Breast Cancer Research and Treatment

, Volume 132, Issue 2, pp 439–448 | Cite as

Gene expression profiling assigns CHEK2 1100delC breast cancers to the luminal intrinsic subtypes

  • Jord H. A. Nagel
  • Justine K. Peeters
  • Marcel Smid
  • Anieta M. Sieuwerts
  • Marijke Wasielewski
  • Vanja de Weerd
  • Anita M. A. C. Trapman-Jansen
  • Ans van den Ouweland
  • Hennie Brüggenwirth
  • Wilfred F. J. van IJcken
  • Jan G. M. Klijn
  • Peter J. van der Spek
  • John A. Foekens
  • John W. M. Martens
  • Mieke Schutte
  • Hanne Meijers-Heijboer
Preclinical Study

Abstract

CHEK2 1100delC is a moderate-risk cancer susceptibility allele that confers a high breast cancer risk in a polygenic setting. Gene expression profiling of CHEK2 1100delC breast cancers may reveal clues to the nature of the polygenic CHEK2 model and its genes involved. Here, we report global gene expression profiles of a cohort of 155 familial breast cancers, including 26 CHEK2 1100delC mutant tumors. In line with previous work, all CHEK2 1100delC mutant tumors clustered among the hormone receptor-positive breast cancers. In the hormone receptor-positive subset, a 40-gene CHEK2 signature was subsequently defined that significantly associated with CHEK2 1100delC breast cancers. The identification of a CHEK2 gene signature implies an unexpected biological homogeneity among the CHEK2 1100delC breast cancers. In addition, all 26 CHEK2 1100delC tumors classified as luminal intrinsic subtype breast cancers, with 8 luminal A and 18 luminal B tumors. This biological make-up of CHEK2 1100delC breast cancers suggests that a relatively limited number of additional susceptibility alleles are involved in the polygenic CHEK2 model. Identification of these as-yet-unknown susceptibility alleles should be aided by clues from the 40-gene CHEK2 signature.

Keywords

Breast cancer CHEK2 1100delC Intrinsic subtypes 

Introduction

At least 10% of breast cancers arise within a familial clustering of multiple breast cancers. Inherited mutations of the BRCA1 or BRCA2 genes are identified in about one-quarter of the families with breast cancer (OMIM 113705 and 600185) [1, 2, 3, 4, 5]. Female carriers of mutant BRCA1 or BRCA2 genes have a lifetime risk of 50–85% to develop breast cancer, classifying both genes as high-risk breast cancer susceptibility genes. In 2002, we and others identified the CHEK2 gene as a breast cancer susceptibility gene (OMIM 604373) [5, 6, 7, 8, 9]. A single founder mutation, CHEK2 1100delC, was identified in about 5% of families with breast cancer that did not have mutations in either BRCA1 or BRCA2. In contrast to BRCA1 and BRCA2, CHEK2 1100delC was estimated to confer only a moderate 20–25% risk to develop breast cancer for female mutation carriers. Although this classified CHEK2 1100delC as a moderate-risk breast cancer susceptibility allele, the mutation was found to be particularly prevalent among families with a high-risk breast cancer inheritance pattern, with mutation frequencies rising to more than 20% among families with four or more cases of breast cancer [5, 6, 7, 9, 10]. Segregation of the CHEK2 1100delC mutation with the cancer phenotype typically was incomplete in the high-risk breast cancer families, suggesting the inheritance of an additional breast cancer susceptibility allele(s) in these families. Independent investigations indeed have implied that a vast amount of non-BRCA1/BRCA2 familial breast cancers likely arises within a context of polygenic breast cancer susceptibility, where multiple moderate-risk or low-risk susceptibility alleles act in concert to confer a high risk to develop breast cancer [11, 12]. By now, several other moderate-risk breast cancer genes have been identified, including the ATM, BRIP1, and PALB2 genes, and recently also ten low-risk loci, and each of them appeared to operate in a polygenic setting [13, 14, 15, 16, 17, 18, 19, 20, 21, 22]. An intriguing question is whether these moderate-risk and low-risk susceptibility genes operate in a polygenic setting wherein each risk allele may act in concert with any other risk allele, or are there also risk alleles that are less promiscuous and operate with a limited set of risk alleles? The issue certainly is not trivial, as polygenic breast cancers likely would be far more biologically homogeneous in the latter setting and thus be anticipated to have a more predictable clinical outcome.

