# Tumor-expressed immune checkpoint B7x-mediated resistance to anti-CTLA-4 therapy.

> **NIH NIH R01** · ALBERT EINSTEIN COLLEGE OF MEDICINE · 2022 · $378,000

## Abstract

Tumor-expressed immune checkpoint B7x-mediated resistance to
 anti-CTLA-4 therapy
Immune checkpoint blockade of CTLA-4 and PD-1/PD-L1 have advanced
the treatment of cancer patients. However, one of the biggest challenges is that
the majority of cancer patients do not respond to these treatments. Based on our
new results, our central hypothesis of this proposed revision research is that
tumor-expressed immune checkpoint B7x induces resistance to anti-CTLA-4
therapy and that combination treatment of anti-B7x and anti-CTLA-4 leads to
synergistic therapeutic efficacy and overcomes the resistance to anti-CTLA-4
therapy. Guided by our published clinical and basic research, and our strong
preliminary data, we will pursue two specific aims: 1) Dissect the mechanisms
underlying tumor-expressed B7x mediated resistance to anti-CTLA-4 therapy;
and 2) Develop novel combination therapies of anti-B7x and anti-CTLA-4 to
overcome the resistance. We have generated a number of novel tools for this
project. The outcomes of this project will reveal new mechanisms underlying
tumor-expressed B7x-mediated resistance to anti-CTLA-4 therapy and will
develop into new immunotherapies overcoming the resistance.

## Key facts

- **NIH application ID:** 10429780
- **Project number:** 3R01CA175495-09S1
- **Recipient organization:** ALBERT EINSTEIN COLLEGE OF MEDICINE
- **Principal Investigator:** Xingxing Zang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $378,000
- **Award type:** 3
- **Project period:** 2014-04-01 → 2025-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10429780

## Citation

> US National Institutes of Health, RePORTER application 10429780, Tumor-expressed immune checkpoint B7x-mediated resistance to anti-CTLA-4 therapy. (3R01CA175495-09S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10429780. Licensed CC0.

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