# Harnessing protease activity for predictive monitoring of cancer immunotherapy

> **NIH NIH R01** · GEORGIA INSTITUTE OF TECHNOLOGY · 2020 · $340,630

## Abstract

Project Summary/Abstract
The blockade of inhibitory immune checkpoints has transformed the treatment of cancer for patients across a
broad range of malignancies. Immune checkpoint blockade (ICB) is achieved by administering antibodies that
block the cytotoxic T lymphocyte-associated protein 4 (CTLA-4) or the programmed cell death 1 (PD-1) pathway
to reinvigorate antitumor T cell activity. Despite treatment responses that are unprecedented and durable, the
majority of patients do not experience a clinical benefit from treatment, and some responders relapse and acquire
resistance. Moreover, response patterns of tumors treated with ICB are unconventional, and can be
misinterpreted as disease progression by radiographic imaging. To maximize the precision and benefit of ICB
therapy, identification of predictive and pharmacodynamic biomarkers to objectively assess immune responses
has rapidly emerged as a clinical priority. The proposal aims to leverage protease activity as predictive
biomarkers for monitoring ICB response and resistance. Proteases play a central role in the underlying biology
of immunity, oncology, and anti-tumor responses. The mark of a “hot” tumor is signified by an effective immune
infiltrate of cytotoxic T cells that lyse cancer cells via the classical perforin- and granzyme-mediated pathway –
the latter of which comprise a family of potent serine proteases. Tumor expression of proteases, including
inflammatory and matrix degrading proteases, is well-established as a hallmark of fundamental tumor processes
including angiogenesis, growth, and metastasis. The central hypothesis is that quantifying the activity of T cell
and tumor proteases early-on-treatment will allow identification of activity biomarkers that predict treatment
efficacy and indicate resistance to ICB therapy. To achieve these goals, this proposal aims to develop a new
class of checkpoint blockade antibodies that are endowed with the dual capacity to inhibit immune checkpoints
and sense protease activity during treatment responses. These activity sensing ICB diagnostics, or IDB-Dx,
comprise -PD-1 or -CTLA-4 antibodies that are site-specifically functionalized with a library of mass-barcoded
peptide substrates. During responses to ICB, these peptides are cleaved by T cell and tumor proteases that are
elevated in “hot” tumors, liberating a unique fingerprint of mass barcodes that are then filtered into the recipient’s
urine for quantification by mass spectrometry. By applying machine learning algorithms, these signatures of
protease activity are trained and validated as predictive classifiers to discriminate “hot” and “cold” tumors,
responders from non-responders, and resistance to therapy.

## Key facts

- **NIH application ID:** 9897497
- **Project number:** 5R01CA237210-02
- **Recipient organization:** GEORGIA INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Gabriel A Kwong
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $340,630
- **Award type:** 5
- **Project period:** 2019-03-20 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9897497

## Citation

> US National Institutes of Health, RePORTER application 9897497, Harnessing protease activity for predictive monitoring of cancer immunotherapy (5R01CA237210-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9897497. Licensed CC0.

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