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Inferring the Evolutionary History of Cancers: Statistical Methods and Applications

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Cancer is an evolutionary process. Accumulation of genomic mutations coupled with the effects of genetic drift and selection lead to divergent clonal populations of cancer cells in a tumour.

High throughput sequencing (HTS) of both bulk tissue and single cells offers a powerful tool to study this diversity, and opens the possibility of reconstructing the evolutionary history of tumours. In particular, it is now possible to reconstruct the phylogeny (evolutionary tree) of extant clones in a tumour. Understanding the phylogeny of clonal populations can provide insight into the ontogeny of a tumour, mechanisms of metastasis, and modes of therapeutic resistance. However, inferring phylogenies using HTS is challenging due to issues such as admixed populations in bulk sequencing and noisy measurements in single cell experiments.

I will present three statistical methods which leverage data from different HTS assays to provide complementary information about the population structure and phylogeny of clones in a tumour.

First, I will discuss the PyClone model which uses targeted deep sequencing data to infer what proportion of cells in a biopsy sample harbour a mutation, and which mutations originate at the same point in the evolutionary history of tumour [1]. I will present current work on scaling PyClone to whole genome scale data using recently developed statistical inference methods [2]. I will also discuss the PhyClone model, an extension of PyClone which attempts to explicitly model the clonal phylogeny using a novel non-parametric Bayesian process.

Second, I will present the single cell genotyper (SCG) model which can be used to analyse targeted single cell sequencing data of known point mutations [3]. The model accounts for several sources of noise, including doublet cells and allele drop-out. This model allows for robust inference of the clonal genotype, which in turn can be used as input for classical phylogenetic algorithms.

Finally, I will consider the problem of mutation loss and present a novel model based on the Stochastic Dollo process for inference of lost mutations. I will show how using this approach, coupled with the PyClone and SCG models, the migration of clones in the peritoneal cavity of patients with High Grade Serous Ovarian Cancer can be tracked [4]. Keywords: cancer, genomics, Bayesian statistics, high grade serous ovarian cancer, phylogenetics, single cell sequencing, high throughput sequencing [1] Roth et al., PyClone: statistical inference of clonal population structure in cancer , Nature Methods, 2014 [2] Bouchard-Côté, Doucet and Roth, Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models , Journal of Machine Learning Research, 2017 [3] Roth et al., Clonal genotype and population structure inference from single-cell tumor sequencing , Nature Methods, 2016 [4] McPherson & Roth et al., Divergent modes of clonal spread and intraperitoneal mixing in high-grade serous ovarian cancer , Nature Genetics, 2016

Speaker : Dr. Andrew Roth Department of Statistics and Ludwig Institute for Cancer Research, University of Oxford

I completed my PhD at the University of British Columbia under the supervision of Sohrab Shah. During that time I developed computational statistical methods for analysing high throughput genomics data from cancer. In particular, I developed methods such as PyClone for studying intra-tumour heterogeneity and clonal evolution. I have also been involved in applying these methods to study triple negative breast and high grade serous ovarian cancer. I am currently a post-doctoral fellow in the Department of Statistics and Ludwig Institute for Cancer Research at the University of Oxford, supervised by Chris Holmes and Xin Lu. I am continuing to develop methods, particularly to study the effects of treatment induced selection in oesophageal cancer.

This talk is part of the Seminars on Quantitative Biology @ CRUK Cambridge Institute series.

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