Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023)

Background . Cancer is a disease of the genome - more specifically mutations acquired by the genomic DNA in the cells of a human over a lifetime, which disrupt the cells' key functions and make them grow uncontrollably. DNA sequencing projects have led us to discover that cancer calls involve many genetic changes and that even in a single tumour, there are often multiple cancer cell populations that each carry their own mutations. Understanding this collection of mutations is important because we need to select therapies that kill all of the cancer cells, not just some of them. Unfortunately, existing computer programs for analyzing "normal" human genome data generated by genome sequencing technologies are limited in scope because they cannot fully characterize all the mutations present in the individual cells of a tumour tissue. Ideally, researchers would like to monitor how the genomes of cancer cells mutate over time, forming distinct tumour clones within the same tissue, and how cancer cells travel through the blood stream or the urinary tract and colonize other tissues, forming metastatic tumours. The new liquid biopsy technology has made it possible to capture tumour DNA circulating in the blood stream and to sequence it, however analyzing such data and identifying and cataloguing the spectrum of mutations in an individual patient will require new mathematical and computational approaches.

Research Project. We propose to develop novel computational methods for (i) detecting SNVs, indels, microSVs, gene fusions and CNVs specific to a tumour (Objective 1) , (ii) detecting the clonal composition of the tumour based on this mutational profile (Objective 2) , and (iii) monitoring the tumour’s progression during treatment (Objective 3) - via a composition of solid tumour WGSS and liquid biopsies sequencing provided as time series data.
The s hort term goal is to develop mathematical models, computational algorithms and software (Objective 1-3) in order to analyze the next generation sequencing data obtained from “liquid biopsy”. These methods will be used by life scientist to better understand the biology underlying tumour evolution. The long term goal is to build a comprehensive and simple to use platform (potentially based on machine learning approaches) capable of predicting patient’s response to different treatments depending on its genomic profile.

Deliverables. The computational pipeline CVEMCT (Clonal Variant calling and Evolutionary Modeling based on CTDNA) which includes algorithms and open source software for clonality detection and tumour progression monitoring. All these tools will be also made available through the CGC – The Cancer Genome Collaboratory, a Genome Canada funded project to host the entire ICGC cancer genome data first for the Canadian and then for the International research community.