# (PQ2) PD-L1/PD-1 signals in aged hosts undergoing cancer immunotherapy

> **NIH NIH R01** · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · 2021 · $565,310

## Abstract

This proposal combines a team with expertise in aging, tumor immunology, tumor immunotherapy, specific
genetically modified animal models and early phase clinical trials with a computational team having great
expertise in analyzing and modeling aging of the immune system. We will study age effects on PD-L1/PD-1
signaling in the host and the tumor focusing on melanoma with some bladder cancer work, two tumors that are
highly responsive to αPD-1 and/or αPD-L1 as proofs-of-concept, and residing in distinct anatomic compartments.
In Aim 1 we study tumor PD-L1 intrinsic effects on αPD-L1 and αPD-1 treatment in melanoma and bladder
cancer using transplantable B16 and inducible Nras/Cdk2n melanoma models, and transplantable MB49 and
BBN-induced tumors for bladder cancer studies. We also use novel melanoma and BC models with tumor cell-
specific PD-L1KO. We study 3 cohorts of elderly versus younger humans getting αPD-L1 or αPD-1 for melanoma
or bladder cancer for human validation. We measure high-dimensional cell phenotypes and signaling responses,
proteins and genes to maximize the information collected from human samples and mice using 23-color FACS,
CyTOF, Luminex, Nanostring and other approaches. In Aim 2 we use all the above models and analytic
strategies in young and aged PD-L1KO mice and WT or bone marrow chimeras to test hematopoietic and non-
hematopoietic (host) PD-L1 signals in treatment outcomes in melanoma and bladder cancer. In Aim 3 the
Systems Immunology team will use their innovative and successful computational modeling to identify age-
related co-predictors of immunotherapy response and to identify candidate mechanisms for responders and non-
responders. We will define a trajectory of immune system aging in mice at ultra-high resolution by performing a
systems level integrative analysis of aging in Collaborative Cross and BL6 mice tracked in a combined
longitudinal and cross-sectional study. This trajectory will be used to understand how tumor response and
treatment outcomes vary as a function of age, and to build a simple, low parameter (i.e., easily testable and
clinically translated), predictive models of treatment response. We will test insights by analyzing immune data
from aged versus young patients undergoing αPD-L1 and αPD-1 cancer immunotherapy in novel machine
learning approaches that we pioneered to identify insights from mouse data that are relevant to humans.
Coupling this disease information with the healthy human aging trajectory that we recently defined will allow us
to adapt our mouse data to predict optimal treatments in humans based on chronological and immune aging.
This combined trans-disciplinary approach will identify common age-related disabilities that reduce PD-L1/PD-1
based immunotherapy responses and suggest tailored treatments for optimal efficacy that could later be tested
in validation sets. These data can also be applied to other types of immunotherapy as we will also test.

## Key facts

- **NIH application ID:** 10247570
- **Project number:** 5R01CA231325-04
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCIENCE CENTER
- **Principal Investigator:** Tyler J. Curiel
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $565,310
- **Award type:** 5
- **Project period:** 2018-09-19 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10247570

## Citation

> US National Institutes of Health, RePORTER application 10247570, (PQ2) PD-L1/PD-1 signals in aged hosts undergoing cancer immunotherapy (5R01CA231325-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10247570. Licensed CC0.

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