Historically, breast cancers had been classified by their expression of the estrogen and progesterone hormone receptors (ER and PGR) and the ERBB2/HER2/NEU receptor. An important breakthrough in breast cancer classification came with the advent of microarray technology, allowing genome wide expression analysis of a tumor sample. Seminal gene expression profiling studies by Sørlie, Perou, and their colleagues have revealed that breast cancers might be classified by their global gene expression program, distinguishing two subsets of breast cancers among ER-positive tumors (luminal A and B), two subsets among ER-negative tumors (basal-like and normal-like) and the ERBB2 subset being mainly ER negative [23, 24, 25]. These intrinsic subtypes were shown to be relevant in prognosis and prediction of clinical outcome of breast cancer patients [25, 26, 27, 28, 29], although not as powerful as gene signatures that had been defined based on prognosis or therapy responses of patients [30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42]. Gene expression profiles have also been associated with genetic alterations present in breast cancers, including alterations of BRCA1, ERBB2, p53, RAS, and MYC [25, 43, 44, 45, 46, 47]. Classification of BRCA1 breast cancers as being predominantly of the basal-like intrinsic subtype [25] was particularly instrumental because it implied biological homogeneity among BRCA1 tumors.

Here, we have generated global gene expression profiles of a cohort of 155 familial breast cancers, including BRCA1, BRCA2, and CHEK2 mutant tumors. We aimed to ascertain if and to what degree CHEK2 mutated tumors share biology with each other and with other hereditary tumor-subtypes. We specifically investigated whether CHEK2 1100delC breast cancers are biologically homogeneous as this may provide clues to the nature of polygenic CHEK2 cancer susceptibility and its genes involved.

Results

Definition of a 40-gene signature for CHEK2 1100delC breast cancers

Global gene expression profiles of 155 familial breast cancers were generated by using Affymetrix U133 Plus 2.0 GeneChips. Familial breast cancer cases were classified by the presence of an oncogenic germline BRCA1 or BRCA2 mutation (n = 47 and n = 6), by the presence of the CHEK2 1100delC founder mutation (n = 26), or by a family history of breast cancer when no mutations were detected in either gene (n = 76). Unsupervised clustering of the 155 tumors by Pearson’s correlation based on the top 10% variably expressed probe sets (n = 5,467) revealed two highly distinct clusters of tumor samples, designated “hormone receptor positive” and “hormone receptor negative” (HR-pos and HR-neg; Fig. 1; reproducibility measure R = 0.95). Based on microarray transcript expression levels, 96% of the 100 tumors in the HR-pos cluster were considered positive for expression of estrogen receptor alpha (ESR1) compared with 9% of the 55 tumors in the HR-neg cluster (Fisher’s exact P < 0.0001 and ESR1 cut-off 1,000; Fig. 1, Table 1), strongly suggesting that the molecular dichotomy among the breast cancers was related to their ER status. In concordance with this notion and with literature reports, univariate t-testing revealed that the differential gene expression programs between the two tumor clusters included not only ESR1 but also its downstream target genes, such as PGR, FOXOA1, GATA3, TFF3, NAT1, and XBP. Importantly, all 26 CHEK2 1100delC tumors and all six BRCA2 tumors were located in the HR-pos cluster, whereas 36 of the 47 BRCA1 tumors were located in the HR-neg cluster (Fig. 1 and Table 1). This cluster division among mutation-positive familial breast cancers was in concordance with their ER status, known to be predominantly ER-positive for CHEK2 1100delC and BRCA2 tumors and predominantly ER-negative for BRCA1 tumors [48, 49, 50, 51].
Fig. 1

Unsupervised Pearson correlation matrix of 155 familial breast cancers. The correlation visualization tool displays pairwise correlations between the 155 tumors, where red cells indicate positive correlation and blue cells indicate negative correlation. The matrix revealed two main clusters of breast cancers, containing 100 and 55 tumor samples and designated HR-pos and HR-neg, respectively. Color-coding mutation status (left bar): redCHEK2 1100delC tumors, blueBRCA1 tumors, greenBRCA2 tumors, and yellow non-mutant tumors

Table 1

Receptor expression among familial breast cancers

Tumors by mutation status

Hormone receptor-positive tumors

Hormone receptor-negative tumors

N

ER

PGR

ERBB2

N

ER

PGR

ERBB2

 

+ve

%

+ve

%

+ve

%

 

+ve

%

+ve

%

+ve

%

CHEK2 1100delC

26

26

100

23

88

6

23

0

BRCA1

9

8

89

7

78

1

11

38

3

8

14

37

0

0

BRCA2

6

6

100

5

83

0

0

0

Non-mutant

59

56

95

53

90

8

14

17

2

12

5

29

0

0

All tumors

100

96

96

88

88

15

15

55

5

9

19

35

0

0

Receptor transcript expression cut-offs were 1,000 for ESR1 (probe set 205225_at), 20 for PGR (probe set 208305_at), and 13,000 for ERBB2 (probe set 216836_s_at)

To determine the CHEK2 1100delC gene expression signature, we performed supervised class comparison of the 26 CHEK2 1100delC tumors with all 129 non-CHEK2 tumors from the familial breast cancer cohort. We have evaluated several class comparison and class prediction methods but the generated gene signatures were all strongly dominated by ESR1 response genes. The assignment of all 26 CHEK2 1100delC breast cancers to the HR-pos tumor cluster suggested molecular homogeneity among these tumors, albeit that it was unclear whether this homogeneity reflected their ER-positive hormone receptor status, their CHEK2 mutation status, or both. We therefore restricted the supervised analysis to the 100 HR-pos familial breast cancers from the cohort. Univariate t-test comparison of the 26 CHEK2 1100delC tumors with the 74 non-CHEK2 HR-pos breast cancers now allowed identification of a CHEK2 signature of 40 differentially expressed genes, represented by 43 probe sets (P < 0.001 and global test P = 0.03; Online Resource Table S1). Hierarchical clustering of all 100 HR-pos breast cancers based on the 40-gene CHEK2 signature was reproducible (R = 0.73) and assigned 88% (23 of 26) CHEK2 1100delC breast cancers to a single cluster branch (Fig. 2a), which was highly significant (Fisher’s exact P < 0.0001). The robustness of the CHEK2 signature was also evaluated by simulating signature identification based on 26 randomly selected HR-pos breast cancers instead of CHEK2 1100delC tumors. Three simulation experiments revealed gene signatures of 9, 5, and 3 differentially expressed genes, and none of the three gene signatures were significant by Fisher’s exact testing.
Fig. 2

Hierarchical clustering of 100 HR-positive familial breast cancers. Gene expression heatmaps, with red and blue cells indicating overexpression and underexpression of probe sets relative to the log-2 transformed geometrical mean expression among all samples. a Clustering based on the 40-gene CHEK2 signature (represented by 43 probe sets). b Clustering based on the 69-gene CHEK2-minus-BRCA1 signature (represented by 71 probe sets). c Unsupervised clustering based on the top 10% variably expressed probe sets (n = 5,467). Color-coding mutation status (bars above each heatmap): redCHEK2 1100delC tumors, blueBRCA1 tumors, greenBRCA2 tumors, and yellow non-mutant tumors. Color-coding intrinsic subtypes (bottom bar): yellow luminal A tumors, red luminal B tumors, and blue basal-like tumors

Figure 2a also assigned six of the nine HR-pos BRCA1 breast cancers to the CHEK2 tumor cluster, with very similar expression patterns of the signature genes when compared with the CHEK2 samples. This might suggest a functional relationship between the BRCA1 and CHEK2 proteins. Under the hypothesis that the HR-positive BRCA1 samples share biology with the CHEK2 samples, putting the BRCA1 samples in the contrast group when identifying differentially expressed genes as we did in Fig. 2a will dampen the differences in expression levels of truly differential genes. Thus, to further evaluate the putative relationship between CHEK2 and BRCA1, we also defined a gene signature by t-test comparison of the 26 CHEK2 1100delC tumors with all HR-pos breast cancers but with exclusion of the nine HR-pos BRCA1 breast cancers. The resulting genes were then used to cluster all the HR-positive samples, including the BRCA1 samples (Fig. 2b). Interestingly, the CHEK2-minus-BRCA1 signature included more differentially expressed genes than the CHEK2 signature (69 vs. 40 genes with an overlap of 37 genes; global test P = 0.008; Online Resource Table S2), implying that the CHEK2-minus-BRCA1 classification yielded biologically more homogeneous tumor clusters. After all, the more distinct two sample groups, the more genes that are expressed exclusively in either sample group, while increasing heterogeneity within a sample group diminishes the number of genes whose expression typifies that group. The biological homogeneity was also reflected by the improved performance of the CHEK2-minus-BRCA1 signature over the CHEK2 signature in clustering the 100 HR-pos breast cancers, with two more CHEK2 1100delC breast cancers and two more BRCA1 breast cancers that located into the CHEK2 tumor cluster (Fig. 2a, b; R = 0.73).

Further evaluation of the 40 genes from the CHEK2 signature by ingenuity pathway analysis revealed that the CHEK2 gene itself was the top most differentially expressed gene (Online Resource Table S1). The most prominent gene from the CHEK2 signature known to be involved in CHEK2 functions was RECQ5L, a member of the RecQ family of DNA helicases that also includes RECQL, RECQL4, BLM, and WRN [52]. The RecQ helicases are involved in processing of aberrant DNA structures that arise during DNA replication and repair, where RECQL5’s function is thought to be in mitotic recombination events. Importantly, germline mutations in the RECQL4, BLM, and WRN genes each give rise to ageing disorders with an increased cancer risk: Rothmund–Thomson, Bloom, and Werner syndromes, respectively [53, 54, 55]. Other genes from the CHEK2 signature had been associated with BRCA1’s function in maintenance of a normal, inactive X chromosome, including the non-coding XIST gene and the polycomb group family member EED [56, 57, 58, 59]. Ingenuity pathway analysis of the 40 genes from the CHEK2 signature identified “Cell cycle G2/M DNA damage checkpoint regulation” as the most prominent canonical pathway that associated with the signature, consistent with the well-defined kinase function of CHEK2 in the G2 phase of the cell cycle [60, 61]. “Fibroblast Growth Factor signaling” and “p53 signaling” featured as top biological networks associated with the CHEK2 signature, which again was not surprising because FGF signaling has previously been associated with breast cancer susceptibility [16, 62, 63, 64], whereas the p53 protein is a well-known downstream phosphorylation target of CHEK2 kinase [60, 61]. The concordance of the functional assignments of the CHEK2 signature genes with current knowledge further supports the validity of the 40-gene CHEK2 signature.

CHEK2 1100delC tumors are luminal subtype breast cancers

Unsupervised hierarchical clustering of the 100 HR-pos familial breast cancers based on the top 10% variably expressed probe sets showed two tumor clusters, with 17 CHEK2 1100delC tumors in one cluster and 9 in the other cluster (Fig. 2c). A dichotomy among hormone receptor-positive breast cancers had also been observed by Sørlie et al. [23, 24, 25]. Therefore, we classified all 155 familial breast cancers from our cohort according the intrinsic gene signatures defined by Sørlie et al. and indicated the observed subtypes below Fig. 2c. Hierarchical clustering based on these genes identified 32 (21%) luminal A subtype tumors, 66 (42%) luminal B, and 57 (37%) basal-like subtype tumors among the 155 familial breast cancers and, notably, no ERBB2 or normal-like breast cancers (Table 2). The 15 ERBB2-positive samples (Table 1) were clustered as luminal A (n = 6), luminal B (n = 8), and the final one as basal. The observation that ERBB2 overexpressing samples can be clustered among the luminal subtypes has also been seen in the original articles of Sørlie et al.
Table 2

