'Computational Geeks' Might Have Uncovered a Therapy for Deadly Breast Cancer

The team believe the computer model will eventually become an app that clinicians can use to match the best combinations of drugs for individual patients. (Credit: Pixabay)

Computational researchers have developed a computer program which has revealed a previously unknown combination of drugs that may be the answer to triple-negative breast cancer. 

Triple-negative breast cancer has no targeted drug therapy, which leaves chemotherapy as the only hope for these patients. For those that chemotherapy doesn’t work, the survival rate remains only 12 months. 

Doctors are turning to combination therapies — cocktails of drugs — in an effort to kill the cancer. But there is no reliable way to predict which combinations, amongst hundreds, that will work — and work quickly, for an individual patient. 

Using genetic and treatment data from triple-negative breast cancer cells grown in labs, and from hundreds of patients worldwide, the researchers from Monash Biomedicine Discovery Institute have now developed a computer program, which has revealed previously unknown combinations of drugs that could be the answer to the disease.

Their research is described in PLOS Computational Biology. 

 

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Triple-negative breast cancer cells can develop resistance to a single targeted drug within days, sometimes hours, largely by re-routing the signalling pathways within the cells, say the researchers. 

“It’s similar to when there’s a car accident, and the traffic manages to re-route itself around it without causing gridlock,” said Dr. Lan Ngyen. 

“But how exactly these cancer cells find new routes to avoid the drug effect remains largely unknown,” he added. 

 

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In collaboration with colleagues at the Weizmann Institute in Israel, the team have characterised a key signalling network that drives the growth of triple-negative breast cancers and developed a computer model that can predict how the network re-routes in response to a particular drug agent. 

The model and its predictions then allowed them to rank various combination of drugs as to which are the most likely to defeat the cancer, by blocking the new route undertaken by the cancer cells. 

 

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Using data from the Cancer Genome Atlas, the researchers tested their league table of drug combinations to determine their success in people who had survived triple-negative breast cancer.

By inputting patients’ genomic and proteomic information into their computer model, the researchers can tell who may benefit from this drug combination — or not, saving time. 

The researchers found a previously unknown combination of two drugs that the model predicts could be successful in treating the aggressive and deadly cancer: 

“We hope to have this new combination in clinical trials in 2-5 years,” Dr. Nguyen said.

The team believe the computer model will eventually become an app that clinicians can use to match the best combinations of drugs for individual patients. 

 


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