. 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 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.
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 cells' 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.
