Marcel Smid

Marlous Hoes

Anieta M Sieuwerts

Stefan Sleijfer

Yi Zhang

Yixin Wang

John A Foekens

PhD John W M Martens
email: j.martens@erasmusmc.nl

Patterns and incidence of chromosomal instability and their prognostic relevance in breast cancer subtypes

Keywords: Chromosomal instability, subtypes, prognosis, SNP copy number

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Chromosomal instability (CIN) is a well known phenomenon observed in many types of cancer and has a pivotal role in tumor onset and progression [START_REF] Lengauer | Genetic instabilities in human cancers[END_REF]. With respect to breast cancer, literature concerning CIN is mostly based on comparative genomic hybridization (CGH) techniques, which have demonstrated specific gains and losses of chromosomal areas in diverse breast cancer subtypes [START_REF] Bergamaschi | Distinct patterns of DNA copy number alteration are associated with different clinicopathological features and geneexpression subtypes of breast cancer[END_REF][START_REF] Chin | Genomic and transcriptional aberrations linked to breast cancer pathophysiologies[END_REF][START_REF] Kallioniemi | Detection and mapping of amplified DNA sequences in breast cancer by comparative genomic hybridization[END_REF][START_REF] Loo | Array comparative genomic hybridization analysis of genomic alterations in breast cancer subtypes[END_REF][START_REF] Naylor | High resolution genomic analysis of sporadic breast cancer using arraybased comparative genomic hybridization[END_REF][START_REF] Pollack | Genome-wide analysis of DNA copynumber changes using cDNA microarrays[END_REF][START_REF] Andre | Molecular characterization of breast cancer with high-resolution oligonucleotide comparative genomic hybridization array[END_REF]. In addition, Carter et al. [START_REF] Carter | A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers[END_REF] have shown that a gene expression profile associated with the incidence of CIN has prognostic relevance in breast cancer. However, this latter study was based on a diverse panel of cancer cell lines and by establishing CIN through a gene expression profile; thus, CIN was assessed indirectly [START_REF] Carter | A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers[END_REF]. Very recently, whole genome paired-end sequencing data of breast cancer samples showed complex somatic rearrangements with some samples showing a large number of deletions, tandem duplications or amplifications, while other samples have virtually no rearrangements [START_REF] Stephens | Complex landscapes of somatic rearrangement in human breast cancer genomes[END_REF].

In this study, we investigated the patterns and prevalence of CIN in subtypes of breast cancer and the relevance of these 2 measures of CIN with respect to patient outcome. To this end, we used a high-resolution 100k single nucleotide polymorphism (SNP) microarray dataset of 313 lymph-node negative primary breast cancer patients with known outcome [START_REF] Zhang | Copy number alterations that predict metastatic capability of human breast cancer[END_REF]. Since the patients in this retrospective cohort did not receive any kind of adjuvant systemic therapy after surgery, this study addresses the pure prognostic value, without the potential confounding effects of therapy. Hierarchical clustering showed 4 groups with a distinct landscape of abnormalities. We elaborated on this by devising a CIN-score for individual samples. This CIN-score was determined by enumerating the total number of genomic areas that showed a concordant loss or gain according to the cop y number of the SNPs present therein. Given the well-known clinical relevance of the diverse breast cancer subtypes such as the five molecular subtypes [START_REF] Perou | Molecular portraits of human breast tumours[END_REF], estrogen receptor (ER)-positive and -negative disease [START_REF] Zhang | Copy number alterations that predict metastatic capability of human breast cancer[END_REF][START_REF] Gruvberger | Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns[END_REF][START_REF] Van 't Veer | Gene expression profiling predicts clinical outcome of breast cancer[END_REF][START_REF] Wang | Gene-expression profiles to predict distant metastasis of lymphnode-negative primary breast cancer[END_REF] as well as triple-negative disease (negative for ER, progesterone receptor (PR) and her2/neu), we analyzed the CIN-score and its association with prognosis in these pathological and molecular subtypes. Our data provide evidence for a highly unstable genome in ER-negative, triple-negative and basal-like tumors, whereas prognostic relevance is only present in patients with ERpositive breast cancer and of the luminal B and her2/neu subtype. Furthermore, to understand the biology related to CIN, we tested whether biologically meaningful pathways are related to CIN in ER-positive breast cancer, the cancer subtype in which CIN was strongly prognostic.

Materials and Methods

Patient and tumor material

Primary tumors of lymph node-negative breast cancer patients were stored in liquid nitrogen at the Erasmus Medical Center. 344 tumors of which Affymetrix U133a gene expression data were available [START_REF] Wang | Gene-expression profiles to predict distant metastasis of lymphnode-negative primary breast cancer[END_REF][START_REF] Yu | Pathway analysis of gene signatures predicting metastasis of node-negative primary breast cancer[END_REF] were initially selected for subsequent analysis of DNA copy number alteration using Affymetrix GeneChip Mapping 100K Array Set (Affymetrix, Santa Clara, CA, USA). Good quality DNA was available for hybridization of 328 of the 344 tumors and reliable SNP calls could be obtained from 313 of these tumors, which were included in the previous analysis of copy number alteration [START_REF] Zhang | Copy number alterations that predict metastatic capability of human breast cancer[END_REF] and in the present study on CIN. All 313 patients were treated from 1980 to 1995 with the same guidelines for local primary treatment, implying that none of these patients did receive any systemic (neo)adjuvant therapy. Patients who first developed a local recurrence before distant metastasis were not included to avoid the possibility that the distant metastasis later on could have originated from the local recurrence. The study was approved by the medical ethics committee of Erasmus MC (MEC 02.953) and conducted in accordance to the code of conduct of Federation of Medical Scientific Societies in the Netherlands (www.fmwv.nl).

Whenever possible, we adhered to the Reporting Recommendations for Tumor Marker Prognostic Studies REMARK [START_REF] Mcshane | Statistics Subcommittee of the NCIEWGoCD (2005) Reporting recommendations for tumor marker prognostic studies (REMARK)[END_REF]. Full clinical and pathological characteristics are described elsewhere [START_REF] Zhang | Copy number alterations that predict metastatic capability of human breast cancer[END_REF]. Briefly, mean age at surgery was 54 years (standard deviation 12y), 49% of patients were premenopausal, 201 tumors were ER positive (according to expression measured on microarray [START_REF] Foekens | Multicenter validation of a gene expression-based prognostic signature in lymph node-negative primary breast cancer[END_REF]) and 112 ER negative. Of all surviving patients (n=220), median follow-up time was 99 months (range 20-169 months). Of the 313 included patients, 114 (36%) developed a distant metastasis, of whom 77 were ER-positive and 37 ER-negative.

