Triple negative breast cancer is a particularly aggressive breast
cancer type that has no targeted treatments. Patients with this disease
do have higher response rates to chemotherapy, however, compared with
some other subtypes. While targeted treatments are designed to attack
specific molecular features that help drive cancer, chemotherapy more
widely attacks all rapidly dividing cells.
University of North Carolina Lineberger Comprehensive Cancer Center
researchers and collaborators are working to predict, before treatment,
whether triple negative breast cancer will respond to
‘A gene expression signature pattern in triple negative breast cancer cells may help researchers predict who's going to respond to chemotherapy prior to actually giving the treatment.’
In a study presented at the 2016 San Antonio Breast Cancer
Symposium, researchers report they developed a model that can predict
which triple negative breast cancer patients will respond to
chemotherapy. Katherine Hoadley, a UNC Lineberger member and
assistant professor in the UNC School of Medicine Department of
Genetics, said the model was moderately successful at predicting
response, but more work is needed to improve its accuracy.
"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
Hoadley, the study's first author.
Hoadley said knowing in advance which triple negative breast cancer
patients will respond to chemotherapy could help physicians determine
the best course of treatment.
To develop the prediction model, researchers analyzed the expression
of genes from breast cancer samples drawn from 389 patients before
treatment, and they also drew upon data on how those patients responded
to treatment. They split the sample data into training and test sets.
Gene expression signatures were analyzed in the training set to identify
the signatures that best associated with a complete response to
treatment. They then used the signatures they uncovered to determine the
ability to predict response in the remaining samples.
They found the model could predict which samples had a complete
pathologic response for 68% of patients who actually did achieve
pathologic complete response to the treatment. And for patients who did
have residual disease after chemotherapy, the test successfully
indicated they did not have pathologic complete response for 64%
of those cases.
Hoadley said the researchers will continue to work on the model to
improve its accuracy. She said they plan to include other features of
cancer cells in their model, 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.