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Venous invasion is three times more common in pancreatic cancer than it is in other major cancers of the gastrointestinal tract, and venous invasion may explain why pancreatic cancer is so deadly. To characterize the patterns of venous invasion in pancreatic cancer, 52 thick slabs (up to 5 mm) of tissue were harvested from 52 surgically resected human ductal adenocarcinomas, cleared with a modified iDISCO method, and labeled with fluorescent-conjugated antibodies to cytokeratin 19, desmin, CD31, p53 and/or e-cadherin. Labeled three-dimensional (3D) pancreas cancer tissues were visualized with confocal laser scanning or light sheet microscopy. Multiple foci of venous and even arterial invasion were visualized. Venous invasion was detected more often in 3D (88%, 30/34 cases) than in conventional 2D slide evaluation (75%, 25/34 cases, P < 0.001). 3D visualization revealed pancreatic cancer cells crossing the walls of veins at multiple points, often at points where preexisting capillary structures bridge the blood vessels. The neoplastic cells often retained a ductal morphology (cohesive cells forming tubes) as they progressed from a stromal to intravenous location. Although immunolabeling with antibodies to e-cadherin revealed focal loss of expression at the leading edges of the cancers, the neoplastic cells within veins expressed e-cadherin and formed well-oriented glands. We conclude that venous invasion is almost universal in pancreatic cancer, suggesting that even surgically resectable PDAC has access to the venous spaces and thus the ability to disseminate widely. Furthermore, we observe that sustained epithelial-mesenchymal transition is not required for venous invasion in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest forms of cancer. Accumulating evidence indicates the tumor microenvironment is highly associated with tumorigenesis through regulation of cellular physiology, signaling systems, and gene expression profiles of cancer cells. Yet the mechanisms by which the microenvironment evolves from normal pancreas architecture to precursor lesions and invasive cancer is poorly understood. Obtaining high-content and high-resolution information from a complex tumor microenvironment in large volumetric landscapes represents a key challenge in the field of cancer biology. To address this challenge, we established a novel method to reconstruct three-dimensional (3D) centimeter-scale tissues containing billions of cells from serially sectioned histological samples, utilizing deep learning approaches to recognize eight distinct tissue subtypes from hematoxylin and eosin stained sections at micrometer and single-cell resolution. Using samples from a range of normal, precancerous, and invasive pancreatic cancer tissue, we map in 3D modes of cancer invasion in the tumor microenvironment, and emphasize the need for further 3D quantification of biological systems.

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Spatial transcriptomics (ST) is a powerful approach for cancers molecular and cellular characterization. Pancreatic intraepithelial neoplasia (PanIN) is a pancreatic ductal adenocarcinoma (PDAC) premalignancy diagnosed from formalin-fixed and paraffin-embedded (FFPE) specimens limiting single-cell based investigations. We developed a new FFPE ST analysis protocol for PanINs complemented with novel transfer learning approaches. The first transfer learning approach, to assign cell types to ST spots and integrate the transcriptional signatures, shows that PanINs are surrounded by PDAC cancer associated fibroblasts (CAFs) subtypes, including the rare antigen-presenting CAFs. Furthermore, most PanINs are of the classical PDAC subtype while one sample expresses cancer stem cell markers. A second transfer learning approach, to integrate ST PanIN data with PDAC scRNA-seq data, identifies a shift between inflammatory and proliferative signaling as PanINs progress to PDAC. Our data support a model of inflammatory signaling and PanIN-CAF interactions promoting premalignancy progression and PDAC immunosuppressive characteristics.

Uniquely among mammalian organs, skin is capable of marked size change in adults, yet the mechanisms underlying this notable capacity are unclear. Here, we use a system of controlled tissue expansion in mice to uncover cellular and molecular determinants of skin growth. Through machine learning–guided three-dimensional tissue reconstruction, we capture morphometric changes in growing skin. We find that most growth is driven by the proliferation of the epidermis in response to mechanical tension, with more limited changes in dermal and subdermal compartments. Epidermal growth is achieved through preferential activation and differentiation of Lgr6 + stem cells of the epidermis, driven in part by the Hippo pathway. By single-cell RNA sequencing, we uncover further changes in mechanosensitive and metabolic pathways underlying growth control in the skin. These studies point to therapeutic strategies to enhance skin growth and establish a platform for understanding organ size dynamics in adult mammals.