DNA copy number analysis and CIN-score

All tumors were confirmed to have sufficient (>70%) tumor content. The CIN-score was established using SNPs present on the Affymetrix GeneChip Mapping 100K Array Set (Affymetrix, Santa Clara, CA, USA). Copy number data was obtained using previously described methods [START_REF] Zhang | Copy number alterations that predict metastatic capability of human breast cancer[END_REF] and is available in the National Center for Biotechnology Information/Gene Expression Omnibus database (GEO) entry GSE10099. For each SNP, the estimate diploid copy number was established [START_REF] Zhang | Copy number alterations that predict metastatic capability of human breast cancer[END_REF] and the distance of the actual copy number for that SNP to the estimate was calculated for each tumor. Hierarchical clustering of copy number data was performed using Nexus (BioDiscovery, El Segundo, CA, USA). For the CIN-score, a SNP was called gain or loss, if the copy number was 0.4 higher or lower than the estimate diploid copy number, respectively. This cut-off was established using Nexus by visual inspection of many samples showing a gain of a complete chromosomal arm. Next, for each patient, the number of genomic segments showing CIN was counted. A CIN-area met these criteria; presence of at least 2 SNPs and sequential SNPs lied within 100 kb distance. Then the status of the SNPs was classified as either "gain", "loss" or "normal". Due to the inherited noise within these arrays, one normal-call was allowed within two SNPs having a gain (or loss) call. Thus, two sequential SNPs with a "normal" call signaled the end of that area. Likewise if a "gain" SNP followed a "loss" SNP, or vice versa. The total number of affected areas in a tumor genome was considered a reliable measure for CIN. We tested if the CINscore was influenced by the selected criteria. First, the maximum of 100 kb distance between 2 sequential SNPs was omitted. The resulting CIN-scores had a Pearson correlation of 0.946 with the original score. Similarly, when including regions which had only a single SNP showing gain or loss, the Pearson correlation with the original score was 0.998. Thus, irrespective of the inclusion criteria, highly consistent CINscores were retrieved. In our view, the CIN-score based on including the 100 kb distance criterion and exclusion of single SNP regions, is less likely to be overly optimistic and was therefore used in this study.

Gene expression data

mRNA expression data available for this entire cohort [START_REF] Wang | Gene-expression profiles to predict distant metastasis of lymphnode-negative primary breast cancer[END_REF][START_REF] Yu | Pathway analysis of gene signatures predicting metastasis of node-negative primary breast cancer[END_REF] were used to establish ER, PR and ERBB2 status, to determine the intrinsic molecular subtypes [START_REF] Perou | Molecular portraits of human breast tumours[END_REF][START_REF] Smid | Subtypes of breast cancer show preferential site of relapse[END_REF], to associate genes with CIN and to conduct pathway analysis. Gene Set Enrichment Analysis (GSEA) [START_REF] Subramanian | Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles[END_REF] was used to identify which of the public gene profiles as described in the database (http://www.broad.mit.edu/gsea/msigdb/index.jsp) complemented with the prognostic breast cancer gene sets [START_REF] Carter | A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers[END_REF][START_REF] Perou | Molecular portraits of human breast tumours[END_REF][START_REF] Van 't Veer | Gene expression profiling predicts clinical outcome of breast cancer[END_REF][START_REF] Wang | Gene-expression profiles to predict distant metastasis of lymphnode-negative primary breast cancer[END_REF][START_REF] Chang | Gene expression signature of fibroblast serum response predicts human cancer progression: similarities between tumors and wounds[END_REF][START_REF] Chi | Gene expression programs in response to hypoxia: cell type specificity and prognostic significance in human cancers[END_REF][START_REF] Liu | The prognostic role of a gene signature from tumorigenic breast-cancer cells[END_REF][START_REF] Paik | A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer[END_REF][START_REF] Sotiriou | Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis[END_REF][START_REF] Winter | Relation of a hypoxia metagene derived from head and neck cancer to prognosis of multiple cancers[END_REF], are associated with the CIN-scores. Using default parameters, ER-positive samples with the top 25% CIN-scores were compared to the bottom 25% CIN-scores of ERpositive samples (50 samples in each group). Next, the genes within the public profiles were assessed to see if they were significantly enriched in the top (or bottom) ranked order of significant genes. The Global Test [START_REF] Goeman | A global test for groups of genes: testing association with a clinical outcome[END_REF] program was used (version 4.2.0) to associate Biocarta pathways (http://www.biocarta.com) to the same sample-groups as were used in the GSEA analysis. SAM analysis [START_REF] Tusher | Significance analysis of microarrays applied to the ionizing radiation response[END_REF] was used to identify differentially expressed genes between the samples with the highest and lowest CIN-scores (50 samples in each group for ER-positive tumors and 28 samples in each group for ER-negative tumors). Independent datasets [START_REF] Sotiriou | Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis[END_REF][START_REF] Schmidt | The humoral immune system has a key prognostic impact in node-negative breast cancer[END_REF] were downloaded from GEO (GSE11121 and GSE2990) and processed with the same parameters as our own dataset [START_REF] Wang | Gene-expression profiles to predict distant metastasis of lymphnode-negative primary breast cancer[END_REF][START_REF] Yu | Pathway analysis of gene signatures predicting metastasis of node-negative primary breast cancer[END_REF]. The top 40 significant genes (20 genes higher expressed in high CIN and 20 genes higher expressed in low CIN tumors) were selected and were used in BRB-ArrayTools (v3.7.1 developed by Dr. Richard Simon and the BRB-ArrayTools Development Team) using the Compound Covariate Predictor to predict the status of the samples in the external datasets as "high CIN"

or "low CIN". Our samples were used as training data.

STATA, release 10.1 (StataCorp, Texas, USA) was used to calculate the Cox proportional HR. MFS was used as endpoint and was defined as the first detection of a distant metastasis that was confirmed after symptoms were reported by the patient or when clinical signs of metastasis were detected at regular follow-up. Each of the subgroups showed a sufficient number of events (more than 20) to use the HR tests, except for the normal-like subtype (7 events). Log-rank tests for trend were used to test the equality of survivor functions across more than two groups. A two-sided P value of < .05 was considered statistically significant.

Results

CIN distribution among breast cancer subtypes

Unsupervised hierarchical clustering of SNP copy number data of 313 breast cancer patients identified 4 main groups, of which the frequency of copy number alterations are shown in Figure 1. Most striking features of the clustered groups were the large percentage of samples showing a gain of either chromosome (chr) 8q (first cluster), chr 1q (cluster 4) or both (second cluster-group). Association of these cluster-groups with diverse subtypes shows a highly variable division; e.g. 90% (57 out of 63) of samples in cluster 4 are ER-positive, or 73% (16 out of 22) of the normal-like subtype are present in cluster 3, the cluster with the samples showing very few abnormalities (full data in Table 1). Figure 1 also makes clear that a large variety in chromosomal instabilities is present in the different samples (see supporting information; Online Resource Fig. 1 for 2 extreme examples). We found no significant differences in prognosis between the 4 clustergroups (Online Resource Fig. 2). We next established a CIN-score for each individual sample . The CIN-score in our cohort ranged from 66 to 3380 (median 907). ER-negative tumors, as well as the triple-negative tumors, had significantly higher CIN-scores than those that were positive for one or more of these receptors (Mann-Whitney test P < 0.0001 in both analyses) (Fig. 2A andB). Among the intrinsic subtypes, basal and luminal B cancers had higher CIN-scores (Fig. 2C). A Kruskal-Wallis test for difference in median levels between all the molecular subgroups was significant, P < 0.0001. Furthermore, post-hoc pairwise comparisons (Bonferroni corrected for multiple testing) of the subtypes showed a significant difference in median CIN-scores of basal vs. luminal B samples (P = 0.001). In fact, all pairwise comparisons were significant (P = 0.005 or lower), except for her2/neu vs. luminal A, her2/neu vs. luminal B, and luminal A vs. normal-like.

