MIRS: an AI scoring system for predicting the prognosis and therapy of breast cancer

Abstract

Current scoring systems for prognosis of breast cancer are available but usually consider only one prognostic feature. We aim to develop a novel prognostic scoring system based on both immune-infiltration and metastatic features to not only assess the patient prognoses more accurately but also guide therapy for patients with breast cancer. Computational immune-infiltration and gene profiling analysis identified a 12-gene panel firstly characterizing immune-infiltrating and metastatic features. Neural network model yielded a precise prognostic scoring system called metastatic and immunogenomic risk score (MIRS). The influence of MIRS on the prognosis and therapy of breast cancer was then comprehensively investigated. MIRS significantly stratifies patients into high risk-group (MIRShigh) and low risk-group (MIRSlow) in both training and test cohorts. The MIRSlow patients exhibit significantly improved survival rate compared with MIRShigh patients. A series of analyses demonstrates that MIRS can well characterize the metastatic and immune landscape of breast cancer. Further analysis on the usage of MIRS in chemotherapy suggests that MIRShigh patients may benefit from three chemotherapeutic drugs (Cisplatin, Tamoxifen and Vincristine). Higher immune infiltration and significantly prolonged survival are observed in MIRSlow patients, indicating a better response in immune checkpoint inhibitor therapy. Our analysis demonstrates that MIRS could effectively improve the accuracy of prognosis for patients with breast cancer. Also, MIRS is a useful webtool, which is deposited at https://lva85.github.io/MIRS/, to help clinicians in designing personalized therapies for patients with breast cancer.