# Optimization and Validation of an indicator cell assay for blood-based diagnosis of lung cancer

> **NIH NIH R44** · PRECYTE, INC. · 2021 · $488,587

## Abstract

Project Summary. The Indicator Cell Assay Platform (iCAP) is an inexpensive blood-based assay that can be
used for early detection of disease, disease stage stratification, prognosis and response to therapy for a variety
of diseases. The iCAP uses cultured, standardized cells as biosensors, capitalizing on the ability of cells to
respond to disease signals present in serum with exquisite sensitivity, as opposed to traditional assays that rely
on direct detection of molecules in blood. Developing the iCAP involves exposing cultured cells to serum from
normal or diseased subjects, measuring a global differential response pattern, and using it to train a reliable
disease classifier based on the expression of a small number of genes. Deploying the iCAP involves measuring
only expression of classifier genes using cost-effective tools. We have demonstrated the iCAP by pre-
symptomatic detection of disease in an amyotrophic lateral sclerosis mouse model, and early detection of
Alzheimer's disease in humans, which we are currently validating.
 Here, we are developing an iCAP for diagnosis of lung cancer (LC). Blood biomarkers of LC are critically
needed for use in combination with existing imaging tools to improve diagnostic accuracy. Our goal is to develop
an iCAP for use on patients who have indeterminate pulmonary nodules (IPNs) identified by imaging that cannot
be confidently classified as malignant or benign from the data. For clinical utility, the iCAP needs to distinguish
malignant from benign nodules with 1) High sensitivity and negative predictive value (NPV) to minimize the
number of patients with malignant tumors that have negative test results, and 2) A specificity that will provide
economic impact and actionable results for patients correctly identified with benign nodules. We have
demonstrated proof of concept for the LC iCAP and achieved 96% NPV, 92% sensitivity and 52% specificity in
distinguishing non-small cell lung cancer from benign nodules (with independent samples). Potential for clinical
utility is high with low risk of missing malignant tumors (8% FNR), actionable results for 52% of patients with
benign nodules, and performance that is better or similar to other assays on the market and in development.
 For Phase II, we propose to optimize and validate the assay with larger cohorts from independent sites
to position us to commercialize the assay as a clinical test. We aim to: 1) Optimize experimental, technical, and
computational parameters of the iCAP, 2) Train and test an improved iCAP classifier using optimal conditions,
and 3) Validate the classifier with blind independent samples. Our goal is to achieve clinical utility and greater
performance than competing tests in development with ≥95% sensitivity and >60% specificity, with >90% NPV.
Our ultimate goal is to develop a test that can be offered to patients at the time of finding an IPN by imaging. Our
simple blood-based assay will give patients a probability of disease using ...

## Key facts

- **NIH application ID:** 9985026
- **Project number:** 5R44CA203455-04
- **Recipient organization:** PRECYTE, INC.
- **Principal Investigator:** Jennifer Joy Smith
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $488,587
- **Award type:** 5
- **Project period:** 2016-01-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9985026

## Citation

> US National Institutes of Health, RePORTER application 9985026, Optimization and Validation of an indicator cell assay for blood-based diagnosis of lung cancer (5R44CA203455-04). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/9985026. Licensed CC0.

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