Open Access

Comprehensive characterization of the DNA amplification at 13q34 in human breast cancer reveals TFDP1 and CUL4A as likely candidate target genes

  • Lorenzo Melchor1, 6,
  • Laura Paula Saucedo-Cuevas1,
  • Iván Muñoz-Repeto1,
  • Socorro María Rodríguez-Pinilla2,
  • Emiliano Honrado1,
  • Alfredo Campoverde3,
  • Jose Palacios2,
  • Katherine L Nathanson4,
  • María José García1, 5 and
  • Javier Benítez1, 5Email author
Breast Cancer Research200911:R86

https://doi.org/10.1186/bcr2456

Received: 14 July 2009

Accepted: 8 December 2009

Published: 8 December 2009

Abstract

Introduction

Breast cancer subtypes exhibit different genomic aberration patterns with a tendency for high-level amplifications in distinct chromosomal regions. These genomic aberrations may drive carcinogenesis through the upregulation of proto-oncogenes. We have characterized DNA amplification at the human chromosomal region 13q34 in breast cancer.

Methods

A set of 414 familial and sporadic breast cancer cases was studied for amplification at region 13q34 by fluorescence in situ hybridization (FISH) analysis on tissue microarrays. Defining the minimal common region of amplification in those cases with amplification at 13q34 was carried out using an array-based comparative genomic hybridization platform. We performed a quantitative real-time - polymerase chain reaction (qRT-PCR) gene expression analysis of 11 candidate genes located within the minimal common region of amplification. Protein expression levels of two of these genes (TFDP1 and CUL4A) were assessed by immunohistochemical assays on the same tissue microarrays used for FISH studies, and correlated with the expression of a panel of 33 antibodies previously analyzed.

Results

We have found 13q34 amplification in 4.5% of breast cancer samples, but the frequency increased to 8.1% in BRCA1-associated tumors and to 20% in basal-like tumors. Tumors with 13q34 amplification were associated with high grade, estrogen receptor negativity, and expression of EGFR, CCNE, CK5, and P-Cadherin, among other basal cell markers. We have defined a 1.83 megabases minimal common region of genomic amplification and carried out mRNA expression analyses of candidate genes located therein, identifying CUL4A and TFDP1 as the most likely target genes. Moreover, we have confirmed that tumors with 13q34 amplification significantly overexpress CUL4A and TFDP1 proteins. Tumors overexpressing either CUL4A or TFDP1 were associated with tumor proliferation and cell cycle progression markers.

Conclusions

We conclude that 13q34 amplification may be of relevance in tumor progression of basal-like breast cancers by inducing overexpression of CUL4A and TFDP1, which are both important in cell cycle regulation. Alternatively, as these genes were also overexpressed in non-basal-like tumor samples, they could play a wider role in cancer development by inducing tumor proliferation.

Introduction

Breast cancer is a complex and heterogeneous disease, which is one of the most frequent causes of cancer deaths in developed countries. Most of breast cancer cases are sporadic; around 5% of breast cancer patients are considered as having hereditary breast cancer. These patients carry mutations in either BRCA1 [1] or BRCA2 [2] genes, but there also are familial breast cancer patients who do not carry mutations in BRCA1/2 and presumably have mutations in another unknown gene or gene(s) (termed non-BRCA1/2 or BRCAX patients). Over the last decade, many studies have shown that sporadic breast cancer can be grouped using molecular profiling into subtypes: basal-like, HER2-overexpressing, luminal A, and luminal B [35]. Researchers have identified molecular features that differentiate sporadic breast cancer from each group of familial breast cancers (BRCA1-, BRCA2-, or BRCAX-associated) [610]. However, our recent analyses have pointed out the striking similarities between sporadic and familial breast cancer in terms of the existence of breast cancer subtypes in both groups, as well as common patterns of genomic aberrations [11]. This finding may emphasize the interest in identifying molecular features that discriminate each of the sporadic and familial breast cancer subtypes, rather than comparing each group of familial breast cancer with sporadic tumors.

One common genomic aberration in breast cancer is high-level amplification. These aberrations may activate a profound increase in expression of genes within the amplification boundaries, crucial for the origin and progression of breast tumors. The most common regions of high-level amplification in breast cancer are 17q12 (targeting ERBB2), 11q13 (CCND1), 8q24 (MYC); 8p11-p12; 17q22-25; and 20q13 [12, 13]. Interestingly, recent genomic analyses have shown that the chromosomal amplification sites tend to differ among the molecular breast cancer subtypes as for instance: 17q12 in HER2-overexpressing tumors, 20q13 in luminal-B tumors, 11q13 in both luminal A and B tumors, or 13q34 in basal-like tumors [5, 11, 1416]. The definition of these chromosomal aberrations may elucidate genes crucial for the origin and progression of each breast cancer subtype. Taking this into account, we focused on a comprehensive characterization of 13q34 amplification (Amp13q34), a genomic aberration that we have recently found to be associated with basal-like breast cancers [11]. This amplification has been previously reported in squamous cell carcinomas [17], adrenocortical carcinomas [18], childhood medulloblastoma [19], hepatocellular carcinomas [20], and breast cancer [21], using conventional comparative-genomic hybridization (cCGH). Genes proposed to be the target genes of Amp13q34 include CUL4A, LAMP1, TFDP1, or GAS6 [20, 2226].

We have further characterized Amp13q34 in sporadic and familial breast cancer and defined its overall frequency, as well as its boundaries by fluorescence in situ hybridization (FISH) and array-based comparative genomic hybridization (aCGH) techniques. We have analyzed the gene- and protein-expression levels of candidate genes as well as their correlation with clinical and immunohistochemical (IHC) features. We propose that CUL4A and TFDP1 are likely the driver genes for this genomic amplification, leading higher tumor aggressiveness through deregulation of cell cycle.

Materials and methods

Patients and tumor samples

A total of 188 familial breast cancer patients belonging either to families with at least three women affected with breast and/or ovarian cancer, one of them diagnosed before 50 years of age, or to families with women affected with breast and/or ovarian cancer and at least one case of male breast cancer. All patients were screened for point mutations and large rearrangements in the BRCA1/2 genes using standard methods [27]. A series of 277 sporadic breast cancer samples came from the Spanish National Cancer Center (Spain) (172 samples), the Cancer Center of Solca (Ecuador) (86 cases), and the University of Pennsylvania (USA) (19). Written consent for tumor analyses and publication was obtained from the patient or their relative. This research study has been performed with the approval of an ethics committee.

DNA isolation from formalin-fixed paraffin embedded tumor tissues

Genomic DNA isolation from formalin-fixed paraffin embedded (FFPE) tumors was carried out as previously described [28]. Briefly, two 30-μm sections were obtained from FFPE tumors, treated with xylene, incubated in Glycine Tris-EDTA and NaSCN, and finally digested with proteinase K and purified with phenol chloroform. All sections were previously examined and dissected with a scalpel to ensure at least 70% content of tumor cells.

Array-based comparative genomic hybridization (aCGH)

Comparative genomic hybridization was done onto the 1 megabase (Mb) Bacterial Artificial Chromosome (BAC) clone array platform developed at the University of Pennsylvania. In few words, the platform is composed of 4134 BAC clones spaced at 1 Mb intervals, including a direct coverage of approximately 400 known cancer genes [29]. DNA probe labeling, aCGH protocol, and array data analysis have been described elsewhere [28]. Briefly, normalized aCGH data were analyzed using the Binary Segmentation algorithm implemented in the Insilico CGH software [30]. This algorithm depicts genomic segments showing estimative copy number values in log2ratio, which are calculated from the mean log2ratio of all the clones within that segment. As a result, it decreases both the dynamic range of the hybridization and the high noise usually present in hybridizations of DNA from FFPE tissues [31, 32]. Therefore, we considered those segments with log2ratio ≥ 0.1 as gains, whereas those with log2ratio ≤ -0.1 were categorized as losses. Segments altered with DNA amplifications were considered when log2ratio ≥ 0.4. A collection of genomic imbalances previously confirmed in breast cancer cell lines and breast tumor samples allowed us to set these thresholds.

Fluorescence in situhybridization (FISH)

FISH analysis was carried out on the original group of six tumor samples with Amp13q34 found in the aCGH studies to verify their amplification levels. Then, we performed the FISH study on two familial and three sporadic breast cancer tissue microarrays (TMA) containing 156 and 258 breast tumor samples respectively, in order to quantify the Amp13q34 rate in a larger collection. From the familial breast cancer series, 44 samples were already included in our aCGH analysis [28]. These TMA have been described elsewhere [9, 33, 34]. Briefly, tumor areas of the breast tumor samples were carefully selected from hematoxilin and eosin-stained sections and delimited on the individual paraffin blocks. Then, two tissue cores from the selected tumor area of each specimen were obtained and included in the TMA.

