MAP4K2 Silencing Overcomes IMiDs-Resistance in Multiple Myeloma

Recently, mitogen-activated protein kinase kinase kinase kinase 2 (MAP4K2) has emerged as an important key regulator of the stress-activated MAPK core signaling pathways. MAP4K2, also called Germinal Center Kinase (GCK), is predominantly and highly expressed in the germinal center of B cells. Recently we have shown that MAP4K2 knockdown in K- or N-RAS mutated MM cells induces MM cell growth inhibition, associated with the downregulation of critical transcriptional factors including IKZF1/3, BCL-6, and c-MYC proteins (Li et al. Blood 2021). Importantly, MAP4K2 silencing induces IKZF1 protein degradation without affecting IKZF1 mRNA level. Furthermore, IMiDs-resistant K-RAS Mut MM cells are sensitive to MAP4K2 inhibition induced IKZF1 degradation and cell growth suppression, suggesting that MAP4K2 inhibition overcomes IMiDs resistances in MM. To further validate MAP4K2 inhibition as a novel strategy to overcome IMiDs-resistance, we generated lenalidomide-resistant human myeloma cell lines. In this model, MM1S-LEN RES cells showed significantly decreased expression of CRBN protein compared to the parent cells. Accordingly, upon lenalidomide treatment, CRBN-mediated down-regulation of IKZF1, c-MYC, IKZF3 and IRF4 were abrogated in the MM1S-LEN RES cells. As expected, MM1S-LEN RES cells were resistant to lenalidomide induced growth inhibition in cell proliferation assay. In contrast, MAP4K2 inhibition using TL4-12 potently induced IKZF1, c-MYC, and IRF4 downregulation as well as cell proliferation inhibition, demonstrating that MAP4K2 regulates IKZF1 and cell growth independently of CRBN. These results indicate that MAP4K2 is a novel therapeutic target to overcome IMiDs-resistance MM. Iberdomide (CC-220) is an orally available IMiD® compound under development for the treatment of relapsed/refractory multiple myeloma. Previous biochemical and structural studies demonstrated that Iberdomide binds to cereblon with a higher affinity than lenalidomide or pomalidomide. Here, we evaluated the combination effects of Iberdomide with MAP4K2 silencing in MM. Tet-on sh-MAP4K2 lentivirus were introduced into RAS Mut MM cells to establish the inducible MAP4K2 knockdown cells upon doxycycline treatment. To address the combination effects, Tet-on sh-MAP4K2 RAS Mut MM cells were treated with doxycycline and different dosages of iberdomide. We found that MAP4K2 silencing strongly increased iberdomide-induced apoptosis (iberdomide alone vs. with MAP4K2 KD: 32% vs 92). Similar, in western blot assays, MAP4K2 silencing combined with Iberdomide significantly enhanced downregulation of IKZF1, c-MYC, and IRF4 compared to the iberdomide treatment alone. These data suggest that combination of iberdomide and MAP4K2 inhibition have synergetic anti-MM effects. Taken together, our findings demonstrate that MAP4K2 is a novel therapeutic target to bypass IMiDs resistance in RAS mutated MM. Combination of MAP4K2 inhibition with Iberdomide results in synergetic anti-cancer effects in MM, therefore could be a potent novel therapeutic regimen for patients with relapsed/refractory multiple myeloma. Disclosures Marcireau: Sanofi: Current Employment.

Modeling drug combination effects via latent tensor reconstruction

Motivation: Combination therapies have emerged as a powerful treatment modality to overcome drug resistance and improve treatment efficacy. However, the number of possible drug combinations increases very rapidly with the number of individual drugs in consideration which makes the comprehensive experimental screening infeasible in practice. Machine learning models offer time- and cost-efficient means to aid this process by prioritising the most effective drug combinations for further pre-clinical and clinical validation. However, the complexity of the underlying interaction patterns across multiple drug doses and in different cellular contexts poses challenges to the predictive modelling of drug combination effects. Results: We introduce comboLTR, highly time-efficient method for learning complex, nonlinear target functions for describing the responses of therapeutic agent combinations in various doses and cancer cell-contexts. The method is based on a polynomial regression via powerful latent tensor reconstruction. It uses a combination of recommender system-style features indexing the data tensor of response values in different contexts, and chemical and multi-omics features as inputs. We demonstrate that comboLTR outperforms state-of-the-art methods in terms of predictive performance and running time, and produces highly accurate results even in the challenging and practical inference scenario where full dose-response matrices are predicted for completely new drug combinations with no available combination and monotherapy response measurements in any training cell line.