At the 2016 San Antonio Breast Cancer Symposium, researchers announced the creation of a model to identify patients with triple negative breast cancer who are most likely to respond to chemotherapy.
At the 2016 San Antonio Breast Cancer Symposium, researchers announced the creation of a model to identify patients with triple negative breast cancer (TNBC) most likely to respond to chemotherapy. The model was developed by analyzing gene expression in 389 samples drawn from breast cancer patients before treatment, then using data on their responses to chemotherapy to predict which gene expression signatures correlated with successful treatment.
“Our goal was to identify a gene expression signature pattern in cancer cells that might be able to help us predict who’s going to respond to chemotherapy prior to actually giving the treatment,” said lead study author Katherine Hoadley, PhD, in a statement released by the researchers’ institution, the UNC Lineberger Comprehensive Cancer Center.
Patients with TNBC, which is a particularly aggressive subtype, tend to have higher response rates to chemotherapy than patients with other subtypes, whose cancers may respond better to targeted treatments. With this model, the researchers hoped to help physicians assess, prior to treatment, whether chemotherapy is likely to be effective in a particular patient, so they can then weigh the risks and benefits of a chemotherapy regimen.
Researchers analyzed half of the 389 samples from TNBC patients to identify the gene expression signatures that were most strongly associated with complete response to treatment. After predicting response in the remaining samples based on these signatures, the predictive power of this model was assessed. They found that 68% of patients who achieved a pathologic complete response to treatment were identified by the model as being likely to respond. The model indicated that full response would not be achieved for 64% of the patients whose cancer persisted following chemotherapy.
According to Hoadley, the researchers will continue to refine the model and hopefully improve its accuracy by including other factors, “such as molecular indicators of how the immune system is responding to the cancer, genetic mutations, and the number of copies of each gene.”
“If we can validate our model in future data sets, our work could help us identify patients who are likely to respond to existing, or even less, chemotherapy and those who could benefit from more chemotherapy or novel approaches,” she said in the press release.