title: SNP panel identification assay (SPIA): a genetic-based assay for the identification of cell lines.
abstract: Translational research hinges on the ability to make observations in model systems and to implement those findings into clinical applications, such as the development of diagnostic tools or targeted therapeutics. Tumor cell lines are commonly used to model carcinogenesis. The same tumor cell line can be simultaneously studied in multiple research laboratories throughout the world, theoretically generating results that are directly comparable. One important assumption in this paradigm is that researchers are working with the same cells. However, recent work using high throughput genomic analyses questions the accuracy of this assumption. Observations by our group and others suggest that experiments reported in the scientific literature may contain pre-analytic errors due to inaccurate identities of the cell lines employed. To address this problem, we developed a simple approach that enables an accurate determination of cell line identity by genotyping 34 single nucleotide polymorphisms (SNPs). Here, we describe the empirical development of a SNP panel identification assay (SPIA) compatible with routine use in the laboratory setting to ensure the identity of tumor cell lines and human tumor samples throughout the course of long term research use.
authors.affiliation: Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA.
authors.forename: Francesca
authors.initials: F
authors.lastname: Demichelis
authors.forename: Heidi
authors.initials: H
authors.lastname: Greulich
authors.forename: Jill A
authors.initials: JA
authors.lastname: Macoska
authors.forename: Rameen
authors.initials: R
authors.lastname: Beroukhim
authors.forename: William R
authors.initials: WR
authors.lastname: Sellers
authors.forename: Levi
authors.initials: L
authors.lastname: Garraway
authors.forename: Mark A
authors.initials: MA
authors.lastname: Rubin
mesh_terms: D045744:Cell Line, Tumor; D003627:Data Interpretation, Statistical; D015894:Genome, Human; D005838:Genotype; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D020641:Polymorphism, Single Nucleotide