Purpose: Classic lobular carcinomas (CLC) account for 10% to 15% of all breast cancers. At the genetic level, CLCs show recurrent physical loss of chromosome16q coupled with the lack of E-cadherin (CDH1 gene) expression. However, little is known about the putative therapeutic targets for these tumors. The aim of this study was to characterize CLCs at the molecular genetic level and identify putative therapeutic targets.

Experimental Design: We subjected 13 cases of CLC to a comprehensive molecular analysis including immunohistochemistry for E-cadherin, estrogen and progesterone receptors, HER2/neu and p53; high-resolution comparative genomic hybridization (HR-CGH); microarray-based CGH (aCGH); and fluorescent and chromogenic in situ hybridization for CCND1 and FGFR1.

Results: All cases lacked the expression of E-cadherin, p53, and HER2, and all but one case was positive for estrogen receptors. HR-CGH revealed recurrent gains on 1q and losses on 16q (both, 85%). aCGH showed a good agreement with but higher resolution and sensitivity than HR-CGH. Recurrent, high level gains at 11q13 (CCND1) and 8p12-p11.2 were identified in seven and six cases, respectively, and were validated with in situ hybridization. Examination of aCGH and the gene expression profile data of the cell lines, MDA-MB-134 and ZR-75-1, which harbor distinct gains of 8p12-p11.2, identified FGFR1 as a putative amplicon driver of 8p12-p11.2 amplification in MDA-MB-134. Inhibition of FGFR1 expression using small interfering RNA or a small-molecule chemical inhibitor showed that FGFR1 signaling contributes to the survival of MDA-MB-134 cells.

Conclusions: Our findings suggest that receptor FGFR1 inhibitors may be useful as therapeutics in a subset of CLCs.

Grade 1 invasive ductal breast carcinomas and classic lobular carcinomas (CLC) are remarkably similar at the molecular genetic level. Like grade 1 invasive ductal breast carcinoma, CLC show deletions on chromosome 16q, but the genes affected by the deletion are different (14). Although the 16q target gene of grade 1 invasive ductal breast carcinoma remains elusive (4), there are several lines of evidence to suggest that CDH1, which maps to 16q22.1 and encodes the transmembrane intercellular adhesion glycoprotein, E-cadherin, is the target gene in CLCs (57). Loss of E-cadherin is a common feature of several types of malignancies (48), and may explain some of the hallmark features of CLC, i.e., the discohesiveness of the cells and the typical single cell file invasion pattern, as well as the peculiar proclivity of metastatic spread to serosal cavities (57).

Although several mechanisms of CDH1 gene inactivation and E-cadherin down-regulation have been reported in lobular carcinomas, including CDH1 gene promoter methylation, CDH1 gene mutations, loss of heterozygosity (LOH) on 16q22, and deletion (physical loss) of 16q (1, 2, 57, 913), little is known about the pathogenic role of other genetic alterations in this special type of breast cancer. Amplification and/or overexpression of HER2 (14, 15) and epidermal growth factor receptor are rare in CLCs (15). There is evidence to suggest that cyclin D1 (CCND1) gene amplification and overexpression is frequently found in CLCs (16, 17). Moreover, the involvement of tumor suppressor genes other than CDH1 in the biology of CLCs has not been evaluated in detail.

Currently, the mainstay of treatment for CLCs is with adjuvant endocrine therapy as they are usually positive for estrogen receptors. However, they continue to pose a therapeutic challenge due to the lower than expected response rates to endocrine therapy (18) and neoadjuvant chemotherapy (19, 20). Hence, there is a pressing need for more therapeutic options in this disease. In this study, we subjected a well-characterized series of CLCs to a comprehensive genetic analysis with the aims of characterizing their molecular genetic features and identifying potential novel therapeutic targets in this subtype of breast cancer.

Tumor samples

Eighteen paraffin-embedded cases of infiltrating lobular carcinoma were retrieved from the Department of Pathology, La Paz Hospital, Madrid, Spain. Paired normal tissue samples (unaffected lymph nodes or normal breast tissue) were available for all tumors. The study had approval from the local ethics committees.

