The Lung Cancer Diagnostic Test 1 (LCDT1) works by measuring the activity of specific proteins in a plasma sample and using a proprietary LCDT1 algorithm to generate a qualitative result of High Risk (Positive) or Low Risk (Negative) for early stage NSCLC. Our device consist of a combination of software, a multiplex immunoassay instrument, assay reagents, and a proprietary algorithm.
We take the patient’s plasma to quantify specific biomarkers with potential for diagnosing lung cancer. This is then incubated with special beads that have antibodies (Purple Y) that will capture the proteins of interest (sun). A detection protein (Green Y) with a tracer (Biotin) binds to another site on the protein of interest. This is known as a sandwich ELISA method, -only we are simultaneously looking at multiple proteins. Any unbound proteins are washed off from mixture. A streptavidin (SAPE) binds to the tracer on the detection protein and produces a fluorescence which is read on a flow cytometer such as a Luminex platform. These beads bounded to the proteins of interest go through the flow cytometer in a single file and pass through the detection chamber allowing the particles to be measured discreetly. Two lasers, red and green, are used to classify the bead number based on the dye ratio on the bead and quantify, respectively.
A biomarker is a measurable indicator of your health. It reflects the normal or abnormal processes in the body and may indicate signs of underlying condition or disease. Biomarkers are found throughout the body in tissues and bodily fluids and can include a variety of molecules such as proteins, DNA, or hormones.
In the LCDT technology, we use proteins as measurable biomarkers. The advantages of using protein biomarkers are:
Our LCDT1 algorithm takes the levels and patterns of protein biomarkers from an individual’s plasma sample and uses machine learning to determine their lung cancer risk. An algorithm is a series of instructions, such as a computer program, for problem solving. At LCP, we have developed a proprietary machine learning algorithm, which when combined with patient demographic data and biomarker values, generates a result of High Risk (Positive) or Low Risk (Negative) for early stage NSCLC.
Lung Cancer Proteomics, LLC
105 Washington Street
Michigan City, IN 46360
Bay Area Laboratory
780 Montague Expressway
Building 7, Suite 703
San Jose, CA 95131