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

> **NIH NIH U01** · HARVARD MEDICAL SCHOOL · 2024 · $500,943

## Abstract

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...

## Key facts

- **NIH application ID:** 10928272
- **Project number:** 5U01CA284207-02
- **Recipient organization:** HARVARD MEDICAL SCHOOL
- **Principal Investigator:** Hanspeter Pfister
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $500,943
- **Award type:** 5
- **Project period:** 2023-09-12 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10928272

## Citation

> US National Institutes of Health, RePORTER application 10928272, Visual Analytics for Exploration and Hypothesis Generation Using Highly MultiplexedSpatial Data of Tissues and Tumors (5U01CA284207-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10928272. Licensed CC0.

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