LUSC/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 8 38290643 38329070 31.728107 1.77741 2.161259 0.408451
Amp 8 38343904 38366263 31.728107 1.771282 2.155292 0.408451
Amp 8 38369607 38389970 30.458883 1.718822 2.104214 0.408451
Amp 8 38284229 38289678 30.016184 1.700781 2.084005 0.408451
Amp 8 38397918 38398857 29.137204 1.660195 2.047561 0.408451
Amp 8 38393432 38397898 29.137204 1.648417 2.035663 0.408451
Amp 3 184087177 184312238 29.137204 1.642966 1.802809 0.852113
Amp 3 183853170 183931470 28.972385 1.628232 1.79645 0.852113
Amp 3 183945255 183962603 28.972385 1.626656 1.803682 0.84507
Amp 3 184313822 184507464 28.972385 1.622031 1.793181 0.852113
Del 9 21947144 21949051 33.922976 0.753291 0.77765 0.584507
Del 9 21953430 21968443 32.335187 0.730673 0.768841 0.577465
Del 9 21946078 21946914 32.262045 0.727666 0.767258 0.584507
Del 9 21993981 21995394 31.623516 0.71538 0.763761 0.584507
Del 9 21968615 21993713 31.390753 0.709073 0.764535 0.577465
Del 9 21941384 21944953 31.17171 0.706067 0.762191 0.584507
Del 9 21999960 22025479 31.170112 0.704185 0.764122 0.577465
Del 9 21923125 21939761 30.487667 0.694872 0.762533 0.577465
Del 9 21908580 21922525 29.336198 0.679811 0.758316 0.577465
Del 9 22027369 22062730 29.258482 0.677099 0.755057 0.577465
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