The pseudokinase NRBP1 activates Rac1/Cdc42 via P-Rex1 to drive oncogenic signalling in triple-negative breast cancer

Yang, Xue; Cruz, Miguel I.; Nguyen, Elizabeth V.; Schittenhelm, Ralf B.; Luu, Jennii; Cowley, Karla J.; Shin, Sung-Young; Nguyen, Lan K.; Sian, Terry C.C. Lim Kam; Clark, Kimberley C.; Simpson, Kaylene J.; Ma, Xiuquan; Daly, Roger J.

doi:10.1038/s41388-023-02594-w

Cited by 9 publications

(7 citation statements)

References 46 publications

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“…Previously, we demonstrated that PEAK1 gene knock-out significantly inhibited growth of MDA-MB-231 TNBC xenografts, as well as experimental metastasis of these cells in a tail vein injection assay 7 . To test RA306, we utilized the highly metastatic variant of this cell line, MDA-MB-231-HM 35 , so that we could assay metastatic spread from the primary tumour. In addition, we generated PEAK1-deficient MDA-MB-231-HM cells via CRISPR editing (Supp.…”

Section: Resultsmentioning

confidence: 99%

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

Yang,

Ma,

Zhao

et al. 2024

Preprint

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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/PLCg1/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.

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Section: Resultsmentioning

confidence: 99%

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

Yang,

Ma,

Zhao

et al. 2024

Preprint

Self Cite

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“…Accordingly, we assumed positive regulation of Rac1 by Cdc42 in our model. However, the opposite regulation (from Rac1 to Cdc42) cannot be excluded because bidirectional crosstalk is possible through the interaction of Rac1 and Cdc42 with their common GEFs [ 10 , 59 ].…”

Section: Resultsmentioning

confidence: 99%

Spatiotemporal Coordination of Rac1 and Cdc42 at the Whole Cell Level during Cell Ruffling

Hladyshau

Stoop

Kamada

et al. 2023

Cells

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Rho-GTPases are central regulators within a complex signaling network that controls cytoskeletal organization and cell movement. The network includes multiple GTPases, such as the most studied Rac1, Cdc42, and RhoA, along with their numerous effectors that provide mutual regulation through feedback loops. Here we investigate the temporal and spatial relationship between Rac1 and Cdc42 during membrane ruffling, using a simulation model that couples GTPase signaling with cell morphodynamics and captures the GTPase behavior observed with FRET-based biosensors. We show that membrane velocity is regulated by the kinetic rate of GTPase activation rather than the concentration of active GTPase. Our model captures both uniform and polarized ruffling. We also show that cell-type specific time delays between Rac1 and Cdc42 activation can be reproduced with a single signaling motif, in which the delay is controlled by feedback from Cdc42 to Rac1. The resolution of our simulation output matches those of time-lapsed recordings of cell dynamics and GTPase activity. Our data-driven modeling approach allows us to validate simulation results with quantitative precision using the same pipeline for the analysis of simulated and experimental data.

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“…New discoveries regarding the mechanisms [35,36], biomarkers [12], and expression patterns [37] of breast cancer are emerging with the use of intrinsic subtyping tools, speci cally the commonly used PAM50 and AIMS models. However, misclassi cation of intrinsic subtypes could result in decreased research accuracy.…”

Section: Discussionmentioning

confidence: 99%

The Importance of Development and Application of Subtyping Models for Breast Cancer on the Same Platform

2023

Preprint

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Background Intrinsic subtypes have played an important role in breast cancer research. The accuracy of breast cancer research findings depends on the accuracy of intrinsic subtype classification of breast cancer samples. Popular intrinsic subtype models, such as PAM50 and AIMS, were mainly developed on Microarray but are widely used in other platforms. The transferability of these models to RNA-seq and other platforms has rarely been studied. We aim to assess the effectiveness of popular intrinsic subtype models on RNA-seq data and improve the accuracy of breast cancer subtyping on this platform.Methods Assuming that one breast cancer sample only belongs to one subtype irrespective of the expression measuring platform, we assessed the consistency of subtype predictions of PAM50 and AIMS for TCGA Microarray and RNA-seq data from the same samples using Kappa statistic. We also built 12 models using common and intrinsic genes on both Microarray and RNA-seq data, and evaluated their performance under the same assumption.Results Both PAM50 and AIMS failed to produce consistent predictions for Microarray and RNA-seq data from the same samples, with Kappa values of 0.33 and 0.21, respectively. Data normalization improved prediction consistency but introduced false classification. Microarray-developed models showed low prediction consistency when applied to both Microarray and RNA-seq profiles of the same samples. However, prediction consistency between predictions of RNA-seq developed model predicting RNA-seq data and Microarray developed model predicting Microarray data was high with most Kappa values above 0.85.Conclusion Great caution should be taken when using PAM50 and AIMS for the subtyping of breast cancer RNA-seq data. Breast cancer subtyping models should be developed and applied on the same platform, using unnormalized expression data for accurate subtype prediction purpose.

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The pseudokinase NRBP1 activates Rac1/Cdc42 via P-Rex1 to drive oncogenic signalling in triple-negative breast cancer

Cited by 9 publications

References 46 publications

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

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

Spatiotemporal Coordination of Rac1 and Cdc42 at the Whole Cell Level during Cell Ruffling

The Importance of Development and Application of Subtyping Models for Breast Cancer on the Same Platform

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