CIN and prognosis

Next, we related the CIN-score to prognosis in the various subtypes. Of the 313 patients, 114 (36%) developed a distant metastasis and were counted as failures in the analysis of distant metastasis-free survival (MFS). Log-transformed CIN-scores as continuous variable in Cox regression analysis were associated with MFS and showed a HR of 2.16 (95% Confidence Interval (CI): 1.53-3.04, P < 0.0001) for ER-positive and a HR of 1.05 (95% CI: 0.70-1.58, P = 0.81) for ERnegative patients. Kaplan-Meier plots, using three equally sized quantiles of the CINscore, showed a significant difference in MFS-curves in ER-positive patients (Fig. 3a), but not in ER-negative patients (Fig. 3b). Analysis within other subgroups of patients showed an association of CIN-scores as continuous variable with MFS in luminal B (HR: 2.59, CI: 1.37-4.88, P = 0.003) and in the her2/neu subtype (HR: 2.47, CI: 1.15-5.30, P = 0.02) while within basal, luminal A, normal-like and triplenegative breast cancer no such relation was seen (HRs of the latter 4 subgroups:

1.06, 1.55, 0.99 and 1.01, respectively; all statistically not significant). Analysis of the survival curves within the subtypes using the CIN-quantiles suffered from low numbers of patients but similar trends as in the ER subgroups were seen (Fig. 3c and3d). Further analysis also revealed that with regard to prognosis, CIN probably explains the difference between the luminal A and luminal B breast cancer since the difference in MFS between these groups (P = 0.01) was no longer present after correcting for CIN-score (Online Resource Fig. 3) (P = 1.00). To validate our association of CIN with prognosis in ER-positive breast cancer, we employed gene expression data of two external datasets [START_REF] Sotiriou | Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis[END_REF][START_REF] Schmidt | The humoral immune system has a key prognostic impact in node-negative breast cancer[END_REF] with in total 254 ER-positive and 57 ER-negative tumors, from patients who were all lymph-node negative and did not receive systemic treatment after surgery. We developed a 40-gene classifier in our own ER-positive cohort, which, using a Compound Covariate Predictor [START_REF] Radmacher | A paradigm for class prediction using gene expression profiles[END_REF] showed a 86% sensitivity and 78% specificity in correctly assigning "low CIN" samples. Next, this same classifier was used to predict the status of the ER-positive samples in the external datasets as "high CIN" or "low CIN". The CIN-signature significantly stratifies the ER-positive samples in the independent datasets according to outcome (P < 0.001, Fig. 3e and3f). The same procedure for the ER-negative samples yielded no significant log-rank test (P = 0.23, Online Resource Fig. 4).

To further explore the biology associated with the CIN process, we correlated transcriptome expression also available from this cohort [START_REF] Wang | Gene-expression profiles to predict distant metastasis of lymphnode-negative primary breast cancer[END_REF][START_REF] Yu | Pathway analysis of gene signatures predicting metastasis of node-negative primary breast cancer[END_REF] with the CIN-score.

For this, gene expression patterns were compared between ER-positive samples with the highest CIN-score versus those with the lowest CIN-scores. Pathway analyses were performed using GSEA [START_REF] Subramanian | Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles[END_REF] and global testing [START_REF] Goeman | A global test for groups of genes: testing association with a clinical outcome[END_REF]. These analyses showed association of CIN with gene sets related to the genomic grade of breast cancer, a CIN-profile derived from a diverse set of cancer cell lines, gene sets related to outcome in breast, multiple myeloma and gastric cancer, a wound response signature and gene sets more generally related to cell cycle progression and those involved in pyrimidine metabolism (see Online Resource Table 1 and 2 for full details). In addition, genes linked to CIN were enriched for E2F transcription factor binding sites. Pathways negatively associated with CIN were related to cellular differentiation, to extrinsically induced cell death and to T-cell function. The strong relation between CIN and the genomic grade in our cohort prompted us to investigate, on a beforehand unplanned analysis, the influence of CIN after grouping our samples according to the genomic grade signature. It appeared that the CINscore and genomic grade were independently significant in the analysis for MFS in ER-positive patients in a multivariate analysis (HR: 2.29, 95% CI: 1.33-3.90, P = 0.002 and HR: 1.90, 95% CI: 1.19-3.01, P = 0.007 for CIN and genomic grade, respectively). The CIN-score was significantly associated with MFS in the goodgrade group (logrank P = 0.004), but not in the poor-grade group, although the MFS curves based on CIN also in this latter group tended to separate (Online Resource Fig. 5).

Discussion.

Chromosomal instability is a long described concept in tumor biology [START_REF] Lengauer | Genetic instabilities in human cancers[END_REF]. In breast cancer, more recent publications describe results based on aCGH copy number data [2-3, 8, 31]. As shown in Figure 1, we observed four major groups of instability profiles; two of these were formerly described by Andre et al. [START_REF] Andre | Molecular characterization of breast cancer with high-resolution oligonucleotide comparative genomic hybridization array[END_REF] who used 106 samples. They described a group with very few abnormalities (our clustergroup 3) and two groups of samples, which both had the characteristic gains of chr 1q and 8q (our second clustergroup). We identified two additional groups, one characterized by many samples with a gain in chr 1q but without a gain of chr 8q and a group with the opposite pattern. Although the ER-status of the tumors or the intrinsic subtypes do not fall exclusively in one of the clustergroups, there are clear non-random patterns.

For example group 4 contains 63 samples of which 57 are ER-positive, in which 90% of which have a gain of the q-arm of chr 1, and almost 50% of samples a gain of 16p. This suggests a very different etiology for these cancers than for ER-negative tumors, where only 19% of cases fall in cluster 2 or 4 (which show the chr 1q gain).

Since other studies already described specific genomic aberrations which are implicated in the different subtypes of breast cancer [2-3, 5, 8, 31], and we knew from our recent work that relatively few specific regions with altered copy number were found to be prognostic [START_REF] Perou | Molecular portraits of human breast tumours[END_REF], our prime objective based on the large variability among samples was to assess the clinical significance of the number of chromosomal abnormalities in individual tumors as well as the biology related to this phenomenon. The 4 clustergroups already showed a trend with 118 tumors showing very few abnormalities to tumors having genomes that were greatly rearranged.