Intrinsic subtypes among familial breast cancers

Tumors by mutation status

Intrinsic subtypes

N

Lum A

Lum B

ERBB2

Basal-like

Normal-like

CHEK2 1100delC

26

8

18

0

0

0

BRCA1

47

1

7

0

39

0

BRCA2

6

1

5

0

0

0

Non-mutant

76

22

36

0

18

0

All tumors

155

32

66

0

57

0

In concordance with previous reports, all 38 HR-neg BRCA1 tumors and a single HR-pos BRCA1 tumor classified as basal-like subtype breast cancers [25]. Importantly, the CHEK2 1100delC tumors were all classified as luminal subtype breast cancers (with 8 luminal A and 18 luminal B tumors), suggesting considerable biological homogeneity among CHEK2 1100delC breast cancers. The division of CHEK2 1100delC tumors over the two luminal breast cancer subtypes only partially coincided with their observed cluster divisions on unsupervised hierarchical clustering of the 100 HR-pos tumors based on the top 10% variably expressed probe sets: seven of eight CHEK2 1100delC tumors of the luminal A subtype were found in the major “top 10%” cluster branch, whereas the 18 luminal B subtype tumors equally divided over both cluster branches (Fisher’s exact P = 0.08). It is important to note that the “top 10%” clustering involved 5,467 probesets that were variably expressed among the 100 HR-pos breast cancers and thus likely also reflects tumor biology unrelated to CHEK2 1100delC mutation status. So even though the 40-gene CHEK2 signature implies homogeneity among CHEK2 1100delC breast cancers, there apparently still exists some degree of heterogeneity among these tumors.

Discussion

Hormone receptor status of breast cancers may confound gene signatures

We here have defined a 40-gene CHEK2 signature that was significantly associated with CHEK2 1100delC breast cancers. However, definition of the CHEK2 signature had not been possible without restriction of the analyses to the so-called hormone receptor-positive (HR-pos) cohort of familial breast cancers because of confounded expression of ESR1 response genes. We successfully circumvented the issue by classifying each breast cancer as either HR-pos or HR-neg based on their global gene expression program (Fig. 1) and then defined the CHEK2 signature by using only HR-pos breast cancers. Although there was a strong concordance between this hormone receptor status classification and ER status of the tumors, we believe that using the full set of probes on the microarray is more reliable than using only ESR1 transcript or ER protein expression data (Fig. 1, with 96% and 9% ESR1-positive breast cancers in either tumor cluster). Indeed, hormone receptor status of breast cancers not only depends on ER status but also on PGR status and likely also on other factors, such as FOXOA1, GATA3, TFF3, NAT1, and XBP. The global gene expression program of breast cancers includes all of these known and unknown biological factors that determine their hormone receptor-related biology. Exploitation of the complete expression data set therefore conceivably results in more accurate hormone receptor classification of breast cancers.

CHEK2 signature defines a relative homogenous subgroup among luminal breast cancer that shares biology with HR-pos BRCA1 cases

The very existence of a CHEK2 signature and the similar expression patterns of the signature genes in these samples indicate a degree of homogeneity among the CHEK2 cases. Interestingly, several BRCA1 cases clustered with the CHEK2 samples after clustering with the CHEK2 signature genes, indicating shared biology between CHEK2 and HR-pos BRCA1 mutated cases. Because the BRCA1 samples were part of the other class in the class comparison to identify the CHEK2 specific genes, re-analysis of the data was performed by removing the BRCA1 cases before identifying differentially expressed genes between CHEK2 samples versus the rest of the HR-pos cases. Indeed, excluding confounding luminal BCRA1 cases increased number of differentially expressed genes associated with CHEK2 status was identified. Furthermore, using these signature genes improved clustering of the CHEK2 mutated samples and close co-clustering of luminal BCRA1 with the CHEK2 cases was observed (Fig. 2b). These observations are supportive of our statements of homogeneity among the CHEK2 mutated samples and shared biology between CHEK2 and HR-pos BRCA1 cases.

