Algorithm aids treatment of breast cancer

By finding this hormone receptor, known as oestrogen receptor positive, physicians can prescribe anti-oestrogen drug therapies and improve patient outcomes, said researchers from the University of Alberta and Alberta Health Services.

Since each cell in the body contains 23,000 genes, identifying the specific genes involved in cancer growth is a complex task. Researchers used machine learning to identify three genes that allowed them to determine whether a tumour was fed by oestrogen.

‘People can’t possibly sort through all this information and find the important patterns,’ said senior author Russ Greiner, a professor in the Department of Computing Science and investigator with the Alberta Innovates Centre for Machine Learning. ‘Machines have other limitations, but what they can do is go through high-dimensional data. With our techniques, we can find combinations of biomarkers that can predict important properties of specific breast cancers.’

According to a statement, Greiner’s team created an algorithm that proved 93 per cent accurate in predicting the oestrogen receptor status of tumours. To do this, they relied on data gathered from 176 frozen tumour samples stored at the Canadian Breast Cancer Foundation Tumor Bank at the Cross Cancer Institute in Edmonton.