Five breast cancer subtypes have been described in sporadic breast cancer (SBC) using expression arrays: basal-like, ERBB2, normal breast-like, luminal A and B. These molecular subtypes show different genomic aberration patterns (GAPs). Recently, our group described these breast cancer subtypes in 50 non-BRCA1/2 familial tumors using immunohistochemistry assays. We extended this study to the other classes of familial breast cancer (FBC), including 62 tumors (18 BRCA1, 16 BRCA2 and 28 non-BRCA1/2), with the same panel of 25 immunohistochemical (IHC) markers and histological grade obtaining a similar classification. We combined these data with results generated by a 1 Mb BAC array-based CGH study to evaluate the genomic aberrations of each group. We found that BRCA1-related tumors are preferentially basal-like, whereas non-BRCA1/2 familial tumors are mainly luminal A subtype. We described distinct GAPs related to each IHC subtype. Basal tumors had a greater number of gains/losses, while luminal B tumors had more high-level DNA amplifications. Our data are similar to those obtained in SBC studies, highlighting the existence of distinct genetic pathways of tumor evolution, common to both SBC and FBC.
Human breast cancer is a heterogeneous disease encompassing different pathological entities and a range of clinical behavior. Studies in sporadic breast cancer (SBC) based on expression profiling reflect this heterogeneity (Perou et al., 2000; Sorlie et al., 2001, 2003; Sotiriou et al., 2003). Five distinct SBC subtypes defined by different expression patterns and clinical outcomes have been reported: luminal A and B, ERBB2, basal-like and normal breast-like (Sorlie et al., 2003). Recently, two different studies have described distinct spectra of DNA copy number alterations associated with each SBC subtype. A higher number of gains/losses were associated with basal-like tumors, while high-level DNA amplification was more frequent in luminal-B subtype tumors (Bergamaschi et al., 2006; Chin et al., 2006).
Familial breast cancer (FBC) includes tumors from patients carrying mutations in the two known breast cancer susceptibility genes: BRCA1 (Miki et al., 1994) and BRCA2 (Wooster et al., 1995). However, most of FBC patients do not carry mutations in these genes, and are known as non-BRCA1/2 or BRCAX cases. BRCA1-associated tumors can be differentiated from BRCA2, BRCAX and SBC based on their immunohistochemical (IHC) profiles (see reviews (Honrado et al., 2005a; Lacroix and Leclercq, 2005)). Most reports suggest that BRCA1-associated tumors have a basal-like phenotype because they share many expression, IHC and clinical features with basal-like cancers (see reviews (Tischkowitz and Foulkes, 2006; Turner and Reis-Filho, 2006)). On the other hand, BRCA2-associated tumors are only distinguished from BRCAX and SBC by the expression of DNA repair proteins such as RAD51 and CHEK2 (Honrado et al., 2005b). The association of BRCA2-associated tumors with luminal A subtype has been suggested in a single expression study (Sorlie et al., 2003). Finally, recent IHC studies have underlined the heterogeneous of the BRCAX tumors, which resembles the one obtained in SBC studies (Oldenburg et al., 2006; Honrado et al., 2007).
Although the genomic characterization of BRCA1-, BRCA2- and BRCAX-associated tumors has been reported in different studies (Tirkkonen et al., 1997; Alvarez et al., 2005; Jonsson et al., 2005; Melchor et al., in press; van Beers et al., 2005), the possible correlation between the molecular subtypes of FBC and the genomic aberrations has not been clarified yet. In the present study, we have classified 62 FBC samples by IHC analysis into five subgroups. We have demonstrated a correlation between IHC subtypes and specific Genomic Aberration Patterns (GAPs). Our results are very similar to those obtained previously in SBC, and support that breast cancer arises from several distinct biological mechanisms, which are common to both FBC and SBC.
Data from 62 FBC samples (18 BRCA1-, 16 BRCA2- and 28 BRCAX-associated tumors) previously studied by IHC and aCGH have been correlated to establish their IHC subtypes and the pattern of genomic aberrations.