The CHEK2 oncogenic pathway includes p53 and likely also BRCA1

Functional pathway analysis of the 40 genes from the CHEK2 signature identified p53 signaling among the top biological networks associated with the signature. This was not an unexpected result as p53 is a well-known phosphorylation target of CHEK2 kinase [60, 61, 65, 66, 67]. A function of both proteins in the same oncogenic pathway would predict that most CHEK2 1100delC tumors carry wild-type p53 alleles because a p53 mutation would not confer a further selective survival advantage to the tumorigenic cells. Indeed, p53 transcript expression was average among the CHEK2 1100delC breast cancers (Online Resource Table S3). In contrast, and consistent with their predominantly p53 mutant status [68, 69, 70, 71, 72], p53 transcript expression was lower in a substantial fraction of the BRCA1 and BRCA2 breast cancers (Online Resource Table S3). In fact, it could be that the apparent clustering of BRCA1 tumors with the CHEK2 1100delC tumors in the hierarchical clustering based on the 40-gene CHEK2 signature simply reflects their p53 pathway deficiency (Fig. 2a). Alternatively, the BRCA1 protein may also partake in the CHEK2 oncogenic pathway. Indeed, BRCA1 is another known phosphorylation target of CHEK2 kinase [73, 74] and the CHEK2 signature also included the two BRCA1-associated genes XIST and EED. In addition, we and others had observed that BRCA1 mutation carriers may be less likely to also carry the CHEK2 1100delC mutation [5, 6, 8, 9], again hinting to a functional association between CHEK2 and BRCA1. Either way, the convergence of the functional pathway analysis on the well-documented CHEK2 functions in cell cycle control and DNA damage responses [60, 61], that typically also include p53 and BRCA1 proteins, is rather impressive and illustrates the central role of these processes in oncogenesis of the mammary gland.

CHEK2 1100delC breast cancers are of the luminal intrinsic subtypes

The CHEK2 1100delC mutation is thought to confer breast cancer susceptibility in concert with another as-yet-unknown susceptibility allele or alleles [5, 6, 8, 9]. Therefore, identification of a gene signature that significantly associated with CHEK2 1100delC breast cancers implied an unexpected biological homogeneity among these tumors. The very existence of a CHEK2 signature suggests that the CHEK2 1100delC mutation substantially contributes to the oncogenesis of CHEK2 1100delC breast cancers. It is notable that there still appears to be biological heterogeneity among CHEK2 1100delC breast cancers; based on the global expression patterns of thousands of genes (Fig. 2c) the CHEK2 cases were found in both main clusters. Another mark of heterogeneity is their classification as luminal A or luminal B subtype breast cancers (Table 2). The heterogeneity may reflect differences among the additional susceptibility alleles present in CHEK2 1100delC breast cancers or differences in the epithelial cell compartment from which the tumors originated. Still, the classification of all 26 CHEK2 1100delC tumors as breast cancers of the luminal subtypes suggests that only a limited number of additional susceptibility alleles are operative in the polygenic CHEK2 model or, in case of still a substantial number of additional susceptibility alleles, that these alleles partake in only a few highly similar oncogenic pathways. The 40-gene CHEK2 signature—when proven robust—could be used to preselect families that show the CHEK2 phenotype. Those families might be searched for risk factors acting in conjunction with CHEK2. The signature thus increases the likelihood of finding genes that act together with CHEK2. Thus, perhaps the most encouraging implication is that we may be able to identify additional susceptibility alleles in the polygenic CHEK2 model in a not too far future.

