tumor types. We reasoned that this approach would be more solid for the identiﬁcation of tumor cell lines and tissue samples than taking SNPs based on their genotype frequencies in normal human populations (29), because genomic alterations, as loss of heterozygosity, gain of heterozygosity and, less frequently, double mutations aﬀect tumor genotypes. The SNP selection process we presented focused on the task of general cell line identiﬁcation. However, if a research group works on a speciﬁc tumor type (e.g. glioma) the assays could be tailored such that known mutations at speciﬁc loci can be added to the panel. By adjusting the model constraints, one can ask for more or less conservative output calls and by increasing the number of SNPs the identity test can become more accurate (30). The general selection rule would still, however, apply to these tailored SPIA assays. The SNPs must still be genetically unlinked and systematically spread across the genome so as to represent all the chromosomes. Regions of known recurring deletion in tumor samples must be avoided. SNP probes with similar annealing temperatures can be selected to facilitate the test implementation in case of multiplex PCR-based assay. SPIA can track genomic drift across passages, look for replicated samples (in a set of patients) and verify matched tissues from the same individual (for example, normal and tumor tissue of the same patient). In this last case, one can adjust SPIA in order to force false positive identity calls. One current alternative application of SPIA is in the upfront analytical phase of a large genomic study. For example, in the process of performing extensive genomic characterization of prostate cancer cell lines, xenografts and human tissues using SNP array analysis and extensive sequencing for known mutations, SPIA was employed to look for similar samples (unpublished data). Interestingly, SPIA identiﬁed a xenograft sample as being genotypically very similar to one of the tumor tissue samples (see Supplementary Material ‘Genotype distance between a rapid autopsy tissue and a xenograft’). Further investiga- tion determined that the xenograft and primary tumor samples were derived from the same individual. Without the SPIA analysis, the relationship between these samples would have gone unnoticed and may have led to over- interpretations of the associated genomic alterations. Thus, SPIA allows rapid and accurate barcoding of many sample types, including cell lines, embryonic stem cells and DNA from large numbers of clinical samples. We envision that researchers would run the SPIA assay in their laboratory on each new DNA sample at the beginning of experiments and refer to an on-line publicly available data bank for correct identiﬁcation of their cell line as a quality assurance measure. Investigators can check the identity of a given cell line over many passages and conﬁrm that no contamination has occurred. This might be critically important when two or more labora- tories are comparing results of experiments using the same cell lines. In addition to individual research groups, large organizations or institutes that maintain cell lines such as the ATCC can employ this method to credential the lines that they carry. We also envision that core facilities at research centers may provide this assay as a service. Nucleic Acids Research, 2008, Vol. 36, No. 7 2455 Researchers would send out an aliquot of DNA to external facilities, which using such platforms as the ABI PRISM SNaPshotTM Multiplex kit (Applied Biosystems) (31), Sequenom mass spectrometer system or other systems, would obtain conﬁrmation of the cell line identity. As a proof of principle, we demonstrated that the Sequenom mass spectrometer system can be used of this purpose by testing 34 SNPs from the SPIA assay. As the NIH and other governmental agencies explore means of making the best use of limited research funds, careful annotation of samples will play a critical role. The SPIA assay and other similar methods should become the standard for validation of sample provenance. SUPPLEMENTARY DATA Supplementary Data are available at NAR Online. ACKNOWLEDGEMENTS Supported by the NIH Prostate SPORE at the Dana- Farber/Harvard Cancer Center NCI P50 CA090381 (FD, RB, WRS and MAR), and the University of Michigan NCI P50 CA69568 (MAR), R01AG21404 (FD, MAR), R01CA109038 (WRS), the Prostate Cancer Foundation (FD) and the Department of Defense Fellowship Award PC040638 (RB). The authors would like to acknowledge the support of Robert Vessella (NCI/NIH Prostate SPORE, University of Washington and Fred Hutchinson Cancer Research Center grant P50 CA097186), Kirsten D Mertz and Barbara Weir for providing us with cell line genotype data. The authors are grateful to Liuda Ziaugra, Alissa C Baker and to Kelly Lamb for technical support and to Andrea Sboner, Wendy M Winckler and Sunita Setlur for critical discussion on this study. Funding to pay the Open Access publication charges for this article was provided by Prostate Cancer Foundation (FD). Conﬂict of interest statement. None declared. REFERENCES 1. 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