Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

A novel SNP analysis method to detect copy number alterations with an unbiased reference signal directly from tumor samples

Lisovich, A and Chandran, UR and Lyons-Weiler, MA and LaFramboise, WA and Brown, AR and Jakacki, RI and Pollack, IF and Sobol, RW (2011) A novel SNP analysis method to detect copy number alterations with an unbiased reference signal directly from tumor samples. BMC Medical Genomics, 4.

Published Version
Available under License : See the attached license file.

Download (4MB) | Preview
[img] Plain Text (licence)
Available under License : See the attached license file.

Download (1kB)


Background. 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. Methods. 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. Results. 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. Conclusions. 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. © 2011 Lisovich et al; licensee BioMed Central Ltd.


Social Networking:
Share |


Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Lisovich, A
Chandran, URchandran@pitt.eduCHANDRAN
Lyons-Weiler, MA
LaFramboise, WAwal9@pitt.eduWAL90000-0002-6024-810X
Brown, AR
Jakacki, RI
Pollack, IFipollack@pitt.eduIPOLLACK
Sobol, RWrws9@pitt.eduRWS9
Centers: Other Centers, Institutes, or Units > Hillman Cancer Center
Other Centers, Institutes, or Units > Pittsburgh Cancer Institute
Date: 2 February 2011
Date Type: Publication
Journal or Publication Title: BMC Medical Genomics
Volume: 4
DOI or Unique Handle: 10.1186/1755-8794-4-14
Schools and Programs: Graduate School of Public Health > Human Genetics
School of Medicine > Biomedical Informatics
School of Medicine > Neurological Surgery
School of Medicine > Pathology
School of Medicine > Pediatrics
School of Medicine > Pharmacology and Chemical Biology
Refereed: Yes
Date Deposited: 17 Nov 2016 19:52
Last Modified: 02 Feb 2019 16:56


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item