Scientists identify proteins that would predict the response to standard treatment in one of the most aggressive breast cancers

The study, carried out by CONICET specialists, would help better adapt therapies for a specific subtype called HER2 positive.

Through bioinformatics and in vitro studies, CONICET researchers identified two proteins that could predict whether standard treatment for a subtype of breast cancer, called HER2 positive, would be effective for people suffering from this disease. The study was published in the journal Cancers.

“The usual treatment for HER2-positive breast cancer involves the use of a drug called trastuzumab, which blocks the action of the HER2 protein. This type of therapy is selective since it affects only tumor cells, because normal cells do not present this protein in large quantities. However, there is a growing concern since a significant fraction of patients do not respond favorably to this treatment, or respond initially, but then the therapy stops working, giving rise to what is known as resistance,” says Marina Flamini, CONICET researcher at the Instituto de Medicina y Biología Experimental de Cuyo (IMBECU, CONICET-UNCUYO) and corresponding author of the study.

According to the scientist, there are various subtypes of breast cancer and this classification is essential, since it allows doctors to better understand the biology of the disease and choose the most appropriate treatment. Among them, one of the most challenging, due to its aggressiveness, is HER2 positive, which represents between 15 and 20 percent of diagnosed cases and has a lower probability of survival than other subtypes.

The research team, which also includes fellow Carla Castro Guijarro and researcher Matías Sanchez, both from IMBECU, identified two key proteins that could predict whether HER2-positive treatment would be effective. “We found two proteins, called vinculin and Cortactin, whose expression differed between patients with a higher and lower probability of survival. Furthermore, the expression of these proteins was also useful to distinguish between patients who respond favorably to treatment with trastuzumab and those who do not. People with lower expression of these proteins had a better prognosis and showed an effective response to treatment. In the scientific field, these types of molecules that allow predicting the aggressiveness of the disease and the response to a certain treatment are called prognostic and predictive “biomarkers”, respectively. In this case, we discovered these two proteins that could be involved in the type of response of tumors to treatments, making these molecules an interesting target to improve therapy,” says Sánchez.

The study is relevant because currently there are no biomarkers, other than HER2, available to guide oncological decisions. “This research is promising because, for some time, a personalized approach to cancer treatment has been sought, adapting therapies to the molecular characteristics of each patient. This is essential since cancer is a very heterogeneous disease and, although two tumors appear similar, they can respond differently to the same treatment. In this way, biomarkers are essential tools to ensure that the right treatment is administered to the right person, avoiding ineffective treatments. Even more so in this type of disease where accurate and early treatment makes the difference,” says Castro Guijarro.

The team highlights that these proteins could be contributing to resistance to trastuzumab in breast cancer cells that have the HER2 receptor. “Now, we seek to go further to evaluate whether these results obtained from bioinformatics and cell line assays are validated in patient samples. If so, these proteins could be considered as therapeutic targets in the development of future therapies for the treatment of HER2-positive breast tumors resistant to trastuzumab,” concludes Flamini.


Castro-Guijarro, A.C.; Sanchez, A.M.; Flamini, M.I. Potential Biomarkers Associated with Prognosis and Trastuzumab Response in HER2+ Breast Cancer. Cancers 202315, 4374.

By Leonardo Fernández