# AstroPath Integration Resource Core

> **NIH NIH U54** · JOHNS HOPKINS UNIVERSITY · 2024 · $268,378

## Abstract

SUMMARY
The AstroPath-Genomics Core will be a hub to disseminate powerful molecular pathology, informatics, big data
science, and genomics methods tailored to accomplish the goals of the Prostate TBEL Program. It will provide
critical support for ALL three projects. The key strategy is to leverage existing infrastructure, equipment and
services at the SKCCC, while also providing specialized approaches and capabilities that are highly tailored to
support the activities in the TBEL Program. This model will allow efficient and cost-effective harnessing of
these powerful technologies through the dedicated effort and expertise of the current AstroPath-Genomics
Core personnel through the close interaction with all Projects and Cores here, without having to establish the
equipment, administrative infrastructure, biospecimen SOPs, and laboratory informatics management systems
(LIMS), from scratch. This centralization of the molecular pathology, informatics and big data science, and
mathematical/computational integration methods will yield cost-effective strategies that avoid duplicating effort
in each component of the TBEL Program. The Core will carry out its work in three major Aims. Aim 1 will
deploy advanced digital pathology tools for multi-analyte immunohistochemistry, immunofluorescence, and in
situ hybridization approaches and image analysis for deep characterization of the microenvironmental
transitions from inflammation to neoplasia. Specific technologies include multi-spectral multi-analyte imaging,
Chromogenic iterative multiplex-IHC(ChIM-IHC), and multiplex ACD in situ hybridization. Aim 2 will utilize bulk,
single cell, and spatial genomics approaches to measure the genomic and epigenomic alterations to the
epithelial and stromal compartments of PIA, PIN, and cancer lesions. Specific technologies include, genome
wide and ultra-deep targeted DNA methylation analysis, multi-modal single nucleus ATAC- and coupled RNA-
seq (snATAC-/snRNA-seq), and spatial transcriptomics. Integration across the multi-modal data streams will
be possible through implementation of the innovative Bayesian non-negative matrix factorization and transfer
learning approaches through the CoGAPS and ProjectR framework. Aim 3 will implement and extend the
AstroPath framework for computational analysis of high dimensional single cell and bulk
genomics/epigenomics, multi-analyte digital pathology, and advanced imaging data. To accomplish these
goals, the core leaders have assembled an impressive team of experts to support the TBEL Center, with
complementary expertise in big data science/informatics, molecular pathology, genomics technologies,
immunopathology, computational/systems biology and applied mathematics.

## Key facts

- **NIH application ID:** 10918245
- **Project number:** 5U54CA274370-03
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Alexander S Szalay
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $268,378
- **Award type:** 5
- **Project period:** 2022-09-15 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10918245

## Citation

> US National Institutes of Health, RePORTER application 10918245, AstroPath Integration Resource Core (5U54CA274370-03). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10918245. Licensed CC0.

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