Gene expression profiling assigns CHEK2 1100delC breast cancers to the luminal intrinsic subtypes
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.
KeywordsBreast cancer CHEK2 1100delC Intrinsic subtypes
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  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.
Definition of a 40-gene signature for CHEK2 1100delC breast cancers
Receptor expression among familial breast cancers
Tumors by mutation status
Hormone receptor-positive tumors
Hormone receptor-negative 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 . 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
Intrinsic subtypes among familial breast cancers
Tumors by mutation status
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 . 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.
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 . 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.
Screening for the CHEK2 1100delC mutation was performed by allele-specific oligonucleotide (ASO) hybridization as previously described . 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 . 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 .
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).
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.
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 . 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].
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
- 3.Tavtigian SV, Simard J, Rommens J, Couch F, Shattuck-Eidens D, Neuhausen S, Merajver S, Thorlacius S, Offit K, Stoppa-Lyonnet D, Belanger C, Bell R, Berry S, Bogden R, Chen Q, Davis T, Dumont M, Frye C, Hattier T, Jammulapati S, Janecki T, Jiang P, Kehrer R, Leblanc JF, Goldgar DE et al (1996) The complete BRCA2 gene and mutations in chromosome 13q-linked kindreds. Nat Genet 12(3):333–337PubMedCrossRefGoogle Scholar
- 6.Meijers-Heijboer H, van den Ouweland A, Klijn J, Wasielewski M, de Snoo A, Oldenburg R, Hollestelle A, Houben M, Crepin E, van Veghel-Plandsoen M, Elstrodt F, van Duijn C, Bartels C, Meijers C, Schutte M, McGuffog L, Thompson D, Easton D, Sodha N, Seal S, Barfoot R, Mangion J, Chang-Claude J, Eccles D, Eeles R et al (2002) Low-penetrance susceptibility to breast cancer due to CHEK2*1100delC in noncarriers of BRCA1 or BRCA2 mutations. Nat Genet 31(1):55–59PubMedCrossRefGoogle Scholar
- 7.Vahteristo P, Bartkova J, Eerola H, Syrjakoski K, Ojala S, Kilpivaara O, Tamminen A, Kononen J, Aittomaki K, Heikkila P, Holli K, Blomqvist C, Bartek J, Kallioniemi OP, Nevanlinna H (2002) A CHEK2 genetic variant contributing to a substantial fraction of familial breast cancer. Am J Hum Genet 71(2):432–438PubMedCrossRefGoogle Scholar
- 10.Oldenburg RA, Kroeze-Jansema K, Kraan J, Morreau H, Klijn JG, Hoogerbrugge N, Ligtenberg MJ, van Asperen CJ, Vasen HF, Meijers C, Meijers-Heijboer H, de Bock TH, Cornelisse CJ, Devilee P (2003) The CHEK2*1100delC variant acts as a breast cancer risk modifier in non-BRCA1/BRCA2 multiple-case families. Cancer Res 63(23):8153–8157PubMedGoogle Scholar
- 12.Smith P, McGuffog L, Easton DF, Mann GJ, Pupo GM, Newman B, Chenevix-Trench G, Szabo C, Southey M, Renard H, Odefrey F, Lynch H, Stoppa-Lyonnet D, Couch F, Hopper JL, Giles GG, McCredie MR, Buys S, Andrulis I, Senie R, Goldgar DE, Oldenburg R, Kroeze-Jansema K, Kraan J, Meijers-Heijboer H et al (2006) A genome wide linkage search for breast cancer susceptibility genes. Genes Chromosomes Cancer 45(7):646–655PubMedCrossRefGoogle Scholar
- 13.Renwick A, Thompson D, Seal S, Kelly P, Chagtai T, Ahmed M, North B, Jayatilake H, Barfoot R, Spanova K, McGuffog L, Evans DG, Eccles D, Breast Cancer Susceptibility C, Easton DF, Stratton MR, Rahman N (2006) ATM mutations that cause ataxia-telangiectasia are breast cancer susceptibility alleles. Nat Genet 38(8):873–875PubMedCrossRefGoogle Scholar
- 14.Seal S, Thompson D, Renwick A, Elliott A, Kelly P, Barfoot R, Chagtai T, Jayatilake H, Ahmed M, Spanova K, North B, McGuffog L, Evans DG, Eccles D, Breast Cancer Susceptibility C, Easton DF, Stratton MR, Rahman N (2006) Truncating mutations in the Fanconi anemia J gene BRIP1 are low-penetrance breast cancer susceptibility alleles. Nat Genet 38(11):1239–1241PubMedCrossRefGoogle Scholar
- 15.Rahman N, Seal S, Thompson D, Kelly P, Renwick A, Elliott A, Reid S, Spanova K, Barfoot R, Chagtai T, Jayatilake H, McGuffog L, Hanks S, Evans DG, Eccles D, Breast Cancer Susceptibility C, Easton DF, Stratton MR (2007) PALB2, which encodes a BRCA2-interacting protein, is a breast cancer susceptibility gene. Nat Genet 39(2):165–167PubMedCrossRefGoogle Scholar
- 16.Easton DF, Pooley KA, Dunning AM, Pharoah PD, Thompson D, Ballinger DG, Struewing JP, Morrison J, Field H, Luben R, Wareham N, Ahmed S, Healey CS, Bowman R, Collaborators S, Meyer KB, Haiman CA, Kolonel LK, Henderson BE, Le Marchand L, Brennan P, Sangrajrang S, Gaborieau V, Odefrey F, Shen CY et al (2007) Genome-wide association study identifies novel breast cancer susceptibility loci. Nature 447(7148):1087–1093PubMedCrossRefGoogle Scholar
- 17.Hunter DJ, Kraft P, Jacobs KB, Cox DG, Yeager M, Hankinson SE, Wacholder S, Wang Z, Welch R, Hutchinson A, Wang J, Yu K, Chatterjee N, Orr N, Willett WC, Colditz GA, Ziegler RG, Berg CD, Buys SS, McCarty CA, Feigelson HS, Calle EE, Thun MJ, Hayes RB, Tucker M et al (2007) A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat Genet 39(7):870–874PubMedCrossRefGoogle Scholar
- 18.Stacey SN, Manolescu A, Sulem P, Rafnar T, Gudmundsson J, Gudjonsson SA, Masson G, Jakobsdottir M, Thorlacius S, Helgason A, Aben KK, Strobbe LJ, Albers-Akkers MT, Swinkels DW, Henderson BE, Kolonel LN, Le Marchand L, Millastre E, Andres R, Godino J, Garcia-Prats MD, Polo E, Tres A, Mouy M, Saemundsdottir J et al (2007) Common variants on chromosomes 2q35 and 16q12 confer susceptibility to estrogen receptor-positive breast cancer. Nat Genet 39(7):865–869PubMedCrossRefGoogle Scholar
- 19.Stacey SN, Gudbjartsson DF, Sulem P, Bergthorsson JT, Kumar R, Thorleifsson G, Sigurdsson A, Jakobsdottir M, Sigurgeirsson B, Benediktsdottir KR, Thorisdottir K, Ragnarsson R, Scherer D, Rudnai P, Gurzau E, Koppova K, Hoiom V, Botella-Estrada R, Soriano V, Juberias P, Grasa M, Carapeto FJ, Tabuenca P, Gilaberte Y, Gudmundsson J et al (2008) Common variants on 1p36 and 1q42 are associated with cutaneous basal cell carcinoma but not with melanoma or pigmentation traits. Nat Genet 40(11):1313–1318PubMedCrossRefGoogle Scholar
- 21.Thomas G, Jacobs KB, Kraft P, Yeager M, Wacholder S, Cox DG, Hankinson SE, Hutchinson A, Wang Z, Yu K, Chatterjee N, Garcia-Closas M, Gonzalez-Bosquet J, Prokunina-Olsson L, Orr N, Willett WC, Colditz GA, Ziegler RG, Berg CD, Buys SS, McCarty CA, Feigelson HS, Calle EE, Thun MJ, Diver R et al (2009) A multistage genome-wide association study in breast cancer identifies two new risk alleles at 1p11.2 and 14q24.1 (RAD51L1). Nat Genet 41(5):579–584PubMedCrossRefGoogle Scholar
- 22.Ahmed S, Thomas G, Ghoussaini M, Healey CS, Humphreys MK, Platte R, Morrison J, Maranian M, Pooley KA, Luben R, Eccles D, Evans DG, Fletcher O, Johnson N, dos Santos Silva I, Peto J, Stratton MR, Rahman N, Jacobs K, Prentice R, Anderson GL, Rajkovic A, Curb JD, Ziegler RG, Berg CD et al (2009) Newly discovered breast cancer susceptibility loci on. 3p24 and 17q23.2. Nat Genet 41(5):585–590PubMedCrossRefGoogle Scholar
- 23.Perou CM, Sørlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, Fluge O, Pergamenschikov A, Williams C, Zhu SX, Lonning PE, Borresen-Dale AL, Brown PO, Botstein D (2000) Molecular portraits of human breast tumours. Nature 406(6797):747–752PubMedCrossRefGoogle Scholar
- 24.Sørlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS, Thorsen T, Quist H, Matese JC, Brown PO, Botstein D, Eystein Lonning P, Borresen-Dale AL (2001) Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA 98(19):10869–10874PubMedCrossRefGoogle Scholar
- 25.Sørlie T, Tibshirani R, Parker J, Hastie T, Marron JS, Nobel A, Deng S, Johnsen H, Pesich R, Geisler S, Demeter J, Perou CM, Lonning PE, Brown PO, Borresen-Dale AL, Botstein D (2003) Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA 100(14):8418–8423PubMedCrossRefGoogle Scholar
- 30.Van’t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415(6871):530–536CrossRefGoogle Scholar
- 31.van de Vijver MJ, He YD, van’t Veer LJ, Dai H, Hart AA, Voskuil DW, Schreiber GJ, Peterse JL, Roberts C, Marton MJ, Parrish M, Atsma D, Witteveen A, Glas A, Delahaye L, van der Velde T, Bartelink H, Rodenhuis S, Rutgers ET, Friend SH, Bernards R (2002) A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347(25):1999–2009PubMedCrossRefGoogle Scholar
- 35.Ma XJ, Wang Z, Ryan PD, Isakoff SJ, Barmettler A, Fuller A, Muir B, Mohapatra G, Salunga R, Tuggle JT, Tran Y, Tran D, Tassin A, Amon P, Wang W, Wang W, Enright E, Stecker K, Estepa-Sabal E, Smith B, Younger J, Balis U, Michaelson J, Bhan A, Habin K et al (2004) A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen. Cancer Cell 5(6):607–616PubMedCrossRefGoogle Scholar
- 36.Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, Baehner FL, Walker MG, Watson D, Park T, Hiller W, Fisher ER, Wickerham DL, Bryant J, Wolmark N (2004) A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351(27):2817–2826PubMedCrossRefGoogle Scholar
- 38.Chang JC, Wooten EC, Tsimelzon A, Hilsenbeck SG, Gutierrez MC, Elledge R, Mohsin S, Osborne CK, Chamness GC, Allred DC, O’Connell P (2003) Gene expression profiling for the prediction of therapeutic response to docetaxel in patients with breast cancer. Lancet 362(9381):362–369PubMedCrossRefGoogle Scholar
- 39.Wang Y, Klijn JG, Zhang Y, Sieuwerts AM, Look MP, Yang F, Talantov D, Timmermans M, Meijer-van Gelder ME, Yu J, Jatkoe T, Berns EM, Atkins D, Foekens JA (2005) Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365(9460):671–679PubMedGoogle Scholar
- 40.Jansen MP, Foekens JA, van Staveren IL, Dirkzwager-Kiel MM, Ritstier K, Look MP, Meijer-van Gelder ME, Sieuwerts AM, Portengen H, Dorssers LC, Klijn JG, Berns EM (2005) Molecular classification of tamoxifen-resistant breast carcinomas by gene expression profiling. J Clin Oncol 23(4):732–740PubMedCrossRefGoogle Scholar
- 41.Foekens JA, Atkins D, Zhang Y, Sweep FC, Harbeck N, Paradiso A, Cufer T, Sieuwerts AM, Talantov D, Span PN, Tjan-Heijnen VC, Zito AF, Specht K, Hoefler H, Golouh R, Schittulli F, Schmitt M, Beex LV, Klijn JG, Wang Y (2006) Multicenter validation of a gene expression-based prognostic signature in lymph node-negative primary breast cancer. J Clin Oncol 24(11):1665–1671PubMedCrossRefGoogle Scholar
- 42.Hess KR, Anderson K, Symmans WF, Valero V, Ibrahim N, Mejia JA, Booser D, Theriault RL, Buzdar AU, Dempsey PJ, Rouzier R, Sneige N, Ross JS, Vidaurre T, Gomez HL, Hortobagyi GN, Pusztai L (2006) Pharmacogenomic predictor of sensitivity to preoperative chemotherapy with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide in breast cancer. J Clin Oncol 24(26):4236–4244PubMedCrossRefGoogle Scholar
- 45.Miller LD, Smeds J, George J, Vega VB, Vergara L, Ploner A, Pawitan Y, Hall P, Klaar S, Liu ET, Bergh J (2005) An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. Proc Natl Acad Sci USA 102(38):13550–13555PubMedCrossRefGoogle Scholar
- 48.Verhoog LC, Brekelmans CT, Seynaeve C, van den Bosch LM, Dahmen G, van Geel AN, Tilanus-Linthorst MM, Bartels CC, Wagner A, van den Ouweland A, Devilee P, Meijers-Heijboer EJ, Klijn JG (1998) Survival and tumour characteristics of breast-cancer patients with germline mutations of BRCA1. Lancet 351(9099):316–321PubMedCrossRefGoogle Scholar
- 49.Lakhani SR, Van De Vijver MJ, Jacquemier J, Anderson TJ, Osin PP, McGuffog L, Easton DF (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(9):2310–2318PubMedCrossRefGoogle Scholar
- 50.de Bock GH, Schutte M, Krol-Warmerdam EM, Seynaeve C, Blom J, Brekelmans CT, Meijers-Heijboer H, van Asperen CJ, Cornelisse CJ, Devilee P, Tollenaar RA, Klijn JG (2004) Tumour characteristics and prognosis of breast cancer patients carrying the germline CHEK2*1100delC variant. J Med Genet 41(10):731–735PubMedCrossRefGoogle Scholar
- 56.Ganesan S, Silver DP, Greenberg RA, Avni D, Drapkin R, Miron A, Mok SC, Randrianarison V, Brodie S, Salstrom J, Rasmussen TP, Klimke A, Marrese C, Marahrens Y, Deng CX, Feunteun J, Livingston DM (2002) BRCA1 supports XIST RNA concentration on the inactive X chromosome. Cell 111(3):393–405PubMedCrossRefGoogle Scholar
- 59.Silva J, Mak W, Zvetkova I, Appanah R, Nesterova TB, Webster Z, Peters AH, Jenuwein T, Otte AP, Brockdorff N (2003) Establishment of histone h3 methylation on the inactive X chromosome requires transient recruitment of Eed-Enx1 polycomb group complexes. Dev Cell 4(4):481–495PubMedCrossRefGoogle Scholar
- 64.Greenman C, Stephens P, Smith R, Dalgliesh GL, Hunter C, Bignell G, Davies H, Teague J, Butler A, Stevens C, Edkins S, O’Meara S, Vastrik I, Schmidt EE, Avis T, Barthorpe S, Bhamra G, Buck G, Choudhury B, Clements J, Cole J, Dicks E, Forbes S, Gray K, Halliday K et al (2007) Patterns of somatic mutation in human cancer genomes. Nature 446(7132):153–158PubMedCrossRefGoogle Scholar
- 69.Crook T, Brooks LA, Crossland S, Osin P, Barker KT, Waller J, Philp E, Smith PD, Yulug I, Peto J, Parker G, Allday MJ, Crompton MR, Gusterson BA (1998) p53 mutation with frequent novel condons but not a mutator phenotype in BRCA1- and BRCA2-associated breast tumours. Oncogene 17(13):1681–1689PubMedCrossRefGoogle Scholar
- 75.Meijers-Heijboer H, Wijnen J, Vasen H, Wasielewski M, Wagner A, Hollestelle A, Elstrodt F, van den Bos R, de Snoo A, Fat GT, Brekelmans C, Jagmohan S, Franken P, Verkuijlen P, van den Ouweland A, Chapman P, Tops C, Moslein G, Burn J, Lynch H, Klijn J, Fodde R, Schutte M (2003) The CHEK2 1100delC mutation identifies families with a hereditary breast and colorectal cancer phenotype. Am J Hum Genet 72(5):1308–1314PubMedCrossRefGoogle Scholar