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Neutral evolution in colorectal
cancer, how can we distinguish
functional from non-functional
variation?
Andrea Sottoriva
Group Leader, Evolutionary Genomics and Modelling Group
Centre for Evolution and Cancer,
The Institute of Cancer Research, London, UK
Nottingham Pathology, 2016.06.29
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Introduction
Buzzword #1:
Cancer Evolution
Evolution: a theory of change
• Random variation of inheritable traits
• Non-random selection
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Evolutionary models (of phenotypes!)
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Gradualism (Charles Darwin)
Hopeful monsters (Richard Goldschmidt)
Punctuated equilibrium (Stephen Jay Gould)
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• Phenotypes are stable inherited traits
• Phenotypes are measured over time (fossil record)
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Molecular evolution
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DNA mutation
Recombination
Genetic drift
Selection
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Mutations are neutral in origin and are then
selected (Luria-Delbruck experiment)
Genotypes and not phenotypes is what we
see with genomics
We need to consider models of molecular
evolution as well
We need to do maths: population genetics
and phylogenomics!
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The cancer evolution paradigm: tumours
are peculiar evolutionary systems
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Evolution in cancer repeats itself in different patients
The starting point is always the same: the human genome
Tumours are very large populations, sometimes 1011
Cancer cells divide asexually: no recombination!
Tumours are expanding populations
Evolution in tumours is relatively quick
Difficult to study cancer evolution through time in humans
We study cancer largely via molecular evolution only (genotypes)
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The wealth of cancer ‘omics data
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Large scale genomic cross-sectional datasets are piling up (e.g. TCGA)
Multiple data types (e.g. WES, WGS, RNA-seq, methylation, miRNA,
arrays, chromatin…)
Cancer genomes appear extremely complex (e.g. chromothrypsis,
chromoplexy, etc…)
Extensive between-patient variation (Vogelstein 2013)
Widespread intra-tumour heterogeneity (Burrell et al. 2013)!
How can we make
sense of all these data?
Buzzword #2:
Sequencing more!
Buzzword #3:
Cancer is complex!
Can we make sense of existing data?
Can we do more than statistics?
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Making sense of the data: an analogy
from astrophysics
Next-generation sequencing
Quantitative data
Evolutionary theory of cancer?
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Measuring cancer evolutionary dynamics
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Hard to follow directly unperturbed evolution of tumours in humans
Can we determine what did cancer cells do in the past?
Majority of cancer genomic studies focus on driver alterations
Cancer drivers are elusive, and may be unique to individual patients
Use the distribution of all available genomic alterations in the
tumour:
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Tumour ancestry is written in the genomes of cancer cells
Genomes contain information about the past
Orthogonal genomic profiling techniques
Multiple sampling and/or deep sequencing
E.g. tracing mtDNA mutations has been used to understand human
origins and migrations
Tracing the Ancestry of Populations
Populations
Compare
genomes
Infer dynamics
Multi-region genomic profiling: turning
space into time
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Phylogenetic trees from genomic data
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They are hard to construct and often off-the-shelf methods are not
appropriate
They are difficult to interpret and are often counter intuitive
They represent genotypes, not phenotypes
They are sensitive to confounding factors such as population
bottlenecks, stochastic variation and sampling error
We need to analyse them with rigorous methods
Common phylogenomics studies have >100 samples for a single
evolutionary process (Lamichhaney et al. 2016, Science)
Phylogenomics experts spend careers analysing single trees!
Still, what we have done so far (ITH studies) is impressive, but we can do
much more!
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What ITH is functional and what is nonfunctional?
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Buzzword #4:
Subclones!
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“What do you define a clone?”
[Simon Tavaré, 2008]
What is a cancer clone?
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Group of cells with the same driver alteration (e.g. KRAS)
Group of cells with the same genotype
Group of cells that share a common ancestor
Group of cells expressing the same phenotype
Group of cells expressing the same phenotype since their most
recent common ancestor
Nor the best…but could be a useful definition…
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A model of non-functional genomic ITH:
what happens when nothing happens?
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The null model for genetic diversity is neutral evolution (Motoo Kimura)
What’s neutral evolution?
Applies to evolution at the molecular level
Most molecular variation is neutral (passenger) or deleterious
Purifying (negative) selection is ubiquitous and purges deleterious
mutations
Positive selection is rare but remains at the origin of adaptation and hence
of great interest!
Nei et al. 2010 (Ann. Rev. of Genomics and Human Genetics), Hughes
2007 (Heredity)
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Neutral evolution in population
genetics
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Neutral evolution has been extensively explored in evolutionary biology
(Kimura 1968, Ohta & Gillespie 1996, Donnelly & Tavare’ 2003, Durrett &
Schweinsberg 2004)
Models of neutral evolution in expanding populations such as cancer have
been already developed (Griffiths & Tavare’ 1998, Durrett 2013)
Largely neglected in current cancer genomic efforts
Now we have tons of next generation sequencing data (e.g. TCGA)
Can we develop a model of ITH that can be used in next-generation
sequencing data from single bulk tumour samples?
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A mathematical law of neutral tumor
evolution
M(t): number of mutations from λ: growth rate
first cancer cell to time t
β: rate of “effective” cell division
N(t): population at time t
μ: mutation rate per division
π: ploidy
dM
= m p l N(t)
dt
1
1
f=
= lb t
p N(t) p e
mæ 1 ö
M( f ) = ç - p ÷
bè f
ø
M( f ) ∼
m
f
N(t) = e
lb t
mp lb t
M (t) =
(e -1)
b
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Neutral evolution 101 whole-exome TCGA
colon cancers (purity > 70%)
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Neutral evolution in 78 WGS Gastric
Cancers (from Wang et al. 2014)
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Validation of neutrality: S vs NS
mutations in gastrics
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Neutral evolution in 849 cancers and
14 types from TCGA
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Lessons from astrophysics: reproducing
the data (signal & noise) using modelling
What’s real and what’s simulation?
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Conclusions
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Neutral growth predicts a 1/f distribution of subclonal variants
A signature of neutral evolution is clearly detectable in a significant
proportion of tumours
In neutral cancers, all tumour-driving alterations are already present in the
first malignant cell
Not all cancers are neutral and some types more than others
This allows making measurements on patient data such as mutation rates
and strength of selection
In line with our previous study in CRC (Sottoriva et al. 2015)
New multi-region sequencing studies in liver (Ling et al. 2015, PNAS) and
colorectal (Uchi et al. 2016, PLoS Genetics) support neutral evolution
Apparently complex data can be explained with relatively simple rules
Can be used as a null model to distinguish functional from nonfunctional ITH
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in partnership with
ICR
Benjamin Werner
Inma Spiteri
Kate Chkhaidze
Ahmet Acar
Alexandra Vatsiou
BARTS
Marc Williams
Trevor Graham
UCL
Chris Barnes
in partnership with
Chris Rokos Fellowship (Andrea Sottoriva)
Geoffrey W. Lewis Trust (Benjamin Werner)