Targeting the Ubiquitin-Proteasome System Using the UBA1 Inhibitor TAK-243 is a Potential Therapeutic Strategy for Small-Cell Lung Cancer

PURPOSE

Small cell lung cancer (SCLC) is an aggressive disease with an overall 5-year survival rate of less than 10%. Treatment for SCLC with cisplatin/etoposide chemotherapy (C/E) ± radiotherapy has changed modestly over several decades. The ubiquitin-proteasome system is an underexplored therapeutic target for SCLC. We preclinically evaluated TAK-243, a first-in-class small molecule E1 inhibitor against UBA1.

EXPERIMENTAL DESIGN

We assessed TAK-243 in 26 SCLC cell-lines as monotherapy and combined with C/E, the PARP-inhibitor, olaparib, and with radiation using cell viability assays. We interrogated TAK-243 response with gene expression to identify candidate biomarkers. We evaluated TAK-243 alone and in combination with olaparib or radiotherapy with SCLC patient-derived xenografts (PDX).

RESULTS

Most SCLC cell lines were sensitive to TAK-243 monotherapy (EC50 median 15.8 nmol/L; range 10.2 nmol/L-367.3 nmol/L). TAK-243 sensitivity was associated with gene-sets involving the cell cycle, DNA and chromatin organization, and DNA damage repair, while resistance associated with cellular respiration, translation, and neurodevelopment. These associations were also observed in SCLC PDXs. TAK-243 synergized with C/E and olaparib in vitro across sensitive and resistant SCLC cell lines. Considerable TAK-243-olaparib synergy was observed in an SCLC PDX resistant to both drugs individually. TAK-243 radiosensitization was also observed in an SCLC PDX.

CONCLUSIONS

TAK-243 displays efficacy in SCLC preclinical models. Enrichment of gene sets is associated with TAK-243 sensitivity and resistance. TAK-243 exhibits synergy when combined with genotoxic therapies in cell lines and PDXs. TAK-243 is a potential therapeutic strategy to improve SCLC patient outcomes, both as a single agent and in combination with existing therapies.

Mixed-integer nonlinear optimisation approach to coarse-graining biochemical networks

Quantitative modelling and analysis of biochemical networks is challenging because of the inherent complexities and nonlinearities of the system and the limited availability of parameter values. Even if a mathematical model of the network can be developed, the lack of large-scale good-quality data makes accurate estimation of a large number of parameters impossible. Hence, coarse-grained models (CGMs) consisting of essential biochemical mechanisms are more suitable for computational analysis and for studying important systemic functions. The central question in constructing a CGM is which mechanisms should be deemed 'essential' and which can be ignored. Also, how should parameter values be defined when data are sparse? A mixed-integer nonlinear-programming (MINLP) based optimisation approach to coarse-graining is presented. Starting with a detailed biochemical model with associated computational details (reaction network and mathematical description) and data on the biochemical system, the structure and the parameters of a CGM can be determined simultaneously. In this optimisation problem, the authors use a genetic algorithm to simultaneously identify parameter values and remove unimportant reactions. The methodology is exemplified by developing two CGMs for the GTPase-cycle module of M1 muscarinic acetylcholine receptor, Gq, and regulator of G protein signalling 4 [RGS4, a GTPase-activating protein (GAP)] starting from a detailed model of 48 reactions. Both the CGMs have only 17 reactions, fit experimental data well and predict, as does the detailed model, four limiting signalling regimes (LSRs) corresponding to the extremes of receptor and GAP concentration. The authors demonstrate that coarse-graining, in addition to resulting in a reduced-order model, also provides insights into the mechanisms in the network. The best CGM obtained for the GTPase cycle also contains an unconventional mechanism and its predictions explain an old problem in pharmacology, the biphasic (bell-shaped) response to certain drugs. The MINLP methodology is broadly applicable to larger and complex (dense) biochemical modules.

Reduced-order modelling of biochemical networks: application to the GTPase-cycle signalling module

Biochemical systems embed complex networks and hence development and analysis of their detailed models pose a challenge for computation. Coarse-grained biochemical models, called reduced-order models (ROMs), consisting of essential biochemical mechanisms are more useful for computational analysis and for studying important features of a biochemical network. The authors present a novel method to model-reduction by identifying potentially important parameters using multidimensional sensitivity analysis. A ROM is generated for the GTPase-cycle module of m1 muscarinic acetylcholine receptor, Gq, and regulator of G-protein signalling 4 (a GTPase-activating protein or GAP) starting from a detailed model of 48 reactions. The resulting ROM has only 17 reactions. The ROM suggested that complexes of G-protein coupled receptor (GPCR) and GAP--which were proposed in the detailed model as a hypothesis--are required to fit the experimental data. Models previously published in the literature are also simulated and compared with the ROM. Through this comparison, a minimal ROM, that also requires complexes of GPCR and GAP, with just 15 parameters is generated. The proposed reduced-order modelling methodology is scalable to larger networks and provides a general framework for the reduction of models of biochemical systems.