02 Aug Cancer diagnosis and treatment could get a boost from machine learning

By Chris Barncard, UW–Madison

Thanks to machine learning algorithms, short pieces of DNA floating in the bloodstream of cancer patients can help doctors diagnose specific types of cancer and choose the most effective treatment for a patient. 

The new analysis technique, created by University of Wisconsin–Madison researchers and published recently in Annals of Oncology, is compatible with “liquid biopsy” testing equipment already approved in the United States and in use in cancer clinics. This could speed the new method’s path to helping patients. 

Liquid biopsies rely on simple blood draws instead of taking a piece of cancerous tissue from a tumor with a needle. 

“Liquid biopsies are much less invasive than a tissue biopsy — which may even be impossible to do in some cases, depending on where a patient’s tumor is,” says Marina Sharifi, a professor of medicine and oncologist in UW–Madison’s School of Medicine and Public Health. “It’s much easier to do them multiple times over the course of a patient’s disease to monitor the status of cancer and its response to treatment.” 

Cancerous tumors shed genetic material, called cell-free DNA, into the bloodstream as they grow. But not all parts of a cancer cell’s DNA are likely to tumble away. Cells store some of their DNA by coiling it up in protective balls called histones. They unwrap sections to access parts of the genetic code as needed. Read more …