Multiplex imaging of breast cancer lymph node metastases identifies prognostic single-cell populations independent of clinical classifiers.

Although breast cancer mortality is largely caused by metastasis, clinical decisions are based on analysis of the primary tumor and on lymph node involvement but not on the phenotype of disseminated cells. Here, we use multiplex imaging mass cytometry to compare single-cell phenotypes of primary breast tumors and matched lymph node metastases in 205 patients. We observe extensive phenotypic variability between primary and metastatic sites and that disseminated cell phenotypes frequently deviate from the clinical disease subtype. We identify single-cell phenotypes and spatial organizations of disseminated tumor cells that are associated with patient survival and a weaker survival association for high-risk phenotypes in the primary tumor. We show that p53 and GATA3 in lymph node metastases provide prognostic information beyond clinical classifiers and can be measured with standard methods. Molecular characterization of disseminated tumor cells is an untapped source of clinically applicable prognostic information for breast cancer.

An Engineered Paper-Based 3D Coculture Model of Pancreatic Cancer to Study the Impact of Tissue Architecture and Microenvironmental Gradients on Cell Phenotype.

The spatial configuration of cells in the tumor microenvironment (TME) affects both cancer and fibroblast cell phenotypes contributing to the clinical challenge of tumor heterogeneity and therapeutic resistance. This is a particular challenge in stroma-rich pancreatic ductal adenocarcinoma (PDAC). Here, a versatile system is described to study the impact of tissue architecture on cell phenotype using PDAC as a model system. This fully human system encompassing both primary pancreatic stellate cells and primary organoid cells using the TRACER platform to allow the creation of user-defined TME architectures that have been inferred from clinical PDAC samples and are analyzed by CyTOF to characterize cells extracted from the system. High dimensional characterization using CyTOF demonstrates that tissue architecture leads to distinct hypoxia and proliferation gradients. Furthermore, phenotypic markers for both cell types are also graded in ways that cannot be explained by either hypoxia or coculture alone. This demonstrates the importance of using complex models encompassing cancer cells, stromal cells, and allowing control over architecture to explore the impact of tissue architecture on cell phenotype. It is anticipated that this model will help decipher how tissue architecture and cell interactions regulate cell phenotype and hence cellular and tissue heterogeneity.

Genetic perturbations go spatial.

Tissue-tumor interactivity is the culmination of cell intrinsic features and their extrinsic interactions with the environment. Recently in Cell, Dhainaut and Rose et al. established a strategy to track pooled CRISPR-modified cells in vivo using protein barcodes (Pro-Codes) and measure their impact on the tumor microenvironment through multiplexed imaging and spatial transcriptomics of intact tissues.1.

Simultaneous Multiplexed Imaging of mRNA and Proteins with Subcellular Resolution in Breast Cancer Tissue Samples by Mass Cytometry.

To build comprehensive models of cellular states and interactions in normal and diseased tissue, genetic and proteomic information must be extracted with single-cell and spatial resolution. Here, we extended imaging mass cytometry to enable multiplexed detection of mRNA and proteins in tissues. Three mRNA target species were detected by RNAscope-based metal in situ hybridization with simultaneous antibody detection of 16 proteins. Analysis of 70 breast cancer samples showed that HER2 and CK19 mRNA and protein levels are moderately correlated on the single-cell level, but that only HER2, and not CK19, has strong mRNA-to-protein correlation on the cell population level. The chemoattractant CXCL10 was expressed in stromal cell clusters, and the frequency of CXCL10-expressing cells correlated with T cell presence. Our flexible and expandable method will allow an increase in the information content retrieved from patient samples for biomedical purposes, enable detailed studies of tumor biology, and serve as a tool to bridge comprehensive genomic and proteomic tissue analysis.