Machine Learning Models for Predicting Multiple Myeloma Staging and MGUS Progression Using Gene Expression Data

Abstract

In this study, we developed and evaluated Machine Learning (ML) models aimed at predicting the stage of multiple myeloma (MM) and the progression of monoclonal gammopathy of undetermined significance (MGUS) to MM. Accurate staging of MM is critical for determining appropriate treatment strategies, and our models, employing algorithms such as ElasticNet, Random Forest, Boosting, and Support Vector Machines, demonstrated high efficacy in capturing the biological differences across disease stages. Among these, the ElasticNet model exhibited strong generalizability, achieving consistent multiclass AUC values across various datasets and data transformations. Predicting MGUS progression to MM presents a significant challenge due to the scarcity of MGUS cases that have progressed. We employed a two-pronged approach to address this: developing models using a limited dataset containing progressing MGUS patients and training models on combined MGUS and MM datasets. The models achieved AUC values slightly above 0.8, particularly with ElasticNet, Boosting and Support Vector Machines, indicating their potential in stratifying MGUS patients by progression risk. This study is original in integrating MM data with MGUS cases to enhance the predictive accuracy of MGUS progression, offering a novel methodology with potential clinical applications in patient monitoring and early intervention. Our feature selection and enrichment analyses further revealed that the identified genes are involved in key signaling pathways, including PI3K-Akt, MAPK, Wnt, and mTOR, all of which play crucial roles in MM pathogenesis. These findings align with established biological knowledge, suggest possible therapeutic targets and increase the explainability of our models.