Classification of ductal carcinoma in situ by gene expression profiling

Ductal carcinoma in situ (DCIS) of the breast represents a heterogeneous group of non-invasive breast tumours commonly detected in women undergoing screening mammography. DCIS is characterised by malignant epithelial cells accumulating in the ducts of the breast without invading through the basement membrane into the surrounding tissue. DCIS accounts for approximately 3% of symptomatic breast malignancies and for approximately 20% of breast malignancies in patients from population-based screening programs [1].

Different histological types of DCIS can be recognised, and a variety of classification systems have been developed [2]. Due to subjective interpretation of the morphology of the lesions, even experienced pathologists differ in their classification of DCIS [3]. Therefore, histological classification of DCIS may not be sufficient, and additional classification approaches could assist pathological classification.

Several models of the evolution of DCIS to invasive cancer have been suggested. One model suggests the linear progression from low-nuclear-grade DCIS to high-nuclear DCIS and the subsequent development of invasive cancer [4]. Based on specific genetic alterations found in the different types of DCIS, a more likely scenario is the evolution of well-, moderately, and poorly differentiated DCIS via distinct pathways. Following this idea, well-differentiated DCIS can give rise to low-grade invasive carcinoma, whereas poorly differentiated DCIS can give rise to high-grade invasive breast cancer [5, 6].

Several specific genetic alterations have been found in DCIS. HER2 gene amplification and protein overexpression are detected in up to 70% of poorly differentiated DCIS cases [7], and cyclin D1 is amplified and overexpressed in DCIS [8] in approximately 20% of the cases. Inactivating mutations of the E-cadherin gene are detected in almost all cases of lobular carcinoma in situ (LCIS) [9]. Several other genetic alterations in oncogenes (for example, C-MYC) and tumour suppressor genes (for example, p53) have been found in DCIS and are reviewed in Reis-Filho and colleagues [10] and Allred and colleagues [11].

Gene expression profiling has been shown to be a powerful tool for identifying profiles of tumour subtypes [12‐15] and for correlating gene expression profiles with outcome in breast cancer [16‐18]. The identification of specific gene expression patterns correlated with the different types of DCIS may help to elucidate the processes underlying the evolution of in situ carcinomas of the breast and also lead to a more reproducible classification of DCIS lesions.

To date, only a few studies of gene expression profiling of DCIS and a comparison with the gene expression pattern of invasive samples have been published and these are based on a small number of samples [19, 20].

This study was performed to identify differences in gene expression (a) between DCIS and invasive breast cancer and (b) between different histological types of DCIS.

Although studies to identify gene expression signatures in DCIS are limited by difficulties in obtaining frozen material from DCIS, we were able to collect a relatively large series of DCIS cases for this purpose. It should be kept in mind that we did not have a sufficient number of cases to validate the gene expression signatures that we identified.

We were able to show that well- (n = 6) and poorly (n = 14) differentiated DCIS show different gene expression profiles and can be distinguished by a classifier of 43 genes. Most of the genes differentially expressed between well- and poorly differentiated DCIS are involved in metabolism (for example, BTD, ETFA, GMFG, and PLAT) and cell communication (for example, ESR1, ACK1, CELSR2, and CCL19).

One of the top genes in the 43-gene classifier is BCL2. The mRNA expression of this anti-apoptotic protein is upregulated in the well-differentiated samples. In addition to its anti-apoptotic function, BCL2 has a suggested role in neuro-endocrine differentiation in colon carcinomas [31] and its downregulation is associated with poor prognosis in breast cancer [32].

Twenty-eight of the 43 genes are upregulated and 15 genes are downregulated in the well-differentiated samples (Figure 3a). Whereas a number of the 28 upregulated genes are involved in DNA binding, no genes fulfilling this function are on the list of the 15 downregulated genes. Conversely, genes involved in phosphate metabolism (for example, GMFG, ACK1, and ATP5B) can be found within the 15 downregulated, but not in the 28 upregulated, genes.