Immunohistochemistry

Sections from representative areas were cut at 4 μm and mounted on silane-coated slides. Immunohistochemistry was done according to the streptavidin-biotin-peroxidase complex method as previously described (6, 21), with antibodies raised against E-cadherin (4A2C7, 1:200, Zymed, San Francisco, CA), β-catenin (C19220, 1:1000; Transduction Labs, Lexington, KY), estrogen receptor (1D5, 1:40; Dako, Glostrup, Denmark), progesterone receptor (PgR636, 1:150; Dako), HER2 (polyclonal, 1:1200; Dako), p53 (DO7, 1:150; Dako), cyclin D1 (CCND1, SP4, 1:50, LabVision, Fremont, CA), and fibroblast growth factor receptor 1 (FGFR1, polyclonal, 1:600; Abcam, Cambridge, United Kingdom).

Positive and negative controls were included in each slide run. For estrogen receptors, progesterone receptors, p53, and CCND1, only nuclear staining was considered positive, whereas only membranous staining was considered specific for Her2/neu, E-cadherin, and FGFR1. A cutoff of ≥10% of positive neoplastic cells was adopted for estrogen receptors, progesterone receptors, p53, CCND1, and FGFR1; Her2/neu was analyzed according to the Herceptest scoring system, and E-cadherin and β-catenin were semiquantitatively analyzed as previously described (6); briefly, the intensity (compared with normal luminal epithelial cells) and the extent of immunoreactive cells was determined. Cases were considered negative for E-cadherin or β-catenin when strong membrane staining was observed in <5% of tumor cells; as “reduced” when the staining intensity was clearly weaker than that observed in normal luminal epithelial cells or when <20% are positively stained; and as “conserved” when >50% of cells showed positive staining with an intensity equal to that of normal luminal epithelial cells (6).

DNA extraction

DNA was isolated from needle microdissected whole tumor sections followed by phenol/chloroform extraction and ethanol precipitation as previously described (6, 21). Normal DNA was obtained from paired normal tissue when available.

Molecular analysis

Microarray-based comparative genomic hybridization. The microarray-based comparative genomic hybridization (aCGH) platform used in this study was constructed by the Breakthrough Breast Cancer Research Centre, UK and comprised one platform comprised of ∼5,600 BAC clones spaced at ∼0.9 Mb throughout the genome. BAC clones were spotted in triplicate onto Corning GAPSII-coated glass slides (Corning, NY). Labeling, hybridization, and washes were carried out as previously described (21). Arrays were scanned with a GenePix 4000A scanner; fluorescence data were processed with GenePix 4.1 image analysis software (Axon Instruments, Inc., Union City, CA).

Data analysis

The log2 ratios were normalized for spatial and intensity-dependent biases using a group row local regression. The median of BAC clone replicate spots was calculated, after exclusion of excessively flagged clones (flagged in >70% of samples). The median log2 ratio for each clone in each “dye-swap” was rescaled according to the local median absolute deviation, and averaged across the replicates (dye-swaps). In the study of CLCs and breast cancer cell lines, this left a final data set of 4,816 clones and 3,527 clones with unambiguous mapping information according to the May 2004 build of the human genome (hg17), respectively. Data were smoothed using a local polynomial adaptive weights smoothing procedure for regression problems with additive errors. A categorical analysis was applied to the BACs after classifying them as representing gain, loss, or no-change according to their smoothed log2 ratio values. For the CLCs, median absolute deviation–centered log2 ratio values less than −0.8 were categorized as losses, those >0.8 as gains, and those in between as unchanged, whereas for the normalized cell line data, the cutoff described by Reis-Filho et al. (21) was used. Data preprocessing (normalization, filtering, and rescaling) was done using S Plus (version 6.2.1, Insightful Corporation, Seattle, WA). Data statistical analysis was carried out in R 2.0.1 (http://www.r-project.org/) and BioConductor 1.5 (http://www.bioconductor.org/), making extensive use of modified versions of the packages, in particular aCGH, marray, and adaptive weights smoothing (21).

Associations between genomic loci were assessed by a calculating a Euclidean distance metric between thresholded values for each clone, assigned as 1, 0, or −1 for gain, no change, or loss in copy number. Only those clones with a DNA copy number change in at least 2 out of 13 samples were plotted. Positive associations (small Euclidean distance) are shown in orange, negative associations (large Euclidean distance) are shown in purple. White represents no association or no (<2 out of 13 cases) alterations. Hierarchical clustering was done on categorical data (i.e., gain, no change, and loss) based on a Euclidean distance measure using Ward's minimum variance method.