The test FISH probe was composed of three BAC clones mapping to the 13q34 chromosomal region, labeled with dUTP-SpectrumOrange (Vysis, Inc. Downers Grove, IL, USA): RP11-391H12 (AL136221.38, mid-position 113.96 Mb), RP11-102K13 (AL160251.29, 114.09 Mb) and RP11-230F18 (AL442125.13, 114.21 Mb). In addition, three BAC clones from the 13q12.11 chromosomal region were used as copy number reference for chromosome 13, labeled with dUTP-SpectrumGreen (Vysis, Inc. Downers Grove, IL, USA): RP11-301J16 (AL137001.23, 19.63 Mb), RP11-408E5 (AL139327.18, 19.77 Mb), and RP11-385E5 (AL356259.11, 19.92 Mb). FISH analysis was done according to Vysis' instructions, with slight modifications. An average of 110 (50 to 200) well-defined nuclei was analyzed by scoring the number of single copy 13q34 and 13q12.11 signals. We considered DNA amplification when > 50% of tumor cells showed more than three times as many 13q34 signals as 13q12.11 copy signals.

RNA isolation from formalin-fixed paraffin embedded tissues and cDNA synthesis

Total RNA was obtained from six breast tumors with Amp13q34 (three BRCA1-associated, one BRCA2-associated, one BRCAX-associated, and one sporadic breast tumor samples) and one breast cancer cell line that also carried the amplification (MDA-MB-157); as well as from 13 breast tumors that did not exhibit DNA copy number changes at 13q34. Importantly, 13q34 DNA copy number status for all tumoral specimens had been assessed by either aCGH or FISH analyses. All tumor samples were FFPE material, and RNA isolation was carried out as described before [35]. Briefly, a standard tissue sample deparaffinization was performed using xylene and alcohols. Then, samples were incubated in a digestion buffer (10 mM TrisHCl pH 8.0; 0.1 mM EDTA; 2% SDS and 500 μg/ml proteinase K). Nucleic acids were purified with standard phenol-chloroform-isoamyl alcohol procedures, followed by a precipitation with isopropanol, glycogen and sodium acetate. After DNase treatment (Ambion Inc., Austin, TX, USA) at 37°C for 30 minutes, RNA was finally processed for cDNA synthesis using M-MLV retrotranscriptase enzyme (Invitrogen, Carlsbad, CA, USA).

Quantitative real time polymerase chain reaction (qRT-PCR) for mRNA expression

Assays were designed using the Roche Applied Science Universal Probe Library website [36] (Roche Diagnostics, Basel, Switzerland) for all target genes and endogenous control (see Additional file 1). qRT-PCR assays were set up in triplicates and performed using the ABI Prism 7900HT Sequence Detection System (Applied Biosystems, Foster City, CA, USA). Relative expression was determined using the free access software qBase [37] based on a modification of the classic delta-delta Ct method that allows for PCR efficiency correction. Appropriate positive and negative controls including non-retrotranscribed RNA for each sample were run for the experiment.

Histology and immunohistochemistry

To associate genomic copy number aberration at 13q34 with changes in protein levels of the candidate genes, we carried out antibody specific staining on the same TMA used in the FISH analyses.

IHC assays were performed by the Envision method (Dako, Glostrup, Denmark) with a heat-induced antigen retrieval step. TMA sections were immersed in 10 mM boiling sodium citrate at pH 6.5 for two minutes in a pressure cooker. Between 150 and 200 cells per core were scored to determine the percentage of cells with positive nuclei or cytoplasm, depending upon the marker. We evaluated the protein expression level for the two candidate genes within Amp13q34 that were significantly overexpressed in the qRT-PCR analyses: CUL4A and TFDP1. As CUL4A expression in the cytoplasm was found in most of cases, we delineated four levels of staining: 0 (null staining), 1 (low staining), 2 (medium staining), and 3 (strong staining), which we grouped into negative (0), mid-level (1, 2) and positive (3) in subsequent analyses. TFDP1 is localized in the cell nucleus, so the median percentage of positive nuclei in all cases was used as the threshold to discriminate between negative (< 25%) or positive expression (≥ 25%). Tumor samples with more than 60% of stained cells (the median percentage of stained cells in only positive cases) were further categorized as highly expressed for the association with Amp13q34.

Other proteins also were studied to establish potential associations. We had previously evaluated nuclear staining for estrogen receptor (ER), progesterone receptor (PR), p53, Ki-67, cyclins D1, D3, E, and A; p16, p27, p21, CDK1, CDK2, CDK4, Skp2, retinoblastoma protein (Rb), E2F1, E2F6, MDM2, topoisomerase IIα, survivin, and CHEK2; cytoplasmic staining for BCL2, vimentin, cytokeratin 5/6 (CK5/6), cytokeratin 8 (CK8), and cyclin B1; and membrane staining for E-cadherin, P-cadherin, B-catenin and G-catenin [34]. HER-2 expression was evaluated according to the four-category (0 to 3+) DAKO system proposed for the evaluation of the HercepTest, and HER-2 expression of 3+ was the only value considered positive [34]. Antibodies, dilutions, suppliers, and thresholds used for analyses are listed in Additional file 2.

Statistical analyses

In order to compare the mRNA expression levels between the different groups (tumors with or without Amp13q34), we applied the U-Mann Whitney test. We also determined associations between tumor groups with both clinical features and IHC markers using the chi square test, with the two-tailed Fisher's exact test correction when needed. The statistical software SPSS for Windows (SPSS, Inc., Chicago, IL, USA) was used to perform these statistical comparisons.

Results

Frequency and genomic definition of the 13q34 amplification

We have previously reported that about 5% of both familial and sporadic breast cancers contain a high-level amplification at 13q34 (Amp13q34) using chromosomal- and array-based comparative genomic hybridization techniques (cCGH, and aCGH, respectively) (Table 1 and Figure 1a) [21, 28]. Now, we have extended the study of the rate of Amp13q34 to a larger cohort of 414 familial and sporadic breast cancer samples using FISH technique. We have found that Amp13q34 is present in around 4.5% of breast cancer samples (Figure 1b-c), although the rate differs slightly among tumor classes, being higher in BRCA1- (8.1%) than in BRCA2- or non-BRCA1/2- (< 3.0%) associated cancers (Table 1). However, these differences are not statistically significant, maybe due to the low number of cases (data not shown). Noteworthy, the level of amplification in this tumor cohort never exceeded a 13q34:13q12.11 copy ratio of seven, being the median ratio of 4.5 as many 13q34 as 13q12.11 copy signals (Figure 1c). This shows that 13q34 region, albeit that it is affected with amplification, is not altered with the same magnitude as other classical high-level DNA amplification sites, such as 17q12 (ERBB2). As expected, no tumor sample analyzed by both cCGH/aCGH and FISH showed discrepancies in copy number values.
Figure 1

Genomic definition of the human 13q34 amplification I. (a) Chromosome 13 genomic profiles by conventional-comparative genomic hybridization (cCGH) of four breast cancer cases analyzed in a previous study by our group [21]. (b) FISH assays, detecting the 13q34 region (red probe) and the 13q12.11 region as a reference (green probe), show amplification at the 13q34 region in nuclei cell from a breast cancer sample (left, seven to eight red and one to two green signals) and in a metaphase of the MDA-MB-157 breast cancer cell line (right, six red and two green signals). (c) Boxplots displaying the range of copy signals for 13q12.11 (green box) and 13q34 (red box) in those cases with Amp13q34 found in our FISH screening.

Table 1

DNA amplification rates at the region 13q34 in different analyses performed by our group

  

DNA amplification rate % (n)

Chromosomal region

Analysis

BRCA1

BRCA2

Non-BRCA1/2

Sporadic

Total

13q31-q34

cCGH*

11.6 (3/26)

5.6 (1/18)

0 (0/36)

-

5.0 (4/80)

13q34

aCGH**

15.8 (3/19)

4.2 (1/24)

0 (0/31)

10.5 (2/19)

6.5 (6/93)

13q34

FISH+

9.5 (2/21)

0 (0/14)

4.8 (3/66)

4.2 (8/192)

4.4 (13/293)

Total

 

8.1 (3/37)

2.6 (1/38)

3.3 (3/92)

4.7 (10/211)

4.5 (17/378)

The percentage of cases with Amp13q34 is shown.

