Over the past several years, I have designed and conducted a series of studies to define biological mechanisms of multiple myeloma (MM) and the transformation from myeloma precursor disease (monoclonal gammopathy of undetermined significance [MGUS] and smoldering myeloma) to MM. We continue to model and integrate clinical, immune microenvironment, and genomic data to better characterize the pathogenesis and (sub)clonal evolution of MM and its precursor disease to better evaluate the mutagenic impact of different chemotherapeutic agents and to improve clinical outcomes. Our group has strong expertise in both clinical and genome profiling analysis. In addition, we have a proven track record on the development of high-throughput laboratory and analytical pipelines (including bioinformatics, statistical modeling, and artificial intelligence (AI)), and we have the skills and experience on how to integrate large and complex genomic datasets.
For more than a decade, I have launched several early-intervention clinical trials for patients with high-risk smoldering myeloma, with the aim to delay or prevent transformation. Furthermore, as part of my interest in effective, non-intense therapies, I have designed clinical studies to seek rapid and deep responses in patients with smoldering myeloma as well as newly diagnosed MM. Beyond traditional clinical criteria for complete remission, I have developed novel strategies to define minimal residual disease (MRD) detection post-therapy in MM and in patients with related disorders, using cell-, molecular-, and imaging-based methods. My goal is to define sensitive MRD methods that can be used for longitudinal monitoring without the need for invasive biopsies. The intention is to use AI to integrate these data with genomic, demographic, clinical, and therapeutic features to develop individualized prognostication, to redefine the concept of high-risk disease, and to design, for the first time, patient-tailored therapeutic strategies.