A multigene model for response stratification to neoadjuvant chemotherapy in triple negative breast cancer

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-oneout 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.