The bone ecosystem facilitates multiple myeloma relapse and the evolution of heterogeneous proteasome inhibitor resistant disease
Miller, A. K.; Bishop, R. T.; Li, T.; Shain, K. T.; Nerlakanti, N.; Lynch, C.; Basanta, D.
Show abstract
Multiple myeloma (MM) is an osteolytic plasma cell malignancy that, despite being responsive to therapies such as proteasome inhibitors, frequently relapses. Understanding the mechanism and the niches where resistant disease evolves remains of major clinical importance. Cancer cell intrinsic mechanisms and bone ecosystem factors are known contributors to the evolution of resistant MM but the exact contribution of each is difficult to define with current in vitro and in vivo models. However, mathematical modeling can help address this gap in knowledge. Here, we describe a novel biology-driven hybrid agent-based model that incorporates key cellular species of the bone ecosystem that control normal bone remodeling and, in MM, yields a protective environment under therapy. Critically, the spatiotemporal nature of the model captures two key features: normal bone homeostasis and how MM interacts with the bone ecosystem to induce bone destruction. We next used the model to examine how the bone ecosystem contributes to the evolutionary dynamics of resistant MM under control and proteasome inhibitor treatment. Our data demonstrates that resistant disease cannot develop without MM intrinsic mechanisms. However, protection from the bone microenvironment dramatically increases the likelihood of developing intrinsic resistance and subsequent relapse. The spatial nature of the model also reveals how the bone ecosystem provides a protective niche for drug sensitive MM cells under treatment, consequently leading to the emergence of a heterogenous and drug resistant disease. In conclusion, our data demonstrates a significant role for the bone ecosystem in MM survival and resistance, and suggests that early intervention with bone ecosystem targeting therapies may prevent the emergence of heterogeneous drug resistant MM.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Basigin Links Altered Skeletal Stem Cell Lineage Dynamics with Glucocorticoid-induced Bone Loss and Impaired Angiogenesis 94%
- Developmental hematopoietic stem cell variation explains clonal hematopoiesis later in life 93%
- Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones 93%
Similar papers in this journal
- PAX3-FOXO1 drives targetable cell state-dependent metabolic vulnerabilities in rhabdomyosarcoma 92%
- A biophysical model uncovers the size distribution of migrating cell clusters across cancer types 91%
- Activation of retinoic acid receptor reduces metastatic prostate cancer bone lesions through blocking endothelial-to-osteoblast transition 91%
Similar papers in this journal
- Population Dynamics of Immunological Synapse Formation Induced by Bispecific T-cell Engagers Predict Clinical Pharmacodynamics and Treatment Resistance 93%
- Anti-resonance in developmental signaling regulates cell fate decisions 93%
- Periosteal skeletal stem cells can migrate into the bone marrow and support hematopoiesis after injury 93%
Similar papers in this journal
- Multi-scale dynamical modelling of T-cell development from an early thymic progenitor state to lineage commitment 93%
- YAP1 is a key regulator of EWS::FLI1-dependent malignant transformation upon IGF-1 mediated reprogramming of bone mesenchymal stem cells 93%
- Mechanism for evolution of diverse autologous antibodies upon broadly neutralizing antibody therapy of people with HIV 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.