# Cancer precision medicine through spatially informative single cell image and transcriptomics data analysis

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $320,088

## Abstract

Abstract
Human cancers are highly heterogeneous, arising from genetic, epigenetic, genomic, and gene environment
interactions. Studying single-cell cancer heterogeneity is essential for effective diagnosis, prognosis and
development of personalized anti-cancer therapy. However, single-cell level tumor heterogeneity in situ with the
original spatial context is not addressed until very recently, with development of new frontier technological
platforms such as spatial transcriptomics (ST) and single cell imaging mass cytometry (IMC). Due to complexity
of these new spatial data types, computation is a major bottle neck to bring these technologies to precision
therapeutic interventions in the clinical space. In this project, we take a three-pronged approach to propose a
series of novel computational methods that will harness the power of spatially informative omics and imaging
data, for drug treatment and cancer patient prognosis predictions. Building upon a previously highly productive
R01 project, we aim to continue investigations in single cell research, with a new focus on single-cell spatial
data analysis. First, we will develop a novel personalized drug repurposing algorithm called STADS using
cancer spatial transcriptomics data. Next, build a new computational model STimpute to impute spatial
transcriptomics data from easily accessible histopathology image data, using transfer learning and graph neural
network (GNN) models. STimpute will allow predictions of drugs from histopathology data, by using imputed ST
data as the proxy input of STADS. Lastly, we will build a new computational framework scImageProg to predict
patient survival at the population level from the single-cell image cytometry data, accomplishing multi-scale
modeling to link single-cell data to population health. The work will be expected to have transformative clinical
impacts from various cutting-edge spatially informative and complex genomics and imaging data types.

## Key facts

- **NIH application ID:** 10930112
- **Project number:** 5R01LM012373-08
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Lana X Garmire
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $320,088
- **Award type:** 5
- **Project period:** 2016-09-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10930112

## Citation

> US National Institutes of Health, RePORTER application 10930112, Cancer precision medicine through spatially informative single cell image and transcriptomics data analysis (5R01LM012373-08). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10930112. Licensed CC0.

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