Computer program breaks new ground in treatment for triple-negative breast cancer
Friday, August 10, 2018
Breast cancer is the most common cancer in women worldwide, with about 1.7 million new cases diagnosed in 2012. Triple negative breast cancer (TNBC) represents 15-20 percent of all breast cancers.
A 2007 study of more than 50,000 women with all stages of breast cancer found that 77 percent of women with TNBC survived at least five years.
A 2009 study, however, found that the five-year survival rate for women with TNBC was similar to the survival rates for women with other cancers of similar stages. The 2009 study only included 296 women, a lot smaller than the 2007 group.
Because the estrogen receptor, the progesterone receptor, and the human epidermal growth factor are not present on the cell, nonchemotherapy treatments that block these receptors, such as hormone therapy or targeted therapy, do not work, and the only treatment available is chemotherapy. More aggressive than other forms of breast cancer, TNBC may spread beyond the breast, may return within three years of chemotherapy, and may be fatal within the first five years.
Chemotherapy has no guarantee of success, and even drug cocktails cannot predict which combinations, among hundreds, will work. Like other types of breast cancer, TNBC can often be treated successfully if caught early.
But in general, survival rates tend to be lower with TNBC compared to other forms of breast cancer. TNBC has no targeted drug therapy, in part, because the cells can develop resistance to a single-targeted drug within days or even hours. How the cancer cells find new routes to avoid the drug effects is still unclear.
Monash University researchers in Australia have used genetic and treatment data from triple-negative cancer cells grown in the lab and from hundreds of patients worldwide to develop a computer program that may uncover a therapy for TBNC.
Dr. Lan Nguyen from the Monash Biomedicine Discovery Institute and his team believe the computer model will eventually become an app that clinicians can use to match the best combinations of drugs for individual patients who present with the disease.
This new model and its predictions then allowed the researchers to rank various combinations of drugs would most likely defeat the cancer by blocking the new route undertaken by the cancer cells.
Using data from The Cancer Genome Atlas, a database of cancer genes and patient histories run by the National Institutes of Health in the United States, the researchers tested their drug combinations to determine success in those who had survived TNBC. Then, by examining a patient's genomic and proteomic information and inputting this information into their computer model, the researchers were able to tell who may benefit from this drug combination and who may not, therefore saving time by not treating with the wrong drugs.
The researchers found a previously unknown combination of two drugs that their model predicts could be successful in treating TNBC and hope to have the new combination in clinical trials in two to five years.
In addition to detecting the best drug combinations for individuals with TNBC, the computer model can be adapted to determine effective drug combinations for other cancers such as lung and melanoma in which cell rerouting to evade drug effect has also been observed.
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