Open Access

Classification of ductal carcinoma in situ by gene expression profiling

  • Juliane Hannemann1,
  • Arno Velds2,
  • Johannes BG Halfwerk1,
  • Bas Kreike1, 3,
  • Johannes L Peterse4 and
  • Marc J van de Vijver1, 4Email author
Breast Cancer Research20068:R61

DOI: 10.1186/bcr1613

Received: 19 July 2006

Accepted: 30 October 2006

Published: 30 October 2006

Abstract

Introduction

Ductal carcinoma in situ (DCIS) is characterised by the intraductal proliferation of malignant epithelial cells. Several histological classification systems have been developed, but assessing the histological type/grade of DCIS lesions is still challenging, making treatment decisions based on these features difficult. To obtain insight in the molecular basis of the development of different types of DCIS and its progression to invasive breast cancer, we have studied differences in gene expression between different types of DCIS and between DCIS and invasive breast carcinomas.

Methods

Gene expression profiling using microarray analysis has been performed on 40 in situ and 40 invasive breast cancer cases.

Results

DCIS cases were classified as well- (n = 6), intermediately (n = 18), and poorly (n = 14) differentiated type. Of the 40 invasive breast cancer samples, five samples were grade I, 11 samples were grade II, and 24 samples were grade III. Using two-dimensional hierarchical clustering, the basal-like type, ERB-B2 type, and the luminal-type tumours originally described for invasive breast cancer could also be identified in DCIS.

Conclusion

Using supervised classification, we identified a gene expression classifier of 35 genes, which differed between DCIS and invasive breast cancer; a classifier of 43 genes could be identified separating between well- and poorly differentiated DCIS samples.

Introduction

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.

It is assumed that most cases of DCIS will progress to invasive breast cancer. Because this progression may take many years and may not occur within the lifetime of a patient, elucidating the mechanisms of progression from in situ lesions to invasive disease and developing diagnostic tests would be of great clinical benefit.

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 [1215] and for correlating gene expression profiles with outcome in breast cancer [1618]. 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].

In the study presented here, gene expression profiling was performed on one LCIS and 39 DCIS samples to identify differentially expressed genes between well-, intermediately, and poorly differentiated DCIS. In addition, differences in gene expression between these cases of carcinoma in situ and 40 invasive breast carcinomas were studied.

Materials and methods

Selection of samples

Cases of DCIS were selected from the tissue bank of the Netherlands Cancer Institute (Amsterdam, The Netherlands). These samples were obtained within 1 hour after surgery from patients who underwent wide local excision (n = 16) or mastectomy (n = 24). All samples were reviewed by two pathologists independently to determine the histological classification of the samples according to Holland and colleagues [21]; samples were classified as well, intermediately, or poorly differentiated. For analysis purposes, the intermediately differentiated DCIS cases were subclassified as those cases that were in part well differentiated (well to intermediately differentiated) and those that were in part poorly differentiated (moderately to poorly differentiated) in some areas. In cases in which there was a discrepancy in classification between the two pathologists, the histological slides were reviewed together to reach an agreement.

In addition, 40 cases of primary invasive breast cancer were selected; these were all cases of invasive ductal carcinoma (IDC) measuring between 1 and 5 cm and were graded as grade 1, 2, or 3 according to the method described by Elston and Ellis [22]. The study was approved by the medical ethical committee of the Netherlands Cancer Institute.

RNA isolation and amplification

RNA isolation and amplification were performed essentially as described by Weigelt and colleagues [23]. Thirty tissue sections of 30 μm of frozen material were cut. The first and the last tissue sections were 6 μm in thickness and were stained with haematoxylin and eosin to determine the percentage of tumour cells and to exclude invasive growth. Only samples with greater than or equal to50% of tumour cells were used for gene expression profiling.

Immunohistochemistry

The procedures applied are described in the supplementary information provided online [24].

Microarray hybridisation

Labeling of the amplified cRNA and microarray hybridisations were performed as previously described [25]. Equal amounts of amplified cRNAs of 100 invasive breast carcinomas were pooled and used as a reference. All hybridisations were performed on 18K human cDNA arrays (Central Microarray Facility, Netherlands Cancer Institute) [26].

Microarrays were scanned with the DNA Microarray Scanner G2565B (Agilent Technologies, Santa Clara, CA, USA). Self-self hybridisations were used to validate the quality of the hybridisations and as a negative control in the error model.

Processing of microarray data

Information on data processing is provided in the supplementary information [24].

Unsupervised hierarchical clustering

Two-dimensional unsupervised hierarchical clustering using Pearson correlation as distance function and complete linkage was performed using Genesis software (Technical University, Graz, Austria) [27, 28].