These observations are backed by the results of a recent publication [START_REF] Stephens | Complex landscapes of somatic rearrangement in human breast cancer genomes[END_REF], which described genome-wide completely paired-end sequenced breast cancer genomes of cell lines and primary breast tumors; some samples show hardly any events, while others show many aberrations. Thus, notwithstanding specific abnormalities, CIN in itself is not necessarily a common denominator in all breast cancer cases, confirming the notorious heterogeneous nature of breast cancer. The measure for CIN we used here for individual samples showed highly variable CIN-scores and we observed a clear uneven distribution within the different predefined breast cancer subtypes. Significantly higher CIN-scores for ER-negative and triple-negative samples were found, indicating that CIN is a more common event in receptor negative breast cancers. This suggests that this subtype is more prone to accumulation of genomic instabilities. Higher CIN-scores were also observed in basal and luminal B samples, which corroborates previous data [START_REF] Bergamaschi | Distinct patterns of DNA copy number alteration are associated with different clinicopathological features and geneexpression subtypes of breast cancer[END_REF][START_REF] Chin | Genomic and transcriptional aberrations linked to breast cancer pathophysiologies[END_REF], though these were based on low-density CGH data of in total 234 patients. Collectively, accumulating evidence suggests that the breast cancer cell type influences the copy number landscape of a breast cancer.

Apparently, not only specific genomic aberrations [START_REF] Chin | Genomic and transcriptional aberrations linked to breast cancer pathophysiologies[END_REF][START_REF] Zhang | Copy number alterations that predict metastatic capability of human breast cancer[END_REF][START_REF] Al-Kuraya | Prognostic relevance of gene amplifications and coamplifications in breast cancer[END_REF], e.g. ERBB2 amplification, are linked to a poor outcome in breast cancer, but also the sheer number of chromosomal instabilities associated with prognosis in ERpositive patients and in patients with a her2/neu or luminal B subtype (Fig. 3). These results confirm but also add substantially to the findings of Carter et al. [START_REF] Carter | A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers[END_REF] who first reported on the clinical significance of CIN in breast cancer. Literature pertaining CIN and prognosis based on copy number data is scarce. Two p ublicly available aCGH breast cancer datasets were investigated; the dataset by Chin et al. [START_REF] Chin | Genomic and transcriptional aberrations linked to breast cancer pathophysiologies[END_REF] has 174 samples, but only 11 of these were eligible (ER-positive, lymph-node negative, no adjuvant hormonal or chemotherapy treated patients), and none of these patients relapsed. The other dataset was from Chin et al. [START_REF] Chin | High-resolution aCGH and expression profiling identifies a novel genomic subtype of ER negative breast cancer[END_REF] describing 171 patients, of whom 60 were eligible, but only 8 patients showed a relapse. We deemed both these studies not robust enough to validate our results. To be able to confirm our observations in other datasets, we therefore employed two external gene-expression datasets with sufficient cases and appropriate follow-up [START_REF] Sotiriou | Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis[END_REF][START_REF] Schmidt | The humoral immune system has a key prognostic impact in node-negative breast cancer[END_REF]. In these data, the pure prognostic relevance of CIN inferred from associated differentially expressed genes in ER-positive cases was clearly confirmed (Fig. 3). Pathway analysis point to an excess of proliferation, including overrepresented E2F directed transcriptional regulation and increased nucleic acid synthesis, and lack of apoptotic and immune cell responses as prominent features in ER-positive breast cancer. These pathways are considered the traditional hallmarks of unconstrained growth of cells [START_REF] Hanahan | The hallmarks of cancer[END_REF].

The genomic grade signature [START_REF] Sotiriou | Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis[END_REF] was found to be the most significant signature associated with CIN, suggesting a strong interaction between CIN and histological tumor grade. Indeed, within our cohort CIN-scores were significantly higher in the poor grade group (Online Resource Fig. 6), an observation also recently reported by Ellsworth et al. [START_REF] Ellsworth | Correlation of levels and patterns of genomic instability with histological grading of invasive breast tumors[END_REF], based on a limited amounts of chromosomal areas (n=26).

Since our cohort is a retrospective cohort obtained from various peripheral hospitals we do not have all histological grading available according to current standards. Therefore, we clustered our samples according to the genomic grade profile.

Interestingly, the CIN-score and genomic grade were, in a multivariate MFS analysis, independent of each other significantly associated with MFS in ER-positive patients.

Of note, since we have analyzed the genomic grade instead of the histopathological grade, these data should be interpreted with caution. The surprising observation that CIN is prognostic in good genomic grade, but not in luminal A samples, while most luminal A samples have a good grade, may be explained by the fact that luminal A samples are not exclusively of good grade; in our cohort, 19% of luminal A samples are of poor grade, while of all good-grade samples, 47% is of luminal A type. The differences i n luminal A / good-grade cohorts probably explains the difference in prognostic value of CIN in these groups. Furthermore, it probably also implies that intrinsinc gene expression differences between luminal A and B breast cancer capture the biological and prognostic differences in chromosomal instability quite well. The observation that CIN may be able to discern differences within the goodgrade group was also reported by Carter et al. [START_REF] Carter | A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers[END_REF]. Their study showed that CIN was able to distinguish outcome in grade 2 tumors in three different datasets (total of 512 patients) and also in grade 1 tumors in a dataset of 161 patients. CIN was not prognostic in the grade 3 samples of the different datasets. This further supports the notion that histopathological grading of well-differentiated tumors in particular can be improved by molecular tools such as gene expression signatures [START_REF] Carter | A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers[END_REF][START_REF] Sotiriou | Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis[END_REF] or as described here, the use of SNP copy number data. Taken together, the data suggest that an ER-positive tumor cell with a poor genomic integrity may still phenotypically appear as well differentiated. But despite that appearance, such a tumor is likely to progress. Poorly differentiated tumor cells on the other hand virtually all display an unstable genome.

In conclusion, we confirm that CIN is a prominent event in many breast cancer samples and our observations add to the current paradigm of the role of chromosomal instability in breast cancer. From our work it becomes clear that ERnegative tumors as well as the basal/triple-negative tumors have a highly unstable genome, but unanticipated, this instability does not seem to aggravate prognosis within ER-negative patients. This signifies an association of CIN with the processes regulating the integrity of the genome possibly in the origin, but not in the progression of the disease. In ER-positive, her2/neu and luminal B tumors the genome is to a lesser degree unstable, but if CIN is present, prognosis is poor.

Obviously, the rather extensive methodology to obtain the CIN-score described in this study calls for easier assayable markers to query CIN if it were to be used in diagnostic routine. Nevertheless, assessment of CIN is a useful tool to gain insight into breast cancer biology and may further contribute to a more tailored treatment approach of breast cancer patients.

Acknowledgments:

The work was supported by Veridex LLC for tissue processing and isolating RNA and DNA for microarray analysis as well as by a research grant from the Netherlands Genomics Initiative (NGI) / Netherlands Organization for Scientific Research (NWO). This organization had no role in study design, analysis of data or preparation of the manuscript.

Conflict of interest:

Authors Y.Zhang and Y.Wang are employees of, and hold stock in Veridex LLC, which financially supported this work for tissue processing and microarray analysis.

The other authors declare no conflict of interest.

