Project 2 Human Tumor Analysis

SUMMARY/ABSTRACT: PROJECT 2 Metastasis is the primary cause of cancer-related death. Our recent evidence establishes a new paradigm, “lymph node tolerization,” in which the lymph node (LN) tissue environment creates a conditioned, systemic metastatic state across tissues and organs. This proposal aims to identify and characterize unidentified

changes that create tolerize LNs, leading to the discovery of new biomarkers, drug targets, and treatment strategies. Using a spatial systems biology approach that combines multiplexed in situ imaging with single-cell RNA sequencing technologies, we will characterize tumor microenvironments across the host metastatic

ecosystem, in HNSCC and LUAD. We hypothesize that spatially resolved stromal-immune interactions in LNs together with stromal-malignant properties in a primary tumor set the stage for metastasis. We will explore mechanistic properties of these processes using organoid models of human-derived cells and mouse models.

In Aim 1, we will use using single-cell spatial proteomics to identify a pro-metastatic microenvironment in uninvolved LNs of HNSCC and LUAD cancer patients by reconstructing and comparing spatially resolved tumor-stroma-immune colocalization patterns of patient-derived uninvolved LNs, involved LNs, and primary

tumors. These analyses will compare cell types and cell-cell co-localization patterns in the tissue environments of N0 and N+ patients. In addition, we will probe cell composition and colocalization patterns together with extracellular matrix architecture to ascertain the role of ECM in establishing and maintaining the pro-metastatic

microenvironment in uninvolved LNs. In Aim 2, we will discover cell-cell interactions in uninvolved LNs that predispose them to colonization by malignant cells by reconstructing and comparing cell-cell interactions uninvolved LNs, involved LNs, and primary tumors inferred through integrative analysis of spatial information

with single-cell RNA-sequencing. We will develop novel biocomputational approaches to integrate spatial features from CODEX with single cell RNA sequencing data to identify proximal cell-cell interactions among tumor-stromal and stromal-immune cell types associated with LN metastases. We will then evaluate selected

cell-cell interactions in organoid models of human-derived cells, including perturbation with CRISPR-facilitated gene editing to reveal mechanistic insights. In Aim 3, we will predict spatiotemporal progression in tumor- stromal and stromal-immune colocalization patterns through spatially-aware Markov modeling and relate these

patterns to changes in human-derived LNs and primary tumors associated with metastatic progression. We will build a spatially aware Markov model using spatially resolved time series data generated using tumor-stromal and immune-stromal organoids generated from human-derived cells, to identify spatial motifs of tumor-stromal

and immune-stromal spatial patterning. This approach will illuminate spatiotemporal features of human LN metastasis, toward defining mechanistic features of the metastatic cascade.