# Project 3: Integrative Computational Analysis of Tumor-Mediated Immunosuppression

> **NIH NIH U54** · STANFORD UNIVERSITY · 2020 · $397,549

## Abstract

ABSTRACT/SUMMARY – Project 3: Integrative Computational Analysis of Tumor-Mediated
Immunosuppression
Lymph nodes are typically assessed in cancer patients to determine disease stage and treatment plan, yet
they are poorly understood and largely understudied in the context of metastatic progression. Most public-
domain high throughput cancer datasets, including The Cancer Genome Atlas (TCGA), profile the primary
tumor and sometimes distant metastatic sites, but rarely positive lymph nodes. In this proposal, we turn our
attention to the lymph nodes with a cancer systems biology approach that tests the hypothesis that lymph
nodes metastasis establishes the gateway to distant metastases by enabling immune tolerance to tumor-
associated antigens. Our Research Center will generate complex high dimensional datasets in both the
primary tumor and lymph nodes of patients with melanoma and head and neck squamous cell carcinoma
(HNSCC) to explore this hypothesis in both human and mouse studies. From each tissue specimen, in both
the human and mouse, we will generate RNAseq data of sorted malignant and immune subpopulations
(Projects 1, 3) and high dimensional in situ images at the level of single cells on the bulk tissue (Project 2).
The goal of this Research Project is to develop and apply computational tools to integrate these complex
datasets in order to identify candidate mediators of tumor-immune interactions that induce immunosuppression
for functional validation. To enrich our ability for interpretation, we will explore signatures of the immune
system in a pan-cancer analysis using the TCGA datasets annotated with time to distant metastasis, in the
context of node-negative and node-positive patients. We hypothesize that pan-cancer genes whose
expression is strongly associated with time to distant metastasis are more likely to be associated with
tumor-intrinsic or microenvironmental processes driving metastasis progression; thus we will
prioritize these genes in our integrative computational analysis of our melanoma and head and neck
squamous cell carcinoma datasets. Using the RNAseq data generated by our study, we will develop and
apply novel network-based computational methods for reconstructing the interactions between malignant and
immune subpopulations. Moreover, we will develop and apply new approaches to integrate the spatial
information from high dimensional single cell in situ images with the gene expression datasets to further refine
our inferences of candidate mediators of immunosuppression. The datasets and computational resources
developed by our Research Project, and Center at large, will not only enable use to deeply explore the role of
lymph nodes in tumor-mediated immunosuppression, but will also provide the community with powerful
resources for understanding systemic influences on the forces governing metastatic dissemination.

## Key facts

- **NIH application ID:** 9982083
- **Project number:** 5U54CA209971-05
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** SYLVIA KATINA PLEVRITIS
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $397,549
- **Award type:** 5
- **Project period:** — → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9982083

## Citation

> US National Institutes of Health, RePORTER application 9982083, Project 3: Integrative Computational Analysis of Tumor-Mediated Immunosuppression (5U54CA209971-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9982083. Licensed CC0.

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