# Automated plasma EV-PDL1 analysis for cancer immunotherapy

> **NIH NIH R43** · ACCURE HEALTH, INC. · 2022 · $399,913

## Abstract

ABSTRACT
Challenges. There are over 4000 clinical trials testing anti-PD1/PD-L1 immune checkpoint inhibitors (ICI),
either alone or in combination with other therapies. While many patients benefit, the vast amount do not, all at
a considerable cost. The most validated and FDA-approved biomarker to guide patient selection is through
immunohistochemical (IHC) staining and scoring of tissue biopsies for PD-L1 (e.g. Tumor Proportion Score,
TPS). Unfortunately, TPS is an imperfect biomarker: i) it requires surgical or image guided tissue biopsy which
is sometimes difficult to perform; ii) the site and timing of tissue acquisition and staining protocols can influence
the accuracy of TPS; iii) IHC takes days to process, delaying treatment; iv) many TPS-positive patients do not
respond to ICI treatment; and v) TPS can change during chemo, targeted and ICI therapies.
Phase I goals. Accure Health proposes to explore an alternative approach: circulating PD-L1 biomarker assay
based on Technology-integrated magneto-electronic sensing (TiMES) of extracellular vesicles (EVs).
Supported by promising clinical data, we hypothesize that circulating EV analysis integrating PD-L1 expression
from primary and metastatic lesions can be a more comprehensive marker. We propose two specific aims. Aim
1. Develop an automated TiMES assay to analyze pan EV-PDL1 and cell type-specific EV-PDL1. Aim 2.
Establish TiMES EV-PDL1 scores and correlate with TPS. We envision the automated TiMES EV-PDL1 assay
and integrated scores can be utilized in clinical trials testing anti-PD1/PD-L1 mono- or combination therapies. It
can provide a faster and more reliable solution for evaluating treatment response, and help accelerate
regulatory decision-making.

## Key facts

- **NIH application ID:** 10545706
- **Project number:** 1R43CA275548-01
- **Recipient organization:** ACCURE HEALTH, INC.
- **Principal Investigator:** Liyun Jessica Sang
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $399,913
- **Award type:** 1
- **Project period:** 2022-09-19 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10545706

## Citation

> US National Institutes of Health, RePORTER application 10545706, Automated plasma EV-PDL1 analysis for cancer immunotherapy (1R43CA275548-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10545706. Licensed CC0.

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