*Melchor et al., 2005 [21]. Genomic amplification is considered when CGH ratio > 1.5 (see Figure 1a).

**Melchor et al., 2007 [28]. Amplification is regarded as genomic segments with log2ratio ≥ 0.4.

+Current study. Numbers correspond to cases in the tissue microarrays that were analyzable. We considered amplification when in tumor areas > 50% tumor cells showed > 3 times as many 13q34 signals as 13q12.11 copy signals.

To define the minimal common region of amplification, we used data from a previous aCGH analysis [28]. A genomic representation of those cases with Amp13q34 (three BRCA1-, one BRCA2-, one BRCAX-, one sporadic-breast cancers and one breast cancer cell line, MDA-MB-157) is shown in Figure 2a. Chromosome 13 has two different genomic profiles based on the start position of the genomic gain: a) loss from the centromere to the 13q31 or 13q32 chromosomal band, with gain or amplification extending from 13q31 to the telomere (cases 1, 3, 4, 6, and 7); or b) copy neutral from the centromere to 13q21 and gain or amplification from 13q21 to the telomere (cases 2 and 5). A detailed analysis of the genomic values on each of the studied cases allowed us to narrow down the minimal common region of DNA amplification to 1.83 Mb, mapping entirely within the 13q34 chromosomal band. Two samples delimited the Amp13q34 region: tumor #3 showed the Amp13q34 starting at RP11-520D2 (spanning 3.55 Mb, and 29 genes), whereas tumor #5 finally defined the minimal 1.83 Mb region from RP11-375A8 to the telomere that contains 22 genes (Figure 2b) (Table 2). As tumor material was limited to carry out a whole characterization of all the genes therein located, we selected those related to tumorigenesis or cell transformation according to either previous studies or their hypothetical function. Our final list of selected genes included: ARHGEF7, ATP11A, MCF2L, CUL4A, LAMP1, GRTP1, DCUN1D2, TFDP1, GAS6, RASA3, and CDC16 (bold genes in Table 2). Although ARHGEF7 is not located within the boundaries of the 1.83 Mb region, it was selected to check a possible correlation for those genes located outside the minimal region, but inside the 3.55 Mb defined by the case #3. Noteworthy, the 13q34 FISH probe used in this study covered CUL4A, LAMP1, GRTP1, DCUN1D2, and TFDP1 (Figure 2 and Table 2).
Figure 2

Genomic definition of the human 13q34 amplification II. (a) Genomic pattern of chromosome 13 by array-based comparative genomic hybridization (aCGH) in seven breast cancer samples (six familial breast tumors from the Spanish National Cancer Research Center, one sporadic breast tumor sample from the University of Pennsylvania, and MDA-MB-157 cell line) from centromere (top, CEN) to telomere (bottom, TEL). An idiogram showing chromosomal bands is depicted on the left. Each row represents a BAC array-clone sorted by their position in the UCSC Genome Browser (Human Feb 2009, assembly (hg 19)). Each clone is colored for each sample according to its level of genomic copies: loss (red), normal (white), gain (light green), amplification (dark green). Grey cells reflect clone data rejected after quality tests for signal intensity and replicate reproducibility. The green and red stars correspond to the location of the BAC probes used for FISH analyses. (b) aCGH genomic pattern of the 13q31-q34 chromosomal region. The BAC clone name and mid-position Mb is displayed. Colors mean the same as in A. The red star shows names and locations of the FISH clones used for the 13q34 probe. The striped dark green box represents the 1.83 Mb minimal common region of DNA amplification within the 13q34 site. A list of genes located within this minimal region is supplied in Table 2.

Table 2

List of genes located in the minimal common region of amplification at 13q34

Gene Name*

Reference Sequence

Chromosome location (start pb - end pb)**

Name

Additional information

ANKRD10

NM 017664

111530888-111567416

ankyrin repeat domain 10

 

ARHGEF7

NM 145735.2

111767624-111947542

Rho guanine nucleotide exchange factor (GEF) 7

Rho GTPase

C13orf16

NM 152324.1

111973015-111996593

chromosome 13 open reading frame 16

 

SOX1

NM 005986.2

112721913-112726020

SRY (sex determining region Y)-box 1

Transcription factor involved in the regulation of embryonic development and in the determination of the cell fate

C13orf28

NM 145248.3

113030669-113089001

chromosome 13 open reading frame 28

 

TUBGCP3

NM 006322.4

113139328-113242481

tubulin, gamma complex associated protein 3

 

C13orf35

NM 207440.1

113301358-113338811

chromosome 13 open reading frame 35

 

ATP11A

NM 032189.3

113344643-113541480

ATPase, class VI, type 11A

Integral membrane ATPase

MCF2L

NM 001112732.1

113622757-113752862

MCF.2 cell line derived transforming sequence-like

 

F7

NM 000131.3

113760105-113774994

coagulation factor VII (serum prothrombin conversion accelerator)

Coagulation factor. Defects in this gene can cause coagulopathy

F10

NM 000504.3

113777113-113803841

coagulation factor X

Coagulation factor. Mutations of this gene result in factor X deficiency, a hemorrhagic condition of variable severity.

PROZ

NM 003891.1

113812968-113826694

protein Z, vitamin K-dependent plasma glycoprotein

 

PCID2

NM 018386.2

113831925-113863029

PCI domain containing 2

 

CUL4A

NM 001008895.1

113863931-113919391

cullin 4A

 

LAMP1

NM 005561.3

113951469-113977741

lysosomal-associated membrane protein 1

Membrane glycoprotein. It may also play a role in tumor cell metastasis

GRTP1

NM 024719.2

113978506-114018463

growth hormone regulated TBC protein 1

 

ADPRHL1

NM 138430.3

114076586-114107839

ADP-ribosylhydrolase like 1

Reversible posttranslational modification used to regulate protein function

DCUN1D2

NM 001014283.1

114110134-114145023

DCN1, defective in cullin neddylation 1, domain containing 2 (S. cerevisiae)

 

TMCO3

NM 017905.4

114145308-114204542

transmembrane and coiled-coil domains 3

 

TFDP1

NM 007111.4

114239056-114295786

transcription factor Dp-1

Transcription factor that heterodimerizes with E2F proteins to enhance their DNA-binding activity and promote transcription from E2F target genes.

ATP4B

NM 000705.2

114303123-114312501

ATPase, H+/K+ exchanging, beta polypeptide

Encodes the beta subunit of the gastric H+, K+-ATPase

GRK1

NM 002929.2

114321597-114438636

G protein-coupled receptor kinase 1

Ser/Thr protein kinase that phosphorylates rhodopsin and initiates its deactivation. Defects in GRK1 are known to cause Oguchi disease 2

LOC100130386

NR 028064.1

114451484-114454062

hypothetical protein LOC100130386

 

GAS6

NM 000820.2

114523524-114567046

growth arrest-specific 6

Gamma-carboxyglutamic acid (Gla)-containing protein thought to be involved in the stimulation of cell proliferation, and may play a role in thrombosis

FLJ44054

NR 024609.1

114586610-114626485

hypothetical protein LOC643365

 

RASA3

NM 007368.2

114747195-114898095

RAS p21 protein activator 3

Member of the GAP1 family of GTPase-activating proteins. The gene product stimulates the GTPase activity of normal RAS p21 but not its oncogenic counterpart. Acting as a suppressor of RAS function, the protein enhances the weak intrinsic GTPase activity of RAS proteins resulting in the inactive GDP-bound form of RAS, thereby allowing control of cellular proliferation and differentiation.

CDC16

NM 003903.3

115000362-115038150

cell division cycle 16 homolog (S. cerevisiae)

Component protein of the APC complex, a cyclin degradation system that governs exit from mitosis

UPF3A

NM 080687.1

115047078-115071281

UPF3 regulator of nonsense transcripts homolog A (yeast)

Component of a post-splicing multiprotein complex involved in both mRNA nuclear export and mRNA surveillance

ZNF828

NM_032436.2

115079965-115092802

zinc finger protein 828

 

*Genes in bold were selected for candidate gene analyses by qRT-PCR.

