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Pancreatic Reference Set Applicant-Haab (2012)

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The panel consists of two distinct assays for CA 19-9 and three different glycoforms of the protein MUC5AC CA 19-9
Genomics
Proteomics
G.I. and Other Associated Cancers Research Group
1

This study will address the clinical problems of early detection and diagnosis. The samples were obtained from patients with early-stage pancreatic cancer, benign pancreatic diseases (primarily chronic pancreatitis), and no disease. The ability to accurately distinguish the cancer from the control cases would be useful both for screening for cancer among high risk populations and for determining whether patients with pancreatic abnormalities have cancer.

Antibody-Lectin Sandwich Arrays: We will use the Antibody-Lectin Sandwich Array (ALSA, Fig. 1) to acquire marker measurements on the reference set samples. A five-marker panel for pancreatic cancer: Our technology allows us to look at the levels of several specific glycans on proteins captured on antibody arrays. This approach can give better performance than standard methods for certain proteins that display disease-associated glycan changes. The multiplexed technology allows us to screen through many candidate markers using low sample volumes. CA 19-9 determination: CA 19-9 is the current best single marker for pancreatic cancer, so it will be important to determine the CA 19- 9 levels in the reference set to allow comparison to the new markers. The typical clinical assays required several hundred microliters of sample, which is considerable consumption of the valuable reference specimens. Our microarray assay requires only a few microliters of sample per replicate, and we previously have optimized and validated the CA 19-9 assay on the antibody microarray platform. Therefore another goal of our work with the reference set will be to obtain high quality measurements of CA 19-9.
Antibody-Lectin Sandwich Arrays We will use the Antibody-Lectin Sandwich Array (ALSA, Fig. 1) to acquire marker measurements on the reference set samples. In the antibody-lectin sandwich array method (ALSA, Fig. 1) [1], immobilized antibodies on microscope slides capture proteins out of biological solutions, and detection antibodies or lectins are used to probe the levels of the proteins or the glycans attached to the proteins (the example in Fig. 1 shows the detection of the CA 19-9 carbohydrate antigen on individual proteins [2]). The ability to measure glycans on specific proteins is valuable for developing effective biomarkers, as we have found that the detection of a glycan on a specific protein can perform better as a biomarker than the detection of the protein alone [1, 3-7]. Because some patients who do not have elevations of a certain protein do have glycan alterations on that protein, the measurement of both types of information gives better overall performance. The ALSA platform lends itself well to biomarker development and validation, since only 6 μl of diluted sample is used on each array, and many samples can be run in high throughput. The data analysis plan and the statistical analysis of the sample size were described previously in the setaside fund requests. The data and our calls of cases and controls will be sent to the DMCC for analysis, who will determine the performance of our biomarker panel. We will test the hypothesis that the new marker panel does not perform better than CA 19-9 alone (null hypothesis). Rejection of this hypothesis within the 95% confidence interval will give strong evidence that the new panel is significantly better than CA 19-9, the current best marker for pancreatic cancer. (Sample size calculations previously were provided to show that our anticipated performance can be assessed with at least 0.8 power using this reference set.) Such a result would provide a strong impetus for broad validation of the marker. The DMCC also will compare performance with other biomarkers panels applied by other groups and investigate the potential value of combining these diverse markers. The use of a common set of samples for all the markers enables this analysis. It will be extremely valuable to see which of the candidate markers performs best and to see how they fit together. If they have complementary performance, they could be used together to achieve better results than any individual marker. For example, if a subgroup of patients is detected by panel A, and a different, non-overlapping group is detected by panel B, the panels could be used together for improved performance, provide false-positive detection did not increase correspondingly. Validation studies will be planned for promising markers coming out of these analyses.

There are currently no biomarkers annotated for this protocol.

No datasets are currently associated with this protocol.


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