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You are here: Home › Protocols › Autoantibody Approach for Serum-Based Detection of Head and Neck Cancer

Autoantibody Approach for Serum-Based Detection of Head and Neck Cancer

236
Tainsky, Michael A. — Karmanos Cancer Institute
Autoantibodies
Case/control
Other, Specify
Lung and Upper Aerodigestive Cancers Research Group

Our long term goal is to improve survival of patients with head and neck squamous cell carcinoma (HNSCC) through early detection using simple noninvasive serum assays in an ELISA-like platform. The objective of this proposal is to improve and confirm the validity of a diagnostic serum assay based on a panel of cancer-specific biomarkers for early cancer detection in patients with HNSCC. Our central hypothesis is that the detection of antibody responses to HNSCC-specific antigens, using a panel of biomarkers, can provide sufficient sensitivity and specificity suitable for clinical testing in the primary setting to screen and diagnose HNSCC in high risk populations to improve early detection.


Aim 1: Assess the sensitivity of the current diagnostic assay using some additional informative biomarkers added to the current diagnostic panel. Aim 2: Confirm the validity of this diagnostic strategy for early HNSCC detection based on pattern of serum immunoreactivity against a biomarker panel in collaboration with 2 other EDRN sites.
We are using IgG molecules specific to cancer patients as a bait to clone antigens derived from phage T7 display cDNA libraries of mRNA from tumor tissue that we use as biomarkers for the early detection of cancer. In a sense we are using the immune system as a biosensor. The immune system elaborates antibodies which we detect as they react with the antigens we clone. The antigens are expressed and then robotically spotted on microarrays. The microarrays are treated with sera from other patients and the binding of IgGs in those sera is used to find the most commonly reactive antigens. We print replicates of thousands of antigens on the microarrays and analyze them with a two-color detection system. The patients' IgGs bound to the spotted antigens are detected using a secondary antibody against human IgG labeled with Alexa-647, a red dye. The second color provides a control for each spot. We use a monoclonal antibody to the N-terminal 11 amino acids of phage backbone protein onto which each antigen is cloned as a fusion protein. That monoclonal antibody is detected with an Alexa-532 (green dye) labeled antibody against murine IgG. The dye ratios provide a control for variations in spotting so as to quantitate the IgG binding to each clone using sera of each patient. We also have a negative control clone that contains no additional amino acids in the cloning vector. The dye ratios on the chips are normalized and those data used in a t-test using sera from patients and healthy controls. Those clones significant in a t-test are used on a validation set of patients and controls not including the previous training samples. The antibody binding data of those subjects in the validation set are tested using machine learning techniques such as neural networks and n-fold cross validation to determine the accuracy of the classifiers.

There are currently no biomarkers annotated for this protocol.

No datasets are currently associated with this protocol.


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