Supervised classification

We performed supervised classification applying methods described previously [16, 29, 30]. Pathological features (histological type of the DCIS samples, histological grade of the invasive samples) were used to define groups for supervised classification. Genes were rank-ordered based on their signal-to-noise statistic. Safe cutoffs were determined by comparing the signal-to-noise ratio (SNR) values with the results from 2,000 sample label permutations (Monte Carlo randomisation). For each group and a number of genes, a centroid is defined as the mean ratio per gene over all samples in that group. Correlation or Euclidean distance of each sample to those centroids determines their predicted group. Leave-out cross-validation was used to determine the optimal number of genes separating the groups. The number of left-out samples in this cross-validation procedure was dependent on the number of samples within the analysis set. SNR calculation, Monte Carlo randomisation, and cross-validation have been described previously [25].

Supplementary information

The microarray data, additional information on the methods, and the filtering results are provided as supplementary information [24].

Results

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.

Tumour characteristics

Thirty-nine cases of DCIS of the breast were included in the analyses. By histological examination, they were assigned to the following groups: well differentiated (n = 6), intermediately differentiated (n = 18), and poorly differentiated (n = 14). For analysis purposes, the group of intermediately differentiated cases was further subdivided in well-intermediately (n = 10), true intermediately (n = 2), and intermediately-poorly (n = 6) differentiated type. One sample contains a mixture of well- and poorly differentiated DCIS components in the same tissue specimen. In addition, one case of LCIS was included.

To be able to compare DCIS with invasive breast cancer, 40 cases of invasive breast cancer were studied. Five tumours were histological grade 1, 11 samples were grade 2, and 24 samples were grade 3. Patient and tumour characteristics are summarised in Table 1.
Table 1

Patient characteristics

In situ samples

Invasive samples

Differentiation

Number (percentage)

Histological grade

Number (percentage)

Well

6 (15%)

1

5 (12.5%)

Intermediately

18 (45%)

2

11 (27.5%)

Poorly

14 (35%)

3

24 (60%)

Good/poor component

1 (2.5%)

  

LCIS

1 (2.5%)

  

IHC

 

IHC

 

ER-positive

28 (70%)a

ER-positive

22 (55%)c

PR-positive

24 (60%)a

PR-positive

19 (47.5%)d

Her2/neu-positive (3+)

12 (30%)b

Her2/neu-positive (3+)

4 (10%)d

p53-positive

11 (27.5%)b

p53-positive

9 (22.5%)d

Tumour detection

   

Palpation

17 (42.5%)

  

Microcalcifications

18 (45%)

  

Others

5 (12.5%)

  

Tumour diameter (mm)

   

Range

10 to 80

  

Median

45

  

Average

42.8

  

Treatment

   

Mastectomy

24 (60%)

  

Breast conserving treatment

6 (15%)

  

Local excision followed by mastectomy

10 (25%)

  

a5% not assessable, b2.5% not assessable, c27.5% not assessable, d30% not assessable. ER, oestrogen receptor; IHC, immunohistochemistry; LCIS, lobular carcinoma in situ; PR, progesterone receptor.

Molecular subtypes of breast cancer

Several subtypes of breast cancer have been identified by gene expression profiling and have been correlated with clinical outcome [13, 14]. This classification has been translated to classical immunohistochemistry (IHC): basal-type tumours are characterised by negative staining for oestrogen receptor (ER), progesterone receptor, and HER2 and are often positive for keratin 5/6; ERB-B2 tumours are HER2-positive, and luminal A and B tumours are ER-positive and HER2-negative. In our set of 40 in situ tumours, only two tumours are positive for CK5/6 by IHC. Both of them are poorly differentiated and negative for HER2 and ER by IHC. From the intrinsic gene set identified by Perou and colleagues [12], we could match 403 identifiers to our array platform. This set of genes was used to perform unsupervised hierarchical clustering of the 40 in situ samples. We clearly see a discrimination between tumours highly expressing genes of the luminal/ESR1 cluster and tumours negative for these genes, whereas the discrimination for the HER2-overexpressing groups was much less clear (Figure 1 in the supplementary information [24]). We could not identify a large basal-type group, which is in agreement with the data obtained using IHC.
https://static-content.springer.com/image/art%3A10.1186%2Fbcr1613/MediaObjects/13058_2006_Article_1579_Fig1_HTML.jpg
Figure 1

Unsupervised hierarchical clustering of in situ and invasive samples. (a) Dendrogram of all in situ (n = 40) and all invasive (n = 40) samples. (b) Scaled-down representation of the entire cluster shown in (a) (1,706 genes). (c) Dendogram of poorly differentiated ductal carcinoma in situ (n = 14) and grade 3 invasive (n = 24) samples. (d) Entire cluster of (c) (1,119 genes). Yellow indicates in situ samples, and blue indicates invasive samples. i, intermediately differentiated; IHC, immunohistochemistry; i/p, intermediately/poorly differentiated; LCIS, lobular carcinoma in situ; p, poorly differentiated; w, well differentiated; w/i, well/intermediately differentiated.

Unsupervised hierarchical clustering

Unsupervised hierarchical clustering of in situand invasive samples

First, the whole group of DCIS and invasive samples was clustered (Figure 1a). As can be seen, the invasive samples cluster in three different groups (indicated as I, II, and III in Figure 1a). Ten out of 14 poorly differentiated DCIS samples cluster together in a fourth group, and a fifth group consists of 13 out of 18 cases of intermediately differentiated DCIS and four out of six of the well-differentiated in situ samples. The clustering seems not to be driven mainly by the ER status or the HER2 status of the samples. These results suggest that poorly differentiated DCIS samples show an overall gene expression profile other than that of the intermediately and well-differentiated DCIS samples.