Legends:

Fig. 1 Chromosomal instability patterns of 313 primary breast tumors

Frequency of gains and losses among hierarchically clustered groups are plotted from chromosome 1 to X (left to right). Green and red indicate gain and loss, respectively.

Fig. 2 Association of CIN with breast cancer subtypes

CIN-scores according to ER status (A), triple-negative status (B), and molecular subtype (C) are shown. Boxes show 1 st -3 rd quartile with the median marked as a horizontal line, notches show the 95% confidence interval of the median.


Mohammed A Aleskandarany

Emad A Rakha

R Douglas Macmillan

Desmond G Powe

Ian O Ellis

Andrew R Green
email: andrew.green@nottingham.ac.uk

Douglas Macmillan

R Green

MIB1/ Ki-67 Labelling Index Can Classify Grade 2 Breast Cancer into Two Clinically Distinct Subgroups

Keywords: Breast carcinoma, Growth fraction, Grade, Immunohistochemistry, Ki67

come

Results:

Using a 10% cut-point of MIB1-LI, grade 2 b reast cancers were classified into lo w (49.8%) and high (50.2%) p roliferat ive subgroups. Univariate and mult ivariate survival analysis revealed st atistically significant differences between these subgroups regarding patients' breast cancer specific survival (p<0.001), and metastasis free survival (p< 0.001) wh ich was independent of the well-established prognostic factors (HR=2.944, 95% CI= 1.634-5.303, p<0.001).

Conclusions:

Our results further demonstrate that grade 2 breast cancers may represent at least two biological or behaviourally different entities. Assay of growth fraction in breast cancer using MIB1/Ki67 immunohistochemistry is a robust cost-effective diagnostic tool that subdivides grade 2 tumours into low and high risk populations providing additional prognostic information in planning therapies and outcome prediction.

Introduction:

Histologic tu mour grade is well recognized as one of the powerfu l prognostic factors in human breast cancer (BC) [START_REF] Soerjomataram | An overview of prognostic factors for long-term survivors of breast cancer[END_REF], which has been validated in mult iple independent studies [START_REF] Pereira | Pathological prognostic factors in breast cancer. IV: Should you be a typer or a grader? A comparative study of two histological prognostic features in operable breast carcinoma[END_REF][START_REF] Dalton | Histologic grading of breast carcinoma. A reproducibility study[END_REF][START_REF] Frierson | Interobserver reproducibility of the Nottingham modification of the Bloom and Richardson histologic grading scheme for infiltrating ductal carcinoma[END_REF]. In current routine BC diagnostics, over half of all BC cases are assigned histologic grade 1 or 3 status, with low and high risk of early recurrence, respectively. Ho wever, the remaining substantial percentage of tumours (30%-60%) are classified as histologic grade 2, which have an intermed iate risk of recurrence but remain less informative for clin ical decision than grade 1 and 3 tu mours [START_REF] Ignatiadis | Understanding the molecular basis of histologic grade[END_REF].

One of the features common to breast carcinomas is the increased rate of proliferation over that seen in normal breast epithelia [START_REF] Whitfield | Common markers of proliferation[END_REF]. In the current practice, mitotic frequency score (MS) is incorporated with tubule format ion and nuclear pleo morphis m into the designation of histological grade in the widely adopted Elston and Ellis modification of the Scarff-Bloo m-Richardson histologic grading system [START_REF] Elston | Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up[END_REF]. However, the number of mitotic figures is not linearly correlated with the rate of proliferation, especially in aneuploid tu mours due to wide variation in cell cycle duration, particularly the mitotic phase of the cell cycle [START_REF] Whitfield | Common markers of proliferation[END_REF]. Therefore, other methods of assessing proliferat ive activity in BC have been previously studied with their documented advantages and disadvantages [START_REF] Whitfield | Common markers of proliferation[END_REF][START_REF] Hall | Review: assessment of cell proliferation in histological material[END_REF], with some focus on histologic grade 2 refinement [START_REF] Meyer | Breast carcinoma malignancy grading by Bloom-Richardson system vs proliferation index: reproducibility of grade and advantages of proliferation index[END_REF][START_REF] Lynch | Mitotic counts provide additional prognostic information in grade II mammary carcinoma[END_REF].

Over the recent years, gene expression profiling has resulted in a paradigm shift in researchers' understanding of BC bio logy and prognostication. Despite the fact that several gene signatures, developed using different microarray platforms, have been shown to correlate with patient outcome, many of these have a relatively small gene overlap. A feature common to these signatures is their ability to identify h igh-risk patients but generally identify a greater nu mber of lo w-risk patients compared with current clinical guidelines [START_REF] Haibe-Kains | Comparison of prognostic gene expression signatures for breast cancer[END_REF]. Moreover these global gene expression analysis studies have demonstrated the prime ro le of proliferation signatures in BC prognosis and prediction of response to therapy [START_REF] Dai | A Cell Proliferation Signature Is a Marker of Extremely Poor Outcome in a Subpopulation of[END_REF][START_REF] Bonnefoi | Predictive signatures for chemotherapy sensitivity in breast cancer: are they ready for use in the clinic?[END_REF]. For instance, a recent meta-analysis of publicly available BC gene expression studies showed that the key biological drivers in nine prognostic signatures were proliferation related genes, in addition to ER signalling and ERBB2 amp lification [START_REF] Wirapati | Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures[END_REF]; results which have been reinforced by other authors [START_REF] Cheang | Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer[END_REF]. As a consequence, attempts, such as genomic grade index (GGI) and mo lecular grade index (M GI), have been made to stratify grade 2 BC into lo w and high risk subgroups to imp rove clin ical relevance [START_REF] Sotiriou | Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis[END_REF][START_REF] Ma | A five-gene molecular grade index and HOXB13:IL17BR are complementary prognostic factors in early stage breast cancer[END_REF]. In their study, Sotiriou et al concluded that the gene signature characteristics displayed by grade 2 BC appears to be a mixture of signatures displayed by grade 1 and 3, rather than being independent or intermediate between t he two. Moreover, the M GI appears to be as powerful prognostically as the GGI and relies only on five genes; all involved in different cell cycle phases, evaluated using quantitative PCR [START_REF] Ma | A five-gene molecular grade index and HOXB13:IL17BR are complementary prognostic factors in early stage breast cancer[END_REF]. Ho wever, the co mplexity, quality assurance requirements and interpretation issues of expression array-based profiling and assays using nucleic acids (RNA and DNA) extracted fro m formalin-fixed paraffin embedded samples, coupled with its cost, potentially limit the use of this technology in the routine clin ical setting [START_REF] Rakha | Expression profiling technology: its contribution to our understanding of breast cancer[END_REF][START_REF] Srinivasan | Effect of Fixatives and Tissue Processing on the Content and Integrity of[END_REF].

For this study, we have assessed proliferat ive activity using the well characterized routinely available MIB1 antibody which binds to Ki-67 protein in grade 2 invasive breast carcinomas to determine whether it could improve the accuracy of predicting clinical outcome for such a group of patients with a varied and an unpredictable course of disease progression. This retrospective study adheres to REMARK criteria [START_REF] Mcshane | REporting recommendations for tumor MARKer prognostic studies (REMARK)[END_REF].

