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. 2012 Jun;14(6):701-11.
doi: 10.1093/neuonc/nos072. Epub 2012 May 8.

DNA Fingerprinting of Glioma Cell Lines and Considerations on Similarity Measurements

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Free PMC article

DNA Fingerprinting of Glioma Cell Lines and Considerations on Similarity Measurements

Pierre Bady et al. Neuro Oncol. .
Free PMC article

Abstract

Glioma cell lines are an important tool for research in basic and translational neuro-oncology. Documentation of their genetic identity has become a requirement for scientific journals and grant applications to exclude cross-contamination and misidentification that lead to misinterpretation of results. Here, we report the standard 16 marker short tandem repeat (STR) DNA fingerprints for a panel of 39 widely used glioma cell lines as reference. Comparison of the fingerprints among themselves and with the large DSMZ database comprising 9 marker STRs for 2278 cell lines uncovered 3 misidentified cell lines and confirmed previously known cross-contaminations. Furthermore, 2 glioma cell lines exhibited identity scores of 0.8, which is proposed as the cutoff for detecting cross-contamination. Additional characteristics, comprising lack of a B-raf mutation in one line and a similarity score of 1 with the original tumor tissue in the other, excluded a cross-contamination. Subsequent simulation procedures suggested that, when using DNA fingerprints comprising only 9 STR markers, the commonly used similarity score of 0.8 is not sufficiently stringent to unambiguously differentiate the origin. DNA fingerprints are confounded by frequent genetic alterations in cancer cell lines, particularly loss of heterozygosity, that reduce the informativeness of STR markers and, thereby, the overall power for distinction. The similarity score depends on the number of markers measured; thus, more markers or additional cell line characteristics, such as information on specific mutations, may be necessary to clarify the origin.

Figures

Fig. 1.
Fig. 1.
Heatmap based on the Sørensen similarity index between cell lines of the glioma cell line panel. The similarity index was computed using the 16 marker set (15 STR markers + AMEL) on 39 glioma cell lines (Glioma-CL) (Table 1). Color key and density plot are provided in the additional graphic (left). Similarity values estimated between cell lines with different origin are comprised in the range from 0.1 (dark blue) to 0.7 (yellow). High similarity values (range, 0.9–1.0) are observed for cell lines with same origin and correspond to the red and dark red squares.
Fig. 2.
Fig. 2.
Heatmap based on the Sørensen similarity index computed between the DSMZ dataset and the glioma cell line panel for markers containing similarity values superior to the cutoff of 0.8. The similarity index was computed on the 9 marker set (8 STR markers + AMEL). Colour key and density plot are provided in the additional graphic (at left). Similarity values estimated between cell lines with different origin are included in the range 0.1 (dark blue) to 0.7 (yellow). High similarity values observed (range, 0.9–1.0) for cell lines with same origin correspond to the red and dark red squares. The similarity score of 0.8 (orange) may or may not reflect similarity (see text for details). Of note, multiple cell lines with identity scores of 0.9 or 1 are extracted from the DSMZ database. Some identify redundant entries, while others reflect genetically modified cell lines of the same origin, or previously known misidentifications. However, 1 previously unknown identity was uncovered between SF767 and ME-180 with a score of 1, suggestive of cross-contamination. (See text for explications)
Fig. 3.
Fig. 3.
Distributions of observed and simulated similarity values. Random similarity distributions were calculated for the 3 datasets (Glioma-CL, NCI-60, and DSMZ) using 16 markers, where available, or 9 markers based on rearranged profiles of markers randomly and independently selected from the set of genotypes in the given dataset. Quantile-quantile plot (QQ-plot) representations shown in the third column of the Figure provide a graphical comparison of observed versus random similarity distributions for each dataset, the red crosses represent observed values above the cutoff of 0.8. In the density plots, the number of similarity values inferior and superior to the cutoff of 0.8 is given in the left and right top of the respective panels. For each dataset, the number of similarity values is equal to (n× nn)/2 where n corresponds to the number of cell lines contained in the collection (more details in statistical section). Red arrows identify the maximal observed similarity (MOS) and maximal simulated similarity (MSS). The grey dotted lines point to the limit of high similarity area (range, 0.8–1.0).
Fig. 4.
Fig. 4.
Representation of the estimation of maximal simulated similarity (MSS) in function of the number of markers. The 3 datasets (Glioma-CL, NCI-60, and DSMZ) were used to simulate similarity scores and to compute their mean and median, as well as the 95% confidence intervals (CIs) for the median (dark grey) and the minimum-maximum intervals (light grey). The arrows indicate the position of the 9 and 16 marker profiles commonly used. For 9 markers we observed that the cutoff of 0.8, suggested by previous studies, (dotted lines) is included in the confidence region defined by the percentile method. This means that at least 1 false positive value >0.8 can be expected in >5% of the datasets. In contrast, this value is outside the confidence region for the 16 markers for both datasets. This means that it is not very probable to obtain a MSS value equal or superior to 0.8 by hazard. The second series of graphics provide accuracy measures of the estimation of MSS, standard deviation (SD), and median absolute deviation (MAD), per number of markers. These figures indicate that for increasing numbers of markers used the SD and MAD are reduced, consequently the MSS values are estimated with better accuracy.

Comment in

  • The value of cell line validation.
    Yung WK. Yung WK. Neuro Oncol. 2012 Jun;14(6):675. doi: 10.1093/neuonc/nos132. Neuro Oncol. 2012. PMID: 22669103 Free PMC article. No abstract available.

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