Unsupervised hierarchical clustering of DCIS

We also performed unsupervised hierarchical cluster analysis to the series of DCIS cases only, resulting in two large groups. One group contains 10 poorly differentiated samples and only one well-differentiated sample, whereas 83% of the well-differentiated samples group in the other, second cluster. Most of the samples in this second group are ER-positive by IHC. In total, our sample set contains 18 cases with an intermediately differentiated component. Of these samples, 12 cluster in the arm of the well-differentiated samples. In accordance with the clustering results presented in Figure 1, these results also indicate that the overall gene expression profiles of in situ samples with an intermediately differentiated component are more similar to those of well-differentiated DCIS than to those of poorly differentiated DCIS. It is clear from these results that there are large differences in gene expression pattern between well- and poorly differentiated DCIS.

Supervised classification

We performed supervised classification on different data sets to identify the genes differentially expressed between the groups of interest. These groups are (a) 40 in situ versus 40 invasive breast carcinomas, (b) 14 poorly differentiated DCIS cases versus 38 invasive grade 3 tumours, and (c) six cases of well-versus 14 cases of poorly differentiated DCIS.

Supervised classification of in situversus invasive carcinomas

We investigated the differences in gene expression between in situ and invasive breast carcinoma samples. We therefore used the whole data set and assigned all 40 in situ samples to one group and all 40 invasive samples to a second group (analysis set 1). To obtain a profile taking into account the expression sets of both tumour types, significantly regulated genes were identified independently for both groups. The 1,706 overlapping genes were used for analysis. Monte Carlo randomisation revealed approximately 300 genes differentially expressed between in situ and invasive samples.

After cross-validation, classifier consisting of 35 genes resulted in a stable prediction of the differences between DCIS and invasive breast carcinomas, with an average performance of 91%. The gene list is provided in Table 2.
Table 2

List of 35 genes able to discriminate between all DCIS and all invasive samples

Rank

NKI ID

Symbol

Annotation

Accession no.

1

116810

ADM

Adrenomedullin

AA446120

2

123346

 

EST

H17315

3

117289

MMP11

Matrix metalloproteinase 11 (stromelysin 3)

AA045500

4

121066

DAPK3

Death-associated protein kinase 3

AA973730

5

123776

PIAS4

Protein inhibitor of activated STAT protein

H30547

6

101837

DHX34

KIAA0134 gene product

AA477623

7

102847

YIF1

Putative transmembrane protein; homolog of yeast Golgi membrane protein Yif1p (Yip1p-interacting factor)

H79351

8

117345

ACTN1

Actinin, alpha 1

AA669042

9

127755

TGFB2

Transforming growth factor, beta 2

W47556

10

108960

GABRD

Gamma-aminobutyric acid (GABA) A receptor, delta

H41122

11

108348

MFAP2

Microfibrillar-associated protein 2

N67487

12

129658

MGC13045

DnaJ (Hsp40) homolog, subfamily C, member 4

AA996059

13

105479

BAT3

HLA-B-associated transcript-3

AA434416

14

120649

KCTD5

Hypothetical protein

AA521027

15

110728

FBXL15

F-box and leucine-rich repeat protein 15

T61547

16

120934

EIF4G1

Eukaryotic translation initiation factor 4 gamma, 1

R37276

17

118584

C9orf115

ESTs, weakly similar to B36298 proline-rich protein PRB3S [Homo sapiens]

AA479713

18

105533

ARF1

ADP-ribosylation factor 1

W45572

19

131909

TUBB2

Tubulin, beta polypeptide

AI672565

20

131540

PRPF31

DKFZP566J153 protein

AI253017

21

110281

HSPA1L

Heat shock 70-kD protein-like 1

H17513

22

107215

KCTD5

Hypothetical protein

AA429470

23

121937

FLJ10374

Hypothetical protein FLJ10374

AA676962

24

100368

GNB2

Guanine nucleotide binding protein (G protein), beta polypeptide 2

N68166

25

105453

PSAP

Prosaposin (variant Gaucher disease and variant metachromatic leukodystrophy)

N72215

26

115391

LMCD1

LIM and cysteine-rich domains 1

AA452125

27

128198

MMP11

Matrix metalloproteinase 11 (stromelysin 3)

AA954935

28

123688

COL1A1

Collagen, type I, alpha 1

R48844

29

127890

PTMS

Parathymosin

AA458981

30

102044

DRAP1

DR1-associated protein 1 (negative cofactor 2 alpha)

AA406285

31

101067

MAP7

Microtubule-associated protein 7

R77252 | R77251

32

129438

IQGAP1

IQ motif containing GTPase activating protein 1

AA478633

33

125700

APC2

Adenomatous polyposis coli like

AA976241

34

127881

NFIC

Nuclear factor I/C (CCAAT-binding transcription factor)

T59427

35

109065

SYT5

Synaptotagmin V

H39018

DCIS, ductal carcinoma in situ; EST, expressed sequence tag; NKI ID, Netherlands Cancer Institute (Amsterdam, The Netherlands) identification number.

