A Neural Network–Based Scoring System for Predicting Prognosis and Therapy in Breast Cancer

Abstract Breast cancer is a prevalent malignancy affecting women worldwide. Currently, there are no precise molecular biomarkers with immense potential for accurately predicting breast cancer development, which limits clinical management options. Recent evidence has highlighted the importance of metastatic and tumor‐infiltrating immune cells in modulating the antitumor therapy response. However, the prognostic value of using these features in combination, and their potential for guiding individualized treatment for breast cancer, remains vague. To address this challenge, we recently developed the metastatic and immunogenomic risk score (MIRS), a comprehensive and user‐friendly scoring system that leverages advanced bioinformatics methods to facilitate transcriptomics data analysis. To help users become familiar with the MIRS tool and apply it effectively in analyzing new breast cancer datasets, we describe detailed protocols that require no advanced programming skills. © 2024 Wiley Periodicals LLC. Basic Protocol 1 : Calculating a MIRS score from transcriptomics data Basic Protocol 2 : Predicting clinical outcomes from MIRS scores Basic Protocol 3 : Evaluating treatment responses and guiding therapeutic strategies in breast cancer patients Basic Protocol 4 : Guidelines for utilizing the MIRS webtool