# Modeling Dynamic Immune Cell Modulation in a 3-D Tissue Engineered Platform to Enhance Patient-specific Immunotherapy for Lung Cancer

> **NIH NIH R21** · UNIVERSITY OF ALABAMA AT BIRMINGHAM · 2022 · $208,271

## Abstract

PROJECT SUMMARY/ABSTRACT
Immune suppression and resistance to immune checkpoint inhibitors (ICI) are major obstacles for successful
immunotherapy for non small cell lung cancer (NSCLC). In NSCLC, the density and diversity of tumor-infiltrating
immune cells in the tumor microenvironment (TME) are closely related to prognosis, prediction of treatment
efficacy of frontline combination therapies with ICI and favorable survival. The patient heterogeneity in the
immune cell composition within the TME indicates that mapping the composition of immune infiltrates and their
functional state within the TME is important for diagnosing and designing treatment strategies and for predicting
biomarkers. The central objective of this project is to utilize our novel three dimensional human tissue model
(3D-LTB) that recapitulates tissue dimensionality and microenvironment of human lung tumors to test the
hypothesis that modulation of tumor-stromal crosstalk and sphingolipid signaling pathways that influence
infiltration of immune suppressive myeloid-derived suppressor cells (MDSCs) in the lung TME alters the spatial
dynamics of resident and recruited effector cells to enhance response to immune targeted therapies for NSCLC.
In Aim1, patient-derived tumors and cutting edge GeoMx Digital Spatial Profiling platform will be utilized to define
the dynamics and spatial profiles of effector T cells within the 3D-LTBs in response to immunotherapy. Studies
in Aim 2 will determine if pharmacological targeting of sphingolipid rheostat alters tumor-stromal crosstalk and
enhances response to immunotherapy using the same platform described in Aim 1. To our knowledge, this is
the first fully developed 3D model of NSCLC that fully recapitulate lung cancer-immune interactions. Our studies
have the power to define patient heterogeneity and identify spatially informed biomarkers in response to ICI in
NSCLC. This optimized model system mimics extrapolatable growth characteristics and molecular signatures of
resistance mechanisms in the human disease.

## Key facts

- **NIH application ID:** 10518637
- **Project number:** 1R21CA263365-01A1
- **Recipient organization:** UNIVERSITY OF ALABAMA AT BIRMINGHAM
- **Principal Investigator:** Jessy Satyadas Deshane
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $208,271
- **Award type:** 1
- **Project period:** 2022-07-27 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10518637

## Citation

> US National Institutes of Health, RePORTER application 10518637, Modeling Dynamic Immune Cell Modulation in a 3-D Tissue Engineered Platform to Enhance Patient-specific Immunotherapy for Lung Cancer (1R21CA263365-01A1). Retrieved via AI Analytics 2026-06-11 from https://api.ai-analytics.org/grant/nih/10518637. Licensed CC0.

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