No abstract

Multiple myeloma remains treatable but incurable. Despite a growing armamentarium of effective agents, choice of therapy, especially in relapse, still relies almost exclusively on clinical acumen. We have developed a system, Mathematical Myeloma Advisor (EMMA), consisting of patient-specific mathematical models parameterized by an assay that reverse engineers the intensity and heterogeneity of chemosensitivity of primary cells from multiple myeloma patients, allowing us to predict clinical response to up to 31 drugs within 5 days after bone marrow biopsy. From a cohort of 52 multiple myeloma patients, EMMA correctly classified 96% as responders/nonresponders and correctly classified 79% according to International Myeloma Working Group stratification of level of response. We also observed a significant correlation between predicted and actual tumor burden measurements (Pearson = 0.5658, < 0.0001). Preliminary estimates indicate that, among the patients enrolled in this study, 60% were treated with at least one ineffective agent from their therapy combination regimen, whereas 30% would have responded better if treated with another available drug or combination. Two clinical trials with experimental agents ricolinostat and venetoclax, in a cohort of 19 multiple myeloma patient samples, yielded consistent results with recent phase I/II trials, suggesting that EMMA is a feasible platform for estimating clinical efficacy of drugs and inclusion criteria screening. This unique platform, specifically designed to predict therapeutic response in multiple myeloma patients within a clinically actionable time frame, has shown high predictive accuracy in patients treated with combinations of different classes of drugs. The accuracy, reproducibility, short turnaround time, and high-throughput potential of this platform demonstrate EMMA's promise as a decision support system for therapeutic management of multiple myeloma..

SUMMARY Drug-tolerant “persister” tumor cells underlie emergence of drug-resistant clones and contribute to relapse and disease progression. Here we report that resistance to the BCL-2 targeting drug ABT-199 in models of mantle cell lymphoma and double-hit lymphoma evolves from outgrowth of persister clones displaying loss of 18q21 amplicons that harbor BCL2. Further, persister status is generated via adaptive super-enhancer remodeling that reprograms transcription and offers opportunities for overcoming ABT-199 resistance. Notably, pharmacoproteomic and pharmacogenomic screens revealed that persisters are vulnerable to inhibition of the transcriptional machinery and especially to inhibition of cyclin-dependent kinase 7 (CDK7), which is essential for the transcriptional reprogramming that drives and sustains ABT-199 resistance. Thus, transcription-targeting agents offer new approaches to disable drug resistance in B-cell lymphomas.

Background: Multiagent therapies, due to their ability to delay or overcome resistance, are a hallmark of treatment in multiple myeloma (MM). The growing number of therapeutic options in MM requires highthroughput combination screening tools to better allocate treatment, and facilitate personalized therapy. Methods: A second-order drug response model was employed to fit patient-specific ex vivo responses of 203 MM patients to single-agent models. A novel pharmacodynamic model, developed to account for twoway combination effects, was tested with 130 two-drug combinations. We have demonstrated that this model is sufficiently parameterized by single-agent and fixed-ratio combination responses, by validating model estimates with ex vivo combination responses for different concentration ratios, using a checkerboard assay. This new model reconciles ex vivo observations from both Loewe and BLISS synergy models, by accounting for the dimension of time, as opposed to focusing on arbitrary time-points or drug effect. Clinical outcomes of patients were simulated by coupling patient-specific drug combination models with pharmacokinetic data. Findings: Combination screening showed 1 in 5 combinations (21.43% by LD50, 18.42% by AUC) were synergistic ex vivo with statistical significance (P < 0.05), but clinical synergy was predicted for only 1 in 10 combinations (8.69%), which was attributed to the role of pharmacokinetics and dosing schedules. Interpretation: The proposed framework can inform clinical decisions from ex vivo observations, thus providing a path toward personalized therapy using combination regimens.

Interactions between the IAP antagonist LCL161 and the histone deacetylase inhibitor (HDACI) panobinostat (LBH589) were examined in human multiple myeloma (MM) cells. LCL161 and panobinostat interacted synergistically to induce apoptosis in diverse MM cell lines including those resistant to bortezomib (PS-R). Similar interactions were observed with other HDACIs (MS-275) or IAP antagonists (birinapant). These events were associated with down-regulation of the non-canonical (but not the canonical) NF-κB pathway and activation of the extrinsic, caspase-8- related apoptotic cascade. Co-exposure of MM cells to LCL161/LBH589 induced TRAF3 up-regulation, TRAF2 and NIK down-regulation, diminished expression of BCL-XL and induction of γH2A.X. Ectopic expression of TRAF2, NIK, or BCL-XL, or shRNA TRAF3 knock-down significantly reduced LCL161/LBH589 lethality, as did ectopic expression of dominant-negative FADD. Stromal/microenvironmental factors failed to diminish LCL161/LBH589-induced cell death. The LCL161/LBH589 regimen significantly increased cell killing in primary CD138+ cells (N = 31) and was particularly effective in diminishing the primitive progenitor cell-enriched CD138-/19+/20+/27+ population (N = 23), but was non-toxic to normal CD34+ cells. Finally, combined LCL161/LBH589 treatment significantly increased survival compared to single-agent treatment in an immunocompetent 5TGM1 murine MM model. Together, these findings argue that LCL161 interacts synergistically with LBH589 in MM cells through a process involving inactivation of the non-canonical NF-κB and activation of the extrinsic apoptotic pathways, up-regulation of TRAF3, and TRAF2/BCL-XL down-regulation. Notably, this regimen overcomes various forms of resistance, is active against primary MM cells, and displays significant in vivo activity. This strategy warrants further consideration in MM.