Materials and methods

Breast cancer samples

Fresh-frozen female primary breast cancers were all selected from the Rotterdam Medical Oncology Tumor (RMOT) bank. Familial breast cancers were identified by linking records of tumor specimens present in the RMOT bank with records of breast cancer patients registered at the Rotterdam Family Cancer Clinic. All familial cases had been screened for mutations in BRCA1 and BRCA2 and for the CHEK2 1100delC mutation (n = 10) [6, 75]. Additional CHEK2 1100delC cases (n = 16) had been identified by genetic screening of 1,706 RMOT cases that were unselected for a family history of cancer [8]. Three CHEK2 1100delC breast cancers have been excluded from the study because they had deleted the mutant allele, rendering the involvement of the CHEK2 1100delC mutation in the oncogenesis of these tumors uncertain. Together, the familial breast cancer cohort included 26 CHEK2 1100delC tumors, 47 BRCA1 tumors, 6 BRCA2 tumors, and 76 non-BRCA1/BRCA2/CHEK2 1100delC tumors designated “non-mutant tumors” (Table 1). The non-mutant breast cancer cases all were from a family with at least two breast cancer cases in first- or second-degree relatives of which at least one had been diagnosed before age 60 years. The Medical Ethical Committee at Erasmus MC has approved the study, which was carried out according the Code of Conduct of the Federation of Medical Scientific Societies in the Netherlands.

Mutation screening

Screening for the CHEK2 1100delC mutation was performed by allele-specific oligonucleotide (ASO) hybridization as previously described [75]. Mutation-positive samples were confirmed by amplification of CHEK2 exons 9–14 by long-range PCR, followed by nested PCR amplification and sequencing of exon 10 [75]. BRCA1 and BRCA2 mutation screens entailed screening of the complete coding sequences of the genes and intron/exon boundaries as well as screening for all known Dutch founder deletions, as previously described [6].

Gene expression profiling

Total RNA was isolated from cryostat sections using RNAzol B (Campro Scientific, Veenendaal, the Netherlands) and RNA quality and quantity was evaluated on an Agilent Bioanalyzer. Antisense biotinylated RNA was prepared and hybridized to Affymetrix U133 Plus 2.0 GeneChips, according to the manufacturer’s guidelines (Affymetrix, Santa Clara, CA). The microarray data have been deposited in NCBI’s Gene Expression Omnibus and are accessible through GEO Series accession number 27830.

Microarray data processing

Intensity values were scaled to an average value of 100 per GeneChip by global scaling normalization, using the R Bioconductor “mas” algorithm (www.bioconductor.org—v1.7; www.r-project.org—v2.4.0). Intensity values less than 30 were set at 30. The geometric mean of intensity values among all samples was calculated for each probe set and intensity values of each sample were then determined relative to the geometric mean and base-2 log transformed using Omniviz (Biowisdom, Maynard, MA).

Immunohistochemical staining

Paraffin-embedded tissue was stained for Her-2 (ERBB2) using the Dako HercepTest™ (K5204, DAKO, Heverlee, Belgium). To verify membrane staining, we used ×20–40 objective magnification. Only invasive tumor cells were scored, according to manufacturer’s protocol. Tissues with IHC 3+ staining were scored positive. Tissues with IHC 2+ staining were checked by DAKO’s Her-2 FISH pharmDx TM Kit (K5331), and were scored positive when Her-2/CEN-17 ratio ≥2. When results were at or near the cut-off (1.8–2.2), 20 additional nuclei were evaluated.

Pearson’s correlation matrix

Omniviz package version 3.6 (Biowisdom, Maynard, MA) was used to calculate pairwise Pearson’s correlation coefficients based on overall gene expression of samples. Omniviz was used to order and visualize a matrix of sample correlations.

Class comparison

Differentially expressed genes were identified from among the top 20% variably expressed probe sets (n = 10,935) using a univariate t-test with 1,000 permutations and univariate P value < 0.001. We also performed a global test to determine differences between expression profiles of CHEK2 1100delC tumors and non-CHEK2 HR-pos tumors, by permuting the class labels. The global test significance level represents the proportion of 1,000 permutations that resulted in at least as many genes as the original gene signature at significance level P < 0.001. As a control, the same analysis was performed trice for 26 randomly selected HR-pos tumors.

