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

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 2R44CA203455-02A1
Agency Tracking Number: R44CA203455
Amount: $1,994,019.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: 102
Solicitation Number: PA17-302
Timeline
Solicitation Year: 2017
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-08-15
Award End Date (Contract End Date): 2022-01-31
Small Business Information
401 TERRY N
Seattle, WA 98109-5263
United States
DUNS: 079209633
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 JENNIFER SMITH
 (206) 920-4347
 jennifer@precyte.net
Business Contact
 ROBERT LIPSHUTZ
Phone: (650) 400-8889
Email: rob@precyte.net
Research Institution
N/A
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
Alzheimerandapos;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 andgt;60% specificity, with andgt;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 a continuous variable. We will develop
a visual depiction of the data that patients and doctors can use to assess risk and decide treatment. With our
collaborator, Dr. Massion, we will work to refine the best clinical approach.Project Narrative
We are developing a novel blood-based diagnostic tool called the indicator cell assay platform
(iCAP) that uses cultured cells as biosensors to detect disease status of patients from blood
samples. The iCAP shows promise to succeed where other methods have failed by exploiting
cellsandapos; natural capability to integrate and amplify external disease signals. Here we will optimize
and validate an indicator cell assay to robustly identify lung cancer in patients that have
indeterminate pulmonary nodules identified by imaging to avoid invasive biopsy and focus further
diagnostic tests on those with much higher likelihood lung cancer.

* Information listed above is at the time of submission. *

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