# Clinical implementations of spatial transcriptomics in tumors

> **NIH NIH R33** · BROAD INSTITUTE, INC. · 2021 · $431,771

## Abstract

Abstract
Tumors reside within a complex multicellular ecosystem comprised of malignant and non-
malignant cells, where interacting cells and molecules are organized in space and time. The
diversity of these cells and their interactions affect cancer progression and drug response and
resistance, and present opportunities for more precise diagnostics and therapeutics. In this
proposal, we will develop Slide-seq, a novel spatial transcriptomic method, into a high-
resolution spatial genomics platform for cancer precision medicine through a set of robust
protocols, pipelines and computational algorithms. Our tools will allow pathologists to apply
Slide-seq on a broad range of tumor specimens in the clinic with standard equipment and
minimal training. Our novel computational pipelines will allow the seamless integration of
molecular, cellular and histological understanding in tumors: they will enable the spatial
localization of cell types within complex tumor environments, the identification of spatially varying
gene expression patterns driven by pathology, as well as the organization of cellular niches.
Applying these approaches will revolutionize our ability to discover changes in tumor spatial and
molecular organization during disease progression and treatment, provide new biomarkers for
diagnostics and prognostics, and highlight new therapeutic avenues.

## Key facts

- **NIH application ID:** 10117213
- **Project number:** 5R33CA246455-02
- **Recipient organization:** BROAD INSTITUTE, INC.
- **Principal Investigator:** Orr Ashenberg
- **Activity code:** R33 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $431,771
- **Award type:** 5
- **Project period:** 2020-03-02 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10117213

## Citation

> US National Institutes of Health, RePORTER application 10117213, Clinical implementations of spatial transcriptomics in tumors (5R33CA246455-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10117213. Licensed CC0.

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