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GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers

Craig H Mermel1234, Steven E Schumacher1234, Barbara Hill1, Matthew L Meyerson1234, Rameen Beroukhim1234* and Gad Getz1*

Author Affiliations

1 Cancer Program, The Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142, USA

2 Department of Medical Oncology, Dana Farber Cancer Institute, 44 Binney Street, Boston, MA 02115, USA

3 Department of Cancer Biology, Dana Farber Cancer Institute, 44 Binney Street, Boston, MA 02115, USA

4 The Center for Cancer Genome Discovery, Dana Farber Cancer Institute, 44 Binney Street, Boston, MA 02115, USA

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Genome Biology 2011, 12:R41  doi:10.1186/gb-2011-12-4-r41

Published: 28 April 2011

Additional files

Additional file 1:

Supplementary Methods. Supplementary Methods contains the full description of the GISTIC2.0 method and details of the specific analyses presented in this manuscript.

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Additional file 2:

Supplementary Figure S1: Ziggurat Deconstruction. (a) A hypothetical segmented chromosome (green line) is deconstructed with the simplified procedure used by Ziggurat Deconstruction (ZD) to initialize background SCNA rates. Dotted red and blue lines denote the length and amplitude of amplified and deleted SCNAs, respectively, while solid red and blue lines denote the result of merging the SCNA with the closest adjacent segment. (b) The same hypothetical segmented chromosome (green line) is deconstructed using the more flexible procedure of subsequent rounds of ZD. Here, the ZD is performed with respect to up to two basal levels (dotted magenta lines) that are fit to the data, allowing for amplified and deleted SCNAs to be superimposed.

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Additional file 3:

Supplementary Figure S2: distribution of SCNA length and amplitudes. Two-dimensional histogram showing the frequency (z-axis) of copy number events as a function of length (x-axis) and amplitude (y-axis). Frequency is plotted on a log-scale to facilitate visualization of very low frequency copy number events.

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Additional file 4:

Supplementary Table S1: comparison of amplitude and length-based filtering of SCNAs. Supplementary Table 1 compares the GISTIC results obtained using low and high amplitude thresholds with those obtained using a focal length threshold on 178 GBM samples.

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Additional file 5:

Supplementary Figure S3: distribution of driver length and amplitudes. Driver SCNAs are typically of shorter length and higher amplitude than random passenger SCNAs. (a,b) Here we show the cumulative frequency distribution of SCNA amplitudes (a) and lengths (b) for SCNAs covering significantly amplified regions identified by GISTIC ('Driver SCNAs', red line) or by a similar number of randomly chosen non-driver regions ('Random SCNAs', blue line).

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Additional file 6:

Supplementary Table S2: comparison of GeneGISTIC and standard GISTIC deletions analysis. Supplementary Table 2 compares the GISTIC results obtained using the standard GISTIC deletions analysis with those obtained using GeneGISTIC on 178 GBM sanples.

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Additional file 7:

Supplementary Figure S4: GeneGISTIC versus standard GISTIC. (a) GeneGISTIC helps identify genes subject to non-overlapping deletion, such as NF1. The left panel shows the 12 samples with focal deletions affecting NF1, many of which do not overlap. As a result, the standard GISTIC marker score (blue line, right panel) has multiple local maxima over NF1. By contrast, the GeneGISTIC score counts all of these deletions as contributing to the NF1 score, resulting in a score for NF1 (red line, right panel) that is significantly greater than that assigned to any of the individual markers covering NF1. (b) GeneGISTIC does not score deletions occurring outside of genes. The left panel shows a region of focal deletion occurring just outside the PCHD9 gene on chromosome 13. These deletions result in a peak in the markers deletion score (blue line, right panel) that is not detected by GeneGISTIC.

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Additional file 8:

Supplementary Table S3: new peaks detected by arbitrated peel-off. Supplementary Table 3 compares the GISTIC results obtained using the standard peel-off algorithm with those obtained using arbitrated peel-off on 178 GBM samples.

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Additional file 9:

Supplementary Figure S5: total recovery of secondary driver peaks. This figure shows the results from 10,000 simulations of 300 samples in which a primary driver event is present in 10% of samples and a secondary driver event is present in 5% of samples. In these simulations, we vary the fraction of overlap between driver events from 100% (total dependence) to 0% (total independence). Here we present to the total recovery of the secondary driver peak in GISTIC runs using arbitrated peel-off (left panel) or the standard peel-off (right panel). The red (left panel) or blue (right panel) lines show the fraction of secondary driver peaks identified in independent GISTIC peaks (that is, not containing the primary driver event), as is shown in Figure 4b. The black lines show the fraction of secondary driver peaks identified in dependent peaks (that is, a peak containing both the primary and secondary driver events), and the green lines show the total recall of secondary driver peaks (in any peak). Error-bars representing the mean ± standard error of the mean are drawn, but may be smaller than the point used to represent the mean and hence not be visible.

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Additional file 10:

Supplementary Figure S6: comparison of RegBounder to theoretically optimal peaks. Comparison between the peak region sizes obtained by RegBounder (green line) with the theoretically minimal peak region sizes (black line) that could be obtained by a similarly confident peak finding algorithm (Supplementary Methods in Additional file 1) at 50% (left) and 95% (right) confidence. Error-bars representing the median ± standard error of the mean are drawn, but may be smaller than the points used to represent the median and hence not be visible.

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