A novel SNP analysis method to detect copy number alterations with an unbiased reference signal directly from tumor samples
1 Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA
2 Department Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA
3 Department of Neurosurgery, University of Pittsburgh School of Medicine, Children's Hospital of Pittsburgh, Pittsburgh, PA 15213, USA
4 Department of Pharmacology & Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA
5 Clinical Genomics Facility, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA
6 University of Pittsburgh Cancer Institute, Hillman Cancer Center, Pittsburgh, PA, 15213, USA
7 Division of Pediatric Hematology/Oncology, Children's Hospital of Pittsburgh, Pittsburgh, PA, 15213, USA
8 Department of Pediatric Neurosurgery, Children's Hospital of Pittsburgh, Pittsburgh, PA, 15213, USA
9 Department of Human Genetics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, 15213, USA
BMC Medical Genomics 2011, 4:14 doi:10.1186/1755-8794-4-14Published: 26 January 2011
Genomic instability in cancer leads to abnormal genome copy number alterations (CNA) as a mechanism underlying tumorigenesis. Using microarrays and other technologies, tumor CNA are detected by comparing tumor sample CN to normal reference sample CN. While advances in microarray technology have improved detection of copy number alterations, the increase in the number of measured signals, noise from array probes, variations in signal-to-noise ratio across batches and disparity across laboratories leads to significant limitations for the accurate identification of CNA regions when comparing tumor and normal samples.
To address these limitations, we designed a novel "Virtual Normal" algorithm (VN), which allowed for construction of an unbiased reference signal directly from test samples within an experiment using any publicly available normal reference set as a baseline thus eliminating the need for an in-lab normal reference set.
The algorithm was tested using an optimal, paired tumor/normal data set as well as previously uncharacterized pediatric malignant gliomas for which a normal reference set was not available. Using Affymetrix 250K Sty microarrays, we demonstrated improved signal-to-noise ratio and detected significant copy number alterations using the VN algorithm that were validated by independent PCR analysis of the target CNA regions.
We developed and validated an algorithm to provide a virtual normal reference signal directly from tumor samples and minimize noise in the derivation of the raw CN signal. The algorithm reduces the variability of assays performed across different reagent and array batches, methods of sample preservation, multiple personnel, and among different laboratories. This approach may be valuable when matched normal samples are unavailable or the paired normal specimens have been subjected to variations in methods of preservation.