April 19, 2018
Largest-ever study of its kind uses a tumour’s past to accurately predict its future
Toronto (April 19, 2018) – Findings from Canadian Prostate Cancer Genome Network (CPC-GENE) researchers and their collaborators, published today in Cell, show that the aggressiveness of an individual prostate cancer can be accurately assessed by looking at how that tumour has evolved. This information can be used to determine what type and how much treatment should be given to each patient, or if any is needed at all.
The researchers analyzed the whole genome sequences of 293 localized prostate cancer tumours, linked to clinical outcome data. These were then further analyzed using machine learning, a type of statistical technique, to infer the evolutionary past of a tumour and to estimate its trajectory. They found that those tumours that had evolved to have multiple types of cancer cells, or subclones, were the most aggressive. Fifty-nine per cent of tumours in the study had this genetic diversity, with 61 per cent of those leading to relapse following standard therapy.
October 23, 2017
The Toronto Bioinformatics User Group’s (TorBUG) 2017-2018 season continues this Wednesday, October 23 with two presentations that promise to be of interest to anyone involved in bioinformatics. Dr. Quaid Morris, Associate Professor at the University of Toronto (U of T) will present “The Genetic Archaeology of Individual Cancers”. Brendan Innes, a PhD Candidate in the Bader Lab at U of T will cover “Cell types in single-cell RNAseq.”