**Gene locations are according to UCSC Genome Browser (Human Feb 2009, assembly (hg 19)).

Analyses of the mRNA expression level for the candidate genes by qRT-PCR

Once the minimal amplification site had been delineated, qRT-PCR was carried out to determine the mRNA expression level of each of the 11 candidate genes, comparing two cohorts of FFPE samples: tumors with Amp13q34 (six tumor samples and MDA-MB-157), and tumors without any genomic aberration at 13q34 region (13 cases). ARHGEF7 and ATP11A showed a non-statistically significant trend to be overexpressed in Amp13q34 tumors (P = 0.242 and P = 0.191, respectively); whereas GAS6 and RASA3 had non-significant tendencies to be downregulated in tumors with Amp13q34 (P = 0.285 and P = 0.052, respectively) (Figure 3). Nevertheless, two genes had significant association between amplification and overexpression: CUL4A (P = 0.007) and TFDP1 (P = 0.019) (Figure 3). At this point, we focused on these two genes as candidate drivers of the 13q34 amplification.
Figure 3

Gene expression analysis by qRT-PCR of the eleven candidate genes located in the minimal common regions of Amp13q34. Relative expression levels of the candidate genes in two sample cohorts: breast tumors with no genomic aberration at 13q34 (grey), and breast tumors with Amp13q34 (black). The expression of the candidate genes was evaluated by real-time quantitative RT-PCR and normalized with a housekeeping gene (ACTB). P values are shown for those genes with statistically significant differences when comparing the two groups in a U-Mann Whitney Test (P < 0.05).

Correlation with protein expression levels and other immunohistochemical features

We performed immunohistochemical analyses of the two candidate genes (CUL4A and TFDP1) on a set of 414 breast cancer samples on TMAs, previously analyzed by FISH (see above). Interestingly, the 75% of the tumors with Amp13q34 showed a strong staining for CUL4A, compared with 34.3% of the tumors without Amp13q34, but overexpressing CUL4A (P = 0.010) (Figure 4a-c and Table 3). A significant association was also found between Amp13q34 and TFDP1, since the 92.3% of the tumors with Amp13q34 (12/13) presented TFDP1 overexpression (P = 0.004), whereas the 51.9% of the tumors without Amp13q34 overexpressed TFDP1 (Figure 4d-f and Table 3). Furthermore, when considering a higher percentage (> 60%) of TFDP1 positive cells as the threshold, 76.9% of Amp13q34 tumors had TFDP1 overexpression as compared with 20.1% of the tumors without Amp13q34 (P = < 0.001) (Table 3). Interestingly, 60% of tumors with Amp13q34 co-overexpressed both CUL4A and TFDP1 (6 out of 10 tumors), although it was not statistically significant maybe due to the low number of samples (data not shown). Altogether, these findings demonstrated that Amp13q34 was significantly associated with a higher expression level of both CUL4A and TFDP1, but given that there are cases without the amplification showing protein overexpression, Amp13q34 was not the exclusive mechanism causing overexpression of these genes.
Figure 4

Immunohistochemical staining for CUL4A (a-c) and TFDP1 (d-f) in tumors with and without Amp13q34. (a) Null CUL4A staining in a non-amplifier tumor sample. (b) Moderate CUL4A staining in a non-amplifier tumor sample. (c) Strong CUL4A staining in a tumor with the Amp13q34. (d) Negative TFDP1 expression in a tumor without Amp13q34. (e) and (f) Strong TFDP1 expression in tumors with Amp13q34, the arrow in (f) points out the absence of expression in a lymphocyte infiltrate compared with the breast tumor cells.

Table 3

Correlation of the presence/absence of Amp13q34 with the protein expression levels of CUL4A and TFDP1

 

CUL4A expression

TFDP1 expression

 

CUL4A

CUL4A

TFDP1

TFDP1

13q34

-ve

+ve

-ve

mid

+ve

-ve

+ve

-ve

+ve1

+ve2

   No amp

155 (65.7)

81 (34.3)

4 (1.7)

151 (64.0)

81 (34.3)

115 (48.1)

124 (51.9)

115 (48.1)

76 (31.8)

48 (20.1)

   Amp

3 (25.0)

9 (75.0)

0 (0)

3 (25.0)

9 (75.0)

1 (7.7)

12 (92.3)

1 (7.7)

2 (15.4)

10 (76.9)

P-values

P = 0.010

P = 0.017*

P = 0.004*

P = < 0.001*

Each cell shows the number of samples and the percentage in the series between brackets.

CUL4A expression levels: a) -ve (negative) (null, low and medium staining) and +ve (positive) (strong staining); b) negative (null staining), mid (low and medium staining) and positive (strong staining).

TFDP1 expression levels: a) negative (< 25%) and positive (> 25%); b) negative (< 25% stained cells), +ve1 (positive) (between 25-60% stained cells), +ve2 (positive) (> 60% stained cells).

*P values from the Chi square of Pearson.

Amp = amplification.

In addition, we associated Amp13q34 with other immunohistochemical and clinical tumor features previously analyzed on a subset of the sample set (260 out of 414 samples) [33, 34]. We observed that tumors with Amp13q34 were characterized by high histological grade; an absence of expression of ER, PR, CCND1, RB, p16, and CK8; and an overexpression of Ki-67, EGFR, P-Cadherin, G-catenin, CCNE, CCNB1, SKP2, survivin, vimentin, and CK5 (Table 4). As most of these markers are associated with basal-like phenotype of breast cancer, these associations may be due to the unique presence of the Amp13q34 in basal-like tumors as we previously reported [11]. CUL4A-overexpressing tumors had higher grade, absence of expression of PR and BCL2, and a higher percentage of cases expressing EGFR, P-Cadherin, CCNB1, SKP2, and CK5; when compared with tumors that did not overexpress CUL4A (Table 4). Last, TFDP1-overexpressing tumors were associated with overexpression of CCNE, CCNB1, and p16 (Table 4). However, no significant association was found between TFDP1 and CUL4A expression levels when studying the overall breast cancer sample set (data not shown). These associations support that the overexpression of CUL4A and TFDP1, even though it is correlated with Amp13q34, is not exclusive of basal-like tumors, but still relates to higher tumor aggressiveness.
Table 4

Protein marker associations between tumors with/without Amp13q34, CUL4A expression, or TFDP1 expression

Clinical & IHC feature

13q34 Amplification

CUL4A expression

TFDP1 expression

 

Absence

Presence

P

Negative

Positive

P

Negative

Positive

P

Grade

         

1

42 (32.1)

0

0.002*

26 (40.6)

10 (19.2)

0.010*

12 (32.4)

27 (33.3)

NS*

2

40 (30.5)

0

 

19 (29.7)

13 (25.0)

 

11 (29.7)

22 (27.2)

 

3

49 (37.4)

8 (100)

 

19 (29.7)

29 (55.8)

 

14 (37.8)

32 (39.5)

 

ER

         

Negative

56 (35.4)

8 (88.9)

0.002

30 (36.6)

29 (48.3)

NS

24 (46.2)

35 (38.0)

NS

Positive

102 (64.6)

1 (11.1)

 

52 (63.4)

31 (51.7)

 

28 (53.8)

57 (62.0)

 

PR

         

Negative

68 (47.2)

8 (88.9)

0.018

30 (43.5)

37 (62.7)

0.034

20 (48.8)

47 (52.3)

NS

Positive

76 (52.8)

1 (11.1)

 

39 (56.5)

22 (37.3)

 

21 (51.2)

42 (47.7)

 

BCL2

         

Negative

82 (56.9)

8 (88.9)

0.082

37 (53.6)

42 (71.2)

0.047

29 (70.7)

52 (58.4)

NS

Positive

62 (43.1)

1 (11.1)

 

32 (46.4)

17 (28.8)

 

12 (29.3)

37 (41.6)

 

Ki-67

         

0-5%

68 (47.2)

1 (11.1)

< 0.001*

35 (50.7)

22 (37.3)

NS*

22 (53.7)

35 (39.3)

NS*

6-25%

56 (38.9)

1 (11.1)

 

24 (34.8)

20 (33.9)

 

13 (31.7)

32 (36.0)

 

> 25%

20 (13.9)

7 (77.8)

 

10 (14.5)

17 (28.8)

 

6 (14.6)

22 (24.7)

 

EGFR

         

Negative

181 (90.0)

4 (40.0)

< 0.001

122 (91.7)

57 (80.3)

0.024

94 (91.3)

89 (85.6)

NS

Positive

20 (10.0)

6 (60.0)

 

11 (8.3)

14 (19.7)

 

9 (8.7)

15 (14.4)

 

Cadherin P

         

Negative

128 (92.1)

3 (33.3)

< 0.001

63 (95.5)

