Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes.

Computational methods have made substantial progress in improving the accuracy and throughput of pathology workflows for diagnostic, prognostic, and genomic prediction. Still, lack of interpretability remains a significant barrier to clinical integration. We present an approach for predicting clinically-relevant molecular phenotypes from whole-slide histopathology images using human-interpretable image features (HIFs). Our method leverages >1.6 million annotations from board-certified pathologists across >5700 samples to train deep learning models for cell and tissue classification that can exhaustively map whole-slide images at two and four micron-resolution. Cell- and tissue-type model outputs are combined into 607 HIFs that quantify specific and biologically-relevant characteristics across five cancer types. We demonstrate that these HIFs correlate with well-known markers of the tumor microenvironment and can predict diverse molecular signatures (AUROC 0.601-0.864), including expression of four immune checkpoint proteins and homologous recombination deficiency, with performance comparable to 'black-box' methods. Our HIF-based approach provides a comprehensive, quantitative, and interpretable window into the composition and spatial architecture of the tumor microenvironment.

A modular approach to creating large engineered cartilage surfaces.

Native articular cartilage has limited capacity to repair itself from focal defects or osteoarthritis. Tissue engineering has provided a promising biological treatment strategy that is currently being evaluated in clinical trials. However, current approaches in translating these techniques to developing large engineered tissues remains a significant challenge. In this study, we present a method for developing large-scale engineered cartilage surfaces through modular fabrication. Modular Engineered Tissue Surfaces (METS) uses the well-known, but largely under-utilized self-adhesion properties of de novo tissue to create large scaffolds with nutrient channels. Compressive mechanical properties were evaluated throughout METS specimens, and the tensile mechanical strength of the bonds between attached constructs was evaluated over time. Raman spectroscopy, biochemical assays, and histology were performed to investigate matrix distribution. Results showed that by Day 14, stable connections had formed between the constructs in the METS samples. By Day 21, bonds were robust enough to form a rigid sheet and continued to increase in size and strength over time. Compressive mechanical properties and glycosaminoglycan (GAG) content of METS and individual constructs increased significantly over time. The METS technique builds on established tissue engineering accomplishments of developing constructs with GAG composition and compressive properties approaching native cartilage. This study demonstrated that modular fabrication is a viable technique for creating large-scale engineered cartilage, which can be broadly applied to many tissue engineering applications and construct geometries.