High-resolution CGH

Amplification and fluorescent labeling of DNA from microdissected tissue was carried out by degenerate oligonucleotide-primed PCR in two rounds and CGH was done as previously described (21).

cDNA microarrays

Total RNA was isolated from breast cancer cell lines using TriZol LS reagent (Invitrogen, Carlsbad, CA) followed by phenol/chloroform extraction. RNA quality was verified using an Agilent Bioanalyzer (Agilent Technologies, Palo Alto, CA). Total RNA was T7-amplified and labeled using the amino allyl MessageAmp kit (Ambion, Huntingdon, United Kingdom) according to the manufacturer's instructions. Five micrograms of amino-allyl aRNA was cohybridized with universal human reference RNA (Stratagene, La Jolla, CA) to a cDNA microarray constructed at The Breakthrough Breast Cancer Research Centre, containing ∼20,000 sequence-validated cDNA clones, corresponding to ∼16,500 known genes. Dye swap duplicates with both Cy3 and Cy5 labeled targets were hybridized separately in 50% formamide and 1× microarray hybridization buffer (Amersham) at 42°C for 18 hours under lifter slips. Following hybridization, arrays were washed twice in 2× SSC, 0.1% SDS for 15 minutes at 42°C, and twice in 0.1× SSC, 0.1% SDS for 15 minutes at 42°C. Following a brief rinse in 0.1× SSC, arrays were dried and scanned on GenePix 4000 (Axon Instruments) dual-color confocal laser scanner and raw intensity measurements were obtained through GenePix software 5.1.

After removing flagged and low-intensity microarray features, data was printTipLoess normalized using the limma package in R 2.0.1 (http://www.r-project.org/). A total of 16,593 clones were mapped according to the May 2004 build of the human genome (hg17). Mapped cDNA clones and BACs were overlaid using a plotting script written in R 2.0.1 (making use of modified versions of aCGH, marray, and limma packages). Heat maps were generated using Java TreeView software.

Fluorescent and chromogenic in situ hybridization

Fluorescent in situ hybridization (FISH) and chromogenic in situ hybridization were used to allow a direct evaluation of the copy number changes in nonneoplastic and invasive lobular carcinoma cells. The probes used included the ready-to-use digoxigenin-labeled SpotLight cyclin D1 amplification probe (Zymed) and an in-house generated probe made up of three contiguous, FISH-mapped BACs (RP11-350N15, RP11-148D21, and RP11-359P11; ref. 22), which map to 8p12-p11.23 (∼38.26 and ∼38.6 Mb). The in-house probe was generated, biotin-labeled, and used in hybridizations as previously described (22). Chromogenic in situ hybridization experiments were analyzed by two of the authors (J.S. Reis-Filho and K. Savage) on a multiheaded microscope. For FISH experiments, representative images were collected sequentially in three channels (4′,6-diamidino-2-phenylindole, FITC, and Cy3) on a TCS SP2 confocal microscope (Leica, Milton Keynes, United Kingdom). Only unequivocal signals were counted. Signals were evaluated at ×400 (chromogenic in situ hybridization) and ×1,000 (FISH), and 60 morphologically unequivocal neoplastic cells were counted for the presence of the gene probe signals. Amplification was defined as more than five signals per nucleus in >50% of cancer cells, or when large gene copy clusters were seen (22). All in situ hybridization were evaluated with observers blinded to the aCGH result.

Molecular characterization of the CDH1 gene

Detailed analysis of the CDH1 gene has been described elsewhere (15). Briefly, the complete CDH1 gene was screened for mutations using single-strand conformational polymorphism–PCR DNA sequencing (15). LOH analysis in tumor and paired normal DNA was carried out using five highly polymorphic microsatellite markers (D16S265, D16S3057, D16S398, D16S496, and D16S752, which map to 16q21.2-q22) and the frequent CDH1 + 2076C/T polymorphism (15). To avoid the underestimation of LOH due to normal cell contamination in the tumor samples, DNA from the infiltrating areas was extracted by LCM using a PixCell laser capture microscope (Arcturus Engineering, Mountain View, CA). CDH1 gene promoter hypermethylation was assessed by methylation-specific PCR of bisulfite-treated DNA (6).