46 (79.3)

0.011

37 (94.9)

74 (86.0)

NS

Positive

11 (7.9)

6 (66.6)

 

3 (4.5)

12 (20.7)

 

2 (5.1)

12 (14.0)

 

G-Catenin

         

Negative

106 (79.1)

4 (44.4)

0.031

59 (86.8)

43 (75.4)

NS

36 (90.0)

69 (78.4)

NS

Positive

28 (20.9)

5 (55.5)

 

9 (13.2)

14 (24.6)

 

4 (10.0)

19 (21.6)

 

Cyclin D1

         

Negative

67 (47.2)

8 (88.9)

0.018

32 (47.1)

27 (46.6)

NS

17 (42.5)

42 (47.7)

NS

Positive

75 (52.8)

1 (11.1)

 

36 (52.9)

31 (53.4)

 

23 (57.5)

46 (52.3)

 

Cyclin E

         

Negative

103 (72.5)

2 (22.2)

0.004

50 (73.5)

34 (58.6)

0.090

32 (80.0)

53 (60.2)

0.043

Positive

39 (27.5)

7 (77.8)

 

18 (26.5)

24 (41.4)

 

8 (20.0)

35 (39.8)

 

Cyclin B1

         

Negative

111 (80.4)

4 (44.4)

0.024

57 (86.4)

40 (70.2)

0.045

37 (92.5)

61 (72.6)

0.010

Positive

27 (19.6)

5 (55.6)

 

9 (13.6)

17 (29.8)

 

3 (7.5)

23 (27.4)

 

RB

         

Negative

33 (23.9)

6 (66.7)

0.011

12 (18.2)

14 (24.1)

NS

11 (27.5)

15 (17.4)

NS

Positive

105 (76.1)

3 (33.3)

 

54 (81.8)

44 (75.9)

 

29 (72.5)

71 (82.6)

 

E2F6

         

Negative

89 (64.5)

4 (44.4)

NS

39 (59.1)

38 (66.7)

NS

30 (75.0)

47 (56.0)

0.049

Positive

49 (35.5)

5 (55.6)

 

27 (40.9)

19 (33.3)

 

10 (25.0)

37 (44.0)

 

P16

         

Negative

53 (39.0)

7 (77.8)

0.033

24 (36.9)

30 (51.7)

NS

23 (59.0)

31 (36.0)

0.020

Positive

83 (61.0)

2 (22.2)

 

41 (63.1)

28 (48.3)

 

16 (41.0)

55 (64.0)

 

SKP2

         

Negative

66 (46.8)

1 (11.1)

0.043

40 (58.8)

22 (37.9)

0.021

24 (60.0)

40 (46.0)

NS

Positive

75 (53.2)

8 (88.9)

 

28 (41.2)

36 (62.1)

 

16 (40.0)

47 (54.0)

 

Survivin

         

Negative

97 (70.8)

1 (11.1)

0.001

49 (72.1)

35 (61.4)

NS

30 (75.0)

57 (64.8)

NS

Positive

40 (29.2)

8 (88.9)

 

19 (27.9)

22 (38.6)

 

10 (25.0)

31 (35.2)

 

CK5

         

Negative

133 (86.4)

3 (33.3)

0.001

74 (90.2)

43 (72.9)

0.011

42 (82.4)

77 (82.8)

NS

Positive

21 (13.6)

6 (66.6)

 

8 (9.8)

16 (27.1)

 

9 (17.6)

16 (17.2)

 

CK8

         

Negative

28 (19.9)

7 (77.8)

0.001

12 (17.6)

17 (29.3)

NS

7 (17.5)

24 (27.0)

NS

Positive

113 (80.1)

2 (22.2)

 

56 (82.4)

41 (70.7)

 

33 (82.5)

65 (73.0)

 

Vimentin

         

Negative

109 (79.0)

3 (33.3)

0.006

53 (77.9)

41 (70.7)

NS

32 (80.0)

63 (72.4)

NS

Positive

29 (21.0)

6 (66.6)

 

15 (22.1)

17 (29.3)

 

8 (20.0)

24 (27.6)

 

ER = Estrogen Receptor; PR = Progesterone Receptor; CK = cytokeratin. RB = retinoblastoma gene protein.

Markers statistically significant in at least one of the tumor group comparisons are shown. P values ≤ 0.10 are depicted, in bold those statistically significant (P ≤ 0.05). NS = non-significant. P values were calculated by Fisher's Exact Test, except histological grade and Ki-67 analyses that were done with Pearson's Chi square (*).

Other markers were also analyzed (such as HER2) but gave no significant associations.

Discussion

In the present study, we have characterized the genomic amplification at the human 13q34 chromosomal region in familial and sporadic breast cancer cases. We have defined a minimal common region of amplification using FISH and aCGH techniques. Moreover, we have characterized the gene and protein expression levels of eleven candidate genes within the region using qRT-PCR and immunohistochemistry assays. The correlations that we have found suggest TFDP1 and CUL4A as likely drivers of this genomic aberration.

Amp13q34 previously has been reported in breast cancer [22, 23] as well as in squamous cell carcinomas, adrenocortical carcinomas, childhood medulloblastoma, and hepatocellular carcinomas [1720]. In our analyses, Amp13q34 is a low frequent genomic event in overall breast cancer (around 4.5%) but the rate increases when analyzing BRCA1-associated breast cancer (8.1%) (Table 1) or basal-like tumors (about 20%) [11]. Although this increase is not statistically significant (data not shown), it is in agreement with previous analyses that describe genomic gains/amplifications at 13q34 in ER-negative tumors, basal-like tumors, BRCA1-associated breast cancers, and medullary carcinomas [10, 16, 38, 39]. Furthermore, we found that Amp13q34 is associated with a high histological grade, hormonal receptor negativity, and an overexpression of basal cytokeratins, cell cycle promoters (CCNE, CCNB1) and EGFR, among other markers (Table 4). These findings suggest that Amp13q34 is biologically important in the progression of basal-like breast cancers. Although a screening in a larger basal-like tumor cohort would be needed; once the prevalence of Amp13q34 is confirmed in this breast tumor phenotype, this genomic aberration could be used both as a clinical marker and as a therapeutic target for Amp13q34 tumors.

As far as we know, this is the first high-resolution genomic characterization of the amplification at the human 13q34 chromosomal site. We narrowed down the minimal common region of Amp13q34 to 1.83 Mb, which included 22 genes, of which 11 may be related to tumorigenesis: ARHGEF7, ATP11A, MCF2L, CUL4A, LAMP1, GRTP1, DCUN1D2, TFDP1, GAS6, RASA3, and CDC16 (Figure 2 and Table 2). In a recent study of mammary tumors arising in a p53-null mouse model, a genomic amplification at mouse chromosomal 8A1 region, syntenic to the human 13q34 region, was defined with a high-resolutive genomic BAC array. The authors reported two minimal clusters of amplification that also covered the aforementioned genes. In addition, they described amplification of CUL4A (25.7%), LAMP1 (13.5%), TFDP1 (31.1%), and GAS6 (13.5%) by DNA qRT-PCR on a set of 74 human breast carcinomas [22]. Noticeably, the amplification and overexpression of CUL4A was also reported in primary breast cancers a few years ago [23]. All these findings support a role for Amp13q34 in human breast cancer, likely through the increased expression of the genes located therein.

Based on the correlations observed in this study, CUL4A and TFDP1 are suggested to be the target genes for this genomic amplification. Both their mRNA (Figure 3) and proteins (Figure 4, and Table 3) are significantly overexpressed in breast tumors with Amp13q34 when compared with breast tumors without 13q34 genomic alterations. Therefore, our data support the importance of these genes as targets for the amplification, consistent with previous findings not only in breast cancer [22], but also in hepatocellular carcinomas [20].

CUL4A is a member of the cullin protein family and composes the multifunctional ubiquitin-protein ligase E3 complex [40]. Our results suggest that CUL4A may play a role in breast cancer progression, based on its amplification and significant overexpression, previously reported in breast malignancies [22, 23], as well as in other carcinomas [20]. It has been shown that CUL4A could contribute to tumor malignancy mediating the activity of tumor suppressors and thus, altering cell cycle checkpoints [4144]. Moreover, a recent study has reported that breast cancer patients with strong expression of CUL4 had a significantly shorter overall and disease-free survival [25]. In our analysis, CUL4A overexpressing tumors were characterized by a high histological grade, an absence of PR and BCL2 expression, and a high expression of cell cycle promoters (cyclin B and SKP2), cell signaling components (EGFR), basal cytokeratins (CK5), and cell adhesion proteins (P-cadherin) (Table 4), suggesting high cell proliferation and tumor aggressiveness. As some of these features are basal-cell markers (such as EGFR, CK5, P-Cadherin), CUL4A may play an important role in the biology of basal-like breast cancers. Furthermore, our results suggest the role for CUL4A as part of the development of breast tumors in general, mainly through deregulation of cell cycle checkpoints.

