Predicting cancer evolution with deep learning algorithms

New proof-of-concept approach offers faster, more accurate inferences about tumour populations.

Excerpt from the Press Release:

The prevalence of mutations in a tumour can provide clues about how the cancer has grown or evolved and how best to treat it.

But making predictions about the evolution of mutated tumours using a single DNA sequenced biopsy requires complex estimates, and current methods are either slow, or don’t always consider why certain subpopulations of cells proliferate more than others.  

This led a pair of researchers at the Ontario Institute for Cancer Research (OICR) to develop a new method for understanding and predicting cancer evolution that harnesses modern deep learning algorithms and incorporates evolutionary modeling.

TumE is a deep learning approach that analyzes the frequency of mutations in a biopsy — known as the variant allele frequency — to identify which subpopulation of mutated cells that are most “fit” and thus most likely to make a tumour stronger.

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