CCB Seminar - Dr Andrew Roth
- Centre for Computational Biology, Haworth building, Room 320
- Engineering and Physical Sciences, Lectures Talks and Workshops, Life and Environmental Sciences, Medical and Dental Sciences, Research
Jaspal Shambi - CCB Administrator
Dr Andrew Roth
Department of Statistics and Ludwig Institute for Cancer Research
University of Oxford
“Inferring the Evolutionary History of Cancers: Statistical Methods and Applications”
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 . I will present current work on scaling PyClone to whole genome scale
data using recently developed statistical inference methods . 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 . 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 .
The seminars are an opportunity for the CCB Community and external speakers to present their work or topics of interest to the bioinformatics community.
Wednesday 1 November 2017 12.00 to 1.00 pm.
CCB Large Meeting & Teaching Room, Haworth Building.
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