Cell lines and reagents

MDA-MB-134, ZR-75-1, CAL51, and MCF7 cell lines (American Type Culture Collection, Manassas, VA) were maintained in DMEM (Sigma, Poole, United Kingdom) supplemented with 10% v/v fetal bovine serum, glutamine, and antibiotics. FGFR1 small interfering RNA (siRNA) SMARTpool reagent (M-003131-02), and nontargeting control siRNA 1 (D-001210-01) were obtained from Dharmacon (Chicago, IL). FGFR1 tyrosine kinase inhibitor SU5402 (ref. 23; Calbiochem, San Diego, CA) was dissolved in DMSO.

RNAi transfection and survival assays

Cells were plated 24 hours before transfection in 96-well plates. Cells were transfected with predesigned siRNA (Dharmacon) at a final concentration of 100 nmol/L using Oligofectamine (Invitrogen) as per the manufacturer's instructions. Five days following transfection, cell survival was assessed using CellTitre-Glo Luminescent cell viability assay (Promega, Madison, WI). To assess the sensitivity to the FGFR1 tyrosine kinase inhibitor, SU5402 (23), cells were plated in 96-well plates and 24 hours after plating exposed to various doses of SU5402. Medium containing drugs were replaced after 48 hours, and cell survival was assessed using CellTitre-Glo Luminescent Cell Viability Assay after 5 days.

Western blotting

Lysates from transfected cell pellets were made 72 hours following transfection, in 50 mmol/L Tris (pH 8.0), 150 mmol/L NaCl, 0.5% sodium deoxycholate, 1% NP40, 0.1% SDS, and protease inhibitors. Lysates were electrophoresed using Novex 4%-12% Bis-Tris precast gels (Invitrogen) and immunoblotted with anti-FGFR1 ab10646 (Abcam) or anti-α-tubulin T9026 (used as a loading control; Sigma), followed by anti-IgG horseradish peroxidase and chemiluminescent detection.

Immunohistochemical characterization of CLCs

We examined 13 cases of CLC that were selected for optimal CGH and aCGH hybridization from 18 previously described cases (6). The immunohistochemical phenotype of these tumors is characteristic of CLC (Table 1). Briefly, 12 cases were positive for estrogen receptors and 6 of these were also positive for progesterone receptors. No p53 nuclear expression was identified. No cases showed overexpression of HER2. Twelve cases completely lacked E-cadherin expression, whereas one showed significantly reduced membrane staining. All cases lacked β-catenin membrane staining.

Table 1.

Summary of the immunohistochemical and molecular findings in 13 cases of CLC

CaseGradeEstrogen receptorProgesterone receptorp53HER-2E-Cadherinβ-CateninCDH1 mutationsCDH1 methylationLOH 16q21-22Loss 16q (HR-CGH)Loss 16q22.1 (aCGH)
LC18 − − − Wild-type UN LOH Loss Loss 
LC22 − − − − Ex12, codon613 UN LOH Loss Loss 
LC32 −. − − Wild-type UN No LOH Loss No 
LC33 − − − − Wild-type UN LOH Loss Loss 
LC38 − − − − − Wild-type MET LOH Loss Loss 
LC40 − − − Wild-type UN LOH Loss Loss 
LC50 − − − − Wild-type MET No LOH Loss Loss 
LC51 − − − − Wild-type MET LOH Loss Loss 
LC57 − − − Ex7, codon288 MET/UN LOH Loss Loss 
LC58 − − − Wild-type MET/UN No LOH Loss Loss 
LC62 − − − Wild-type UN N.I. Loss Loss 
LC71 − − 1+ −/+ − Wild-type UN N.I. No Loss 
LC73 − − − Wild-type MET/UN LOH Loss Loss 
CaseGradeEstrogen receptorProgesterone receptorp53HER-2E-Cadherinβ-CateninCDH1 mutationsCDH1 methylationLOH 16q21-22Loss 16q (HR-CGH)Loss 16q22.1 (aCGH)
LC18 − − − Wild-type UN LOH Loss Loss 
LC22 − − − − Ex12, codon613 UN LOH Loss Loss 
LC32 −. − − Wild-type UN No LOH Loss No 
LC33 − − − − Wild-type UN LOH Loss Loss 
LC38 − − − − − Wild-type MET LOH Loss Loss 
LC40 − − − Wild-type UN LOH Loss Loss 
LC50 − − − − Wild-type MET No LOH Loss Loss 
LC51 − − − − Wild-type MET LOH Loss Loss 
LC57 − − − Ex7, codon288 MET/UN LOH Loss Loss 
LC58 − − − Wild-type MET/UN No LOH Loss Loss 
LC62 − − − Wild-type UN N.I. Loss Loss 
LC71 − − 1+ −/+ − Wild-type UN N.I. No Loss 
LC73 − − − Wild-type MET/UN LOH Loss Loss 

