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. 2001 Jul 3;98(14):7952-7.
doi: 10.1073/pnas.151241198.

Quantitative analysis of chromosomal CGH in human breast tumors associates copy number abnormalities with p53 status and patient survival

A N Jain  1 , K Chin, A L Børresen-Dale, B K Erikstein, P Eynstein Lonning, R Kaaresen, J W Gray
Affiliations

Quantitative analysis of chromosomal CGH in human breast tumors associates copy number abnormalities with p53 status and patient survival

A N Jain et al. Proc Natl Acad Sci U S A. .

Abstract

We present a general method for rigorously identifying correlations between variations in large-scale molecular profiles and outcomes and apply it to chromosomal comparative genomic hybridization data from a set of 52 breast tumors. We identify two loci where copy number abnormalities are correlated with poor survival outcome (gain at 8q24 and loss at 9q13). We also identify a relationship between abnormalities at two loci and the mutational status of p53. Gain at 8q24 and loss at 5q15-5q21 are linked with mutant p53. The 9q and 5q losses suggest the possibility of gene products involved in breast cancer progression. The analytical techniques are general and also are applicable to the analysis of array-based expression data.

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Figures

Figure 1
Figure 1
Full CGH profile for a normal (dashed line) and a tumor (solid line). The tumor shows marked abnormalities on chromosomes 1, 8, and 9 in addition to several other significant deviations.
Figure 2
Figure 2
VMRL display of 52 CGH profiles and 8 normal profiles. Each CGH profile is displayed along the x axis, with the y axis being log (relative copy number), z being overall patient survival, and the color of the lines encoding patient status (purple is deceased, orange is alive, and white indicates a normal control). The top view shows all profiles, with the lowest survival furthest from the viewer. The bottom view shows the data thresholded by using the gray plane, to eliminate the variation caused by noise, and highlights a region on chromosome 9 that appears to be linked with poor survival.
Figure 3
Figure 3
(Upper) Correlation magnitude of copy-number variation with survival (Left). Cumulative distribution of correlation magnitudes under the null hypothesis (Right). None of the loci are significant at P = 0.05. (Lower) CGH variation energy across the genome (Left). Cumulative distribution of correlation magnitudes by using energy cutoffs of 0.0, 3.0, and 6.0 (Right). The P = 0.05 correlation thresholds are 0.35, 0.31, and 0.18, respectively.
Figure 4
Figure 4
Correlation magnitude of copy-number variation with survival (energy greater than 3.0). (Energy greater than 6.0 is marked with a +.) The 8q24 locus meets the P = 0.05 significance test, and the 9q13 locus meets a single-sided test.
Figure 5
Figure 5
Kaplan–Meier plot of patient survival for those with normal (dashed lines) vs. increased (solid lines) copy number at 8q24 (Upper), and normal vs. deleted copy number at 9q13 (Lower). Both aberrations yield statistically significant survival differences (P < 0.01).
Figure 6
Figure 6
Hierarchical clustering of tumors based on copy number across chromosomes 8 and 9. The CGH data are presented on a red to green (deletion to amplification) color scale. Patient survival also is displayed (black is low survival and green is high on a linear scale), and the p53 status of each tumor also is shown [black is wild type (WT) and green is mutant]. Centromeres are indicated with arrows. Both patient survival and p53 status appear to be related to the clustering. The clustering was single-linkage agglomerative clustering, using all 1,225 bins of log-transformed CGH data in computing distances.
Figure 7
Figure 7
Correlation of p53 status with copy number. There is a significant association between mutations in p53 and both 5q15-21 loss and 8q24 gain.
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