A Simple System for the Early Detection of Breast Cancer
Abstract
The immunosignature (IMS) technology is a new approach to diagnosis. Because it measures the profile of antibodies in response to disease, it is particularly suited to detect disease early. The goal of this project is to determine if this technology can be useful in the early detection of breast cancer (BC), and if so, what the optimal protocol for its application is. The first Aim is to determine how early BC can be detected by immunosignatures. Our goal was to use a sample size of case and controls that was large enough to overcome the overfitting problems common with biomarker discovery. 240 sera samples from women up to one year before they were diagnosed with stage I cancer were assayed and compared to 500 samples from non-cancer women in the same PLCO cohort. These samples are from 12 different sites. We have run all these samples 2-3 times. Using leave-portions out of the case and controls, we demonstrated that the IMS is capable of approximately 89% accuracy in diagnosis. We did encounter problems with array consistency and had to exclude approximately 20% defective ones. We also did not have enough arrays to implement the original design and so the distribution of blinded samples was biased. Unfortunately, these problems lead to a bias in the ims of the unknowns. We will need to execute this experiment again with the improved arrays. In the process we have also developed a simple array based diagnostic based on frame-shift neo-antigens which looks promising. After repeating the year 0-1 experiment, if the results merit, we will proceed in analysis of samples taken 1-4 years before Stage 1 diagnosis, using a commercial source of arrays with less variability. For Aim 2 will we determine if there is a signature distinguishing benign, non-cancer and invasive. We have obtained most of the samples for this study from Duke University and started preliminary analysis.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 01, 2015
- Accession Number
- ADA622379
Entities
People
- Stephen A. Johnston
Organizations
- Arizona State University