Lung and Upper Aerodigestive Cancers Research Group
To evaluate the test characteristics of a panel of biomarkers for identifying patients with early stage head and neck squamous cell carcinoma (HNSCC). The primary endpoints are sensitivity, specificity and accuracy of the marker panel. This study of the test characteristics of a modeling strategy for diagnosing HNSCC uses a case-control design, with several types of cases and several types of controls.
AIM 1: Eliminate nonspecific antigen biomarkers that cross-react with sera from patients with nonmalignant head and neck diseases, autoimmune diseases and other types of cancers.
AIM 2: Validate this diagnostic strategy for early HNSCC detection based on pattern of serum immunoreactivity against the biomarker panel.
AIM 3: Assess the use of this diagnostic strategy for early detection of HNSCC in collaboration with other EDRN and non-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.
The National Cancer Institute's Division of Cancer Prevention has released a new funding opportunity to solicit organ-specific applications for Biomarker Developmental Laboratories (BDLs), one of the four scientific units of the recently funded Early Detection Research Network (EDRN). The EDRN is a national infrastructure funded to discover, develop, and validate biomarkers for risk assessment, detection, and molecular diagnosis and prognosis of early cancer. BDLs are responsible for the discovery, development, characterization, and testing of new, or the refinement of existing, biomarkers and biomarker assays for risk assessment, detection, and molecular diagnosis and prognosis of cancers.
The existing BDLs are primarily focused on ovary and gastrointestinal cancers. The proposed BDLs (to be supported under this funding opportunity) should be focused on one or more of the following cancers: breast, prostate and other genitourinary organs, or lung. In addition, cancers with rapidly rising incidence rates, e.g., endometrial, hepatocellular, kidney, thyroid, oropharyngeal cancers, and/or cancers with unique etiology, e.g., mesothelioma, will be considered.
The newly funded units of the Early Detection Research Network will be announced later in April. Successful applicants have already been notified. Those researchers who were not successful during the last round of applications are encouraged to apply to this opportunity.