Autoantibody Approach for Serum-Based Detection of Head and Neck Cancer
- Abbreviated Name
- Autoantibody Approach for Serum-Based Detection of Head and Neck Cancer
- Lead Investigator
- No lead investigator
- Coordinating Investigator
- No coordinating investigator
- Involved Investigators
No abstract availalbe.
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.
- No publications available at this time for this protocol.
- No data collections available at this time for this protocol.
- Start Date
- Mar 1 2008
- Estimated Finish Date
- Feb 28 2009
- Finish Date
- Feb 28 2009
- Protocol ID
- Protocol Type
- Field of Research
- Collaborative Group
- Lung and Upper Aerodigestive Cancers Research Group
- Cancer Types
- Malignant neoplasm of nasal cavities middle ear and accessory sinuses
- Malignant neoplasms of floor of mouth
- Malignant neoplasms of gum
- Malignant neoplasms of hypopharynx
- Malignant neoplasms of lip
- Malignant neoplasms of nasopharynx
- Malignant neoplasms of oropharynx
- Malignant neoplasms of other and ill-defined sites within the lip, oral cavity, and pharynx
- Malignant neoplasms of other and unspecified parts of the mouth
- Malignant neoplasms of tongue