NOTE: −, lack of expression; −/+, reduced expression; +, positive; Grade, Scarff-Bloom-Richardson grade.

aCGH confirms the genetic complexity of CLCs

The frequency of DNA copy number alterations for each clone in our sample set is shown in Fig. 1A. As described by Shelley-Hwang et al. (13) and Loo et al. (12), a significant proportion of the genome showed alterations: a median of 1,178 BACs [range, LC58 (742)-LC57 (3,531) BACs], representing 24.46% of the genome, showed either gains or losses of genetic material. Gains were more frequent than losses, affecting a median of 791 BACs (12.39% of the genome) and 597 BACs (16.42% of the genome), respectively. Overall, the most common copy number changes observed in >30% of the samples were gains of chromosomes 1q, 5p, 7q, 11p, 11q, 12q, 14q, 16p, 18p, 19p+q, and 20p+q, and losses on chromosomes 11q, 13q, 16q, 18q, and Xq (Supplementary Table S1). aCGH analysis confirmed the presence of recurrent regions reported to be gained or lost in low-grade ductal and lobular breast carcinomas (i.e., 1q+, 11q−, and 16q−). Given that DNA amplification methods were not used in the cases analyzed by aCGH, and that aCGH provides more accurate profiles for pericentromeric and telomeric regions than CGH (2, 12, 13), chromosomes 16p, 19p, and 22 could be reliably analyzed in the present study.

Fig. 1.

Frequency of copy number changes in CLCs. Overall frequency of DNA copy number alterations found in 13 CLCs as defined by aCGH (A) and HR-CGH (B). A, individual BAC clones are plotted according to genomic location along the X-axis. The proportion of tumors in which each clone is gained (green bars) or lost (red bars) is plotted along the Y-axis. Vertical dotted lines, chromosome centromeres. B, cytogenetic bands are plotted according to their genomic location along the X-axis. The proportion of tumors in which each cytogenetic band is gained (green bars) or lost (red bars) is plotted along the Y-axis. Vertical dotted lines, chromosome centromeres. C, association matrix of copy number changes based on Euclidean distance. The heat map shows positive associations (small Euclidean distance; orange) and negative associations (large Euclidean distance; purple).

Fig. 1.

Frequency of copy number changes in CLCs. Overall frequency of DNA copy number alterations found in 13 CLCs as defined by aCGH (A) and HR-CGH (B). A, individual BAC clones are plotted according to genomic location along the X-axis. The proportion of tumors in which each clone is gained (green bars) or lost (red bars) is plotted along the Y-axis. Vertical dotted lines, chromosome centromeres. B, cytogenetic bands are plotted according to their genomic location along the X-axis. The proportion of tumors in which each cytogenetic band is gained (green bars) or lost (red bars) is plotted along the Y-axis. Vertical dotted lines, chromosome centromeres. C, association matrix of copy number changes based on Euclidean distance. The heat map shows positive associations (small Euclidean distance; orange) and negative associations (large Euclidean distance; purple).

Close modal

Gains and losses of whole chromosomes or chromosomal arms were reliably detected by aCGH. However, the increased resolution of aCGH also allowed the identification of the most frequently altered regions within these large-scale gains and losses. For instance, the minimal region of gains on 11q mapped to the cytogenetic bands 11q12-q13.4, which encompasses several candidate oncogenes such as cyclin D1 (CCND1), fibroblast growth factor 19 (FGF19), fibroblast growth factor 4 (FGF4), fibroblast growth factor 3 (FGF3), and myeloma overexpressed gene (MYEOV).

