MEK signaling represents a viable therapeutic vulnerability of KRAS-driven somatic brain arteriovenous malformations.

Brain arteriovenous malformations (bAVMs) are direct connections between arteries and veins that remodel into a complex nidus susceptible to rupture and hemorrhage. Most sporadic bAVMs feature somatic activating mutations within KRAS, and endothelial-specific expression of the constitutively active variant KRASG12D models sporadic bAVM in mice. By leveraging 3D-based micro-CT imaging, we demonstrate that KRASG12D-driven bAVMs arise in stereotypical anatomical locations within the murine brain, which coincide with high endogenous Kras expression. We extend these analyses to show that a distinct variant, KRASG12C, also generates bAVMs in predictable locations. Analysis of 15,000 human patients revealed that, similar to murine models, bAVMs preferentially occur in distinct regions of the adult brain. Furthermore, bAVM location correlates with hemorrhagic frequency. Quantification of 3D imaging revealed that G12D and G12C alter vessel density, tortuosity, and diameter within the mouse brain. Notably, aged G12D mice feature increased lethality, as well as impaired cognition and motor function. Critically, we show that pharmacological blockade of the downstream kinase, MEK, after lesion formation ameliorates KRASG12D-driven changes in the murine cerebrovasculature and may also impede bAVM progression in human pediatric patients. Collectively, these data show that distinct KRAS variants drive bAVMs in similar patterns and suggest MEK inhibition represents a non-surgical alternative therapy for sporadic bAVM.

GPU-Accelerated RSF Level Set Evolution for Large-Scale Microvascular Segmentation.

Microvascular networks are challenging to model because these structures are currently near the diffraction limit for most advanced three-dimensional imaging modalities, including confocal and light sheet microscopy. This makes semantic segmentation difficult, because individual components of these networks fluctuate within the confines of individual pixels. Level set methods are ideally suited to solve this problem by providing surface and topological constraints on the resulting model, however these active contour techniques are extremely time intensive and impractical for terabyte-scale images. We propose a reformulation and implementation of the region-scalable fitting (RSF) level set model that makes it amenable to three-dimensional evaluation using both single-instruction multiple data (SIMD) and single-program multiple-data (SPMD) parallel processing. This enables evaluation of the level set equation on independent regions of the data set using graphics processing units (GPUs), making large-scale segmentation of high-resolution networks practical and inexpensive. We tested this 3D parallel RSF approach on multiple data sets acquired using state-of-the-art imaging techniques to acquire microvascular data, including micro-CT, light sheet fluorescence microscopy (LSFM) and milling microscopy. To assess the performance and accuracy of the RSF model, we conducted a Monte-Carlo-based validation technique to compare results to other segmentation methods. We also provide a rigorous profiling to show the gains in processing speed leveraging parallel hardware. This study showcases the practical application of the RSF model, emphasizing its utility in the challenging domain of segmenting large-scale high-topology network structures with a particular focus on building microvascular models.