Neutral evolution in colorectal cancer, how can we distinguish functional from non-functional variation?

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1 in partnership with 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,

2 Introduction 2 Buzzword #1: Cancer Evolution

3 Evolution: a theory of change 3 Random variation of inheritable traits Non-random selection

4 Evolutionary models (of phenotypes!) 4 Gradualism (Charles Darwin) Hopeful monsters (Richard Goldschmidt) Punctuated equilibrium (Stephen Jay Gould) Phenotypes are stable inherited traits Phenotypes are measured over time (fossil record)

5 Molecular evolution 5 DNA mutation Recombination Genetic drift Selection 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!

6 The cancer evolution paradigm: tumours are peculiar evolutionary systems 6 Evolution in cancer repeats itself in different patients The starting point is always the same: the human genome Tumours are very large populations, sometimes 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)

7 The wealth of cancer omics data 7 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?

8 Making sense of the data: an analogy from astrophysics 8 Next-generation sequencing Quantitative data Evolutionary theory of cancer?

9 Measuring cancer evolutionary dynamics o o o o 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: 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

10 Tracing the Ancestry of Populations Populations Compare genomes Infer dynamics

11 Multi-region genomic profiling: turning space into time 11

12 Phylogenetic trees from genomic data 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!

13 What ITH is functional and what is nonfunctional? 13

14 14 Buzzword #4: Subclones!

15 15 What do you define a clone? [Simon Tavaré, 2008]

16 What is a cancer clone? 16 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 bestbut could be a useful definition

17 A model of non-functional genomic ITH: what happens when nothing happens? 17 The null model for genetic diversity is neutral evolution (Motoo Kimura) Whats 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 (Ann. Rev. of Genomics and Human Genetics), Hughes 2007 (Heredity)

18 Neutral evolution in population genetics 18 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?

19 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 19

20 Neutral evolution 101 whole-exome TCGA colon cancers (purity > 70%) 20 v v v v v v

21 Neutral evolution in 78 WGS Gastric Cancers (from Wang et al. 2014) 21 v v v v v v

22 Validation of neutrality: S vs NS mutations in gastrics 22

23 Neutral evolution in 849 cancers and 14 types from TCGA 23

24 Lessons from astrophysics: reproducing the data (signal & noise) using modelling 24 Whats real and whats simulation?

25 Conclusions 25 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

26 in partnership with ICR Benjamin Werner Inma Spiteri Kate Chkhaidze Ahmet Acar Alexandra Vatsiou BARTS Marc Williams Trevor Graham UCL Chris Barnes

27 in partnership with Chris Rokos Fellowship (Andrea Sottoriva) Geoffrey W. Lewis Trust (Benjamin Werner)

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