Machine learning models based on immunological genes to predict the response to neoadjuvant therapy in breast cancer patients.

ABSTRACT: Breast cancer (BC) is the most common malignancy worldwide and neoadjuvant therapy (NAT) plays an important role in the treatment of patients with early BC. However, only a subset of BC patients can achieve pathological complete response (pCR) and benefit from NAT. It is therefore necessary to predict the responses to NAT. Although many models to predict the response to NAT based on gene expression determined by the microarray platform have been proposed, their applications in clinical practice are limited due to the data normalization methods during model building and the disadvantages of the microarray platform compared with the RNA-seq platform. In this study, we first reconfirmed the correlation between immune profiles and pCR in an RNA-seq dataset. Then, we employed multiple machine l

SUBMITTER: Chen J 

PROVIDER: S-EPMC9352856 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature