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[Cancer Research 65, 822-827, February 1, 2005]
© 2005 American Association for Cancer Research

Molecular Biology, Pathobiology and Genetics

Comparative Genomic Hybridization Profiles in Human BRCA1 and BRCA2 Breast Tumors Highlight Differential Sets of Genomic Aberrations

Erik H. van Beers1, Tibor van Welsem1, Lodewyk F.A. Wessels2, Yunlei Li2, Rogier A. Oldenburg3, Peter Devilee3, Cees J. Cornelisse3, Senno Verhoef1, Frans B.L. Hogervorst1, Laura J. van't Veer1 and Petra M. Nederlof1

1 Department of Pathology and Familial Cancer Clinic of the Netherlands Cancer Institute, Amsterdam, the Netherlands; 2 Faculty of Information Technology and Systems, Information and Communication Theory Group, Delft University of Technology, Delft, the Netherlands; and 3 Department of Human and Clinical Genetics and Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands

Requests for reprints: Erik H. van Beers, Department of Experimental Therapy, Netherlands Cancer Institute, Room H604, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands; E-mail: e.v.beers{at}nki.nl.


   Abstract
Top
 Abstract
Introduction
 Patients and Methods
 Results
 Discussion
 References
 
BRCA1 or BRCA2 germline mutations cause ~30% of breast cancers within high-risk families. This represents 5% of total breast cancer incidence. Although BRCA1 and BRCA2 are both implicated in DNA repair and genome stability, it is unknown whether BRCA1 and BRCA2 are associated with similar or distinct diseases. In a previous study we reported that BRCA1-related breast carcinomas show a distinct genomic profile as determined by comparative genomic hybridization (CGH). We now hypothesize that, if functionally equivalent, mutations in BRCA1 and BRCA2 would result in similar genomic profiles in tumors. Here we report the chromosomal gains and losses as measured by CGH in 25 BRCA2-associated breast tumors and compared them with our existing 36 BRCA1 and 30 control profiles. We compared all chromosomal regions and determined the regions of differential gain or loss between tumor classes and controls. BRCA2 and control tumors have very similar genomic profiles. As a consequence, and in contrast to BRCA1-associated tumors, CGH profiles from BRCA2-associated tumors could not be distinguished from control tumors using the classification methodology as we have developed before. The largest number of significant differences existed between BRCA1 and controls, followed by BRCA1 compared with BRCA2, suggesting different tumor development pathways for BRCA1 and BRCA2.

Key Words: hereditary breast cancer • BRCA1 • BRCA2 • comparative genomic hybridization


   Introduction
Top
 Abstract
 Introduction
Patients and Methods
 Results
 Discussion
 References
 
A hallmark of all solid tumors, and therefore also of breast cancer, is genome-wide DNA copy-number instability resulting in large chromosomal gains or losses. It has been suggested that genomic instability results from diminished dsDNA repair due to mutations in DNA repair genes (1–3). This is assumed to explain the etiology of breast cancer among carriers of mutant alleles for either of the two DNA repair genes BRCA1 and BRCA2 (4–6), which impose a highly increased risk for hereditary or familial breast cancer. However, it is unclear whether these genes cause tumors through similar or partially overlapping pathways and thus are associated with similar or different diseases. We have previously shown that the profile of BRCA1-related tumors, as assessed by comparative genomic hybridization (CGH), is distinct from sporadic breast tumors. Application of BRCA1 CGH profiling in classification was further validated on prospective samples (ref. 7, and data not shown). In this study we aim to characterize the CGH profile for BRCA2 tumors and determine its similarity to our previously reported BRCA1 and control breast cancer CGH profiles. Comparison of the genomic profiles in BRCA1- and BRCA2-associated breast tumors may suggest whether germline mutations in BRCA1 and BRCA2 are involved in different pathways, and thus at the genomic level may represent distinct diseases. We suppose that the gains and losses that typify BRCA1 or BRCA2 mutants may yield clues about the locations of other genes involved in the development of these breast tumors. Furthermore, we aim at exploiting the potential differences in CGH profiles for the classification of breast tumors in hereditary cancer families with unconfirmed etiology.


   Patients and Methods
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 Abstract
 Introduction
 Patients and Methods
Results
 Discussion
 References
 
Tumor genomic DNA isolation, probe labeling, metaphase chromosome preparation, and hybridizations were done as described (7). Tumor samples were either from verified BRCA1 or BRCA2 mutation carriers or from control breast tumors. Because control tumors were unselected for family history, we estimate their probability for containing BRCA1 and BRCA2 mutations to be around 5%, equal to their proportion in total breast cancer incidence. Data analysis and subsequent classification procedures are as described (7). In short, a series of 36 BRCA1 and 30 control tumors [most of them also included in our previous study (7)], and 25 BRCA2 tumors were analyzed by metaphase CGH against one normal female reference genomic DNA. The chromosomal copy-number loss and gain thresholds were set at fluorescence ratios 0.85 and 1.15, respectively. The experimental setup yields digital microscopic image fluorescence intensity data for 1,716 equal-size channels across the genome. With a threshold-driven statistical minimum-loss-of-information algorithm all channels were pooled into 81 features (bands) as described elsewhere (7). Band nomenclature consists of variance accounted for (VAF), the level of variance (throughout this article 0.85), and the chromosome number followed by the nth band of that particular chromosome (e.g.,VAF0.85-12.3 for the third VAF band counting from tip of the q-arm of chromosome 12, which we shall further abbreviate to VAF12.3; see Table 1). After hybridization, channel fluorescence ratios were used to designate for each VAF-band whether there was no change, gain (G), loss (L) or both (G and L). Subsequently, mutual information feature ranking and simple Bayesian discrete classifiers were employed in leave-one-out cross-validation procedures to construct class predictors for BRCA1 versus Controls, BRCA1 versus BRCA2, and BRCA2 versus Controls.


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  		Table 1. Data collection resolution, analysis (VAF-) resolution, cyto-bands, band length, and percentage gain or loss for each tumor class

 

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  		Table 2. Fourteen significant genomic regions with differential gain or loss between tumor classes

 
Statistical Procedures. The whole genome ploidy data for three classes of tumors (BRCA1, BRCA2, and Controls) across 81 features (VAF bands) was analyzed to identify tumor class-specific VAF bands. Fisher's exact two-sided P values were computed separately for the following class combinations: BRCA1 versus BRCA2, BRCA1 versus Controls, and BRCA2 versus Controls. For each of these pairwise class comparisons, two cases were considered: gains versus non-gains (the rest) and losses versus non-losses (the rest). This results in 486 P values (3 pairwise class comparisons x 81 bands x2 aberration types). All P values are adjusted for multiple testing by executing 1,000 permutation runs of the Westfall and Young step-down resampling algorithm (8). Through this procedure, the number of (adjusted) significant features was reduced to 14 VAF bands with high likely involvement in tumor type-specific genome stability pathways (Table 2).


   Results
Top
 Abstract
 Introduction
 Patients and Methods
 Results
Discussion
 References
 
CGH results are frequently reported as frequency tables of gains and losses of chromosome arms (41 arms). In a previous study we developed a method to subdivide the genome in smaller regions (81 regions) by clustering adjacent elements based on the correlation of the CGH fluorescence ratios ((7), Table 1). Here, we report losses and gains in those 81 bands for 36 BRCA1, 25BRCA2, and 30 control tumors (Fig. 1). With an average of 34 aberrations per tumor, the BRCA1 class clearly has the highest degree of instability. BRCA2-related tumors showed on average 28 alterations per tumor, and control tumors 25 altered regions. The most pronounced differences between the classes were observed in the bin representing 11 to 20 alterations per tumor. This bin contains only 3 of 36 (8%) BRCA1-associated tumors, but 24% of BRCA2 and 33% of control tumors, respectively. The bin with 41 to 50 alterations contains 3% of the control tumors, 28% of the BRCA1-related tumors, and 12% of the BRCA2-related tumors (Fig. 1).



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  		Figure 1. Frequency of aberrations in all 81 VAF0.85 bands calculated per tumor class. Control tumors (n = 30), BRCA1 (n = 36), BRCA2 (n = 25).

 
We detected genomic gains and losses in all tumors studied. Overall, in 91 tumors with 81 channels each, gains represented 18%,losses 18%, gain-and-loss within one band 0.3% (cf. hatched in Fig. 2), and no aberrations 64% of all genomic regions. The tumors with highest degree of alterations (aneuploidy) contained 55 copy-number aberrations out of 81 bands. The tumor with the lowest number of aberrations showed only two gains (+VAF1.3 and +VAF3.1) and one loss (–VAF2.1). The top-three most frequent gains were +VAF1.3 (82.5% of 91 tumors), +VAF8.3 (73%) and VAF1.4 (65%). The top-three most frequent losses observed were, -VAF8.1 (52.7%), -VAF4.1 (50.5%), and -VAF16.2 (49.5%). Gains were least frequent in VAF3.2 (1%), VAF13.2 (1%), VAF13.3 (2%), and losses were never observed in three bands VAF1.3, VAF3.5 and, VAF3.6.



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  		Figure 2. Individual tumor profiles in 14 significant genome VAF bands. Columns are breast tumors from proven BRCA1, proven BRCA2, or unselected breast cancer patients. Rows represent genomic regions as VAF bands. Gain (grey), loss (black), gain-and-loss (x), or unchanged (white) in 14 significant VAF0.85 bands with two-sided Fisher's exact adjusted P < 0.1.

 
Different Tumor Profiles of Gains and Losses for BRCA1 and BRCA2 Breast Tumors. In testing our hypothesis of differential CGH profiles in BRCA1-, BRCA2-associated, and control breast tumors, we restricted further analyses to the 14 VAF bands with significant two-sided Fisher's exact adjusted P < 0.1 (Table 2). Pairwise comparison between BRCA1 and control tumors (Table 2, column B1vsC) revealed 10 significant VAF bands. Nine of these show differential gains (top panel) and only VAF5.3 showed differential loss (bottom panel). In two of these bands (+8.2 and +16.1) control tumors showed the highest percentage of aberration (Table 2, cf.B1vsC).

The comparison between BRCA2-associated and control tumors revealed just one significant VAF band, namely gain in VAF3.5 (Table 2, column B2vsC), which is also significant in the comparison between BRCA1 and controls.

BRCA1-associated tumors differed significantly from BRCA2-associated tumors (Table 2, column B1vsB2) in four VAF bands, +1.3, +9.4, –12.3, and –14.2. Only in +VAF9.4 the frequency of gain in BRCA2 (8/25 or 32%, see Fig. 2) significantly exceeded that ofBRCA1 (0%) whereas for the three remaining VAF bands (+1.3, –12.3, and –14.2) the highest percentage of aberrations was present in BRCA1 tumors.

Class-Specific Aberrations. Of the 14 significant VAF bands here identified, no band is uniquely associated with a specific tumor class in the sense that it is significant compared with both other classes (Table 2). Only gain of VAF3.5 in control tumors (13%) is significantly less frequent than in BRCA1 (67%) or BRCA2 (58%) and therefore this should not be defined as a unique property of control tumors but rather a property of BRCA1 and BRCA2 tumors.

Construction of Classifiers. The other aim of this study was to evaluate the possibility to classify BRCA1 and BRCA2 breast tumors. With data from this and an earlier study, we built optimal classifiers for each tumor class according to the previously formalized procedures and concluded that, as reported (7), BRCA1 and control tumors can be classified with 83% performance. Attempts in the present study to build classifiers to distinguish BRCA2 from control tumors or distinguish BRCA1 from BRCA2 were not satisfactory based on their limited performance (B2vsC, 68%) or because too many VAF bands, each with marginal contribution, had to be included in the classifier (B1vsB2 consisted of 64 bands). Our findings suggest that BRCA2-associated tumors are a lot more similar to control tumors than BRCA1-associated tumors. This can also be seen in Table 2, where, in contrast to B1vsC, only VAF3.5 proved significantly different between BRCA2-associated tumors and controls. The subsequent differential features between BRCA1 and BRCA2 (Table 2) were not sufficient to further distinguish these tumors from BRCA1 (data not shown).


   Discussion
Top
 Abstract
 Introduction
 Patients and Methods
 Results
 Discussion
References
 
Classification of Individual Breast Tumors. We have identified 10 genomic regions with differential gains and losses between the BRCA1-associated and control tumors. The comparison between BRCA2 and controls yielded just one genomic region (VAF3.5) whereas four genomic regions were significantly different between BRCA1 and BRCA2 (Table 2). These differences, however, were not enough for classification of individual BRCA2 breast tumors (results not shown). We already knew that the differences between BRCA1 and control tumors supported classification of individual BRCA1 tumors with high performance (7), but we learned in this study that the differences between BRCA2 and controls were much smaller and hardly exclusive for BRCA2 tumors because of VAF3.5 gain being also frequent in BRCA1 (Table 2). We currently aim at collecting genomic profiles at a higher resolution using array CGH which detects aberrations beyond the resolution of metaphase CGH and could identify new significant regions between tumor classes to be employed in classification.

BRCA1 and BRCA2 Functions and Breast Cancer. Thus far, there is a limited number of publications on the genomic alterations in BRCA2 tumors of which only one uses CGH (9). Our results largely agree with those findings, namely significantly more losses in BRCA1-associated tumors compared with controls for 5q, 4q, 4p, 2q, and 12q.

Differences with the results of Tirkkonen et al. (9) include results for 6p, with gain in ~10% of their control tumors compared with 40%gain in our control tumors. Also, we found gain of 6p in BRCA2 samples (28% in VAF6.1 and 12% in VAF6.2) in contrast to none of Tirkkonen et al. (9). Among control tumors, in the bands corresponding to 13q, we found 60% (VAF13.3) and 43% (VAF13.4) loss compared with only 25% reported by Tirkkonen et al. Finally, for BRCA1 we observed 31% loss in VAF13.3 and 25% loss in VAF13.4 as opposed to >70% by Tirkkonen et al. (9). These differences could be explained by the small sample sizes but it is not inconceivable that different genetic backgrounds between the populations of the Netherlands and Iceland play a role also, considering that 13 out of 15 BRCA2-associated tumors from the Tirkkonen study (9) represent identical mutations and belong to a single haplotype.

Comparison of our data with a CGH study of ovarian tumors using 46 BRCA1, 18 BRCA2, and 28 control tumors (10) illustrates the limitations in our current understanding of how chromosomal instability pathways are determined by tissue type and/or mutations in BRCA1, BRCA2, or other yet unidentified breast cancer predisposing genes. A comparison of frequencies of aberrations between our data and that of Ramus et al. (10) shows similar loss in BRCA1-associated tumors at 3pter-p22 (cf.VAF3.1), loss at 12q21-31 (cf. VAF12.3) compared with control tumors (10). Further research of BRCA1 and BRCA2-related tumors could prove crucial to the development of the much needed classification methodology for these high-risk familiar ovarian tumors.

Our working hypothesis that BRCA1 and BRCA2 tumors develop through a similar tumor development pathway as a result of decreased DNA repair caused by mutation seems not supported by our CGH data due to the tumors not having similar genomic aberrations, but seems in accordance with a report of distinct expression profiles for BRCA1- and BRCA2-associated breast tumors (11). Whether some of the distinct chromosomal gains and losses in BRCA1 and BRCA2 tumors account for the differences identified at the expression level remains to be evaluated in future studies that perform both DNA and messenger RNA analysis for the same tumor samples. A prominent and significant deviation from control tumor CGH profiles that is shared by both BRCA1 and BRCA2 profiles is gain of 3q, a very large region with many genes. In BRCA1 tumors gain of 3q is associated with loss on chromosome 3p containing BAP1, a BRCA1 binding protein and a tumor suppressor gene. Also located in this 3p region is MLH1, which is part of the BRCA1-associated genome surveillance complex, containing BRCA1, MSH2, MSH6, MLH1, ATM, BLM, PMS2, and the RAD50-MRE11-NBS1 protein complex (12). Other BRCA1 interacting proteins involved in DNA repair are BARD1, located at 2q34-35 and is frequently lost in BRCA2 tumors, and RAD51, a BRCA2 interacting protein located at 15q15.1 for which we found loss of 42% in BRCA1 but only 20% in BRCA2 and 20% in control samples. Interestingly, recent gene-expression profiling studies along with immunohistochemical studies indicate the existence of breast cancer subtypes (13–15) that may relate to pathology and immunohistochemistry (15–18). A phenotype characterized as ER–/erbB2–/PR–/EGFR+/CK5/6+, which seems to be indicative for BRCA1 status and is referred to as "basal-like," was initially found in unsupervised clustering of breast cancer gene expression data (13). An emerging model is that BRCA1 tumors derive from a different cell lineage (basal cells) than sporadic tumors (luminal cells) and are possibly different from BRCA2 tumors as well. However, it remains impossible to identify BRCA1 or BRCA2 tumors solely on the basis of histopathology, and development of other classification methodologies remains urgent. Recently, we have successfully employed CGH profiles to classify individual BRCA1-related tumors in a pool of otherwise unselected (control) tumors (7). Our current CGH study shows a closer molecular resemblance between BRCA2 tumors and controls than between BRCA1 and controls. This study identifies VAF3.5 with a significant difference between BRCA2 and control tumors (Table 2) and four significant differences between BRCA1- and BRCA2-associated tumors. The optimal classifier to distinguish BRCA2-related tumors from control tumors under-performed with only 68% tumors correctly classified (data not shown). One could explain the inability to distinguish BRCA2 from controls by the limited numbers of tumors studied (n = 25), the insufficient resolution of the cCGH technology (1,716 channels), or the similar intrinsic biological properties of these tumors (16). To address these issues, and to possibly build BRCA2 classifiers in the future, we are now increasing the sample size of BRCA2-related tumors and the sampling resolution by using 3.5k Human BAC arrays similar to the recently reported mouse mammary tumor CGH from murine BRCA1 knockout lines (19). One might also argue that sporadic tumors are similar to BRCA2 tumors. Evidence for this is provided by a recent study in which a newly identified gene, EMSY, was found to bind and attenuate BRCA2 function. EMSY seemed to be exclusively amplified in about 13% of sporadic breast tumors (20), which might result in a BRCA2 phenocopy with a "BRCA2-like" molecular phenotype. Future research in our laboratory focuses on profiling non-B1/non-B2 hereditary breast tumors with array CGH, which is likely to be more sensitive and reproducible compared with metaphase CGH, and may help in the classification of familial breast cancers to enable the appropriate risk-reducing strategies for consanguineous individuals in high-risk breast and/or ovarian cancer families.


   Acknowledgments
 
Grant support: Dutch Cancer Society/Koningin Wilhelmina Fonds grant NKB_NKI 2001-2424 (E.H. van Beers and T. van Welsem) and KWF_RUL99_2021 (R.A. Oldenburg, C.J. Cornelisse, and P. Devilee).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

We thank Hans Peterse for review of the tumor samples, and Cecile Ottenheim and Debbie Sprong for help with karyotyping.

Received 6/29/04. Revised 10/28/04. Accepted 11/30/04.


   References
Top
 Abstract
 Introduction
 Patients and Methods
 Results
 Discussion
 References
 

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  2. Weaver Z, Montagna C, Xu X, et al. Mammary tumors in mice conditionally mutant for Brca1 exhibit gross genomic instability and centrosome amplification yet display a recurring distribution of genomic imbalances that is similar to human breast cancer. Oncogene 2002;21:5097–107.[CrossRef][Medline]
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  5. Hussain S, Wilson JB, Medhurst AL, et al. Direct interaction of FANCD2 with BRCA2 in DNA damage response pathways. Hum Mol Genet 2004;13:1241–8.[Abstract/Free Full Text]
  6. Dong Y, Hakimi MA, Chen X, et al. Regulation of BRCC, a holoenzyme complex containing BRCA1 and BRCA2, by a signalosome-like subunit and its role in DNA repair. Mol Cell 2003;12:1087–99.[CrossRef][Medline]
  7. Wessels LF, van Welsem T, Hart AA, et al. Molecular classification of breast carcinomas by comparative genomic hybridization: a specific somatic genetic profile for BRCA1 tumors. Cancer Res 2002;62:7110–7.[Abstract/Free Full Text]
  8. Westfall PH, Young SS. Resampling-Based Multiple Testing: examples and methods for P-value adjustment. New York: John Wiley & Sons, Inc; 1993.
  9. Tirkkonen M, Johannsson O, Agnarsson BA, et al. Distinct somatic genetic changes associated with tumor progression in carriers of BRCA1 and BRCA2 germ-line mutations. Cancer Res 1997;57:1222–7.[Abstract/Free Full Text]
  10. Ramus SJ, Pharoah PD, Harrington P, et al. BRCA1/2 mutation status influences somatic genetic progression in inherited and sporadic epithelial ovarian cancer cases. Cancer Res 2003;63:417–23.[Abstract/Free Full Text]
  11. Hedenfalk I, Duggan D, Chen Y, et al. Gene-expression profiles in hereditary breast cancer. N Engl J Med 2001;344:539–48.[Abstract/Free Full Text]
  12. Wang Y, Cortez D, Yazdi P, et al. BASC, a super complex of BRCA1-associated proteins involved in the recognition and repair of aberrant DNA structures. Genes Dev 2000;15:927–39.
  13. Sørlie T, Tibshirani R, Parker J, et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci U S A 2003;100:8418–23.[Abstract/Free Full Text]
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  16. Lakhani SR, Jacquemier J, Sloane JP, et al. Multifactorial analysis of differences between sporadic breast cancers and cancers involving BRCA1 and BRCA2 mutations. J Natl Cancer Inst 1998;90:1138–45.[Abstract/Free Full Text]
  17. Foulkes WD, Stefansson IM, Chappuis PO, et al. Germline BRCA1 mutations and a basal epithelial phenotype in breast cancer. J Natl Cancer Inst 2003;95:1582–5.
  18. Palacios J, Honrado E, Osorio A, et al. 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;9:3606–15.[Abstract/Free Full Text]
  19. Chung YJ, Jonkers J, Kitson H, et al. A whole-genome mouse BAC microarray with 1-Mb resolution for analysis of DNA copy number changes by array comparative genomic hybridization. Genome Res 2003;15:188–96.
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