(163e) Probing the Spatial Proteomics of Gamma-Secretase/Notch Signaling in Breast Cancer: Coupling Correlative Microscopy with Spatial-Reaction Diffusion Modeling

The overall objective of this research is to investigate the spatiotemporal attributes of the gamma-secretase/Notch pathway and its inhibition in triple negative breast cancer (TNBC) to provide a molecular basis for therapeutic development. The mortality from TNBC in African American (AA) women is higher than in White American (WA) or European-American (EA) women. Recent studies have indicated that Notch signaling plays an important role in this racial disparity. Notch signaling has been associated with tumor transformation, proliferation, survival, angiogenesis and metastasis. We have shown that gamma-secretase cleavage of Notch is highly regulated in breast cancer cells (Villa et al (2014) 8, 1077-1092) and differs significantly in the 2D vs 3D cellular context. Our other recent studies have shown that subtle changes in membrane lipid composition affect the distribution of Notch substrates and gamma-secretase and thus enzymatic activity(Barros et al. (2020) Langmuir 36, 23, 6569–6579). At this point, ethnic differences in gamma-secretase/Notch signaling and its role in the higher incidence of poorer outcomes of AA from TNBC are poorly understood. The major question is exactly how the racial disparities would manifest. The objective of these studies is to expand the scope of our research by bringing in a systems biological modeling component to help discern the molecular underpinnings of this disparity. In essence, in the context of molecular observables of gamma-secretase/notch signaling, we aim to use modeling to search for spatial proteomics-based biomarkers to be used to guide therapeutic intervention.

We hypothesize that the characterization of the overall and intracellular spatial proteomics of g-secretase/Notch and comparison with in silico system biology models could yield molecular signatures of racial disparities. To interpret the underlying biophysical and kinetic underpinnings we propose to compare image-based spatial proteomics of TNBCs to spatial stochastic simulations. These models contain the gamma-secretase/Notch signaling network coded spatially onto cell structure grids obtained from the 3D confocal and EM imagery of the plasma membrane, nucleus, and other relevant intracellular structures. These models can predict the spatial distributions of the protein molecular observables in the 3D imagery, such as the build-up or depletion from cell regions under different gamma-secretase inhibitor (GSI) levels, using known GSI and enzymatic kinetic parameters. This method, in essence, produces generative models of image-based spatial proteomics that can be compared with experimental data. The major advance is that these underlying models incorporate what is known about the biophysics of gamma-secretase/Notch, in the form of membrane protein (MP) diffusivities of the enzyme and cleavage fragments that is dependent on the lipid microenvironment and also MP phase separation/localization, such as obtained in our Confocal Laser Scanning Microscopy/Atomic force microscopy (CLSM/AFM) studies of gamma-secretase/notch in supported lipid bilayers. Another important aspect of the models is that the enzyme/inhibition kinetics are integrated and the dependence of the simulated spatial proteomics on these parameters can be characterized.

This work constitutes the first-ever examination of the image-based and simulated spatial proteomics of TNBCs in the context of racial disparities in Notch and gamma-secretase pathways. We believe that this work could ultimately lead to the basis for therapeutic strategies based on gamma-secretase inhibition characteristics in the overall and spatial proteome.