This study was approved by Nottingham Research Ethics Committee 2 under the title of "Development of a molecu lar genetic classification of breast cancer". None of th e authors has any competing interests.

Materials and Methods:

This study was based on a well-characterized consecutive series of early stage (stage I-III, n=1550) primary operable invasive breast carcinoma fro m patients entered into the Nottingham Tenovus Primary Breast Carcino ma Series between 1990 and 1998 (n =1,550). Patients were under the age of 70 and managed in accordance to a uniform protocol. Patients' clin ical history and tumour characteristics, information on therapies and outcomes were available and prospectively maintained.

The method used for tumour grading is that described by Elston and Ellis [START_REF] Elston | Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up[END_REF] that involves assessment of three components of tumour morphology; tubule formation, nuclear pleo morphism and frequency of mitoses (each scored from 1 to 3). The mitosis was scored based on the number of mitoses per 10 high power fields (M 1; ≤9, M 2; 10-19, and M3≥20, using a microscopic field diameter of 0.59 mm) [START_REF] Ellis | Pathology reporting of breast disease : a joint document incorporating the third edition of the NHS Breast Screening Programme's Guidelines for pathology reporting in breast cancer screening and the second edition of the Royal College of Pathologists' Minimum dataset for breast cancer histopathology[END_REF].

Outcome data include survival status, survival time, cause of death, disease free interval, time to loco-regional recurrence, and distant metastasis and was maintained on a prospective basis. The Breast Cancer Specific Surv ival (BCSS) is defined as the time (in months) from the date of primary surgery to the date of breast cancer-related death. Metastasis free interval (MFI) is defined as the duration (in months) fro m the date of primary surgery to the appearance of distant metastasis. Adjuvant treatment was scheduled on the basis of patient and tumour prognostic /predictive factor status including Nottingham Prognostic Index (NPI), estrogen receptor (ER) status, and menop ausal status.

Patients within the good prognostic group (NPI <3.4) did not receive adjuvant therapy. Hormonal therapy (HT) was prescribed to patients with ER positive tumours and NPI scores of >3.4 (moderate and poor prognostic groups). Premenopausal patients within the moderate and poor prognostic groups The patients had a median age of 54 years (range 18-70 years). Outco me data was available for 1513/1550 patients, with a median overall survival of 123 months (range 4 to 247 months) and the med ian time of metastasis free survival of 110 months (range 2-247 months). Distant recurrence occurred in 483 cases (31 %), 411 (27%) patients died from breast cancer, while 875 (58%) patients were alive at the end of follow-up. Figure 1 shows numbers of patients with available data through the study and different analyses.

Immunohistochemistry (IHC):

Formalin-fixed paraffin tissue sections (4μm) mounted on Superfrost slides (Surigpath) were immunohistochemically stained, employing the standard streptavidin -biotin co mplex method, as previously described [START_REF] Aleskandarany | Growth fraction as a predictor of response to chemotherapy in node negative breast cancer[END_REF]. Heat induced antigen (epitope) retrieval (HIER) was performed in citrate buffer (pH6.0), using microwave oven-assisted heating for 20 minutes. Primary mouse monoclonal anti-Ki-67 antibody (MIB1 clone, M7240, DAKO, Den mark), d iluted 1:100 (optimu m working dilution) in normal swine serum (NSS)/TBS, was applied to each slide and incubated for 60 minutes at room temperature. Strept AB complex (Dako) diluted in 1:100 in NSS/TBS was applied and incubated for 60 minutes. 3-3'Diaminobenzid ine tetrahydrochloride (Dako liquid DAB Plus, K3468) was used as a chromogen. The sections were counterstained with Mayer's haemato xylin. Positive (FFPE tonsil section) and negative (primary antibody replaced by TBS) controls were included in each staining run.

Ki-67/MIB1 scoring

The MIB1-LI was quantitatively determined using human eye light microscopical assessment.

The entire slide was scanned for immunostaining evaluation using a light microscope at low power magnificat ion (x100). All tumour cell nuclei with homogenous granular staining, mult iple speckled staining, or nucleolar staining were regarded as positively stained regardless of intensity, while any cytoplasmic immunoreactivity was considered non-specific, and hence not taken into consideration.

Scoring was performed in the areas with highest number of positive nuclei (hot spot) within the invasive component of the tumour. The M IB1-LI (tu mour gro wth fract ion) was expressed as the percentage of MIB1 positive malignant cells in 1,000 malignant cells assessed under high power magnificat ion (x400).

Statistical analysis

Statistical analysis was performed using SPSS 16.0 statistical software (SPSS Inc., Chicago, IL, USA ). Cut-off values for the different bio markers included in this study were chosen before statistical analysis. Standard cut-offs were used for established prognostic factors and were the same as for previously published patient series [START_REF] El-Rehim | High-throughput protein expression analysis using tissue microarray technology of a large wellcharacterised series identifies biologically distinct classes of breast cancer confirming recent cDNA expression analyses[END_REF][START_REF] Rakha | Triple-negative breast cancer: distinguishing between basal and nonbasal subtypes[END_REF]. Determinat ion of the optimal MIB1-LI cut-off points was performed using X-t ile bio informat ics software; version 3.6.1, 2003-2005, Yale University, USA as previously described [START_REF] Camp | X-Tile: A New Bio-Informatics Tool for Biomarker Assessment and Outcome-Based Cut-Point Optimization[END_REF]. Analysis of categorical variables was performed with the appropriate statistical test. Survival curves were analyzed by Kaplan-Meier method with significance determined by the Log Rank test. Multivariate analysis was performed by Cox hazard analysis. A p-value <0.05 (two sided) was considered significant.

Results

In the current study, the median patients' age at diagnosis of this cohort was 54 years (range 18-70 years), with 60% of patients were postmenopausal. Grade 2 cases constituted 506/ 1550 (33%), while 275 (18%) grade 1 and the remain ing 764 cases (49%) were grade 3. As reported [START_REF] Elston | Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up[END_REF], a statistically significant difference between grades 1, 2 and 3 was found regarding BCSS and distant metastasis free survival (MFS) using Kaplan Meier survival analysis (p<0.001), with grade 2 tu mours having intermed iate risk (Figure 2A&B). Of all tumours, 885 cases (58%) were ductal carcinoma of no special type (duct/NST), with the remainder consisting of tumours of other histologic types including medullary, tubular mixed, lobular mixed, and mixed NST and lobular. ER positive tumours constituted (69.9%), while 177 cases (11.4%) were HER2 positive (based on IHC and HER2 gene amplification detection using CISH). The NPI in the studied cases ranged from 2.04-5.00 (mean, 4.40). Regarding the adjuvant systemic therapy used, 285 patients (20%) received classic CMF adjuvant chemotherapy, while 481 (32%) received tamo xifen as hormonal therapy. Table 1 summarizes the clinico-pathological characteristics of the study series.