Unsupervised cluster analysis
We performed an unsupervised hierarchical cluster analysis with 25 IHC markers (Supplementary Table 1) and histological grade. The 62 tumor samples were classified into two main groups, associated with their estrogen receptor (ER) status. The ER-negative branch (Figure 1, left) included 20 tumors of grade 2 and 3 (brown and red squares, respectively), hormonal receptors negativity and overexpression of proteins that promote cell cycle progression and cell proliferation. The ER-positive branch involved 42 tumors mainly of grade 1 (green squares) or 2, hormonal receptors positivity and expression of proteins related to the inhibition of the cyclin-CDK complexes, and luminal epithelial proteins (cytokeratin 8 (CK8)) (Figure 1, right).
The ER-negative branch can be split into two subgroups: one characterized by ERBB2 overexpression (4 tumors), and a second subgroup (16 tumors) defined by overexpression of basal markers, such as, cytokeratin 5 (CK5), vimentin, survivin and epidermal growth factor receptor (EGFR). The subgroup associated with ERBB2 overexpression was composed exclusively of BRCAX tumors, while the basal-like subgroup included mainly BRCA1-associated tumors (11 of 16 tumors).
Three subgroups were found within the ER-positive branch. One of the subgroups (brown branch in Figure 1) contained 16 tumors that are mostly grade 1, and overexpress hormonal receptors, CK8, BCL2 and proteins that inhibit cell cycle progression (for example, p27 and p16). Of note, most of these tumors were BRCAX samples (13 of 16). A total of 17 tumors composed the second subgroup (purple branch in Figure 1). These samples were characterized by a higher grade than the previous group, low or lack of expression of hormonal receptors as compared with the other groups and overexpression of other proteins such as cyclin A (related to the cell cycle progression) and TOPOII (related to cell growth). A mixture of the three FBC groups was found in this subgroup (Figure 1). We termed these two subgroups as luminal A and B, respectively, according to the parameters (such as grade and differential expression of TOPOII) that have been used previously to discriminate between them (Sorlie et al., 2001; Sotiriou et al., 2003). Finally, a group of nine tumors were described (Figure 1, green branch), which had luminal features (positive for hormonal receptors and CK8), high grade and overexpression of CHEK2 and survivin. This group was composed mainly of BRCA2-associated tumors (5 of 9 samples). We called this group as ‘unclassified’ tumors, since the features did not match with those previously established for ‘normal breast-like’ or the other tumor groups.
A summary of the FBC distribution in the different IHC subtypes is shown in Table 1. Most of BRCA1-associated tumors show a basal-like phenotype, while BRCA2-associated tumors are mainly found in the luminal B or unclassified subtypes. The ERBB2 subtype was composed entirely of BRCAX samples, although most of BRCAX malignancies had a luminal A phenotype. IHC characteristics of the different subtypes and statistical comparisons are shown in Table 2.
Genomic characterization of each FBC subtype
Classifying the FBC tumors in five subtypes according to the IHC clusters, we collected the array-CGH data of these samples (Melchor et al., in press), and assessed their copy number of genomic aberrations (CNA). Basal breast tumors had the highest mean of genomic changes (30.75±3.0 CNA), while luminal A tumors had the lowest mean number of CNA (10.87±1.9). Luminal B, ERBB2 and unclassified tumors had 20.00±2.7, 14.75±6.2 and 14.44±3.7 CNA, respectively (Figure 2). Differences in the amount of CNA were statistically significant (P<0.05, Mann–Whitney U-test) when comparing basal breast tumors with any of the other cancer subtypes.
Next, we plotted the GAPs of each FBC subtype (Figure 3). The most recurrent aberrations (frequency over 50%) in luminal A subtype were gains at 1q and 16p, and losses at 11q23 and 16q. Luminal B tumors exhibited as recurrent aberrations (over 50%): −8ptel-p12, +8q21-qtel, −11q23.3-qtel, −14q31, +16p, −16q, +20q13.13-qtel, −22q. Given that the ERBB2 subtype was composed of only four tumors, the GAP was not informative, though all cases had gain at 17q12 (ERBB2 locus). Basal breast tumors clearly presented an unstable GAP with many aberrations with a frequency over 50%, such as, −3p21-p13, +3q25.1-q26, −4p, −4q22.1-qtel, −5q, −8ptel-p12, +8q22.1-qtel, among others. Finally, unclassified tumors showed an intermediate level of genome instability as compared to luminal A and B tumors. The only two genomic aberrations with a frequency higher than 50% in unclassified tumors were gains at 1q32.2 and 8q21.12-qtel. When we compared the aberration frequencies between the subtypes as determined by IHC, significant differences were only found when compared basal versus non-basal breast tumors (Supplementary Figure 1).
High-level DNA amplifications were more frequently found in luminal B, basal-like and ERBB2 tumors than in luminal A cancers (P=0.021, 0.036 and 0.042, respectively) (Figure 4a). Some regions of high-level DNA amplification tend to be subtype specific such as 20q13 in luminal B tumors, 6p22 and 13q34 in basal-like, and as expected 17q12 in ERBB2 tumors (Figure 4b).
We have shown that FBC can be grouped in the different breast cancer subtypes described previously in SBC using IHC (Perou et al., 2000; Sorlie et al., 2001, 2003; Sotiriou et al., 2003). In addition, we have performed a complete genomic characterization of the subtypes. Differences in the levels of genomic instability, GAPs, and high-level DNA amplification target regions were found associated with FBC subtypes.
Common breast cancer heterogeneity and association of BRCA status with breast cancer subtype
We have previously shown the heterogeneity present in BRCAX and SBC tumors using 25 IHC markers and grade (Honrado et al., 2007). In the current work, we extended the study to the other classes of FBC, including 62 cancers (18 BRCA1-, 16 BRCA2- and 28 BRCAX-associated), and found similar results: five FBC subtypes were established using unsupervised cluster analysis of IHC of multiple proteins and grade (Figure 1). Each FBC subtype was associated with its own characteristic IHC features (Table 2). These subtypes were similar to those obtained in SBC using expression analysis: basal-like, ERBB2, luminal A and B (Perou et al., 2000; Sorlie et al., 2001). Although we were not able to distinguish a normal breast-like subtype, we identified a fifth group with intermediate characteristics between luminal A and B subtypes, which we titled the ‘unclassified group’. Given its higher grade and overexpression of proliferation markers (cyclins, Ki-67 and so on), this subtype could be more aggressive than the luminal A subtype and thus, it could be analogous to the luminal C subtype named by Sorlie et al. (2001) or luminal-like 3 described by Sotiriou et al. (2003). These findings emphasize the existence of different breast tumor subtypes that represent distinct biological entities not only in SBC, but also in FBC.
The proportion of these subtypes in FBC is not the same as in SBC (Sorlie et al., 2001) or BRCAX samples (Oldenburg et al., 2006; Honrado et al., 2007). In the present study, 26% of FBC had a basal-like IHC phenotype compared to 15% in SBC and BRCAX (Sorlie et al., 2001; Oldenburg et al., 2006) (Table 1). This difference can be attributable to the presence of BRCA1-associated tumors in our series, which are prone to have a basal phenotype (Sorlie et al., 2003; Tischkowitz and Foulkes, 2006; Turner and Reis-Filho, 2006). In our sample set, 61% of BRCA1-associated tumors comprised a significant proportion of the basal-like IHC phenotype. Nineteen percent of BRCA2-associated tumors, and 7% of BRCAX samples were also found to be basal-like (Table 1). All BRCAX cancers were studied previously for hypermethylation of the BRCA1 gene promoter and loss of heterozygosity; interestingly, the BRCAX samples that had basal-like phenotype showed biallelic inactivation of the BRCA1 gene (Honrado et al., 2007). This model of carcinogenesis in the BRCAX tumors is in agreement with the low level of BRCA1 mRNA expression reported in basal-like cancers (Staff et al., 2003; Wei et al., 2005; Turner et al., 2006). On the other hand, the ERBB2 subtype comprised only of BRCAX tumors (four cases, ∼14%) (Table 1). This finding is in concordance with the low incidence of ERBB2 amplification in BRCA1/2 mutation carriers described before (Grushko et al., 2002; Lakhani et al., 2002; Palacios et al., 2003; Adem et al., 2004). A significant association was found between most of BRCAX samples (45%) and luminal A phenotype, as seen in previous analyses (Oldenburg et al., 2006; Honrado et al., 2007). Finally, BRCA2- and non-basal BRCA1-associated tumors were mainly related to the luminal B phenotype (37 and 22%, respectively). BRCA2-associated tumors also comprised an important proportion of the unclassified samples (Table 1). In contrast, Sorlie et al. (2003) linked BRCA2-associated tumors to the luminal A subtype. This discrepancy may be caused by the small sample size given that the authors studied only two samples while we have a larger cohort of 16 BRCA2-associated cancers.
Distinct genomic aberration patterns associated with each FBC subtype
Different genomic characteristics have been recently associated with each of the five subtypes of SBC (Bergamaschi et al., 2006; Chin et al., 2006). We have studied the genomic change features of the five FBC subtypes using array-CGH data from a previous analysis (Melchor et al., in press). Basal-like tumors showed the highest genomic instability (Figure 2), consistent with two previous studies in SBC (Bergamaschi et al., 2006; Chin et al., 2006). In contrast, luminal A tumors had the lowest number of genomic aberrations. Bergamaschi et al. (2006) found the lowest number in ERBB2 tumors, but the low number of ERBB2 samples in our study (four cases) did not allow us to draw any significant conclusions.
The FBC subtypes defined by IHC exhibited distinct GAP (Figure 3). Basal-like tumors showed a specific GAP with many altered chromosomal sites such as −3p, +3q, −4p, −4q, −5q, −8p, +8q and so on. Some of these aberrations (for example, −3p25, −4p, −4q22-q35.1, −5q and so on) were significantly associated with basal-like tumors when compared with non-basal tumors (Supplementary Figure 1). The close association between basal-like phenotype and BRCA1-associated tumors explains the similarities that are found when comparing the GAP from basal-like tumors and BRCA1-associated tumors (Jonsson et al., 2005). Luminal A tumors frequently have +1q, +16p, −11q23 and −16q; luminal B tumors show genomic aberrations of other regions such as −8p, +8q, +20q and −22q. These subtype-GAPs and the recurrent chromosomal aberrations within each subtype are in agreement with those previously reported in SBC (Supplementary Table 2) (Bergamaschi et al., 2006; Chin et al., 2006).
Differences in high-level DNA amplifications
High-level DNA amplifications were found more frequently in luminal B tumors than in the other tumor subtypes (Figure 4a). In addition, the regions targeted for amplification differed slightly between the IHC subtypes (Figure 4b). As expected, ERBB2 tumors had a frequent amplification at 17q12 and overexpression of ERBB2. Approximately 20% of the luminal B breast cancer subtype had amplification of regions, such as, 8p11-p12, 8q24, 11q13.3-q13.4, 17q25 and 20q13. Luminal A cancers had few high-level amplifications with the exception of frequent amplification at 11q13 (CCND1 locus), which was also found in luminal B tumors. This finding could explain the CCND1 overexpression present in the luminal tumors (Figure 1), and it is in concordance with the studies that show a negative correlation between CCND1 amplification and basal-like phenotype (Reis-Filho et al., 2006; Elsheikh et al., 2007). Finally, basal-like tumors have high-level DNA amplification frequently at 8q24, 12p13 and 13q34 (Figure 4b). Most of these amplification sites are similar to the regions described in SBC subtypes (Bergamaschi et al., 2006; Chin et al., 2006) (Supplementary Table 2). Amplification of 11q13 appears recurrent in luminal cancers, whereas amplifications of 8p11-12 and 20q13 are more frequently found in the luminal B subtype, and amplification of 17q21 in the ERBB2 subtype. These common amplifications found in both FBC and SBC subtypes could represent targets for therapy, as has already been established with ERBB2 and trastuzumab. A high frequency of amplification at 8q24 (MYC locus) was described in our basal-like FBC, an aberration less common in basal-like SBC (Chin et al., 2006; Rodriguez-Pinilla et al., 2006). Because Al-Kuraya et al. (2004) reported a greater frequency of MYC amplification in medullary carcinomas, a specific subtype of basal-like tumors that is very frequent in BRCA1-associated cancers (Lakhani et al., 1998), we checked whether that difference could be due to the presence of medullary carcinomas in our basal-like FBC. However, MYC amplification was present in 3 of 6 medullary FBC and 4 of 10 non-medullary basal-like FBC in our set (data not shown). A larger series of cases will be necessary to confirm or to rule out this association.
Our findings demonstrate that breast cancer can be subdivided into distinct subtypes independently whether the tumors are familial or sporadic. The FBC subtypes differed in terms of tumor histology, IHC portraits and genomic changes patterns (Table 3). A higher prevalence of basal-like phenotype is present in BRCA1-related tumors, while luminal A phenotype is recurrent in BRCAX-associated cancers. In addition, basal-like malignancies had more gains and losses than the other subtypes, while luminal B cancers showed more high-level DNA amplifications. These characteristics are similar to those recently described in SBC (Bergamaschi et al., 2006; Chin et al., 2006). These findings support the existence of distinct genetic pathways of tumor evolution, common to sporadic and FBC, which underlie the pathogenesis of the different breast tumor subtypes and may explain their distinct biological behavior. Furthermore, we would postulate that gene-expression profiling, clinical presentation and response to therapy also differ in the five FBC IHC subtypes, as already reported in SBC subtypes (Perou et al., 2000; Sorlie et al., 2001, 2003; Carey et al., 2006; Hu et al., 2006). Taking into account these differences, the BC subtypes should be studied as distinct entities to better describe their features, as has been done for basal-like tumors (Turner and Reis-Filho, 2006; Yehiely et al., 2006; Vincent-Salomon et al., 2007).
Materials and methods
Tumor samples and patients
We compiled 62 paraffin-embedded tumor tissues, which had been analysed previously both by IHC (Palacios et al., 2003, 2005; Honrado et al., 2005b) and aCGH (Melchor et al., in press). These breast cancer samples were selected from families with at least three women affected with breast and/or ovarian cancer and at least one of them diagnosed before 50 years of age, or from families with female breast and/or ovarian cancer and at least one case of male breast cancer. All cases were studied for mutations and large rearrangements in the BRCA genes using standard procedures (Osorio et al., 2000; Diez et al., 2003). A total of 18 cases had mutations in the BRCA1 gene, 16 patients presented mutations in the BRCA2 gene and 28 cases were negative for germ-line mutations in the BRCA genes (non-BRCA1/2 or BRCAX).
Morphological evaluation, TMA construction and IHC studies
Histological sections from all 62 samples were reviewed by two pathologists (EH and JP). The Nottingham histological grading system was used to assess the grade of invasive ductal carcinomas.
Representative areas of the 62 tumors were carefully selected on H&E-stained sections and marked on individual paraffin blocks. Two tissue cores were obtained from each specimen and included in Tissue Micro-Arrays (TMA), whose characteristics have been previously published (Palacios et al., 2003, 2005; Honrado et al., 2005b).
Briefly, IHC assays were performed by the Envision method (Dako, Glostrup, Denmark) with a heat-induced antigen retrieval step. Sections from the tissue array were immersed in 10 mM boiling sodium citrate at pH 6.5 for 2 min in a pressure cooker. For the 25 antibodies used in this study, dilutions and suppliers are listed in Supplementary Table 1.
Between 150 and 200 cells per core were scored for the percentage of cells with positive nuclei or cytoplasm, depending upon the marker. We evaluated nuclear staining for ER, progesterone receptor, p53, Ki-67, cyclins D1, D3, E and A, p16, p27, p21, Skp2, retinoblastoma protein, E2F6, MDM2, topoisomerase IIα, survivin and CHEK2; evaluation of cytoplasmic staining was carried out for BCL2, vimentin, CK5/6, CK8 and cyclin B1, as described previously (Palacios et al., 2005). 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, as published earlier (Palacios et al., 2005).
Array comparative genomic hybridization analysis
Genomic DNA isolation from the 62 formalin-fixed paraffin-embedded (FFPE) tumors was performed as previously described (Melchor et al., in press). Briefly, two 30-μm sections were obtained from FFPE tumors, treated with xylene, incubated in Glycine Tris-EDTA (100 mM glycine, 10 mM Tris, pH 8.0, 1 mM EDTA) and NaSCN (1 M) 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.
Comparative genomic hybridization was carried out onto the ‘1 Mb BAC’ array platform developed at the University of Pennsylvania (Greshock et al., 2004). DNA probe labeling, aCGH protocol and array data analysis have been described previously (Melchor et al., in press). Briefly, in the array data analysis, aCGH normalization was carried using the DNMAD application (Vaquerizas et al., 2004). The normalized profiles were processed using the Binary Segmentation algorithm implemented in the Insilico CGH software (Vaquerizas et al., 2005). This algorithm defines genomic segments, which have an estimative copy number value in log2 ratio (that is, the median log2 ratio of the contained clones). Those segments with log2 ratio ⩾0.1 were considered as gains, while those with log2 ratio ⩽0.1 were categorized as losses. High-level DNA amplifications were considered when log2 ratio ⩾0.4.
Hierarchical unsupervised cluster analysis was performed by means of the UPGMA method using correlation distance and Euclidean distance between markers. The cluster was displayed using SOTAARRAY (Herrero et al., 2001) (software available at http://gepas.bioinfo.cipf.es). IHC results were represented by range of color from green to red, the lowest and the highest percentage of positive cells for each marker, respectively. Exceptions were grade that was scaled as 33% ‘expressed’ for grade 1 (green), 66% for grade 2 (brown) and 100% for grade 3 (red), and HER-2 that was scaled as 100% for positive (3+) (red) and 0% for negative (green) (Figure 1). Fisher's exact test was used to determine the differences in the expression of each marker between groups, except for Ki-67 and grade, which were calculated using χ2-square test. The statistical software SPSS for Windows (SPSS Inc., Chicago, IL) was used for this analysis.
Regarding the array-CGH data, we used a non-parametric Mann–Whitney U-test to compare the mean number of genomic alterations among the different established groups. The SPSS software was used for these comparisons. For the analysis of differences in the aberration frequency of specific chromosomal regions, we used the Stat POMELO tool (http://pomelo.bioinfo.cnio.es) (Herrero et al., 2003), applied Fisher's exact test and adjusted P-values for multiple testing using the FDR approach (a P-value <0.05 was considered significant).
array-based comparative genomic hybridization
copy number of genomic aberrations
familial breast cancer
genomic aberration pattern
sporadic breast cancer
Adem C, Soderberg CL, Hafner K, Reynolds C, Slezak JM, Sinclair CS et al. (2004). ERBB2, TBX2, RPS6KB1, and MYC alterations in breast tissues of BRCA1 and BRCA2 mutation carriers. Genes Chromosomes Cancer 41: 1–11.
Al-Kuraya K, Schraml P, Torhorst J, Tapia C, Zaharieva B, Novotny H et al. (2004). Prognostic relevance of gene amplifications and coamplifications in breast cancer. Cancer Res 64: 8534–8540.
Alvarez S, Diaz-Uriarte R, Osorio A, Barroso A, Melchor L, Paz MF et al. (2005). A predictor based on the somatic genomic changes of the BRCA1/BRCA2 breast cancer tumors identifies the non-BRCA1/BRCA2 tumors with BRCA1 promoter hypermethylation. Clin Cancer Res 11: 1146–1153.
Bergamaschi A, Kim YH, Wang P, Sorlie T, Hernandez-Boussard T, Lonning PE et al. (2006). Distinct patterns of DNA copy number alteration are associated with different clinicopathological features and gene-expression subtypes of breast cancer. Genes Chromosomes Cancer 45: 1033–1040.
Carey LA, Perou CM, Livasy CA, Dressler LG, Cowan D, Conway K et al. (2006). Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study. JAMA 295: 2492–2502.
Chin K, DeVries S, Fridlyand J, Spellman PT, Roydasgupta R, Kuo WL et al. (2006). Genomic and transcriptional aberrations linked to breast cancer pathophysiologies. Cancer Cell 10: 529–541.
Diez O, Osorio A, Duran M, Martinez-Ferrandis JI, de la Hoya M, Salazar R et al. (2003). 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 22: 301–312.
Elsheikh S, Green AR, Aleskandarany MA, Grainge M, Paish CE, Lambros MB et al. (2007). CCND1 amplification and cyclin D1 expression in breast cancer and their relation with proteomic subgroups and patient outcome. Breast Cancer Res Treat; e-pub ahead of print.
Greshock J, Naylor TL, Margolin A, Diskin S, Cleaver SH, Futreal PA et al. (2004). 1-Mb resolution array-based comparative genomic hybridization using a BAC clone set optimized for cancer gene analysis. Genome Res 14: 179–187.
Grushko TA, Blackwood MA, Schumm PL, Hagos FG, Adeyanju MO, Feldman MD et al. (2002). Molecular-cytogenetic analysis of HER-2/neu gene in BRCA1-associated breast cancers. Cancer Res 62: 1481–1488.
Herrero J, Al-Shahrour F, Diaz-Uriarte R, Mateos A, Vaquerizas JM, Santoyo J et al. (2003). GEPAS: a web-based resource for microarray gene expression data analysis. Nucleic Acids Res 31: 3461–3467.
Herrero J, Valencia A, Dopazo J . (2001). A hierarchical unsupervised growing neural network for clustering gene expression patterns. Bioinformatics 17: 126–136.
Honrado E, Benitez J, Palacios J . (2005a). The molecular pathology of hereditary breast cancer: genetic testing and therapeutic implications. Mod Pathol 18: 1305–1320.
Honrado E, Osorio A, Milne RL, Paz MF, Melchor L, Cascon A et al. (2007). Immunohistochemical classification of non-BRCA1/2 tumors identifies different groups that demonstrate the heterogeneity of BRCAX families. Mod Pathol 20: 1298–1306.
Honrado E, Osorio A, Palacios J, Milne RL, Sanchez L, Diez O et al. (2005b). Immunohistochemical expression of DNA repair proteins in familial breast cancer differentiate BRCA2-associated tumors. J Clin Oncol 23: 7503–7511.
Hu Z, Fan C, Oh DS, Marron JS, He X, Qaqish BF et al. (2006). The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics 7: 96.
Jonsson G, Naylor TL, Vallon-Christersson J, Staaf J, Huang J, Ward MR et al. (2005). Distinct genomic profiles in hereditary breast tumors identified by array-based comparative genomic hybridization. Cancer Res 65: 7612–7621.
Lacroix M, Leclercq G . (2005). The ‘portrait’ of hereditary breast cancer. Breast Cancer Res Treat 89: 297–304.
Lakhani SR, Jacquemier J, Sloane JP, Gusterson BA, Anderson TJ, van de Vijver MJ et al. (1998). Multifactorial analysis of differences between sporadic breast cancers and cancers involving BRCA1 and BRCA2 mutations. J Natl Cancer Inst 90: 1138–1145.
Lakhani SR, Van De Vijver MJ, Jacquemier J, Anderson TJ, Osin PP, McGuffog L et al. (2002). 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 20: 2310–2318.
Melchor L, Honrado E, Huang J, Alvarez S, Naylor TL, Garcia MJ et al. (in press). Estrogen receptor status could modulate the genomic pattern in familial and sporadic breast cancer: a study based on array CGH. Clin Cancer Res (in press).
Miki Y, Swensen J, Shattuck-Eidens D, Futreal PA, Harshman K, Tavtigian S et al. (1994). A strong candidate for the breast and ovarian cancer susceptibility gene BRCA1. Science 266: 66–71.
Oldenburg RA, Kroeze-Jansema K, Meijers-Heijboer H, van Asperen CJ, Hoogerbrugge N, van Leeuwen I et al. (2006). Characterization of familial non-BRCA1/2 breast tumors by loss of heterozygosity and immunophenotyping. Clin Cancer Res 12: 1693–1700.
Osorio A, Barroso A, Martinez B, Cebrian A, San Roman JM, Lobo F et al. (2000). Molecular analysis of the BRCA1 and BRCA2 genes in 32 breast and/or ovarian cancer Spanish families. BJC 82: 1266–1270.
Palacios J, Honrado E, Osorio A, Cazorla A, Sarrio D, Barroso A et al. (2003). 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 9: 3606–3614.
Palacios J, Honrado E, Osorio A, Cazorla A, Sarrio D, Barroso A et al. (2005). Phenotypic characterization of BRCA1 and BRCA2 tumors based in a tissue microarray study with 37 immunohistochemical markers. Breast Cancer Res Treat 90: 5–14.
Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA et al. (2000). Molecular portraits of human breast tumours. Nature 406: 747–752.
Reis-Filho JS, Savage K, Lambros MB, James M, Steele D, Jones RL et al. (2006). Cyclin D1 protein overexpression and CCND1 amplification in breast carcinomas: an immunohistochemical and chromogenic in situ hybridisation analysis. Mod Pathol 19: 999–1009.
Rodriguez-Pinilla SM, Jones RL, Lambros MB, Arriola E, Savage K, James M et al. (2007). MYC amplification in breast cancer: a chromogenic in situ hybridisation study. J Clin Pathol 60: 1017–1023.
Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H et al. (2001). Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA 98: 10869–10874.
Sorlie T, Tibshirani R, Parker J, Hastie T, Marron JS, Nobel A et al. (2003). Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA 100: 8418–8423.
Sotiriou C, Neo SY, McShane LM, Korn EL, Long PM, Jazaeri A et al. (2003). Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci USA 100: 10393–10398.
