# Overcoming resistance to anti-PD1 immunotherapy

> **NIH NIH R35** · UNIVERSITY OF CHICAGO · 2023 · $970,168

## Abstract

ABSTRACT
Novel immunotherapies for cancer are having a major clinical impact, in particular anti-PD-1 mAbs
which have been FDA-approved for 20 cancer entities. However, the mechanisms that explain
why a subset of patients fails to respond to these therapies is incompletely understood.
Understanding these mechanisms should lead to new therapeutic strategies for expanding
efficacy further. Our prior data indicated that a baseline T cell-inflamed tumor microenvironment
was predictive of response to anti-PD-1, which augments the functionality of CD8+ T cells already
present within the tumor microenvironment. During the previous funding period, we made multiple
novel discoveries that have been paradigm-shifting for the field, which have coalesced to motivate
continued investigation into 5 research directions: investigation of novel T cell immune
checkpoints, innate immune strategies to promote de novo T cell responses in the tumor
microenvironment, tumor cell-intrinsic oncogenic events mediating immune resistance, regulation
of anti-tumor immunity by the commensal microbiota, and germline variants influencing host anti-
tumor T cell responses. Each of these directions is identifying novel therapeutic opportunities
that are expected to expand the circle of efficacy for checkpoint blockade immunotherapy in the
clinic.

## Key facts

- **NIH application ID:** 10737852
- **Project number:** 2R35CA210098-08
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** THOMAS F GAJEWSKI
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $970,168
- **Award type:** 2
- **Project period:** 2016-12-07 → 2030-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10737852

## Citation

> US National Institutes of Health, RePORTER application 10737852, Overcoming resistance to anti-PD1 immunotherapy (2R35CA210098-08). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10737852. Licensed CC0.

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