The MIB1-LI for this series ranged from (0-100%, med ian 15%). Using X-t ile b ioinformat ics software package, a MIB1-LI of 10% was found to distinguish low fro m highly proliferativ e tu mours.

In this study histological grade 1, 2 and 3 BCs were stratified into either a) low (MIB1 -LI<10%, or b) high proliferat ive (MIB1-LI ≥10%, groups: G1a, G1b; G2a, G2b; and G3a, and G3b, respectively, Table 2. Figure 3 shows two different grade 2 cases with low (A) and high (B) MIB1-LI.

Association of MIB1-LI and mitotic scores with patients' outcome in grade 2 tumours:

Using the 10% MIB1-LI cut-point, grade 2 tu mours were split into low pro liferat ive G2a (252, 49.8%) and high proliferat ive G2b (254, 50.8%) groups. Univariate survival analysis showed that Grade 2 tumours with high proliferation (G2b) had significantly shorter BCSS (p < 0.001, Hazard ratio (HR) =3.116, 95% CI= 1.994-4.971, Figure 4A) relat ive to those grade 2 tumours with low proliferation (G2a). G2b tu mours had a 76% 10 year BCSS co mpared to 92% in the G2a cases.

Moreover, a statistically significant difference was observed between G2a and G2b cases regarding MFS (p<0.001, HR=2.152, 95%CI= 1.505-3.079, Figure 4B). In addit ion, un-treated and tamo xifen treated patients had the same pattern of survival with respect to tumour proliferat ion, where patients with G2b tumours experienced shorter BCSS and MFS than those with G2a tumours (Figure 4 C-F).

Similarly, using the mitotic frequency scores (M1, M2, and M3), grade 2 cases were divided into three distinct proliferat ive subgroups with significant survival differences between M1 and M2 subgroups regarding their BCSS (p =0.002, HR=1.502, 95%CI=1.163-1.940) and MFS (p =0.006, HR=1.476, 95%CI=1.173-1.856). However, no significant difference was observed between the M2 and M3 groups regarding BSCC and MFS (p>0.05).

Comparison of outcome between grades using proliferative subgroups:

Similarly, we subdivided grade 1 into low (G1a) and high (G1b) proliferat ive subgroups using 10% MIB1-LI. A significant difference was observed between grade 1 and grade 2 tumours subdivisions (G1a/b, G2a/b) regard ing their BCSS and DFS (p<0.001, LR=69.3, p<0.001, respectively; Figure 5A&B). A significant difference in patient outcome between G1a and G1b was observed regarding BCSS (p=0.007, HR=3.031, 95% CI=1.295-7.095) and MFS (p<0.001, HR=1.311, 95% CI=1.237-3.389). Moreover, there was significant difference between G2b and G3a regarding BCSS (p=0.01). However, no significant differences were noted between G2a and either G1b or G1a (p>0.05). Importantly, patient outcome remained similar between the low and high proliferat ive grade 3 tu mours (G3a&b) regarding their BCSS and DFS (p=0.982& 0.766, respectively).

Multivariate analysis

Multivariate Co x proportional hazard model analysis of predictors of BCSS grade 2 tu mours was performed including MIB1-LI, mitotic scores M1, M2 and M3, nodal stage, tumour size, molecu lar subtype [lu minal (ER + and or PR + ), HER2 + , basal-like BC (BLBC, ER -, PR -, HER2 -, and positive for CK5/ 6, and/or CK14 and/or EGFR), and triple negative non -basal BC (TNnon-B, all negative)] [24], and adjuvant therapy. This analysis demonstrated that MIBLI is the strongest independent predictor of BCSS (HR = 3.251, 95 CI = 1.796-5.886, p<0.001) and MFS (HR =2.188, 95 CI = 1.364-3.509, p= 0.001) in grade 2 BC, (Table 3). Interestingly, the mitotic score which was able to subdivide BC grad e 2 cases in univariate analysis lost its significance in mu ltivariate model analysis (p=0.692 and 0.376 for BCSS and MFS, respectively).

Discussion

Breast cancer is regarded as a heterogeneous group of tumours with diverse behaviour, outcome, and response to therapy [START_REF] Simpson | Molecular evolution of breast cancer[END_REF] and patients with apparently similar clinical and pathological features can show distinct outcomes and varied response to therapy [START_REF] Elston | Pathological prognostic factors in breast cancer[END_REF]. With the increasing focus on personalization of treat ment there is an increasing need for additional refinement of prognostic factors to improve patients' risk stratification and the targeting of treatment for those who will truly benefit, thereby avoiding iatrogenic mo rbidity in thos e who will not [START_REF] Rakha | Expression profiling technology: its contribution to our understanding of breast cancer[END_REF][START_REF] Pusztai | High-dose chemotherapy: how resistant is breast cancer?[END_REF]. Patients with intermediate risk of recurrence , for examp le as exhib ited by patients with histologic grade 2 tu mours pose significant difficulty in the management in balancing risk of relapse with potential to experience side effects [START_REF] Elston | Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up[END_REF].

Further sub-classificat ion of histologic grade 2 tu mours, possibly into low and high risk categories, would be beneficial to increase the prognostic stratificat ion of these patients [START_REF] Sotiriou | Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis[END_REF].

In this study, the cell cycle associated protein Ki-67, as assessed by IHC using the MIB1 antibody, was tested as a marker of g rowth fraction to determine its potential to refine early invasive grade 2 BC. In our series, grade 2 BC constituted 33% of the whole cohort, confirming previous observations that this forms a substantive proportion of cas es in routine practice. Our results demonstrated that Ki-67/MIB1-LI categorized, using a 10% cut-point, BC grade 2 into two distinct subgroups which exhib ited significantly different outcome, which was independent from other factors. Similar methodology applied to grades 1 and 3 cases also stratified into proliferat ive subgroups, however the proportion of cases were different with the majority of the grade 1 being low p roliferat ive and the majority of grade 3 being high proliferative. Significant BCSS and M FS d ifferences were revealed between the subdivisions of the grade 1, while no differences were noticed between grade 3 subdivisions. However, the number of cases in the G1b subgroup was limited (40 patients, 2.5% of whole series), and probably might not be a reflection of b iological difference. Therefore further studies are required in order to determine the clinical relevance of proliferation assessment within low grade BC in routine practice.

It is noteworthy that there was no difference in outcomes of G2 low proliferative (G2a) and the proliferative subdivisions of grade 1 (G1a & G1b). On the contrary, the outcome of grade 2 highly proliferative subgroup (G2b) was significantly different fro m those of grade 3 low proliferative (G3a).