TFDP1 is a heterodimerization partner for members of the E2F family of transcription factors. E2F1/TFDP1 forms a complex involved in cell cycle progression by the regulation of expression of cell cycle promoters (cyclin A, cyclin E, CDK2) [45]. In our study, breast tumors overexpressing TFDP1 had high expression of p16, cyclin E and cyclin B1 (Table 4), as described in hepatocellular carcinoma [20]. These associations point out a deregulation of the cell cycle in these tumors. However, overexpression of TFDP1 did not correlate with altered expression levels either of its protein partner, E2F1, or one of the E2F1/TFDP1 regulators, RB, in our tumor cohort (data not shown). The lack of correlation between TFDP1 and E2F1 expression suggests that TFDP1 may have a function in addition to gene transcription regulation and cell cycle progression in breast cancer. This discordance between E2F1 and TFDP1 protein levels was also described in non-Hodgkin lymphomas [46] and in hepatocellular carcinomas, where only TFDP1 overexpression was associated with a larger tumor size [26]. Additionally, Abba and colleagues analyzed publicly available human breast cancer mRNA expression datasets and found significant associations of TFDP1 overexpression with shorter overall survival, relapse-free survival, and metastasis-free interval [22]. Moreover, TFDP1 overexpression has been related to progression of hepatocellular carcinomas [26], and together with activated HA-RAS, causes oncogenesis in rat embryo fibroblasts [47]. Thus, TFDP1 is not only a strong candidate to drive Amp13q34, but also a gene involved in different components of tumorigenesis, causing higher tumor aggressiveness and a poor patient prognosis. Further functional analyses to elucidate its potential role in breast cancer oncogenesis need to be performed.

The other candidate genes that we studied (ARHGEF7, ATP11A, MCF2L, LAMP1, GRTP1, DCUN1D2, GAS6, RASA3 and CDC16) did not demonstrate overexpression in Amp13q34 tumors, although non-significant trends were found for overexpression of ARHGEF7 and ATP11A, and downregulation of GAS6 and RASA3 (Figure 3). However, the lack of significant correlations for these genes between copy number and expression in our dataset should not rule out their possible role in breast cancer. For example, a recent article associated expression of GAS6 with indicators of good prognosis such as progesterone receptor positivity, small tumor size, low grade, and young patient age [24]. These tumor features do not correspond to the ones found in Amp13q34 tumors, which are mainly ER and PR negative (Table 4); thus, GAS6 may not be the target gene in the Amp13q34, but may still play a role in other breast cancers.

Conclusions

In summary, we have characterized a 1.83 Mb minimal common region of amplification at 13q34 that may play a crucial role in the development of basal-like breast cancers. Analyses of larger series of this tumor subtype will allow us to confirm the specificity of this aberration and, importantly, its possible use as a clinical marker of basal-like tumors. This genomic aberration could facilitate the tumor progression through the overexpression of driver/target genes such as TFDP1 and CUL4A, which are overexpressed not only in Amp13q34 tumors, but also in other breast cancer samples characterized by a high cell proliferation and aggressiveness. Therefore, further functional analyses should be performed to demonstrate their potential oncogenic roles, so that the development of pharmaceutical suppressors of CUL4A or TFDP1 activity could provide a therapeutic target for tumors overexpressing these proteins.

Abbreviations

aCGH: 

array-based comparative genomic hybridization

Amp13q34: 

amplification at human chromosomal 13q34 region

BAC: 

Bacterial Artificial Chromosome

cCGH: 

conventional or chromosomal comparative genomic hybridization

CK: 

cytokeratin

ER: 

estrogen receptor protein

FFPE tumors: 

formalin-fixed paraffin embedded tumors

FISH: 

fluorescence in situ hybridization

IHC: 

immunohistochemical

Mb: 

megabase(s)

PR: 

progesterone receptor protein

qRT-PCR: 

quantitative real time polymerase chain reaction

RB: 

retinoblastoma gene or protein

TMA: 

tissue microarray.

Declarations

Acknowledgements

We thank the Spanish National Tumor Bank Network, and Miguel Urioste from Familial Cancer Unit for the collection of the samples. We also thank the Immunohistological Unit, Carmen Martin and Juan Cruz Cigudosa (from Molecular Cytogenetics Group) at the Spanish National Cancer Center, as well as Ekta Jain (from Breakthrough Research Centre), for their technical assistance.

LM and LPSC Grant sponsor: 'Ministerio Español de Educación y Ciencia' FPU AP-2004-0448, and 'Ministerio Español de Sanidad y Consumo' PFIS FI08/00155, respectively. IMR is the recipient of a post-residency award funded by the 'Obra Social de Caja Navarra'. MJG was supported by the 'Instituto de Salud Carlos III'. This study has been partially funded by projects SAF03-02497 and SAF 06-06149 from the 'Ministerio Español de Educación y Ciencia'; and by a grant from Breast Cancer Research Foundation (BCRF) to KLN.

Authors’ Affiliations

(1)
Human Genetics Group, Human Cancer Genetics Program, Spanish National Cancer Research Center (CNIO)
(2)
Breast and Gynecological Group, Molecular Pathology Program, Spanish National Cancer Research Center (CNIO)
(3)
The Institute of Cancer SOLCA
(4)
Department of Medicine, Medical Genetics, Abramson Cancer Center, University of Pennsylvania (UPENN)
(5)
Center for Biomedical Research on Rare Diseases (CIBERER)
(6)
The Breakthrough Breast Cancer Research Center, The Institute of Cancer Research (ICR)

