An integrated mathematical modeling approach to define how the aging bone ecosystem drives multiple myeloma evolution and treatment response - Project Summary Significance: Multiple myeloma (MM) is an incurable bone destroying plasma cell skeletal malignancy with a peak incidence between ages 65-74 years old. The introduction of therapies such as proteasome inhibitors (PI, e.g. bortezomib, carfilzomib), and bone protecting bisphosphonates (e.g. zoledronate, ZOL) has vastly improved patient outcomes. However, MM often relapses, due in part to the protective effects of the bone ecosystem coupled with clonal evolution. Understanding the interplay between sensitive and resistant clones and the contribution of the surrounding aging bone ecosystem is critical for enhancing drug efficacy and emerging adaptive therapy (AT) regimens that we posit could significantly delay patient relapse. Rationale: Using combined empirical, in vitro, and in vivo data, we generated a hybrid cellular automata (HCA) mathematical model describing the interactions between PI-naïve and sensitive MM clones and the surrounding bone ecosystem. In silico, we also simulated the effect of standard of care treatments (PI and/or ZOL) on MM progression and demonstrated the protective effect of the bone ecosystem to treatment and MM heterogeneity. Our experimental analyses of aged mice also show major differences in the mesenchymal stem cell (MSC) compartment that could have profound effects on MM progression and therapy response. We have also recently optimized spatial transcriptomic protocols for examining population changes and transcriptive states in naïve and MM bearing bones. Based on these data we hypothesize that, an integrated age-scaled mathematical model of the MM-bone microenvironment can optimize adaptive therapy and delay the emergence of resistant disease. To test this, we propose three synergistic and independent aims. Approaches: In Aim 1, data obtained from spatial transcriptomic analysis of young (3 months) and old (24 months) tumor naïve mice will be used to “age” the HCA model. We will also use data from PI-resistant and naïve MM isogenic cell lines, grown in the presence or absence of MSCs derived from young or aged mice. These data will be complemented by ex vivo patient data derived from MM patients’ samples and biopsies. In Aim 2, we will explore the evolution of PI-resistant disease and bone ecosystem effects in the context of PI and/or microenvironmental targeted treatment in young vs. aged mice. Results will be used to calibrate the HCA. In Aim 3, optimization algorithms (OAs) will be used to define AT regimens that outperform standard of care therapy in aged mice, and in “shadow” clinical trials of newly diagnosed Moffitt MM patients (deidentified). Innovation/Impact: Our innovative studies will generate a novel HCA model that can be used to dissect the role of the aging bone ecosystem in MM evolution and therapy response. We also anticipate demonstrating how ATs can optimize standard of care therapies to delay the emergence of resistant disease in an age related manner. We believe the proposed studies will be highly impactful from a scientific and clinical view point.
