High‐resolution genomic and expression analyses of copy number alterations in breast tumors

Abstract

Analysis of recurrent DNA amplification can lead to the identification of cancer driver genes, but this process is often hampered by the low resolution of existing copy number analysis platforms. Fifty-one breast tumors were profiled for copy number alterations (CNAs) with the high-resolution Affymetrix 500K SNP array. These tumors were also expression-profiled and surveyed for mutations in selected genes commonly mutated in breast cancer (TP53, CDKN2A, ERBB2, KRAS, PIK3CA, PTEN). Combined analysis of common CNAs and mutations revealed putative associations between features. Analysis of both the prevalence and amplitude of CNAs defined regions of recurrent alteration. Compared with previous array comparative genomic hybridization studies, our analysis provided boundaries for frequently altered regions that were approximately one-fourth the size, greatly reducing the number of potential alteration-driving genes. Expression data from matched tumor samples were used to further interrogate the functional relevance of genes located in recurrent amplicons. Although our data support the importance of some known driver genes such as ERBB2, refined amplicon boundaries at other locations, such as 8p11-12 and 11q13.5-q14.2, greatly reduce the number of potential driver genes and indicate alternatives to commonly suggested driver genes in some cases. For example, the previously reported recurrent amplification at 17q23.2 is reduced to a 249 kb minimal region containing the putative driver RPS6KB1 as well as the putative oncogenic microRNA mir-21. High-resolution copy number analysis provides refined insight into many breast cancer amplicons and their relationships to gene expression, point mutations and breast cancer subtype classifications. This article contains Supplementary Material available at http://www.interscience.wiley.com/jpages/1045-2257/suppmat.

Frequent genetic differences between matched primary and metastatic breast cancer provide an approach to identification of biomarkers for disease progression

“…Previous publications describing the parallel analysis of gene copy number changes and mRNA expression in primary tumors of breast cancer have reported a strong correlation between the findings derived from both types of analysis. 7,[20][21][22]31 On the other hand, the aberration class I cases are the minority in our study and all the remaining patients showed more or less pronounced differences in gene DNA copy number profiles. This would presumably result in dissimilarity in the levels of transcripts produced from regions affected by gains/amplifications or deletions.…”

“…The experimental approach we took is also sensitive and robust, which is well illustrated by a strong correlation between findings from our work and previously published reports of breast cancers focusing on primary tumors. [7][8][9][10] Our methodology is also insensitive to DNA CNV that might be present in normal DNA of the studied patients.…”

“…We identified common regions of aberrations frequently present as gains at 1q, 8q, 11q, 16p, 17q and 20q as well as deletions at 1p, 8p, 11q, 16q and 17p and the majority of our observations were consistent with previous analyses of primary tumors from breast cancer patients. 7,9,19 Supplementary Figure 2 shows the overall frequency plots of aberrations combined for all primary tumors versus all metastases. No statistically significant difference in frequency of aberrations between these two groups was detected using Fisher's exact test.…”

“…Multiple genomic aberrations found in primary tumors have been linked to poor prognosis for the patient. [7][8][9][10] Primary tumors and matched ALN metastases from breast cancer patients have been studied by low-resolution genomic approaches for assessment of copy number changes, such as metaphase CGH, allelic imbalance and low-resolution array-CGH. [11][12][13][14] In one report using metaphase CGH, 12 divergence between the primary tumors and ALN metastases was noted in several cases.…”

A whole-genome RNAi screen identifies an 8q22 gene cluster that inhibits death receptor-mediated apoptosis

“…Copy number values for specific genes were calculated as the mean copy number value for the probe sets bounding the gene location and all intervening probe sets using the segmented data. Regions of significant gains were identified by using the GISTIC algorithm as described previously (37).…”