Visual Analytics for Exploration and Hypothesis Generation Using Highly MultiplexedSpatial Data of Tissues and Tumors

PROJECT SUMMARY The recent development of highly multiplexed subcellular resolution tissue imaging promises to accelerate research into tumor initiation, progression, and immune surveillance and ultimately aid in the discovery of new biomarkers usable in a clinical setting. In parallel, spatially resolved measurement of transcript and small

molecule abundances is achieving near single-cell resolution. NCI programs such as the Human Tumor Atlas Network (HTAN) are capitalizing on these developments to create public data repositories (“Tissue Atlases”) similar in scope and ambition to The Cancer Genome Atlas (TCGA). The greatest barriers to making such data

routinely accessible to basic and translational cancer biologists lie not in data collection but rather data visualization and analysis. Existing software tools designed for cultured cell experiments or hematoxylin and eosin (H&E) based digital pathology are inadequate for high-plex data applications, and emerging tools do not

meet the needs of either low-cost and efficient data sharing or sophisticated multi-modal machine learning from diverse data. This Informatics Technology for Cancer Research (ITCR) project will therefore develop, harden, and test standards-compliant software tools that make it possible to visualize, annotate, and quantify features of

the tumor microenvironment spanning a 105-fold range in length scale (from ~100 nm to 1 cm) by building on a suite of interoperable, cloud-based MINERVA tools that also work well with existing commercial and open-source software. Key user communities include cell biologists, microscopists, pathologists and oncologists with

expertise in imaging and tissue biology and computational biologists and bioinformaticians who process and integrate image and ‘omic data into atlases. These users work closely together in our laboratories but have distinct needs. Our tools will therefore support three phases of research: (i) initial data exploration via intuitive

and easy-to-deploy web-based tools; (ii) hypothesis generation and testing via sophisticated ML-enabled visual analytics; and (iii) data publication and integration with existing knowledge, databases, and atlases. Our innovations will include the latest advances in visual encoding, ML/AI, and human-computer interfaces that

enable human-in-the-loop analysis and explanatory and exploratory data visualization. Aim 1 will establish light-weight methods for low-cost visualization and communication of multiplex IF, H&E, and spatial omics data collected by HTAN and similar international consortia. Aim 2 will develop new ways for deeply

exploring and analyzing the spatial data for hypothesis generation and testing, with a focus on quantifying morphology and cell-cell interactions in 2D whole-slide and high-resolution 3D images. Aim 3 will expand our MINERVA platform to enable collaborative analysis and data sharing across different audiences and data types

to better understand how tumor architecture changes with disease progression and treatment. Together, our software will promote efficient hypothesis generation and testing from complex, multi-modal datasets as well as their annotation and distribution in accordance with FAIR data principles.