DELINEATE THE TUMOR IMMUNE MICROENVIRONMENT FOR BETTER UNDERSTANDING IMMUNOTHERAPY RESPONSE

This application is being submitted in response to the Notice of Special Interest (NOSI) identified as NOT-CA-24-047. Monoclonal gammopathy of undetermined significance (MGUS) is a clinically asymptomatic precursor to blood malignancies such as multiple myeloma (MM), affecting approximately 3.2% of the population over 50 years old. MGUS progresses to malignant conditions at a rate of about 1% per year, with 90% of these transformations resulting in incurable cancers. Despite the critical need, there are no approved treatments to prevent MGUS from progressing to MM. This is due to two major challenges: the lack of effective methods to identify high-risk MGUS patients and the absence of effective preventive interventions. In this application, we propose to address these two challenges by leveraging computational approaches to integrate diverse genomic and transcriptomic data from previous studies. First, we will apply a Cell-of-Origin gene signature consisting of 20 genes (PBM20) to select MGUS patients with a high risk of progression. By integrating the PBM20 signature with traditional clinical factors, we will use machine learning and statistical methods to build classification and survival regression models, optimizing the accuracy of predicting MGUS progression to MM. Second, we will perform systematic drug repurposing analysis to identify new candidate drugs to prevent MGUS progression from existing drugs approved by the FDA for treating other indications. We will collaborate on this project with the CAP-MGUS Center at Baylor College of Medicine (BCM), led by Dr. Yong Li. This project has the potential to transform the current paradigm of managing MGUS from passive monitoring to proactive prevention, significantly reducing the transformation of MGUS into incurable malignancies.