New genetic classifier can predict the sensitivity of chemotherapy for breast cancer

Led by Prof. Wulin Yang and Haiming Dai from the Hefei Institutes of Physical Science (HFIPS) of the Chinese Academy of Sciences, a team of researchers recently postulated a genetic classifier with the ability to predict the sensitivity of neoadjuvant chemotherapy for breast cancer in the area of tumor molecular markers.

Construction of LASSO Logistic regression model and its predictive power for neoadjuvant therapy. Image Credit: Numstocker/Shutterstock.com

Breast cancer is a type of malignant tumor that exhibits high clinical and biological heterogeneity. Various subtypes respond to chemotherapy with multitude of clinical characteristics.

Hence, it is crucial to devise techniques to estimate treatment sensitivity, which forms the foundation for precision medicine for breast cancer that would enable patients to opt for the most ideal treatment approach and avoid overtreatment.

As part of the research, the team developed a prediction model with a 25-gene signature with the help of least absolute shrinkage and selection operator (LASSO) logistic regression using patient drug response data.

The model has many advantages, “it can predict pathologic complete response to paclitaxel and anthracycline-based neoadjuvant chemotherapy with high accuracy and is applicable to various subtypes of breast cancer, demonstrating an important contribution of the immune ecosystem to chemotherapeutic sensitivity,” stated Wulin Yang, who headed the research team.

This gene signature exhibited optimal predictive abilities for various batches of data and different breast cancer subtypes while exhibiting good generalization ability. Thus, it is expected to be fostered as a new diagnostic tool to predict the sensitive one to chemotherapy in clinical practice.

It can therefore select the optimal scheme for breast cancer and provide patients with chances of precise treatment.”

Wulin Yang, Professor, Hefei Institutes of Physical Science