Supervised classification for poorly differentiated DCIS versus grade 3 invasive carcinoma

Because it is very likely that grade 3 invasive breast cancer arises from poorly differentiated DCIS [5, 6], we applied the supervised classification procedure to the subset of poorly differentiated DCIS (n = 14) and grade 3 invasive tumours (n = 24) (analysis set 2). Again, the filtering procedure was applied to both groups independently. The overlapping fraction of this gene list contains 1,119 genes that were used to perform the analyses. Monte Carlo randomisation showed that 80 genes are differentially expressed between poorly differentiated DCIS and grade 3 invasive breast carcinoma samples. After cross-validation in 14 steps, the best performance of 93% is reached, when at least 50 genes are used to build the classifier. This performance remains stable with increasing numbers of genes. This means that 50 to 80 genes are able to discriminate between poorly differentiated DCIS and invasive grade 3 breast tumours (Figure 2a). These 80 genes are shown in Table 3. Between the 35-gene classifier of all DCIS and invasive samples and the subgroup classifier of 80 genes, 21 genes were present in both classifiers.
https://static-content.springer.com/image/art%3A10.1186%2Fbcr1613/MediaObjects/13058_2006_Article_1579_Fig2_HTML.jpg
Figure 2

Euclidean distance and heatmaps of the in situ and invasive samples using the classifiers obtained after cross-validation. (a) All ductal carcinoma in situ (DCIS) (n = 40) and all invasive (n = 40) samples. The classifiers consist of 80 genes. (b) Poorly differentiated DCIS (n = 14) versus invasive grade 3 samples (n = 24) using a classifier of 35 genes. p, poorly differentiated.

Table 3

List of 80 genes able to discriminate between poorly differentiated DCIS and invasive grade 3 breast tumours

Rank

NKI ID

Symbol

Annotation

Accession no.

1

123776

PIAS4

Protein inhibitor of activated STAT protein

H30547

2

129658

MGC13045

DnaJ (Hsp40) homolog, subfamily C, member 4

AA996059

3

121937

FLJ10374

Hypothetical protein FLJ10374

AA676962

4

102847

YIF1

Putative transmembrane protein; homolog of yeast Golgi membrane protein Yif1p (Yip1p-interacting factor)

H79351

5

127755

TGFB2

Transforming growth factor, beta 2

W47556

6

117289

MMP11

Matrix metalloproteinase 11 (stromelysin 3)

AA045500

7

104973

SYNPO2

Synaptopodin 2

R31679

8

121066

DAPK3

Death-associated protein kinase 3

AA973730

9

128493

GMFG

Glia maturation factor, gamma

AI311932

10

105533

ARF1

ADP-ribosylation factor 1

W45572

11

132031

 

NY-REN-24 antigen

AA918005

12

127881

NFIC

Nuclear factor I/C (CCAAT-binding transcription factor)

T59427

13

120649

KCTD5

Potassium channel tetramerisation domain containing 5

AA521027

14

120934

EIF4G1

Eukaryotic translation initiation factor 4 gamma, 1

R37276

15

105453

PSAP

Prosaposin (variant Gaucher disease and variant metachromatic leukodystrophy)

N72215

16

112695

SYNPO2

H. sapiens cDNA FLJ20767 fis, clone COL06986

AA043349

17

101577

BMI1

Murine leukaemia viral (bmi-1) oncogene homolog

AA478036

18

105479

BAT3

HLA-B-associated transcript-3

AA434416

19

123071

C9orf82

Hypothetical protein FLJ13657

AA135972

20

101638

ID4

Inhibitor of DNA binding 4, dominant negative helix-loop-helix protein

AA464856

21

115306

LRP16

LRP16 protein

AA456318

22

118143

STX1B2

ESTs, moderately similar to ST1B_HUMAN SYNTAXIN 1B [H. sapiens]

H41572

23

128106

DUSP6

Dual specificity phosphatase 6

AA455254

24

115676

RPS15A

Ribosomal protein S15a

AA411682

25

108595

CCL19

Small inducible cytokine subfamily A (Cys-Cys), member 19

AA680186

26

126589

C6orf166

Hypothetical protein FLJ10342

AA984953

27

131540

PRPF31

DKFZP566J153 protein

AI253017

28

109065

SYT5

 

H39018

29

128198

MMP11

Matrix metalloproteinase 11 (stromelysin 3)

AA954935

30

109364

MYST2

Histone acetyltransferase

H11938

31

106989

TNFSF13

Tumour necrosis factor (ligand) superfamily, member 13

AA443577

32

109798

  

T82459

33

131890

CDH1

Cadherin 1, type 1, E-cadherin (epithelial)

