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SUMMARY:Inferring the Evolutionary History of Cancers: Statistical Methods
  and Applications - Dr Andrew Roth\, Ludwig Cancer Research\, University o
 f Oxford 
DTSTART:20171127T160000Z
DTEND:20171127T170000Z
UID:TALK72584@talks.cam.ac.uk
CONTACT:46487
DESCRIPTION:Cancer is an evolutionary process. Accumulation of genomic mut
 ations coupled with the effects of genetic\ndrift and selection lead to di
 vergent clonal populations of cancer cells in a tumour. \n\nHigh throughpu
 t 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 t
 o reconstruct the phylogeny (evolutionary tree) of extant clones in a tumo
 ur. Understanding the phylogeny of clonal populations can provide insight 
 into the ontogeny of a tumour\, mechanisms of metastasis\, and modes of th
 erapeutic resistance. However\, inferring phylogenies using HTS is\nchalle
 nging due to issues such as admixed populations in bulk sequencing and noi
 sy measurements in\nsingle cell experiments.\n\nI will present three stati
 stical methods which leverage data from different HTS assays to provide\nc
 omplementary information about the population structure and phylogeny of c
 lones in a tumour. \n\nFirst\, I will discuss the PyClone model which uses
  targeted deep sequencing data to infer what proportion of cells in a biop
 sy sample harbour a mutation\, and which mutations originate at the same p
 oint in the\nevolutionary history of tumour [1]. I will present current wo
 rk 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 ph
 ylogeny using a novel\nnon-parametric Bayesian process. \n\nSecond\, I wil
 l present the single cell genotyper (SCG) model which can be used to analy
 se targeted single cell sequencing data of known point mutations [3]. The 
 model\naccounts for several sources of noise\, including doublet cells and
  allele drop-out. This model allows for\nrobust inference of the clonal ge
 notype\, which in turn can be used as input for classical phylogenetic\nal
 gorithms. \n\nFinally\, I will consider the problem of mutation loss and p
 resent a novel model based on the\nStochastic 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].\nKey
 words: cancer\, genomics\, Bayesian statistics\, high grade serous ovarian
  cancer\, phylogenetics\, single\ncell sequencing\, high throughput sequen
 cing\n[1] Roth et al.\, PyClone: statistical inference of clonal populatio
 n structure in cancer \, Nature\nMethods\, 2014\n[2] Bouchard-Côté\, Dou
 cet and Roth\, Particle Gibbs Split-Merge Sampling for Bayesian\nInference
  in Mixture Models \, Journal of Machine Learning Research\, 2017\n[3] Rot
 h et al.\, Clonal genotype and population structure inference from single-
 cell tumor\nsequencing \, Nature Methods\, 2016\n[4] McPherson & Roth et a
 l.\, Divergent modes of clonal spread and intraperitoneal mixing in\nhigh-
 grade serous ovarian cancer \, Nature Genetics\, 2016\n\nSpeaker :\nDr. An
 drew Roth\nDepartment of Statistics and Ludwig Institute for Cancer Resear
 ch\, University of Oxford\n\nI completed my PhD at the University of Briti
 sh Columbia under the supervision of Sohrab Shah. During that time I devel
 oped computational statistical methods for analysing high throughput genom
 ics 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 an
 d high grade serous ovarian cancer. I am currently a post-doctoral fellow 
 in the Department of Statistics and Ludwig Institute for Cancer Research a
 t the University of Oxford\, supervised by Chris Holmes and Xin Lu. I am c
 ontinuing to develop methods\, particularly to study the effects of treatm
 ent induced selection in oesophageal cancer.
LOCATION:CRUK CI Room 009/009A
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