Unsupervised hierarchical clustering

The NCI Biometric Research Branch BrB-Array Tool was used to perform unsupervised hierarchical cluster analysis (http://linus.nci.nih.gov/BRB-ArrayTools.html), using average distance linkage and centered correlation measures. Robustness of cluster reproducibility was calculated by perturbing the expression data with Gaussian noise and re-clustering 100 times and then measuring the similarity of the new clusters to the original clusters [76]. Fisher’s exact testing was used to determine the significance of distributions of the tumor samples over clusters.

Biological pathway analysis

The 40-gene CHEK2 signature was evaluated for biological function and network interactions by using Ingenuity pathway analysis software (www.ingenuity.com).

Classification in intrinsic molecular subtypes

To enable classification of the familial breast cancers in intrinsic molecular subtypes reported by Sørlie et al. [23, 24, 25], we linked Genbank accession numbers of the 496 genes from the intrinsic gene sets to Affymetrix ID annotation numbers from the U133 Plus 2.0 GeneChips via Unigene HS numbers, allowing mapping of 451 unique Affymetrix probe sets. Unsupervised hierarchical clustering of all 155 familial breast cancers using this gene set was performed as described above, except that non-centered correlation metric was used to more accurately replicate analyses reported by Sørlie et al. [23, 24, 25].

Notes

Acknowledgments

We are grateful to the breast cancer patients and their clinicians for participation in this research. We thank Marion Meier-van Gelder, Mieke Timmermans, Miranda Arnold, Anneke Goedheer, Roberto Rodriguez-Garcia, Wendy van der Smissen, and Anja de Snoo for their technical assistance. We also thank Wim van Putten and Antoinette Hollestelle for insightful discussions. Funding: Dutch Cancer Society grants DDHK 2002-2687 and DDHK 2003-2862, and partly by the Netherlands Genomics Initiative/Netherlands Organization for Scientific Research NWO. Hanne Meijers-Heijboer is a fellow from the NWO Vidi Research Program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Conflict of interests

None.

Supplementary material

10549_2011_1588_MOESM1_ESM.pdf (69 kb)
Supplementary material 1 (PDF 69 kb)
10549_2011_1588_MOESM2_ESM.pdf (60 kb)
Supplementary material 2 (PDF 60 kb)
10549_2011_1588_MOESM3_ESM.pdf (14 kb)
Supplementary material 3 (PDF 14 kb)

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Copyright information

© Springer Science+Business Media, LLC. 2011

Authors and Affiliations

  • Jord H. A. Nagel
    • 1
  • Justine K. Peeters
    • 2
  • Marcel Smid
    • 1
  • Anieta M. Sieuwerts
    • 1
  • Marijke Wasielewski
    • 1
  • Vanja de Weerd
    • 1
  • Anita M. A. C. Trapman-Jansen
    • 1
  • Ans van den Ouweland
    • 3
  • Hennie Brüggenwirth
    • 3
  • Wilfred F. J. van IJcken
    • 4
  • Jan G. M. Klijn
    • 1
  • Peter J. van der Spek
    • 2
  • John A. Foekens
    • 1
  • John W. M. Martens
    • 1
  • Mieke Schutte
    • 1
  • Hanne Meijers-Heijboer
    • 1
    • 3
    • 5
  1. 1.Department of Medical OncologyJosephine Nefkens Institute, Erasmus University Medical CenterRotterdamThe Netherlands
  2. 2.Department of BioinformaticsErasmus University Medical CenterRotterdamThe Netherlands
  3. 3.Department of Clinical GeneticsErasmus University Medical CenterRotterdamThe Netherlands
  4. 4.Erasmus Center for BiomicsErasmus University Medical CenterRotterdamThe Netherlands
  5. 5.Department of Clinical GeneticsVU Medical CenterAmsterdamThe Netherlands

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