# A deep-transfer-learning framework to transfer clinical information to single cells and spatial locations in cancer tissues

> **NIH NIH R21** · INDIANA UNIVERSITY INDIANAPOLIS · 2022 · $213,483

## Abstract

SUMMARY
In the past 10 years, there has been an explosion of new high-resolution molecular data which revolutionize the
way that cancer is understood and treated. They include, single cell transcriptomics, spatial transcriptomics, and
computational image analysis. However, the study of the association of those data with clinical outcomes such
as survival, relapse, metastasis and drug response were left behind. In the meantime, Deep learning field is
maturing very fast with many diverse applications including on biological data. It frequently utilizes multi-layer
neural network models to learn and extract highly non-linear representations of data. Transfer learning is the
subfield of machine learning, which focuses on transferring knowledge learned from a set of source examples to
another types of samples. Combining these two approaches constitutes deep transfer learning and is a promising
solution to investigate and understand the association of high-resolution components of these new cancer data
with the corresponding clinical outcomes. Here we propose the use of deep transfer learning to transfer patient
outcome information learned from large patient transcriptomics cohorts to the cells, cell types, spatial regions,
and image features, which can then be further prioritized by their assigned risks and be evaluated as potential
targets in the aggressive cancers. Specifically, we will develop deep transfer learning frameworks DEGAS for
cell type prioritization and test on glioblastoma and multiple myeloma single cell data to validate this approach.
Then it will be applied on single cell data of more aggressive cancer types such as triple negative breast cancer,
pancreatic ductal adenocarcinoma, non-small-cell lung cancer, and gastric cancer to prioritize high risk cells and
cell types. Then, it will be further modified for use with spatial transcriptomic (ST) data to prioritize high risk
spatial regions of breast cancer and pancreatic ductal adenocarcinoma tumors. Since ST data can act as a
bridge between single cell to patient-level transcriptomics, and histology images. We will further leverage our
framework to identify high risk image features by linking histology image features to patient risk via ST data.
Finally, our framework will be built into R and Python packages available through GitHub and Bioconductor for
use by the broader cancer research community.

## Key facts

- **NIH application ID:** 10424763
- **Project number:** 1R21CA264339-01A1
- **Recipient organization:** INDIANA UNIVERSITY INDIANAPOLIS
- **Principal Investigator:** Travis Steele Johnson
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $213,483
- **Award type:** 1
- **Project period:** 2022-07-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10424763

## Citation

> US National Institutes of Health, RePORTER application 10424763, A deep-transfer-learning framework to transfer clinical information to single cells and spatial locations in cancer tissues (1R21CA264339-01A1). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10424763. Licensed CC0.

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