Michael P. Snyder

Transformative technologies are enabling the construction of three-dimensional maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible three-dimensional molecular and cellular atlas of the human body, in health and under various disease conditions.

VISTA is a key immune checkpoint receptor under investigation for cancer immunotherapy; however, its signaling mechanisms remain unclear. Here we identify a conserved four amino acid (NPGF) intracellular motif in VISTA that suppresses cell proliferation by constraining cell-intrinsic growth receptor signaling. The NPGF motif binds to the adapter protein NUMB and recruits Rab11 endosomal recycling machinery. We identify and characterize a class of triple-negative breast cancers with high VISTA expression and low proliferative index. In tumor cells with high VISTA levels, the NPGF motif sequesters NUMB at endosomes, which interferes with epidermal growth factor receptor (EGFR) trafficking and signaling to suppress tumor growth. These effects do not require canonical VISTA ligands, nor a functioning immune system. As a consequence of VISTA expression, EGFR receptor remains abnormally phosphorylated and cannot propagate ligand-induced signaling. Mutation of the VISTA NPGF domain reverts VISTA-induced growth suppression in multiple breast cancer mouse models. These results define a mechanism by which VISTA represses NUMB to control malignant epithelial cell growth and signaling. They also define distinct intracellular residues that are critical for VISTA-induced cell-intrinsic signaling that could be exploited to improve immunotherapy.

Abstract Motivation Data normalization is an important step in processing proteomics data generated in mass spectrometry (MS) experiments, which aims to reduce sample-level variation and facilitate comparisons of samples. Previously published methods for normalization primarily depend on the assumption that the distribution of protein expression is similar across all samples. However, this assumption fails when the protein expression data is generated from heterogenous samples, such as from various tissue types. This led us to develop a novel data-driven method for improved normalization to correct the systematic bias meanwhile maintaining underlying biological heterogeneity. Methods To robustly correct the systematic bias, we used the density-power-weight method to down-weigh outliers and extended the one-dimensional robust fitting method described in the previous work of (Windham, 1995, Fujisawa and Eguchi, 2008) to our structured data. We then constructed a robustness criterion and developed a new normalization algorithm, called RobNorm. Results In simulation studies and analysis of real data from the genotype-tissue expression (GTEx) project, we compared and evaluated the performance of RobNorm against other normalization methods. We found that the RobNorm approach exhibits the greatest reduction in systematic bias while maintaining across-tissue variation, especially for datasets from highly heterogeneous samples. Availability https://github.com/mwgrassgreen/RobNorm Contact huatang@stanford.edu and mpsnyder@stanford.edu

Abstract Polygenic risk score (PRS) serves as a valuable tool for predicting the genetic risk of complex human diseases for individuals, playing a pivotal role in advancing precision medicine. Traditional PRS methods, predominantly following a linear structure, often fall short in capturing the intricate relationships between genotype and phenotype. We present PRS-Net, an interpretable deep learning-based framework designed to effectively model the nonlinearity of biological systems for enhanced disease prediction and biological discovery. PRS-Net begins by deconvoluting the genomewide PRS at the single-gene resolution, and then it encapsulates gene-gene interactions for genetic risk prediction leveraging a graph neural network, thereby enabling the characterization of biological nonlinearity underlying complex diseases. An attentive readout module is specifically introduced into the framework to facilitate model interpretation and biological discovery. Through extensive tests across multiple complex diseases, PRS-Net consistently outperforms baseline PRS methods, showcasing its superior performance on disease prediction. Moreover, the interpretability of PRS-Net has been demonstrated by the identification of genes and gene-gene interactions that significantly influence the risk of Alzheimer’s disease and multiple sclerosis. In summary, PRS-Net provides a potent tool for parallel genetic risk prediction and biological discovery for complex diseases.