3D-Tissue Engineered Bone Marrow (3DTEBM) Culture Retrospectively Predicts Treatment Clinical Outcomes of Multiple Myeloma Patients

Background:

Multiple Myeloma (MM) is the second most common hematological malignancy, and continues to be a fatal disease even with the development of novel therapies. Despite promising preclinical data in standard tissue culture models, most drugs fail in clinical trials and show lower efficacy in patients. This highlights the discrepancy between the current in vitro models, the pathophysiology of the disease in the patients, and the urgent need for better in vitro models for drug development and improved prediction of efficacy in patients. We have previously developed a patient-derived 3D-Tissue Engineered Bone Marrow (3DTEBM) culture model, which showed superior properties for proliferation of primary MM cells ex vivo, and better recapitulated drug resistance. The long-term goal of this study is to use the 3DTEBM model as a tool to perform drug screens on BM aspirates of MM patients and prospectively predict the efficacy of different therapies in individual patients, and help treatment providers develop personalized treatment plans for each individual patient. In the current study, we used the 3DTEBM model to, retrospectively, predict clinical responses of MM patients to therapy, as a proof of concept.

Methods:

We used whole-BM, viably frozen tissue banked samples from 20 MM patients with clear clinical response patterns of complete remission, and either very good partial response (sensitive) or progressive disease (non-sensitive). The BM aspirates were used to develop a 3DTEBM that represents each individual patient. The patient-derived 3DTEBM cultures were treated ex vivo with the same therapeutic regimen that the patient received in the clinic for 3 days. The treatment ex vivo was based on combinations at different concentrations which mimic the steady state concentrations (Css) of each drug. The efficacy of the treatment ex vivo was evaluated by digestion of the 3DTEBM matrix, extraction of the cells, and analysis for prevalence of MM cells in the treatment groups compared to the non-treated controls. Patients were defined "sensitive" if the effect reached 50% killing in the range of 10xCss. The ex vivo sensitivity data was then correlated with the clinical response outcomes.

Results:

We found that the 3DTEBM was predictive in approximately 80% of the cases (in about 85% of the combination therapy cases, and in about 70% of the single therapy cases). Broken down by individual drug, it was predictive in 80% of the cases treated with Bortezomib, 78% Lenalidomide, 84% Dexamethasone, 100% Daratumumab, 50% Carfilzomib, 50% Pomalidomide, and 100% Doxorubicin.

Conclusions:

The 3DTEBM is a more pathophysiologically relevant model which predicts clinical efficacy of drugs in multiple myeloma patients, retrospectively. This data provides the bases for future studies which will examine the ability of the 3DTEBM model to predict treatment efficacy, prospectively, for development of personalized treatment plans in individual multiple myeloma patients.