AI671174

34

111513

COG3

H. sapiens clone 25226 mRNA sequence

AA461166

35

108645

HMGCS2

3-Hydroxy-3-methylglutaryl-Coenzyme A synthase 2 (mitochondrial)

AA496149

36

101651

TRAP1

Heat shock protein 75

AA497020

37

105304

LRP16

LRP16 protein

W52182 | AA284285

38

105363

ARL7

ADP-ribosylation factor-like 7

AA485683

39

127890

PTMS

Parathymosin

AA458981

40

118682

NBS1

Nijmegen breakage syndrome 1 (nibrin)

H98655

41

108997

PTTG1IP

Pituitary tumour-transforming 1 interacting protein

AA156461

42

110281

HSPA1L

Heat shock 70-kD protein-like 1

H17513

43

125700

APC2

Adenomatous polyposis coli like

AA976241

44

117139

ALDOB

Aldolase B, fructose-bisphosphate

H72098

45

107595

SOX17

SRY-box 17

AA427400 | AI732705

46

107375

NUCKS

Similar to rat nuclear ubiquitous casein kinase 2

AA137266

47

109238

BSG

Basigin (OK blood group)

AA436440

48

122821

NSE2

ESTs

H30453

49

123689

LOC339123

STIP1 homology and U-Box containing protein 1

R54844

50

115953

LOC146542

Human Chromosome 16 BAC clone CIT987SK-A-635H12

AA455010

51

108960

GABRD

 

H41122

52

128222

GLUL

Glutamate-ammonia ligase (glutamine synthase)

AI000103

53

100222

NFIX

Nuclear factor I/X (CCAAT-binding transcription factor)

AA406269

54

105470

ISYNA1

Myo-inositol 1-phosphate synthase A1

AA454554

55

117998

RBM9

RNA binding motif protein 9

H03903

56

105404

GDF15

Prostate differentiation factor

N26311

57

127811

TOB1

Transducer of ERBB2, 1

W96163

58

105524

RPS6KA4

Ribosomal protein S6 kinase, 90-kD, polypeptide 4

AA443601

59

109232

BCKDHA

Branched chain keto acid dehydrogenase E1, alpha polypeptide (maple syrup urine disease)

AA477298

60

115741

APPL

Adaptor protein containing pH domain, PTB domain and leucine zipper motif

AA436158

61

100898

ELF3

E74-like factor 3 (ets domain transcription factor, epithelial-specific)

AA434373

62

101067

MAP7

Microtubule-associated protein 7

R77252 | R77251

63

109306

AQP1

Aquaporin 1 (channel-forming integral protein, 28 kD)

H24316

64

102326

CYC1

Cytochrome c-1

AA447774

65

108988

MALAT1

Histone deacetylase 3

H88540

66

102253

ACTG2

Actin, gamma 2, smooth muscle, enteric

T60048

67

116834

GPC1

Glypican 1

AA455896

68

105497

HNRPK

Heterogeneous nuclear ribonucleoprotein K

W85697

69

108372

LCP1

Lymphocyte cytosolic protein 1 (L-plastin)

W73144

70

128634

PRCP

Prolylcarboxypeptidase (angiotensinase C)

AI360366

71

106297

PHF17

Hypothetical protein FLJ22479

AA136664

72

101616

KRT19

Keratin 19

AA464250

73

128532

LTB

Lymphotoxin beta (TNF superfamily, member 3)

AI351740

74

102385

F13A1

Coagulation factor XIII, A1 polypeptide

AA449742

75

102673

WHSC1L1

Wolf-Hirschhorn syndrome candidate 1-like 1

T97900

76

109638

CXXC1

CpG binding protein

T60082

77

109116

FBL

Fibrillarin

AA663986

78

109425

TUBB

Tubulin, beta polypeptide

AA427899

79

117500

 

EST

AA621138

80

100656

UBE2C

Ubiquitin carrier protein E2-C

AA430504

DCIS, ductal carcinoma in situ; EST, expressed sequence tag; NKI ID, Netherlands Cancer Institute (Amsterdam, The Netherlands) identification number.

Supervised classification of well-versus poorly differentiated DCIS

We intended to find the most prominent differences between the well- and poorly differentiated DCIS samples. Sixfold cross-validation of six well- and 14 poorly differentiated in situ samples (analysis set 3) resulted in a set of 43 genes separating these groups with a performance of 90% (Figure 3a, Table 4).
https://static-content.springer.com/image/art%3A10.1186%2Fbcr1613/MediaObjects/13058_2006_Article_1579_Fig3_HTML.jpg
Figure 3

Correlation plots and heatmaps of the in situ samples using the classifiers obtained after cross-validation. (a) Well- (n = 6) versus poorly (n = 14) differentiated ductal carcinoma in situ (DCIS). The classifiers consist of 43 genes. (b) Well-/well-intermediately (n = 16) versus intermediately-poorly/poorly (n = 20) differentiated DCIS using a classifier of 78 genes. i-p, intermediately-poorly differentiated; p, poorly differentiated; w, well differentiated; w-i, well-intermediately differentiated.

Table 4

List of 43 genes able to discriminate between well- and poorly differentiated DCIS

Rank

NKI ID

Symbol

Annotation

Accession no.

1

108691

ACK1

Activated p21cdc42Hs kinase

AA427891

2

109246

BCL2

B-cell CLL/lymphoma 2

W63749

3

109268

ALDH3A2

Aldehyde dehydrogenase 3 family, member A2

AA633569

4

109236

BTD

Biotinidase

R17765

5

108595

CCL19

Small inducible cytokine subfamily A (Cys-Cys), member 19

AA680186

6

100524

CELSR2

Cadherin, EGF LAG seven-pass G-type receptor 2, flamingo (Drosophila) homolog

H39187

7

126868

TMC4

DKFZP586J0619 protein

AA991211

8

100708

SLC39A6

LIV-1 protein, oestrogen regulated

H29315

9

109170

C4A

Complement component 4A

AA664406

10

109127

ESR1

Oestrogen receptor 1

AA291749

11

128702

 

EST

AI313031

12

121012

HSHIN1

Hin-1

AA902831

13

128095

PCSK6

Paired basic amino acid cleaving system 4

W85807

14

128052

ARHGEF7

PAK-interacting exchange factor beta

AA452871

15

128493

GMFG

Glia maturation factor, gamma

AI311932

16

123382

HIG1

Likely ortholog of mouse hypoxia induced gene 1

T74105

17

129689

C1orf21

Chromosome 1 open reading frame 21

AA406569

18

102289

ETFA

Electron-transfer-flavoprotein, alpha polypeptide (glutaric aciduria II)

T57919

19

126124

FLJ20152

Hypothetical protein

AA918685

20

127815

PLAT

Plasminogen activator, tissue

R38933

21

101559

NPY1R

Neuropeptide Y receptor Y1

R43817

22

100260

MAL

Mal, T-cell differentiation protein

AA227885

23

127969

CRYAA

Crystallin, alpha A

H84722

24

128244

SERPINA3

Serine (or cysteine) proteinase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 3

AA704242

25

108649

 

Human clone 23948 mRNA sequence

H15114

26

106399

GRTP1

Hypothetical protein FLJ22474

N52651

27

123478

FLJ14712

Hypothetical protein FLJ14712

N79050

28

117207

EMP3

Epithelial membrane protein 3

W73810

29

111787

ZNF451

H. sapiens cDNA FLJ13010 fis, clone NT2RP3000542

AA486412

30

109502

KITLG

H. sapiens cDNA: FLJ21592 fis, clone COL07036

H11088

31

109315

UCP2

Uncoupling protein 2 (mitochondrial, proton carrier)

H61243

32

118532

NUPL1

PRO2463 protein

AA772502

33

100263

MYB

V-myb avian myeloblastosis viral oncogene homolog

N49284

34

128249

CD3E

CD3E antigen, epsilon polypeptide (TiT3 complex)

AA933862

35

131226

IL7R

Interleukin 7 receptor

T65739

36

100104

SELL

Selectin L (lymphocyte adhesion molecule 1)

H00662

37

108671

BCAT2

Branched chain aminotransferase 2, mitochondrial

AA436410

38

116984

ATP5B

ATP synthase, H+ transporting, mitochondrial F1 complex, beta polypeptide

AA708298

39

108376

LAMA3

Laminin, alpha 3 (nicein [150 kD], kalinin [165 kD], BM600 [150 kD], epilegrin)

AA001432

40

104944

SLC7A2

Solute carrier family 7 (cationic amino acid transporter, y+ system), member 2

R26163

41

100840

THOC1

Nuclear matrix protein p84

AA129297

42

100650

SHFM1

Deleted in split-hand/split-foot 1 region

H85464

43

101429

SIAT1

Sialyltransferase 1 (beta-galactoside alpha-2,6-sialytransferase)

AA598652

DCIS, ductal carcinoma in situ; EST, expressed sequence tag; NKI ID, Netherlands Cancer Institute (Amsterdam, The Netherlands) identification number.

Because histological classification of intermediately differentiated DCIS versus well- or moderately differentiated DCIS is most challenging, we investigated whether gene expression profiling could be used to identify markers that could help in making this classification. We therefore included the cases classified as intermediately differentiated DCIS. Subsequently, we divided the sample set into one group of well/well-intermediately differentiated samples (n = 16) and a second group containing poorly/intermediately-poorly differentiated samples (n = 20). Supervised classification of these data revealed a set of 78 genes separating these two groups with an average performance of 89% (Table 5).
Table 5

List of 78 genes able to discriminate between well/well-intermediately and intermediately-poorly/poorly differentiated DCIS

Rank

NKI ID

Symbol

Annotation

Accession no.

1

111275

 

EST

H20757

2

109268

ALDH3A2

Aldehyde dehydrogenase 3 family, member A2

AA633569

3

109236

BTD

Biotinidase

R17765

4

110384

KPNA2

Karyopherin alpha 2 (RAG cohort 1, importin alpha 1)

AA676460

5

131448

PLEKHG1

KIAA1209 protein

AI301815

6

108691

ACK1

Activated p21cdc42Hs kinase

AA427891

7

107840

EPC1

ESTs

AA120875

8

126868

TMC4

DKFZP586J0619 protein

AA991211

9

106257

FLJ32499

H. sapiens cDNA FLJ12749 fis, clone NT2RP2001149

W56590

10

128493

GMFG

Glia maturation factor, gamma

AI311932

11

128702

  

AI313031

12

129547

METAP2

Methionine aminopeptidase; eIF-2-associated p67

AA283030

13

111787

ZNF451

H. sapiens cDNA FLJ13010 fis, clone NT2RP3000542

AA486412

14

103209

RBMS1

H. sapiens mRNA; cDNA DKFZp564H0764 (from clone DKFZp564H0764)

R62566

15

108595

CCL19

Small inducible cytokine subfamily A (Cys-Cys), member 19

AA680186

16

129267

  

AA609203

17

109127

ESR1

Oestrogen receptor 1

AA291749

18

100263

MYB

V-myb avian myeloblastosis viral oncogene homolog

N49284

19

100524

CELSR2

Cadherin, EGF LAG seven-pass G-type receptor 2, flamingo (Drosophila) homolog

H39187

20

100260

MAL

Mal, T-cell differentiation protein

AA227885

21

102995

PIGT

CGI-06 protein

H82992

22

108649

 

Human clone 23948 mRNA sequence

H15114

23

109246

BCL2

B-cell CLL/lymphoma 2

W63749

24

100203

TNFAIP3

Tumour necrosis factor, alpha-induced protein 3

AA476272

25

107809

XBP1

X-box binding protein 1

W90128

26

102921

 

H. sapiens mRNA; cDNA DKFZp434D0818 (from clone DKFZp434D0818)

N95578

27

108671

BCAT2

Branched chain aminotransferase 2, mitochondrial

AA436410

28

101925

EZH2

 

AA430744

29

123382

HIG1

Likely ortholog of mouse hypoxia induced gene 1

T74105

30

131187

KPNA2

Karyopherin alpha 2 (RAG cohort 1, importin alpha 1)

AA489087

31

111288

 

H. sapiens mRNA; cDNA DKFZp564C2063 (from clone DKFZp564C2063)

AA416628

32

109170

C4A

Complement component 4A

AA664406

33

108203

TEGT

Testis enhanced gene transcript (BAX inhibitor 1)

AA629591

34

102639

EML2

Microtubule-associated protein like echinoderm EMAP

R27580

35

131258

PSMA7

Proteasome (prosome, macropain) subunit, alpha type, 7

AI318565

36

123478

FLJ14712

Hypothetical protein FLJ14712

N79050

37

109415

FCGBP

Fc fragment of IgG binding protein

R52030

38

127815

PLAT

Plasminogen activator, tissue

R38933

39

115769

 

ESTs

AA406313

40

106220

GIMAP5

Hypothetical protein FLJ11296

AA150443

41

128641

PTTG1

Pituitary tumour-transforming 1

AI362866

42

105439

TGOLN2

Trans-Golgi network protein (46-, 48-, 51-kD isoforms)

T81338

43

101362

ERBB2

V-erb-b2 avian erythroblastic leukaemia viral oncogene homolog 2 (neuro/glioblastoma derived oncogene homolog)

AA446928

44

108387

IDH2

Isocitrate dehydrogenase 2 (NADP+), mitochondrial

AA679907

45

100352

TGOLN2

Trans-Golgi network protein (46-, 48-, 51-kD isoforms)

H82891

46

107941

PLAC8

Hypothetical protein

AA150263

47

100104

SELL

Selectin L (lymphocyte adhesion molecule 1)

H00662

48

110983

DLEU1

Deleted in lymphocytic leukaemia, 1

AA425755

49

108438

GRB7

Growth factor receptor-bound protein 7

H53703

50

107752

PAG

Phosphoprotein associated with GEMs

N50114

51

128532

LTB

Lymphotoxin beta (TNF superfamily, member 3)

AI351740

52

124620

ASTN2

KIAA0634 protein

AA404602

53

102357

CHN1

Chimerin (chimaerin) 1

AA598668

54

109454

AKR7A2

Aldo-keto reductase family 7, member A2 (aflatoxin aldehyde reductase)

T62865

55

108678

CASP10

Caspase 10, apoptosis-related cysteine protease

H80712

56

131111

CUGBP2

CUG triplet repeat, RNA-binding protein 2

AA047257

57

123475

C9orf87

Hypothetical protein FLJ10493

N53432

58

105013

 

EST

H61003

59

100791

TDG

Thymine-DNA glycosylase

AA496947

60

100528

BCL2L2

BCL2-like 2

AA454588

61

116312

FLJ14299

Hypothetical protein FLJ14299

AA453170

62

100700

TRIB2

GS3955 protein

AA458653

63

102004

PIK3R1

Phosphoinositide-3-kinase, regulatory subunit, polypeptide 1 (p85 alpha)

R54050

64

104569

MYO1B

H. sapiens cDNA FLJ20153 fis, clone COL08656, highly similar to AJ001381 H. sapiens incomplete cDNA for a mutated allele

N95358

65

113907

SNRPB2

Small nuclear ribonucleoprotein polypeptide B"

H00286

66

128683

WASL

Wiskott-Aldrich syndrome-like

AI261600

67

123768

DUSP22

Mitogen-activated protein kinase phosphatase x

H42417

68

105099

RET

Ret proto-oncogene (multiple endocrine neoplasia MEN2A, MEN2B and medullary thyroid carcinoma 1, Hirschsprung disease)

H24956

69

116859

STMN1

Leukaemia-associated phosphoprotein p18 (stathmin)

AA873060

70

111660

FLJ13710

ESTs

AA120866

71

100112

SAA1

Serum amyloid A1

H25546

72

100840

THOC1

Nuclear matrix protein p84

AA129297

73

129239

 

EST, Moderately similar to AF119917 63 PRO2831 [H. sapiens]

W95750

74

115662

GIMAP4

Hypothetical protein FLJ11110

AA406363

75

109607

HTPAP

ESTs

T48412

76

108692

EMP2

Epithelial membrane protein 2

T88721

77

105133

JUNB

Jun B proto-oncogene

N94468

78

129959

LOC283352

EST

AI023540

DCIS, ductal carcinoma in situ; EST, expressed sequence tag; NKI ID, Netherlands Cancer Institute (Amsterdam, The Netherlands) identification number.

We observed a separation of this data set in three distinct groups (Figure 3). One group contains one intermediately-poorly differentiated sample (17%) and 12 out of 14 poorly differentiated samples, and a second group all six well-differentiated samples and seven out of 10 well-intermediately differentiated samples. The third group shows no correlation with both profiles and consists of five out of six intermediately-poorly and three out of 10 well-intermediately differentiated samples. This implies that this third group typifies mainly the intermediately-poorly differentiated samples. Well-intermediately differentiated samples are apparently very similar to well-differentiated DCIS in their gene expression. These results are in accordance with the results of unsupervised hierarchical clustering of all in situ samples (Figure 4a).
https://static-content.springer.com/image/art%3A10.1186%2Fbcr1613/MediaObjects/13058_2006_Article_1579_Fig4_HTML.jpg
Figure 4

Unsupervised hierarchical clustering of the in situ samples. (a) Dendrogram of all 40 in situ samples. (b) Scaled-down representation of the entire cluster of (a) (5,788 genes). (c) Dendrogram of only the well- (n = 6) and poorly (n = 14) differentiated ductal carcinoma in situ (DCIS) cases. (d) Entire cluster of (c) (4,493 genes). Yellow indicates well-differentiated DCIS, brown indicates poorly differentiated DCIS, black indicates DCIS samples with an intermediately differentiated component, and gray indicates special cases. i, intermediately differentiated; IHC, immunohistochemistry; i-p, intermediately-poorly differentiated; LCIS, lobular carcinoma in situ; p, poorly differentiated; w, well differentiated; w-i, well-intermediately differentiated.

Twenty-one genes are overlapping between the 43 genes of analysis set 3 and the 78 genes of analysis set 4. It is known that many poorly differentiated in situ breast carcinomas do not express the ER. In our data set, nine of all 14 poorly differentiated DCIS samples (64%) are negative for ER expression by IHC. There was a slight chance that our classifier would detect mainly the differences of ER-associated genes. We identified only one gene (LIV-1), beside the ER itself, directly ER-regulated in the classifier of 43 genes. Additionally, we compared the 43 genes with 2,460 ER-associated genes identified by van 't Veer and colleagues [16]. Thirteen genes, including the ER itself, have been found in both gene lists. So, most of the genes in this 43-gene classifier have not been correlated to ER expression so far, indicating that the differences between well- and poorly differentiated DCIS samples are not originating from the ER status of the samples.

Remarkably, completely different gene lists are found describing the differences in gene expression between different in situ samples, on one hand, and DCIS and invasive samples on the other hand. These findings may indicate that gene regulation involved in progression from in situ to invasive breast cancer affects molecular mechanisms other than the mechanisms responsible for the development of the different types of DCIS.

Discussion

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.

Conclusion

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.

Abbreviations

DCIS: 

ductal carcinoma in situ

ER: 

oestrogen receptor

IDC: 

invasive ductal carcinoma

IHC: 

immunohistochemistry

LCIS: 

lobular carcinoma in situ

SNR: 

signal-to-noise ratio.

Declarations

Acknowledgements

We thank N. Nasr for help in collecting pathology data and performing microarray hybridisations. This work was supported by the Dutch Cancer Society (02-2575).

Authors’ Affiliations

(1)
Division of Experimental Therapy, Netherlands Cancer Institute
(2)
Central Microarray Facility, Netherlands Cancer Institute
(3)
Division of Radiotherapy, Netherlands Cancer Institute
(4)
Division of Diagnostic Oncology, Netherlands Cancer Institute

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