January 10, 2020
OICR-led international research group develops new open-source software to determine the accuracy of computational methods that can map the genetic history of tumour cells.
A cancer patient’s tumour is often made up of many cells with different genetic traits that can evolve over time. Interest in tumour evolution has grown over the last decade, giving rise to several new computational tools and algorithms that can characterize genetic diversity within a tumour, and infer patterns in how tumours evolve. However, to date there has been no standard way to compare these tools and determine which are most accurate at deciphering these data.
The genetic differences between tumour cells can tell us a lot about a patient’s disease and how it evolves over time – Adriana Salcedo
In a study recently published in Nature Biotechnology, an OICR-led international research group released new open-source software that can be used to judge the accuracy of these novel algorithms.
The group developed a framework and scoring system to determine how accurately each algorithm predicted various measures of a tumour’s genetic diversity and evolution patterns. They incorporated several considerations in to their evaluation, including the proportion of cancerous cells in the tumour sample, the number of genetically different groups of cancerous cells in the tumour sample, the proportion of cells within each of these groups, which genetic mutations were in each group, and the genetic relationship between the groups.
“The genetic differences between tumour cells can tell us a lot about a patient’s disease and how it evolves over time,” says co-first author Adriana Salcedo, PhD Candidate at the University of Toronto and OICR . “As the scientific community works to better understand how tumours evolve, it is important that we collaborate to create and use gold-standard methods for our research.”
The research group built upon existing computer software tools to analyse more than 580 predictions made by several different computational approaches. Their framework, tools and software are all publicly available for the research community to use and has already helped analyse the evolutionary history of more than 2,600 tumour samples in an international pan-cancer research initiative.
The framework developed in this study will continue to serve as a foundation which will enable the research community to benchmark new and existing tools that characterize a tumour’s genetic diversity and map how tumours evolve.
Read more about this study in the Francis Crick Institute’s news release.