References

  1. Miki Y, Swensen J, Shattuck-Eidens D, Futreal PA, Harshman K, Tavtigian S, Liu Q, Cochran C, Bennett LM, Ding W, et al: A strong candidate for the breast and ovarian cancer susceptibility gene BRCA1. Science. 1994, 266: 66-71. 10.1126/science.7545954.View ArticlePubMedGoogle Scholar
  2. Wooster R, Neuhausen SL, Mangion J, Quirk Y, Ford D, Collins N, Nguyen K, Seal S, Tran T, Averill D, et al: Localization of a breast cancer susceptibility gene, BRCA2, to chromosome 13q12-13. Science. 1994, 265: 2088-2090. 10.1126/science.8091231.View ArticlePubMedGoogle Scholar
  3. Hu Z, Fan C, Oh DS, Marron JS, He X, Qaqish BF, Livasy C, Carey LA, Reynolds E, Dressler L, Nobel A, Parker J, Ewend MG, Sawyer LR, Wu J, Liu Y, Nanda R, Tretiakova M, Ruiz Orrico A, Dreher D, Palazzo JP, Perreard L, Nelson E, Mone M, Hansen H, Mullins M, Quackenbush JF, Ellis MJ, Olopade OI, Bernard PS, Perou CM: The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics. 2006, 7: 96-10.1186/1471-2164-7-96.View ArticlePubMedPubMed CentralGoogle Scholar
  4. Perou CM, Sorlie T, Eisen MB, Rijn van de M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, Fluge O, Pergamenschikov A, Williams C, Zhu SX, Lonning PE, Borresen-Dale AL, Brown PO, Botstein D: Molecular portraits of human breast tumours. Nature. 2000, 406: 747-752. 10.1038/35021093.View ArticlePubMedGoogle Scholar
  5. Bergamaschi A, Kim YH, Wang P, Sorlie T, Hernandez-Boussard T, Lonning PE, Tibshirani R, Borresen-Dale AL, Pollack JR: Distinct patterns of DNA copy number alteration are associated with different clinicopathological features and gene-expression subtypes of breast cancer. Genes Chromosomes Cancer. 2006, 45: 1033-1040. 10.1002/gcc.20366.View ArticlePubMedGoogle Scholar
  6. Hedenfalk I, Duggan D, Chen Y, Radmacher M, Bittner M, Simon R, Meltzer P, Gusterson B, Esteller M, Kallioniemi OP, Wilfond B, Borg A, Trent J, Raffeld M, Yakhini Z, Ben-Dor A, Dougherty E, Kononen J, Bubendorf L, Fehrle W, Pittaluga S, Gruvberger S, Loman N, Johannsson O, Olsson H, Sauter G: Gene-expression profiles in hereditary breast cancer. N Engl J Med. 2001, 344: 539-548. 10.1056/NEJM200102223440801.View ArticlePubMedGoogle Scholar
  7. Jonsson G, Naylor TL, Vallon-Christersson J, Staaf J, Huang J, Ward MR, Greshock JD, Luts L, Olsson H, Rahman N, Stratton M, Ringner M, Borg A, Weber BL: Distinct genomic profiles in hereditary breast tumors identified by array-based comparative genomic hybridization. Cancer Res. 2005, 65: 7612-7621.PubMedGoogle Scholar
  8. Lakhani SR, Vijver Van De MJ, Jacquemier J, Anderson TJ, Osin PP, McGuffog L, Easton DF: The pathology of familial breast cancer: predictive value of immunohistochemical markers estrogen receptor, progesterone receptor, HER-2, and p53 in patients with mutations in BRCA1 and BRCA2. J Clin Oncol. 2002, 20: 2310-2318. 10.1200/JCO.2002.09.023.View ArticlePubMedGoogle Scholar
  9. Palacios J, Honrado E, Osorio A, Cazorla A, Sarrio D, Barroso A, Rodriguez S, Cigudosa JC, Diez O, Alonso C, Lerma E, Sanchez L, Rivas C, Benitez J: Immunohistochemical characteristics defined by tissue microarray of hereditary breast cancer not attributable to BRCA1 or BRCA2 mutations: differences from breast carcinomas arising in BRCA1 and BRCA2 mutation carriers. Clin Cancer Res. 2003, 9: 3606-3614.PubMedGoogle Scholar
  10. Joosse SA, van Beers EH, Tielen IH, Horlings H, Peterse JL, Hoogerbrugge N, Ligtenberg MJ, Wessels LF, Axwijk P, Verhoef S, Hogervorst FB, Nederlof PM: Prediction of BRCA1-association in hereditary non-BRCA1/2 breast carcinomas with array-CGH. Breast Cancer Res Treat. 2008Google Scholar
  11. Melchor L, Honrado E, Garcia MJ, Alvarez S, Palacios J, Osorio A, Nathanson KL, Benitez J: Distinct genomic aberration patterns are found in familial breast cancer associated with different immunohistochemical subtypes. Oncogene. 2008, 27: 3165-3175. 10.1038/sj.onc.1210975.View ArticlePubMedGoogle Scholar
  12. Al-Kuraya K, Schraml P, Torhorst J, Tapia C, Zaharieva B, Novotny H, Spichtin H, Maurer R, Mirlacher M, Kochli O, Zuber M, Dieterich H, Mross F, Wilber K, Simon R, Sauter G: Prognostic relevance of gene amplifications and coamplifications in breast cancer. Cancer Res. 2004, 64: 8534-8540. 10.1158/0008-5472.CAN-04-1945.View ArticlePubMedGoogle Scholar
  13. Albertson DG, Snijders AM, Fridlyand J, Jordan R, Pinkel D, Schmidt BL: Genomic analysis of tumors by array comparative genomic hybridization: more is better. Cancer Res. 2006, 66: 3955-3956. 10.1158/0008-5472.CAN-05-3611. author reply 3956.View ArticlePubMedGoogle Scholar
  14. Adelaide J, Finetti P, Bekhouche I, Repellini L, Geneix J, Sircoulomb F, Charafe-Jauffret E, Cervera N, Desplans J, Parzy D, Schoenmakers E, Viens P, Jacquemier J, Birnbaum D, Bertucci F, Chaffanet M: Integrated profiling of basal and luminal breast cancers. Cancer Res. 2007, 67: 11565-11575. 10.1158/0008-5472.CAN-07-2536.View ArticlePubMedGoogle Scholar
  15. Chin K, DeVries S, Fridlyand J, Spellman PT, Roydasgupta R, Kuo WL, Lapuk A, Neve RM, Qian Z, Ryder T, Chen F, Feiler H, Tokuyasu T, Kingsley C, Dairkee S, Meng Z, Chew K, Pinkel D, Jain A, Ljung BM, Esserman L, Albertson DG, Waldman FM, Gray JW: Genomic and transcriptional aberrations linked to breast cancer pathophysiologies. Cancer Cell. 2006, 10: 529-541. 10.1016/j.ccr.2006.10.009.View ArticlePubMedGoogle Scholar
  16. Natrajan R, Lambros MB, Rodriguez-Pinilla SM, Moreno-Bueno G, Tan DS, Marchio C, Vatcheva R, Rayter S, Mahler-Araujo B, Fulford LG, Hungermann D, Mackay A, Grigoriadis A, Fenwick K, Tamber N, Hardisson D, Tutt A, Palacios J, Lord CJ, Buerger H, Ashworth A, Reis-Filho JS: Tiling path genomic profiling of grade 3 invasive ductal breast cancers. Clin Cancer Res. 2009, 15: 2711-2722. 10.1158/1078-0432.CCR-08-1878.View ArticlePubMedGoogle Scholar
  17. Shinomiya T, Mori T, Ariyama Y, Sakabe T, Fukuda Y, Murakami Y, Nakamura Y, Inazawa J: Comparative genomic hybridization of squamous cell carcinoma of the esophagus: the possible involvement of the DPI gene in the 13q34 amplicon. Genes Chromosomes Cancer. 1999, 24: 337-344. 10.1002/(SICI)1098-2264(199904)24:4<337::AID-GCC7>3.0.CO;2-O.View ArticlePubMedGoogle Scholar
  18. Dohna M, Reincke M, Mincheva A, Allolio B, Solinas-Toldo S, Lichter P: Adrenocortical carcinoma is characterized by a high frequency of chromosomal gains and high-level amplifications. Genes Chromosomes Cancer. 2000, 28: 145-152. 10.1002/(SICI)1098-2264(200006)28:2<145::AID-GCC3>3.0.CO;2-7.View ArticlePubMedGoogle Scholar
  19. Michiels EM, Weiss MM, Hoovers JM, Baak JP, Voute PA, Baas F, Hermsen MA: Genetic alterations in childhood medulloblastoma analyzed by comparative genomic hybridization. J Pediatr Hematol Oncol. 2002, 24: 205-210. 10.1097/00043426-200203000-00009.View ArticlePubMedGoogle Scholar
  20. Yasui K, Arii S, Zhao C, Imoto I, Ueda M, Nagai H, Emi M, Inazawa J: TFDP1, CUL4A, and CDC16 identified as targets for amplification at 13q34 in hepatocellular carcinomas. Hepatology. 2002, 35: 1476-1484. 10.1053/jhep.2002.33683.View ArticlePubMedGoogle Scholar
  21. Melchor L, Alvarez S, Honrado E, Palacios J, Barroso A, Diez O, Osorio A, Benitez J: The accumulation of specific amplifications characterizes two different genomic pathways of evolution of familial breast tumors. Clin Cancer Res. 2005, 11: 8577-8584. 10.1158/1078-0432.CCR-05-1278.View ArticlePubMedGoogle Scholar
