Citations

Abstract Atypical Teratoid/Rhabdoid Tumors (ATRTs) are malignant tumors of the central nervous system in children. They are almost universally characterized by genetic loss of SWI/SNF Related, Matrix Associated, Actin Dependent Regulator of Chromatin Subfamily member B1 (SMARCB1) - a core subunit of the ATP-dependent SWI/SNF chromatin remodeling complex, and dysregulation of lineage restricted enhancers. Here, we show that tumor subgroup-specific upregulation of a distant member of the transforming growth factor-β (TGF-β), bone morphogenic factor (BMF) family of proteins drives insensitivity to NK-mediated cytolysis in vitro and in vivo. Mechanistically, subgroup-specific loss of chromatin occupancy by the RE1 Silencing Transcription Factor (REST) to its consensus RE1 motif, allowed deposition of enhancer-specific histone H3 lysine4 (K4) mono-methylation and histone H3K27 acetylation marks to activate an enhancer-promoter interaction. Restoration of REST binding or BET-domain inhibition resulted in a significant reduction in the enhancer marks and enhancer-promoter interaction and involved histone H3K27me3 deposition at the REST binding site, to facilitate gene silencing and sensitization of ATRTs to NK-mediated cytolysis. Thus, our study has identified a novel function for REST as a modulator of tumor-intrinsic enhancer activity and tumor-extrinsic immune microenvironment. Of equal significance, we highlight a role of REST in recruiting the polycomb repressive complex-2 (PRC2) in gene silencing in ATRTs.

T1-weighted MRI has been extensively used to extract imaging biomarkers and build classification models for differentiating Alzheimer’s disease (AD) patients from healthy controls, but only recently have brain connectome networks derived from diffusion-weighted MRI been used to model AD progression and various stages of disease such as mild cognitive impairment (MCI). MCI, as a possible prodromal stage of AD, has gained intense interest recently, since it may be used to assess risk factors for AD. Little work has been done to combine information from both white matter and gray matter, and it is unknown how much classification power the diffusion-weighted MRI-derived structural connectome could provide beyond information available from T1-weighted MRI. In this paper, we focused on investigating whether diffusion-weighted MRI-derived structural connectome can improve differentiating healthy controls subjects from those with MCI. Specifically, we proposed a novel feature-ranking method to build classification models using the most highly ranked feature variables to classify MCI with healthy controls. We verified our method on two independent cohorts including the second stage of Alzheimer’s Disease Neuroimaging Initiative (ADNI2) database and the National Alzheimer’s Coordinating Center (NACC) database. Our results indicated that 1) diffusion-weighted MRI-derived structural connectome can complement T1-weighted MRI in the classification task; 2) the feature-rank method is effective because of the identified consistent T1-weighted MRI and network feature variables on ADNI2 and NACC. Furthermore, by comparing the top-ranked feature variables from ADNI2, NACC, and combined dataset, we concluded that cross-validation using independent cohorts is necessary and highly recommended.