LUAD/00: Gistic2 Copy Number Analysis
Overview
Introduction

This pipeline calculates the significant amplified or deleted genomic regions within the given tumor data, using GISTIC version 2.

Summary

With data describing chromosomal aberrations in large tumor sets, the aberrations that drive tumorigenesis and the oncogenes and TSGs they most likely target can be identified if the following 4 issues are addressed: (i) The aberrations in each of the tumors must be accurately mapped; (ii) Driver aberrations that rise above the background rate of random passenger aberrations must be identified; (iii) For each driver aberration, the loci most likely to contain the targeted oncogenes or TSGs must be identified; (iv) Tumors must be classified as to whether they are aberrant at the predicted driver loci, so that the effects of those aberrations can be studied. The GISTIC algorithm tackles each issue in individual stages as discussed in [1] and [3].

Results

Figure 1.  Genomic positions of amplified regions: the X axis represents the normalized amplification signals (top) and significance by q-value (bottom). The green line represents the significance cutoff at q-value=0.25

Figure 2.  Genomic positions of deleted regions: the X axis represents the normalized deletion signals (top) and significance by q-value (bottom). The green line represents the significance cutoff at q-value=0.25

Table 1.  Get Full Table The top 10 amplified and deleted regions.

Type Chromosome Start End X.log10.q.value. G.score average.amplitude frequency
Amp 14 35019123 35075426 6.334831 1.315172 1.998012 0.303571
Amp 14 36428789 36493378 6.334831 1.261653 1.829873 0.321429
Amp 14 35078830 35136078 6.334831 1.260926 1.926947 0.303571
Amp 14 34953429 35018769 6.334831 1.256537 1.911249 0.303571
Amp 14 35921657 36428449 6.334831 1.243438 1.91117 0.303571
Amp 14 35136254 35918647 6.195262 1.202291 1.850132 0.303571
Amp 14 36618446 36692303 5.420839 1.121729 1.65675 0.321429
Amp 14 36498222 36610671 5.322083 1.107313 1.638914 0.321429
Amp 14 36693340 36781441 4.960675 1.067791 1.590014 0.321429
Amp 14 34220366 34318406 4.878701 1.046146 1.574892 0.321429
Del 9 21953430 21989240 24.714534 0.806208 0.764166 0.5
Del 9 21999960 22037932 24.714534 0.801844 0.764871 0.5
Del 9 22043652 22108058 23.606481 0.772116 0.762044 0.482143
Del 9 21989615 21995394 22.987023 0.756101 0.762162 0.482143
Del 9 21937957 21949051 22.673439 0.746731 0.747208 0.5
Del 9 22108102 22137033 22.673439 0.746454 0.759324 0.464286
Del 9 21753566 21934287 21.642512 0.721069 0.744039 0.482143
Del 9 21747432 21750396 20.337433 0.691342 0.740301 0.464286
Del 9 22137715 22209912 20.241265 0.686977 0.739959 0.464286
Del 9 22210791 22293935 19.422313 0.667702 0.747455 0.446429
Methods & Data

GISTIC takes segmented copy number signals as input and calculates the aggregated significance across samples, probe-by-probe. Statistical significance (q-value) is based on permutation-based test by randomizing probe labels across the genome. As shown above, the method outputs genomic views of significantly amplified and deleted regions, as well as a table of genes with gain or loss scores.

References
[1] Beroukhim et al, Assessing the significance of chromosomal aberrations in cancer: Methodology and application to glioma, Proc Natl Acad Sci U S A. Vol. 104(50):2007
[3] Mermel et al, GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers, Genome Biology In Press:2011