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Cancer Institute Seminar Series - Dr Simone Zaccaria

09 January 2020, 12:00 pm–1:00 pm

Dr Simone Zaccaria

Dr Simone Zaccaria, Princeton University, presents: 'Characterizing the copy-number landscape of cancer genomes from bulk and single-cell DNA sequencing data.'

Event Information

Open to

All

Organiser

Veronica Dominguez

Location

Courtyard Cafe
Paul O'Gorman Building
UCL Cancer Institute 72 Huntley Street
London
WC1E 6DD
United Kingdom

Hosted by: Dr Nicholas McGranahan

A light lunch will be served after the seminar

Cancer arises from an evolutionary process during which somatic mutations of the genome accumulate in a population of cells. During this process, distinct groups of cells, or clones, acquire different complements of mutations, yielding a heterogeneous tumour. As these clones exhibit different behaviours and treatment responses, the accurate reconstruction of the tumour evolution has a critical impact on both diagnosis and prognosis. One common type of somatic mutations is Copy-Number Aberrations (CNAs) that change the number of copies of the two alleles contained in the human genome for every autosomal region. As CNAs are ubiquitous in cancer, these mutations represent fundamental markers to quantify intra-tumour heterogeneity and to reconstruct the tumour evolution.  For this reason, several methods have been proposed to identify CNAs using DNA sequencing data, either from multiple bulk tumour samples from the same patient or from thousands of single cells in parallel.

However, to deal with the challenges introduced by these sequencing data, previous methods have relied on restrictive assumptions or have limited their analysis to allele-unspecific CNAs. To address the critical need for accurate identification of CNAs, I developed two computational methods that  jointly analyse multiple samples/cells that are products of the same evolutionary process by leveraging the features of the most recent sequencing technologies. First, I developed HATCHet, the first comprehensive algorithm to identify CNAs jointly from DNA sequencing data of multiple bulk tumour samples from the same patient. Second, I developed CHISEL, the first algorithm to infer allele-specific CNAs from single-cell DNA sequencing data by jointly analysing all the cells. When applied on DNA sequencing data from multiple cancer patients, HATCHet and CHISEL improve the identification of CNAs and identify previously uncharacterized mechanisms of tumour evolution.
 

About the Speaker

Dr Simone Zaccaria

at Princeton University

More about Dr Simone Zaccaria