Staff S, Isola J, Tanner M . (2003). Haplo-insufficiency of BRCA1 in sporadic breast cancer. Cancer Res 63: 4978–4983.
Tirkkonen M, Johannsson O, Agnarsson BA, Olsson H, Ingvarsson S, Karhu R et al. (1997). Distinct somatic genetic changes associated with tumor progression in carriers of BRCA1 and BRCA2 germ-line mutations. Cancer Res 57: 1222–1227.
Tischkowitz MD, Foulkes WD . (2006). The basal phenotype of BRCA1-related breast cancer: past, present and future. Cell Cycle 5: 963–967.
Turner NC, Reis-Filho JS . (2006). Basal-like breast cancer and the BRCA1 phenotype. Oncogene 25: 5846–5853.
Turner NC, Reis-Filho JS, Russell AM, Springall RJ, Ryder K, Steele D et al. (2006). BRCA1 dysfunction in sporadic basal-like breast cancer. Oncogene 26: 2126–2132.
van Beers EH, van Welsem T, Wessels LF, Li Y, Oldenburg RA, Devilee P et al. (2005). Comparative genomic hybridization profiles in human BRCA1 and BRCA2 breast tumors highlight differential sets of genomic aberrations. Cancer Res 65: 822–827.
Vaquerizas JM, Conde L, Yankilevich P, Cabezon A, Minguez P, Diaz-Uriarte R et al. (2005). GEPAS, an experiment-oriented pipeline for the analysis of microarray gene expression data. Nucleic Acids Res 33: W616–W620.
Vaquerizas JM, Dopazo J, Diaz-Uriarte R . (2004). DNMAD: web-based diagnosis and normalization for microarray data. Bioinformatics 20: 3656–3658.
Vincent-Salomon A, Gruel N, Lucchesi C, Mac Grogan G, Dendale R, Sigal-Zafrani B et al. (2007). Identification of typical medullary breast carcinoma as a genomic sub-group of basal-like carcinomas, a heterogeneous new molecular entity. Breast Cancer Res 9: R24.
Wei M, Grushko TA, Dignam J, Hagos F, Nanda R, Sveen L et al. (2005). BRCA1 promoter methylation in sporadic breast cancer is associated with reduced BRCA1 copy number and chromosome 17 aneusomy. Cancer Res 65: 10692–10699.
Wooster R, Bignell G, Lancaster J, Swift S, Seal S, Mangion J et al. (1995). Identification of the breast cancer susceptibility gene BRCA2. Nature 378: 789–792.
Yehiely F, Moyano JV, Evans JR, Nielsen TO, Cryns VL . (2006). Deconstructing the molecular portrait of basal-like breast cancer. Trends Mol Med 12: 537–544.
We thank the Spanish National Tumor Bank Network, and Luz Álvarez and Miguel Urioste from the Familial Cancer Unit for the collection of the samples. For their technical assistance, we thank the Immunohistological Unit, and Carmen Martin and Juan C Cigudosa from the Molecular Cytogenetics Group, at the Spanish National Cancer Center and C Angelica Medina from Medical Genetics Division of the University of Pennsylvania. We also thank Susanna Leskelä for English assistance. LM Grant sponsor: Spanish Ministry of Education and Science FPU AP-2004-0448. Short stays sponsorships: ICRETT/05/063 and EMBO Short-Term Fellowship ASTF 162-05. MJG was supported by the Fundación Científica de la Asociación Española contra el Cáncer. This study has been partially funded by projects SAF03-02497 and SAF 06-06149 from the Spanish Ministry of Education and Science; and by a grant from Breast Cancer Research Foundation (BCRF) to KLN.
About this article
Cite this article
Melchor, L., Honrado, E., García, M. et al. Distinct genomic aberration patterns are found in familial breast cancer associated with different immunohistochemical subtypes. Oncogene 27, 3165–3175 (2008) doi:10.1038/sj.onc.1210975
- hereditary breast cancer
- breast cancer subtypes
Genetics and Molecular Biology (2019)
RANK-c attenuates aggressive properties of ER-negative breast cancer by inhibiting NF-κB activation and EGFR signaling
Single-base LOH can be used as Specific Marker to Classify BRCAx Familial Breast Cancer into More Homogenous Subtypes
The Breast Journal (2017)
Different Array CGH profiles within hereditary breast cancer tumors associated to BRCA1 expression and overall survival
BMC Cancer (2016)