Therefore, G2b (16% of the whole cohort) showed a distinct outcome that was intermediate between grade 1 and grade 3. In other words, grade 2 BC was not, using MIB1 -LI, totally re-distributed between either grade 1 o r 3, but a substantial cohort of grade 2 cases were shown to have an intermediate risk when compared to grade 1 and grade 3 BC. Interestingly, in the study of Sotiriou and colleagues, the GGI reclassified patients with histologic grade 2 BC into two gro ups with distinct clinical outcomes similar to those of histologic grades 1 & 3 [START_REF] Sotiriou | Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis[END_REF]. Although their observation challenges the existence and clinical relevance of an intermed iate-grade, they have characterized a group of grade 2 cases with GGI intermediate between grades 1 and 3. The existence of an intermediate-risk group, based on the study of tumour growth fraction was demonstrated in our series, but its frequency is lower than that seen using the histologic grading systems alone. Furthermore, our assay is more applicable to the routine histologic practice because MIB1-LI assay is accepted to be a low cost, simple method which is perfectly suitable for standardization in clinical laboratory practice, in contrast to array-based profiling and assays using nucleic acids extracted fro m FFPE t issues [START_REF] Rakha | Expression profiling technology: its contribution to our understanding of breast cancer[END_REF][START_REF] Srinivasan | Effect of Fixatives and Tissue Processing on the Content and Integrity of[END_REF], which require quality assurance and cost/effectiveness appraisal prior to their adoption into routine clinical practice.

The mitotic frequency score, which is incorporated into the Nottingham grading system, also stratified grade 2 BC into three risk groups in this study. However, MIB-LI not only had a higher hazard ratios (~ 2 fo ld) in predict ing the BCSS and MFS of grade 2 BC, but also it eliminated mitotic scores fro m the mult ivariate model rendering it insignificant.

In addition to GGI, other attempts to refine grade 2 BC have been reported, and are thus noteworthy.

Le Doussal et al. were able to separate grade 2 node negative BC into three significantly different subgroups according to their MFS, through excluding tubule format ion fro m the Scarf -Bloo m Richardson histologic grading system [START_REF] Doussal | Prognostic value of histologic grade nuclear components of Scarff-Bloom-Richardson (SBR). An improved score modification based on a multivariate analysis of 1262 invasive ductal breast carcinomas[END_REF]. In addition, Baak et al. reported that Mitotic Activity Index (MAI) was the most important prognostic factor in node negative BC among the 3 morphologic features of the Nottingham co mbined histologic grade, with no and limited additional prognostic value of tubule format ion and nuclear atypia respectively [START_REF] Baak | Prospective multicenter validation of the independent prognostic value of the mitotic activity index in lymph node-negative breast cancer patients younger than 55 years[END_REF]. Moreover, Lynch et al. in their pilot study highlighted the importance of mitotic counting in refining grade 2 cases to improve prognostic accuracy, which was similar to S-phase fraction assessment using flow cytometry, wh ile, no additional significance of Ki-67 evaluation using IHC was noted [START_REF] Lynch | Mitotic counts provide additional prognostic information in grade II mammary carcinoma[END_REF]. Fro m these findings, it could be concluded that proliferation assessment is the most useful and is the prime determinant of outcome in BC patients relative to other grade co mponents. However, these stu dies were restricted to either a part icular stage [START_REF] Doussal | Prognostic value of histologic grade nuclear components of Scarff-Bloom-Richardson (SBR). An improved score modification based on a multivariate analysis of 1262 invasive ductal breast carcinomas[END_REF][START_REF] Baak | Prospective multicenter validation of the independent prognostic value of the mitotic activity index in lymph node-negative breast cancer patients younger than 55 years[END_REF], or carried out on a relatively s mall number of cases [START_REF] Lynch | Mitotic counts provide additional prognostic information in grade II mammary carcinoma[END_REF].

In conclusion, our results further demonstrate that grade 2 breast cancers may represent combination of at least two biological or behaviourally different entities. Assay of growth fraction in breast cancer, using the routinely available anti-Ki-67 MIB1 antibody by IHC, is a robust cost-effective method and potential routine diagnostic tool which can subdivide grade 2 tumours into low and high risk populations, providing additional prognostic informat ion and patient stratificat ion when planning therapy and predicting outcome.

  were candidates for CMF (Cyclophosphamide, Methotrexate, and 5-Flourouracil) chemotherapy; and those ER positive LN positive patients were offered HT in addition to CMF. Conversely, postmenopausal patients with moderate or poor NPI and ER positive were o ffered HT, while ER negative patients received CMF if fit. Data on a wide range of biomarkers of known clinical and biological relevance to breast cancer were also available. These include, hormone receptors [estrogen receptor (ER), progesterone receptor (PR), androgen receptor (AR), ep idermal growth factor receptor family members (HER1 [EGFR], and HER2), cytokeratins [CKs] (basal CKs; CK5/6 and CK14, and lu minal CKs; CK18 and 19), P53, and cadherin family members [E-cadherin and P-cadherin] [22].

Table 1 : Summary of the characteristics of the study cohort and their tumour tissue specimens

 1

	Characteristics	No (% )
	Age:
	Med ian (Range)	54 (18-70)
	Menopausal Status
	Premenopausal	612 (39.5)
	Postmenopausal	936 (60.5)

Table 2 : Breast cancer grades 1, 2 and 3 and their subdi visions by MIB1 -LI:

 2

	Grade	Low proliferative	High Proliferative
		(MIB1-LI<10%)	(MIB1-LI≥10%)
	Grade 1	235 (85.5)	40 (14.5)
	Grade 2	252 (49.8)	254 (50.2)
	Grade 3	81 (10.6)	683 (89.4)

Table 3 : Cox proporti onal hazards analysis for predictors of B CSS and distant metastasis free survi val (MFS): effect of MIB1-LI, mitotic scores, tumour size, nodal stage, molecular subtype and adjuvant therapy in grade 2 BC cases: BCSS MFS

 3

	Vari able (No.)	P value	HR	95% CI	P value	HR	95% CI
	MIB1-LI
	Lo w: (245) High: (248)	< 0.001	3.251	1.796	5.886	0.001	2.188	1.364	3.509
	Mi totic score
	M1: (291)	0.569				0.246
	M2: (180)	0.876	0.957	0.554	1.655	0.965	1.011	0.635	1.608
	M3: (22)	0.358	1.501	0.632	3.564	0.111	1.836	0.870	3.876
	Tumour size	0.041	1.338	1.016	1.813	0.001	1.541	1.187	2.002
	(in centimet res)
	Nodal stage
	1: (295) 2: (160) 3: (38)	0.001 0.080 < 0.001	1.636 4.476	0.943 2.005	2.838 9.994	0.009 0.061 0,003	1.560 2.957	0.980 1.448	2.482 6.039
	Molecul ar subtype
	Lu minal: (422) HER2 Positive: (34) Trip le Negative: (13)	0.143 0.064 0.585	1,828 0.671	0.965 0.161	3.461 2.806	0.028 0.010 0.589	2.127 0.723	1.199 0.223	3.772 2.346
	Endocrine therapy
	No : 307	0.07	0.803	0.633	1.018	0.076	0.840	0.693	1.018
	Yes: 186
	Chemotherapy
	No: (453)	0.138	0.530	0.229	1.227	0.056	0.490	0.236	1.018
	Yes: (40)

Acknowledge ments

MAA is funded by the Ministry of High Education (Egypt). Part of this work has been partially funded by the Breast Cancer Campaign.