Katsumi, Y. | bioRxiv Science

bioRxiv2021

Identification and validation of a non-genetically encoded vulnerability to XPO1 inhibition in malignant rhabdoid tumors - expanding patient-driven discovery beyond the Nof1.

Malignant rhabdoid tumors (MRTs) are rare, aggressive pediatric solid tumors, characterized by a 22q11 deletion that inactivates the SMARCB1 gene. Outcomes remain poor despite multimodality treatment. MRTs are among the most genomically stable cancers and lack therapeutically targetable genetic mutations. We utilized metaVIPER, an extension of the Virtual Inference of Protein-activity by Enriched Regulon (VIPER) algorithm, to computationally infer activated druggable proteins in the tumor of an eight month old patient and then expanded the analysis to TCGA and TARGET cohorts. In vitro studies were performed on a panel of MRT and atypical teratoid/rhabdoid tumor cell lines. Two patient-derived xenograft (PDX) mouse models of MRT were used for in vivo efficacy studies. MetaVIPER analysis from the patients tumor identified significantly high inferred activity of nuclear export protein Exportin-1 (XPO1). Expanded metaVIPER analysis of TCGA and TARGET cohorts revealed consistent elevations in XPO1 inferred activity in MRTs compared to other cancer types. All MRT cell lines demonstrated baseline activation of XPO1. MRT cell lines demonstrated in vitro sensitivity to the XPO1 inhibitor, selinexor which led to cell cycle arrest and induction of apoptosis. Targeted inhibition of XPO1 in patient-derived xenograft models of MRT using selinexor resulted in abrogation of tumor growth. Selinexor demonstrates efficacy in preclinical models of MRT. These results support investigation of selinexor in a phase II study in children with MRT and illustrate the importance of an N-of-1 approach in driving discovery beyond the single patient. Statement of Translational RelevanceWe describe the patient-driven discovery of XPO1 activation as a non-genetically encoded vulnerability in MRTs. The application of metaVIPER analysis to tumors lacking actionable oncogenic alterations represents a novel approach for identifying potential therapeutic targets and biomarkers of response. Our preclinical validation of selinexor confirms XPO1 inhibition as a promising therapeutic strategy for the treatment of MRT.

cancer biology↗

bioRxiv2021

Functional connectivity gradients as a common neural architecture for predictive processing in the human brain

Predictive processing is emerging as a common computational hypothesis to account for diverse psychological functions subserved by a brain, providing a systems-level framework for characterizing structure-function relationships of its distinct substructures. Here, we contribute to this framework by examining gradients of functional connectivity as a low dimensional spatial representation of functional variation in the brain and demonstrating their computational implications for predictive processing. Specifically, we investigated functional connectivity gradients in the cerebral cortex, the cerebellum, and the hippocampus using resting-state functional MRI data collected from large samples of healthy young adults. We then evaluated the degree to which these structures share common principles of functional organization by assessing the correspondence of their gradients. We show that the organizing principles of these structures primarily follow two functional gradients consistent with the existing hierarchical accounts of predictive processing: A model-error gradient that describes the flow of prediction and prediction error signals, and a model-precision gradient that differentiates regions involved in the representation and attentional modulation of such signals in the cerebral cortex. Using these gradients, we also demonstrated triangulation of functional connectivity involving distinct subregions of the three structures, which allows characterization of distinct ways in which these structures functionally interact with each other, possibly subserving unique and complementary aspects of predictive processing. These findings support the viability of computational hypotheses about the functional relationships between the cerebral cortex, the cerebellum, and the hippocampus that may be instrumental for understanding the brains dynamics within its large-scale predictive architecture.

neuroscience↗