Chueh, A. C. | bioRxiv Science

bioRxiv2024

Feed-forward stimulation of CAMK2 by the oncogenic pseudokinase PEAK1 generates a therapeutically "actionable" signalling axis in triple negative breast cancer

The PEAK family of pseudokinases, comprising PEAK1-3, are signalling scaffolds that play oncogenic roles in several poor prognosis human cancers, including triple negative breast cancer (TNBC). However, therapeutic targeting of pseudokinases is challenging due to their lack of catalytic activity. To address this, we screened for PEAK1 effectors by affinity purification and mass spectrometry, identifying calcium/calmodulin-dependent protein kinase 2 (CAMK2)D and CAMK2G. PEAK1 promoted CAMK2D/G activation in TNBC cells via a novel feed-forward mechanism involving PEAK1/PLC{gamma}1/Ca2+ signalling and direct binding via a consensus CAMK2 interaction motif in the PEAK1 N-terminus. In turn, CAMK2 phosphorylated PEAK1 to enhance association with PEAK2, which is critical for PEAK1 oncogenic signalling. To achieve pharmacologic targeting of PEAK1/CAMK2, we repurposed RA306, a second generation CAMK2 inhibitor under pre-clinical development for treatment of cardiovascular disease. RA306 demonstrated on-target activity against CAMK2 in TNBC cells and inhibited PEAK1-enhanced migration and invasion in vitro. Moreover, RA306 significantly attenuated TNBC xenograft growth and blocked metastasis in a manner mirrored by CRISPR-mediated PEAK1 ablation. Overall, these studies establish PEAK1 as a critical cell signalling nexus, identify a novel mechanism for regulation of Ca2+ signalling and its integration with tyrosine kinase signals, and identify CAMK2 as a therapeutically actionable target downstream of PEAK1.

cancer biology↗

bioRxiv2021

Integrative Modelling of Signalling Network Dynamics Identifies Cell Type-selective 1 Therapeutic Strategies for FGFR4-driven Cancers

Oncogenic FGFR4 signalling represents a potential therapeutic target in various cancer types, including triple negative breast cancer (TNBC) and hepatocellular carcinoma (HCC). However, resistance to FGFR4 single-agent therapy remains a major challenge, emphasizing the need for effective combinatorial treatments. Our study sought to develop a comprehensive computational model of FGFR4 signalling and provide network-level insights into resistance mechanisms driven by signalling dynamics. Our integrated approach, combining computational network modelling with experimental validation, uncovered potent AKT reactivation following FGFR4 targeting in the TNBC cell line MDA-MB-453. By systematically simulating the model to analyse the effects of co-targeting specific network nodes, we were able to predict, and subsequently confirm through experimental validation, the strong synergy of co-targeting FGFR4 and AKT or specific ErbB kinases, but not PI3K. Incorporating protein expression data from hundreds of cancer cell lines, we then adapted our model to diverse cellular contexts. This revealed that while AKT rebound is common, it is not a general phenomenon. ERK reactivation, for example, occurs in certain cell types, including the FGFR4-driven HCC cell line Hep3B, where there is a synergistic effect of co-targeting FGFR4 and MEK, but not AKT. In summary, our study offers key insights into drug-induced network remodelling and the role of protein expression heterogeneity in targeted therapy responses. We underscore the utility of computational network modelling for designing cell type-selective combination therapies and enhancing precision cancer treatment. SignificanceThis study underscores the potential of computational predictive modelling in deciphering mechanisms of cancer cell resistance to targeted therapies and in designing more effective, cancer type-specific combination treatments.

systems biology↗