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The complexity of cancer signaling networks limits the efficacy of most single agent treatments and brings about challenges in identifying effective combinatorial therapies. In this study, we used chronic active B cell receptor (BCR) signaling in diffuse large B cell lymphoma (DLBCL) as a model system to establish a computational framework to optimize combinatorial therapy in silico. We constructed a detailed kinetic model of the BCR signaling network, which captured the known complex crosstalk between the NFκB, ERK and AKT pathways and multiple feedback loops. Combining this signaling model with a data-derived tumor growth model, we predicted viability responses of many single drug and drug combinations in agreement with experimental data. Under this framework, we exhaustively predicted and ranked the efficacy and synergism of all possible combinatorial inhibitions of eleven currently targetable kinases in the BCR signaling network. Ultimately, our work establishes a detailed kinetic model of the core BCR signaling network and provides the means to explore the large space of possible drug combinations. Major Findings Using chronic active B cell receptor (BCR) signaling in diffuse large B cell lymphoma (DLBCL) as a model system, we developed a kinetic-modeling based computational framework for predicting effective combination therapy in silico. By integrative modeling of signal transduction, drug kinetics and tumor growth, we were able to directly predict drug-induced cell viability responses at various dosages, which were in agreement with published cell line experimental data. We implemented computational screening methods that identified potent and synergistic combinations in silico and validated our independent predictions in vitro. Quick Guide to Equations and Assumptions A kinetic model was constructed for the BCR signaling network according to the following rules. For protein-protein binding interactions, we assumed that this type of reaction were under equilibrium and solved the steady-state level of protein complex analytically in the model, false[ AB false] = 1 ( 1 + K ( [ T A ] + [ T B ] ) ) - false( 1 + K false( false[ T A false] + false[ T B false] false) false) 2 - 4 K 2 [ T A ] [ T B ] 2 K where [TA] and [TB] stand for the total concentration of protein species A and B respectively, [AB] represents the concentration of the protein complex, K is the inverse of the dissociation constant Kd. For kinase catalyzed reactions, we adopted the classic Michaelis-Menten kinetics, j 1 d P d t = 1 k cat [ E ] [ S ] K m + [ S ] where 1dPdt is the rate of catalytic product formation, kcat is the turnover rate, Km is the Michaelis-Menten constant, [E] and [S] are concentration of enzyme and substrate respectively. Each interaction in the BCR signaling network was depicted by corresponding terms according to above rules. The full kinetic model consists of 28 state...

Aberrant activation of the B cell receptor (BCR) signaling network drives survival and proliferation of many B cell malignancies, such as activated B cell like diffuse large B cell lymphoma (ABC-DLBCL). A number of small molecule inhibitors targeting various kinases in the BCR signaling network have been developed. However, clinical application of these targeted agents is facing several challenges such as low response rate and acquired drug resistance. The limited efficacy of single agent targeted therapy is at least partially due to pathway reactivation through crosstalks and compensatory circuits. By simultaneously repressing multiple nodes in the signaling network, combination therapy has the potential to completely extinguish signaling and induce more potent and durable response. The complexity of BCR signaling network makes it difficult to infer which combinations will be effective and synergistic. Given the large number of possible drug combinations, comprehensive experimental screening – including exploration of multiple dosages – is not practically feasible. Computational models of signaling networks that can accurately reconstruct signaling dynamics in silico may represent a useful alternative to experimental screening and trial-and-error experimental investigation. Here, we developed a computational model that integrates signal transduction, tumor growth, and drug kinetics to accurately simulate BCR signaling dynamics and the effect of drug-induced perturbation on signaling output and cell viability. We used this model to predict effective combinatorial therapy in silicoand validated some of these predictions using in vitro experiments. Based on experimentally verified protein-protein interactions, we constructed the first detailed kinetic model of the BCR signaling network covering three major signaling pathways downstream of BCR, namely NFkB, PI3K/AKT and MAPK. The model captured complex crosstalk between these three pathways and multiple feedback loops. Simulated kinase activation time courses under temporal antigen stimulus successfully recapitulated normal BCR signaling dynamics as reported in literature. Using published drug response data in the BCR signaling-dependent ABC-DLBCL cell line TMD8, we trained a tumor growth model which in combination with the kinetic model enabled reliable prediction of viability response of many drug combinations at various dosages. For example, predicted viability response of BTK inhibitor ibrutinib in combination with inhibitors targeting other kinases in the network, e.g. BKM-120 against PI3K, sotrastaurin against PKC-beta closely matched previously published experimental data in TMD8 (r>0.86,p<1e-11). We then sought to identify synergistic drug combinations by simulating viability response at 10x10 virtual dosages for each drug combination and estimating synergism using Bliss independence model. 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. By integrating expression levels of BCR signaling network components assessed by published primary DLBCL RNA-seq data, we simulated patient-specific drug responses and computed the correlation between expression level of specific components with viability response under specific treatments. 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 summary, this study provides a novel approach to computationally optimize combinatorial targeted therapy against aberrant BCR signaling and paves the way for the discovery of effective patient-specific drug combinations. Disclosures Melnick: Bioreference: Scientific Advisory Board, Scientific Advisory Board Other; Calgene: Consultancy; Janssen: Research Funding; Genentech: Speakers Bureau.

The complexity of cancer signaling networks limits the efficacy of most single agent treatments and brings about challenges in identifying effective combinatorial therapies.Using chronic active B cell receptor (BCR) signaling in diffuse large B cell lymphoma (DLBCL) as a model system, we established a computational framework to optimize combinatorial therapy in silico. We constructed a detailed kinetic model of the BCR signaling network, which captured the known complex crosstalk between the NFκB, ERK and AKT pathways; and multiple feedback loops. Combining this signaling model with a data-derived tumor growth model we predicted viability responses of many single drug and drug combinations that are in agreement with experimental data. Under this framework, we exhaustively predicted and ranked the efficacy and synergism of all possible combinatorial inhibitions of eleven currently targetable kinases in the BCR signaling network. Our work established a detailed kinetic model of the core BCR signaling network and provides the means to explore the large space of possible drug combinations. Author SummaryUsing chronic active B cell receptor (BCR) signaling in diffuse large B cell lymphoma(DLBCL) as a model system, we developed a kinetic-modeling based computational framework for predicting effective combination therapy in silico. By integrative modeling of signal transduction, drug kinetics and tumor growth, we were able to directly predict drug-induced cell viability response at various dosages, which were in agreement with published cell line experimental data. We implemented computational screening methods that identified potent and synergistic combinations in silico and validated our independent predictions in vitro.Besides, results acquired from such screening may be specific to the cell line tested, thus lacking general applicability to highly variable primary tumors found in patients.Computational models of signaling networks that can accurately reconstruct signaling dynamics in silico may represent a useful alternative to experimental screening and trial-and-error experimental investigation. Once proven reliable, these models can be used to exhaustively test the efficacy of a large number of single drug and drug combinations by quantifying signaling output under corresponding network perturbations.Even though computational modeling has been widely used to study the dynamics of signaling network in the past decades, the development of cancer signaling models and its application to predicting effective combinatorial therapies is still lacking. Here we demonstrate the feasibility of this approach using chronic activation of B cell receptor (BCR) signaling in diffuse large B cell lymphoma (DLBCL) as a model system. We adopted a systems biology approach and established a computational framework to optimize anti-DLBCL combinatorial therapy in silico. The proposed approach is broadly applicable and can be used for other malignancies driven by aberrantly active signaling pathways. The deregulation of B cell receptor (...