# Predicting response to anti-PD-1 therapy in triple negative breast cancer by comprehensive profiling of the tumor microenvironment

> **NIH NIH F31** · STANFORD UNIVERSITY · 2020 · $45,520

## Abstract

Abstract
Triple negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, and
affects more than 37,000 women each year. Previous work has shown that the presence of
immune cells in the tumor microenvironment of TNBC influences overall survival. This has
prompted clinical trials testing immunomodulatory drugs for the treatment of this disease.
Although immunotherapy has demonstrated success across a range of tumor types, only a
subset of patients experience significant benefit. Identifying biomarkers to predict which patients
will respond to these drugs has been extremely challenging. Sequencing based approaches
require the tissue to be dissociated prior to analysis, and hence do not capture the spatial
relationships between different cell types. Imaging methods do capture these spatial
relationships, but can only visualize a small number of proteins at a time. This results in an
incomplete picture of the complexity of the tumor microenvironment, since not all cell types can
be identified at once. Our group has recently developed Multiplexed Ion Beam Imaging, which
allows for a nearly 10-fold increase in the number of antibodies that can be visualized
simultaneously. Our hypothesis is that by combining this novel imaging modality with DNA and
RNA sequencing, we will be able to comprehensively profile the tumor microenvironment of
TNBC patients, and thus significantly improve prediction of response. In Aim 1, I will improve the
computational tools our lab uses to identify the boundaries between adjacent cells in tissue, in
order to accurately assign imaging signal to the correct cell. In Aim 2, I will use our lab’s novel
imaging platform to profile samples from patients enrolled in a clinical trial targeting PD-1, a key
immune regulatory protein. I will then use this rich information to predict patient response to
therapy. In Aim 3 I will integrate sequencing data from the same samples with the imaging data
we generated to determine how genetic alterations influence the composition of immune cells
present in the tumor microenvironment. This work will increase our understanding of the
immune interactions in TNBC, and will generate significantly improved models to predict
response to immunotherapy.

## Key facts

- **NIH application ID:** 9907924
- **Project number:** 1F31CA246880-01
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** NOAH GREENWALD
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $45,520
- **Award type:** 1
- **Project period:** 2020-08-19 → 2023-08-18

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9907924

## Citation

> US National Institutes of Health, RePORTER application 9907924, Predicting response to anti-PD-1 therapy in triple negative breast cancer by comprehensive profiling of the tumor microenvironment (1F31CA246880-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9907924. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*