-patient studies of regional bulk genomic assays, or studies
of single-cell variability in single tumours) (FIG. 1) and the type or types of genomic data
profiled (initially, pre-sequencing marker types such as large-scale CGH
66
or fluorescence
in
situ
hybridization (FISH)
69
; now, predominantly next-generation sequencing (NGS)-derived
SNVs
70
or CNVs
71
, and sometimes more exotic variant types such as gene expression, DNA
methylation, or histone marks
10,14,32,72,73
). The diversity also includes variation by
mathematical model; that is, the mathematical representation of the kinds of mutational
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processes one intends to study. The model may capture both the kind of mutations
considered (for example, SNVs versus structural variants (SVs)
20,74
) and basic questions
such as whether those mutations are assumed to be under selection
2,7,11,14,17,72,75
or
selectively neutral
4,76–79
. Furthermore, this diversity of methods includes variation in the
algorithms applied; that is, the computational instructions used to find an optimal tree or
trees consistent with both the data and the model. The importance and utility of
in silico
models to study various phenomena in cancer goes far beyond tumour phylogenetics, and
other kinds of models have been reviewed elsewhere
12,13
. Many of the papers cited therein
take a traditional mathematical modelling approach with emphasis on the mathematics, on
simulation studies, on parameter estimation, and on validating the model. As tumour
phylogenetics has gained in popularity, phylogenetics now tends to show up as a small part
of high-impact studies. These studies are understandably focused on data sets that were
derived from human subjects and were expensive and complicated to collect. One of the
main messages of this Review is that when mathematical models are used in these studies,
the importance of validating the models against simulated and observed data should not be
forgotten.
Most studies of tumour phylogenetics to date have adapted standard algorithms that were
developed for species phylogenetics (for example, maximum par-simony
21,61
, minimum
evolution
73
, neighbour joining
71,80
, UPGMA
21
, or various maximum likelihood or Bayesian
probabilistic inference methods
81,82
), occasionally comparing multiple standard approaches
in a single study
21,83
(TABLES 1,2). Only recently have new phylogeny algorithms emerged
to deal with the peculiarities of tumour versus species evolution
84–88
. In the next section, we
survey the diversity of methods available, with particular focus on those suited to modern
sequencing technologies.
This variety of phylogeny methods has corresponded to a variety of applications. Tumour
evolutionary trees, which were once merely conceptual models
2
, are now central in the
results of many studies
11
. Early uses of phylogeny methods often focused on applying the
new tool of tumour phylogenetics to old problems, such as using evidence of evolutionary
selection to separate driver mutations from passenger mutations
29,50
, or using novel
algorithms to find the order and timing of driver mutations
89–91
or to determine how these
driver mutations associate with progression stages
92
. Other key results have emerged
organically, for example from studies addressing the still controversial question of whether
tumour evolution follows the expectations of classical clonal evolution theory
93–95
in
producing predominantly linear phylogenies
54,76,96,97
, whether it exhibits predominantly
branched evolution exemplified by the early divergence of subclones
30,33,40,42,49,73,83,98–100
,
or whether it occupies some continuum encompassing both extremes in different
tumours
34,101
. Researchers continue to find new applications for phylogeny models, such as
the use of phylogenies prognostically to predict the likely future progression of a
tumour
43,58,85,92,102
; such applications are an evolution of older approaches that have been
used to predict progression from simpler measures of tumour heterogeneity
38,58,59,102–105
.
One worrisome trend among these studies is their seemingly conflicting conclusions about
the evolutionary trajectories of cancers, such as on the questions of linear versus branched
evolution or Darwinian selection versus no selection. The distinctions may be traced to
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differences in the application of phylogenetics, such as looking at distinct marker types (for
example, SNVs versus CNVs) or using distinct evolutionary models or phylogeny
algorithms. For example, the studies that concluded that there was little selection in some
tumours looked mostly at SNVs and CNVs, but perhaps there is selection in those tumours
via evolutionary mechanisms that would be apparent only when looking at other marker
types, such as karyotypes or methylation patterns. Few studies have tested whether the
phylogenetic inferences made are robust to a change of methods, with notable
exceptions
68,106
.
Variations on tumour phylogenetics
Recent years have seen a rapid proliferation of methods for tumour phylogenetics. In this
section, we categorize some of the seminal advances made. We can roughly distinguish three
classes of method, based on the kind of phylogeny study for which they are designed: cross-
sectional methods, which use data on many tumours to build trees describing the common
progression pathways across a population; regional bulk methods, which build trees for
single patients through bulk genomic assays of distinct tumour sites or regions; and single-
cell methods, which build trees from the cell-to-cell variations in single tumours (FIG. 1).
Not all methods fit neatly within one category, but the categories provide a crude
organization for the description of methods below.
Within these high-level categories, we see a diversity of genomic data types (TABLE 3),
evolutionary models, and phylogeny algorithms. Below, we consider a subset of methods
that were of particular importance in introducing new techniques to the field or were of
unique value to likely users. TABLE 1 and the extended version, Supplementary information
S1 (table), provide a more comprehensive list of important methods. TABLE 2 and the
extended version, Supplementary information S2 (table), list important studies that have
made use of tumour phylogeny methods.
Cross-sectional tumour phylogenetics
Key ideas behind cross-sectional tumour phylogenetics originate in the pre-phylogenetic
work of Fearon and Vogelstein, who proposed that bulk analysis of collections of tumours
from multiple patients could allow one to infer the likely orders of aberrations and stages of
progression (for example, from adenoma to carcinoma) so that each aberration is associated
with progression to a specific stage
93
. They proposed a linear (event 2 follows event 1
follows event 0) model for the progression of colorectal cancer. This Fearon–Vogelstein
model, although a simplification
107
, has been highly influential on thinking about tumour
evolution. Phylogenetic methods were first brought to the reconstruction of tumour
progression pathways by Desper
et al.
, who generalized the Fearon–Vogelstein linear
progression model to allow branching in the form of a tree, sometimes called an oncogenetic
tree
66
. FIGURE 1a provides an illustration of the oncogenetic tree model for interpreting
cross-sectional data that has come from multiple patients. In the original oncogenetic tree
model, each tree edge corresponds to a possible aberration with an associated probability of
occurrence. Paths in the tree correspond to possible sequences of accumulating aberrations.
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