三阴性乳腺癌新辅助化疗反应分层的多基因模型

Around half of triple negative breast cancer (TNBC) patients achieve a pathological complete response (pCR) based on neoadjuvant chemotherapy (NAC), which is associated with a good outcome. Conversely, in patients with a poor response to NAC, there is a clear need to administer more effective therapeutic strategies. Accurate prediction of tumor response could enable the implementation of more personalized and effective treatment strategies. In this retrospective multicenter study, formalin-fixed paraffin-embedded tissues of pre-NAC needle biopsies from TNBC patients treated between 2013 and 2022 were analyzed.

Clinical, pathological, and transcriptomic data were combined in a prediction model, using a leave-one-out design, to predict the response to NAC, followed by external validation in an independent dataset. In total, 204 patients were included, comprising 87 good responders and 117 poor responders.

A transcriptomic based prediction model showed that all samples but one clustered correctly in the good or the poor responder category. External validation showed an accuracy of 85% in predicting a good response to NAC, using a 31-gene signature. On the other hand, prediction of having a non-pCR was not substantial in this external cohort, since only 58% were predicted correctly.

This study suggests that a 31-gene prediction model may help identify TNBC patients who are likely to achieve a pCR following NAC alone. These patients may not require therapeutic intensification, such as addition of immunotherapy, thereby minimizing exposure to unnecessary treatment-related toxicity and reducing associated healthcare costs. Nonetheless, further optimization and prospective validation are needed prior to moving towards clinical implementation.