It is known that HER2 is overexpressed in poorly differentiated DCIS in approximately 42% of the cases [7], and it has been suggested that HER2 overexpression is an early step in the evolution of a distinct type of breast carcinoma. In our data set of all in situ samples, we found a positive log2-ratio for HER mRNA expression in six of 14 poorly differentiated DCIS cases (43%) and in one case of intermediately-poorly differentiated DCIS. In all the other in situ samples, the log2-ratios of HER2 are negative. These results are in agreement with the hypothesis that HER2 overexpression is an early event in the development of poorly differentiated in situ breast carcinomas.

Supervised classification of well-, well-intermediately, intermediately-poorly, and poorly differentiated DCIS samples (analysis set 4) showed a separation of these samples in three groups: a 'good' group, a 'poor' group, and an 'intermediate' group containing mostly samples that were identified as intermediately-poorly differentiated samples by pathologists. This group also contains some samples pathologically classified as well-intermediately differentiated, whereas most of these samples fall in the 'good' group. These results indicate that well- and well-intermediately differentiated DCIS are more similar to each other than poorly and intermediately-poorly differentiated DCIS are. Following this idea, well- and well-intermediately differentiated samples may be considered to be one group, whereas poorly and intermediately-poorly differentiated samples seem to be two distinct groups of DCIS. If these results can be validated in additional studies, this classification could help to decrease controversial classification of DCIS due to interobserver variability and to recognise well-differentiated DCIS with more accuracy.

Within the gene lists describing the differences between well- and poorly differentiated DCIS, a number of genes refer to proteins for which antibodies are available. There is no single gene discriminating between the different types of DCIS, but it has to be investigated whether a combination of protein stainings in a patient's tissue can assist in better classification of DCIS. From the study presented here, potential candidates for such an approach are Bcl-2, Ack1, CCL19, and CELSR2, among others.

Thirty-five genes are able to describe the global differences in gene expression between in situ and invasive breast tumour samples. This classifier contains many genes involved in signal transduction (for example, APC2, DAPK3, ADM, ARF1, and IQGAP1) and cell growth and maintenance (TGFB2, PTMS, PSAP, TUBB2, and MAP7).

The most likely model describing the progression from in situ to invasive breast cancer lesions is the existence of distinct pathways for the evolution of well- and poorly differentiated DCIS. Following this idea, well-differentiated in situ lesions develop into grade 1 IDC, whereas poorly differentiated samples develop into grade 3 IDC [5, 6]. We therefore performed supervised classification on the set of poorly differentiated DCIS (n = 14) and grade 3 invasive breast cancer (n = 24).

Approximately 80 genes discriminate poorly differentiated in situ from grade 3 invasive breast carcinomas. Thirteen of these 80 genes are upregulated and 67 genes are downregulated in poorly differentiated DCIS samples. The genes in this classifier are involved mostly in cell growth and protein metabolism. Many of them have a function in protein binding (for example, LCP1, TRAP1, ID4, TOB1, and CDH) and nucleic acid binding (for example, FBL, PIAS4, ELF3, EIF4G1, NBS1, and WHSC1L1).

A limited number of previous studies have addressed gene expression profiles in DCIS, and most of these studies have analysed a small number of samples. One study by Seth and colleagues [20] compared one case of low- to intermediate-grade DCIS with one case of high-grade DCIS with an invasive component and identified genes upregulated or downregulated in the low- to intermediate-grade DCIS case. Adeyinka and colleagues [19] studied six cases of DCIS with necrosis and four samples of DCIS without necrosis and identified a signature of 69 transcripts differentially expressed between these two groups. Ma and colleagues [33] used laser capture microdissection from paraffin-embedded material followed by gene expression profiling to identify molecular signatures in premalignant, preinvasive, and invasive stages of breast cancer. The results of their study suggested that tumour grade, rather than tumour stage, is associated with distinct gene expression patterns and that changes in gene expression required for invasive growth are already present in the DCIS stage [33]. In the study presented here, we compared the gene expression profiles of poorly differentiated DCIS lesions with those in grade 3 invasive breast tumours. In contrast to Ma and colleagues, we did not compare paired samples from the same patient but compared two groups of tumours. The 80-gene signature we identified is different from the signatures describing the differences between different grades of DCIS lesions. Schuetz and colleagues [34] identified gene expression signatures of in situ and invasive breast cancer by using 18 paired samples and combining laser capture microdissection and gene expression profiling on oligonucleotide microarrays. They showed that 546 probes were differentially expressed between DCIS and IDC. From the 18 genes they validated by real-time polymerase chain reaction, four (MMP11, PLAU, BGN, and FAP) are also present in our filtered data sets of significantly regulated probe sets comparing DCIS and invasive samples. They all show the same expression pattern as described by Schuetz and colleagues and are expressed at higher levels in the groups of invasive tumours. One of these genes (MMP11) is also part of the 35-gene and 80-gene classifiers. MMP11 and PLAU have already been correlated to invasion and poor prognosis [35, 36]. FAP (seprase) is a membrane-bound protease that has been suggested to reduce the dependence of breast cancer cells on exogenous growth factors in vitro and thereby to facilitate tumour growth and metastasis [37]. Allinen and colleagues [38] identified comprehensive gene expression profiles of the different cell types in normal breast, DCIS, and invasive breast cancer tissue. These data show that dramatic gene expression changes occur between normal breast tissue and breast carcinomas and that these changes are already present at the DCIS stage. These results also suggest a role of the chemokines CXCL12 and CXCL14 in breast tumourigenesis. Neither chemokine is present on our array platform, but CXCR4, which is the receptor for CXCL12, is. CXCR4 does not appear in the set of significantly regulated genes, indicating that it does not play a crucial role in our series of tumours, which reflects the data of a mixed population of cells enriched for tumour cells, whereas Allinen and colleagues performed gene expression profiling on microdissected cell populations.

A recent study by Nagaraja and colleagues [39] describes gene expression patterns corresponding to normal breast, noninvasive breast cancer, and invasive breast cancer by using several cell lines. They identified genes involved in cell-cell and cell-matrix interactions which were altered in their expression. A set of nine genes was sufficient to distinguish between invasive and non-invasive cell lines [39]. From this set of nine transcripts, six could be matched to our array platform. For three of them (cadherin 11, annexin A1, and vimentin), we observe the same expression pattern as published by Nagaraja and colleagues for the transition from in situ to invasive carcinoma. The other three transcripts (S100A8, claudin 3, and cadherin 1) are upregulated in the invasive cancer cell line in the data set of Nagaraja and colleagues, whereas we see a downregulation in the invasive grade 3 tumours compared with the group of poorly differentiated samples. This may be due to the fact that Nagaraja and colleagues generated in vitro data, which we compared with our human breast cancer data set.

Porter and colleagues [40] identified a subset of genes that are significantly regulated in DCIS or invasive carcinomas. They identified 26 genes that were differentially expressed between normal and DCIS samples or intermediate- and high-grade DCIS, respectively. From these, only XBP1 is present in one of our classifiers (78 genes). Porter and colleagues describe this transcript as tumour-specific, meaning upregulated in in situ and invasive tumours compared with their normal samples. We find that XBP1 is significantly more highly expressed in well- and well-intermediately differentiated DCIS samples than in poorly/intermediately-poorly differentiated ones.

Wulfkuhle and colleagues [41] performed proteomic analyses of six matched normal and DCIS samples of the human breast. They identified proteins that are more highly expressed in individual DCIS samples and that are involved in cytoskeletal regulation or vesicular trafficking or have chaperone activity. From the 15 proteins from which the expression has been validated by IHC, 12 are present as probes on our array platform. Three of those (profilin, stathmin, and prohibitin) are differentially regulated between DCIS and invasive samples, and all three show a higher expression in the invasive samples than in the DCIS samples. This is in line with the paper of Wulfkuhle and colleagues, which describes a higher expression of these proteins in the DCIS samples than in normal tissue. This indicates that changes in gene and protein expression observed in invasive tumours are already present in the transition from normal tissue to DCIS lesions.

We demonstrate here that gene expression profiling can distinguish between in situ breast cancer samples of well-versus poorly differentiated type. There appear to be a group of poorly differentiated samples, a group of well- and well-intermediately differentiated samples, and a third group containing mainly intermediately-poorly differentiated in situ cases. The quantitative differences in gene expression between these groups are mainly between twofold and fourfold. These differences are difficult to detect by classical IHC, because this technique is not very accurate in the quantification of small differences in protein expression. So far, there are no single markers that distinguish between the different types of DCIS, but the possibility of identifying a manageable panel of markers to distinguish the different types of DCIS lesions has to be further investigated.