Virtual B-cell Lymphoma Model Could Help Predict Effective Combination Therapy

Dr. Olivier Elemento
Dr. Olivier Elemento, PhD

The aberrant activation of B-cell receptor (BCR) signaling network drives survival and proliferation of B-cell malignancies including activated B-cell like diffuse large B-cell lymphoma (ABC-DLBCL). While various small molecule inhibitors have been developed to target the BCR targeting signaling network the clinical application of these targeted agents face several challenges. The complexity of the BCR signaling network ensures that individual small molecule inhibitors must be used in combination to produce effective and durable response in patients. Given the large number of possible drug combinations, comprehensive experimental screening is not really feasible.

However, computational models of cell signaling networks that can accurately reconstruct signaling dynamics in silico may represent a useful alternative to experimental screen and trial-and-error experimental investigations. During the 56th Annual Meeting of the American Society of Hematology, we presented results from a computational model to predict effective combinatorial therapy in silico before validating these predictions using in vitro experiments.

Using published drug response data we began by seeking to predict the viability response of the BTK inhibitor ibrutinib in combination with inhibitors targeting other kinases, e.g. BKM-120 against PI3K, sotrastaurin against PKC-beta closely matched previously published experimental data in TMD8 (r>0.86,p<1e-11). Next we sought to identify synergistic drug combinations. Computational screening predicted dual blockage of LYN and SYK as the most synergistic combination, which we confirmed experimentally by treating TMD8 cells with LYN inhibitor Dasatinib and SYK inhibitor R406 at multiple doses.

Finally we sought to use our model to predict biomarkers of sensitivity and resistance to specific treatment strategies. We found that overexpression of PTP1B, which dephosphorylates BTK substrate PLCg2, predicts relative sensitivity to BTK inhibition. Supporting this prediction, we observed increased PTP1B expression in DLBCL cell lines sensitive to ibrutinib treatment, suggesting PTP1B as potential biomarker for ibrutinib sensitivity.

In conclusion, this study provides for a new approach to computationally optimize combinatorial targeted therapy against aberrant BCR signaling. Going forward it will be another method to discover effective patient specific drug combinations.

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