  22. Abba MC, Fabris VT, Hu Y, Kittrell FS, Cai WW, Donehower LA, Sahin A, Medina D, Aldaz CM: Identification of novel amplification gene targets in mouse and human breast cancer at a syntenic cluster mapping to mouse ch8A1 and human ch13q34. Cancer Res. 2007, 67: 4104-4112. 10.1158/0008-5472.CAN-06-4672.View ArticlePubMedPubMed CentralGoogle Scholar
  23. Chen LC, Manjeshwar S, Lu Y, Moore D, Ljung BM, Kuo WL, Dairkee SH, Wernick M, Collins C, Smith HS: The human homologue for the Caenorhabditis elegans cul-4 gene is amplified and overexpressed in primary breast cancers. Cancer Res. 1998, 58: 3677-3683.PubMedGoogle Scholar
  24. Mc Cormack O, Chung WY, Fitzpatrick P, Cooke F, Flynn B, Harrison M, Fox E, Gallagher E, Goldrick AM, Dervan PA, Mc Cann A, Kerin MJ: Growth arrest-specific gene 6 expression in human breast cancer. Br J Cancer. 2008, 98: 1141-1146. 10.1038/sj.bjc.6604260.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Schindl M, Gnant M, Schoppmann SF, Horvat R, Birner P: Overexpression of the human homologue for Caenorhabditis elegans cul-4 gene is associated with poor outcome in node-negative breast cancer. Anticancer Res. 2007, 27: 949-952.PubMedGoogle Scholar
  26. Yasui K, Okamoto H, Arii S, Inazawa J: Association of over-expressed TFDP1 with progression of hepatocellular carcinomas. J Hum Genet. 2003, 48: 609-613. 10.1007/s10038-003-0086-3.View ArticlePubMedGoogle Scholar
  27. Diez O, Osorio A, Duran M, Martinez-Ferrandis JI, de la Hoya M, Salazar R, Vega A, Campos B, Rodriguez-Lopez R, Velasco E, Chaves J, Diaz-Rubio E, Jesus Cruz J, Torres M, Esteban E, Cervantes A, Alonso C, San Roman JM, Gonzalez-Sarmiento R, Miner C, Carracedo A, Eugenia Armengod M, Caldes T, Benitez J, Baiget M: Analysis of BRCA1 and BRCA2 genes in Spanish breast/ovarian cancer patients: a high proportion of mutations unique to Spain and evidence of founder effects. Hum Mutat. 2003, 22: 301-312. 10.1002/humu.10260.View ArticlePubMedGoogle Scholar
  28. Melchor L, Honrado E, Huang J, Alvarez S, Naylor TL, Garcia MJ, Osorio A, Blesa D, Stratton MR, Weber BL, Cigudosa JC, Rahman N, Nathanson KL, Benitez J: Estrogen receptor status could modulate the genomic pattern in familial and sporadic breast cancer. Clin Cancer Res. 2007, 13: 7305-7313. 10.1158/1078-0432.CCR-07-0711.View ArticlePubMedGoogle Scholar
  29. Greshock J, Naylor TL, Margolin A, Diskin S, Cleaver SH, Futreal PA, deJong PJ, Zhao S, Liebman M, Weber BL: 1-Mb resolution array-based comparative genomic hybridization using a BAC clone set optimized for cancer gene analysis. Genome Res. 2004, 14: 179-187. 10.1101/gr.1847304.View ArticlePubMedPubMed CentralGoogle Scholar
  30. Vaquerizas JM, Conde L, Yankilevich P, Cabezon A, Minguez P, Diaz-Uriarte R, Al-Shahrour F, Herrero J, Dopazo J: GEPAS, an experiment-oriented pipeline for the analysis of microarray gene expression data. Nucleic Acids Res. 2005, 33: W616-620. 10.1093/nar/gki500.View ArticlePubMedPubMed CentralGoogle Scholar
  31. Lai WR, Johnson MD, Kucherlapati R, Park PJ: Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data. Bioinformatics. 2005, 21: 3763-3770. 10.1093/bioinformatics/bti611.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Venkatraman ES, Olshen AB: A faster circular binary segmentation algorithm for the analysis of array CGH data. Bioinformatics. 2007, 23: 657-663. 10.1093/bioinformatics/btl646.View ArticlePubMedGoogle Scholar
  33. Honrado E, Osorio A, Palacios J, Milne RL, Sanchez L, Diez O, Cazorla A, Syrjakoski K, Huntsman D, Heikkila P, Lerma E, Kallioniemi A, Rivas C, Foulkes WD, Nevanlinna H, Benitez J: Immunohistochemical expression of DNA repair proteins in familial breast cancer differentiate BRCA2-associated tumors. J Clin Oncol. 2005, 23: 7503-7511. 10.1200/JCO.2005.01.3698.View ArticlePubMedGoogle Scholar
  34. Palacios J, Honrado E, Osorio A, Cazorla A, Sarrio D, Barroso A, Rodriguez S, Cigudosa JC, Diez O, Alonso C, Lerma E, Dopazo J, Rivas C, Benitez J: Phenotypic characterization of BRCA1 and BRCA2 tumors based in a tissue microarray study with 37 immunohistochemical markers. Breast Cancer Res Treat. 2005, 90: 5-14. 10.1007/s10549-004-1536-0.View ArticlePubMedGoogle Scholar
  35. Taron M, Rosell R, Felip E, Mendez P, Souglakos J, Ronco MS, Queralt C, Majo J, Sanchez JM, Sanchez JJ, Maestre J: BRCA1 mRNA expression levels as an indicator of chemoresistance in lung cancer. Hum Mol Genet. 2004, 13: 2443-2449. 10.1093/hmg/ddh260.View ArticlePubMedGoogle Scholar
  36. Roche Applied Science. [http://www.roche-applied-science.com]
  37. Hellemans J, Mortier G, De Paepe A, Speleman F, Vandesompele J: qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Biol. 2007, 8: R19-10.1186/gb-2007-8-2-r19.View ArticlePubMedPubMed CentralGoogle Scholar
  38. Fridlyand J, Snijders AM, Ylstra B, Li H, Olshen A, Segraves R, Dairkee S, Tokuyasu T, Ljung BM, Jain AN, McLennan J, Ziegler J, Chin K, Devries S, Feiler H, Gray JW, Waldman F, Pinkel D, Albertson DG: Breast tumor copy number aberration phenotypes and genomic instability. BMC Cancer. 2006, 6: 96-10.1186/1471-2407-6-96.View ArticlePubMedPubMed CentralGoogle Scholar
  39. Vincent-Salomon A, Gruel N, Lucchesi C, MacGrogan G, Dendale R, Sigal-Zafrani B, Longy M, Raynal V, Pierron G, de Mascarel I, Taris C, Stoppa-Lyonnet D, Pierga JY, Salmon R, Sastre-Garau X, Fourquet A, Delattre O, de Cremoux P, Aurias A: Identification of typical medullary breast carcinoma as a genomic sub-group of basal-like carcinomas, a heterogeneous new molecular entity. Breast Cancer Res. 2007, 9: R24-10.1186/bcr1666.View ArticlePubMedPubMed CentralGoogle Scholar
  40. Kipreos ET, Lander LE, Wing JP, He WW, Hedgecock EM: cul-1 is required for cell cycle exit in C. elegans and identifies a novel gene family. Cell. 1996, 85: 829-839. 10.1016/S0092-8674(00)81267-2.View ArticlePubMedGoogle Scholar
  41. Gupta A, Yang LX, Chen L: Study of the G2/M cell cycle checkpoint in irradiated mammary epithelial cells overexpressing Cul-4A gene. Int J Radiat Oncol Biol Phys. 2002, 52: 822-830. 10.1016/S0360-3016(01)02739-0.View ArticlePubMedGoogle Scholar
  42. Kotake Y, Zeng Y, Xiong Y: DDB1-CUL4 and MLL1 mediate oncogene-induced p16INK4a activation. Cancer Res. 2009, 69: 1809-1814. 10.1158/0008-5472.CAN-08-2739.View ArticlePubMedPubMed CentralGoogle Scholar
  43. Leung-Pineda V, Huh J, Piwnica-Worms H: DDB1 targets Chk1 to the Cul4 E3 ligase complex in normal cycling cells and in cells experiencing replication stress. Cancer Res. 2009, 69: 2630-2637. 10.1158/0008-5472.CAN-08-3382.View ArticlePubMedPubMed CentralGoogle Scholar
  44. Miranda-Carboni GA, Krum SA, Yee K, Nava M, Deng QE, Pervin S, Collado-Hidalgo A, Galic Z, Zack JA, Nakayama K, Nakayama KI, Lane TF: A functional link between Wnt signaling and SKP2-independent p27 turnover in mammary tumors. Genes Dev. 2008, 22: 3121-3134. 10.1101/gad.1692808.View ArticlePubMedPubMed CentralGoogle Scholar
  45. Shan B, Farmer AA, Lee WH: The molecular basis of E2F-1/DP-1-induced S-phase entry and apoptosis. Cell Growth Differ. 1996, 7: 689-697.PubMedGoogle Scholar
  46. Chan JA, Olvera M, Lai R, Naing W, Rezk SA, Brynes RK: Immunohistochemical expression of the transcription factor DP-1 and its heterodimeric partner E2F-1 in non-Hodgkin lymphoma. Appl Immunohistochem Mol Morphol. 2002, 10: 322-326. 10.1097/00022744-200212000-00006.PubMedGoogle Scholar
  47. Johnson DG, Cress WD, Jakoi L, Nevins JR: Oncogenic capacity of the E2F1 gene. Proc Natl Acad Sci USA. 1994, 91: 12823-12827. 10.1073/pnas.91.26.12823.View ArticlePubMedPubMed CentralGoogle Scholar

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