Figure 1B and Supplementary Table S1 summarize the recurrent gains and losses observed in ≥30% of the cases. Gains affecting small regions (<15 Mb) were identified in genomic loci harboring genes which have been reported to be amplified and/or overexpressed in human carcinomas (Table 2). For instance, focal gain of genomic material was identified on 3q27.1 (in 46% of cases), which encompasses two transcription factors: the translation initiation factor 4G (EIF4G-1) and ets variant gene 5 (ETV5). A small gain on chromosome 2q37.1, encoding the WNT6 gene, was observed in 30.8% of the cases. Gain of 5p15.33, the genomic locus of telomerase reverse transcriptase (h-TERT), was seen in 61% of cases. Gains on 12q were observed in 12 (92%) cases, with the minimal region of amplification mapping to 12q13.2-12q14.1, which encompasses multiple gene candidates, including cyclin-dependent kinase 2 (CDK2), cyclin-dependent kinase 4 (CDK4), sarcoma-amplified sequence (SAS), glioma-associated antigen (GLI1), centaurin-γ1 (CENTG1), and C-ERBB3 (HER3). Table 2 summarizes genes mapping to small regions (≤20 Mb) of recurrent losses observed in >30% of the samples. Interestingly, copy number polymorphisms have been described in these regions (refs. 24, 25; http://projects.tcag.ca/variation/). Although increased copy number of genes mapping to these regions may play a role in the biology of lobular cancers, we cannot determine whether these copy number gains represent germ line copy number polymorphisms conferring an elevated susceptibility to lobular carcinomas or are tumor-specific changes. Further studies are required to clarify this issue.

Table 2.

Small recurrent gains (<15 Mb) and losses (<20 Mb) of genomic material in >30% of the samples

Cytogenetic bandsStart (kb)End (kb)Putative oncogene
Gains    
    2q33.1-q33.3 199,000 205,000 DPP4, FZD7 
    2q33.3-q34 207,000 210,000 CREB1, FZD5 
    2q35 216,000 222,000 WNT6,WNT10A, PTPRN, DSP 
    2q37.1 232,000 234,000 EIF4E2 
    3q27.1-q27.3 184,000 187,000 EIF4G1, ETV5, EPHB3, ABCC5, MAP3K13 
    4p16.3-p15.33 248 11,276.68 FGFR3, SNURF, RNF4, GAK, TACC3 
    4p14 39,260 41,111.01 HIP2, UCHL1 
    5p15.33 368 1,869.006 TERT 
    5q31.2-q32 136,000 144,000 FGF1,CDC25, EGR-1 
    5q33.1-q33.2 148,000 154,000 CDX1, PDGFRB, GPX3 
    5q34-q35.3 168,000 181,000 FGF18, FGFR4, FOX1, ETEA, STK10, RAB24, VEGFR-3, SRC2 
    6p21.33-p12.3 31,367 46,265.01 TNF, VEGF 
    7q11.23-q21.11 71,713 77,318.47 FZD9, CLDN4, CLDN3, PTPN12 
    7q22.1 96,873 103,000 ZFP95, SERPINE1,CLDN15 
    8p12-p11.21 33,711 423,48.47 SPFH2, BRF2, RAB11FIP1, FLJ14299, PROSC, EIF4BP1, ASH2L, LSM1, BAG4, DDHD2, HTPAP, WHSC1L1, AP3M2, IKBKB, FGFR1, EIF4EBP1, TACC1, C8ORF4, SFRP1, INDO, IKBKB, POLB, VDAC3, SLC20A2, LOC114926 
    9q31.3 107,000 109,000 KLF4, EPB41L4B 
    11p15.5-p15.3 400 12,022.4 HRAS, EIF3S5, EIF4G2, IGF2, LMO1 
    12p13.31 5,699 9,576.503 VWF 
    12p13.1-p12.3 12,857 15,916.1 EPS8, RERG, ARHGDIB 
    12q13.12-q14.1 46,343 58,299.56 CDK2, CDK4, ERBB3, SAS, GLI-1, MMP19, ITGA7,GPD1 
    12q14.2-q14.3 62,342 64,794.16 HMGA2 
    12q23.3 103,000 103,000 TRA1 
    12q24.23-q24.31 117,000 124,000 EIF2B1, CDK2AP1 
    12q24.33 127,000 132,000 PXN 
    13q34 110,000 114,000 PTPN11 
    14q11.2-q12 19,884 25,192.05 DAD1, BCL2L2, RAB2B, MMP14 
    14q23.2-q24.2 61,087 69,758.86 FUT8, EIF2S1, RAB15 
    14q24.3 72,262 75,186.99 NEK9, ENTPD5, EIF2B2 
    14q32.31-q32.33 98,912 105,000 AKT1, JAG2 
    15q11.2 20,524 21,438.85 MKRN3 
    15q14-q15.3 37,664 41,610.45 RAD51 
    15q21.3 50,631 52,066.41 HNF6 
    15q22.2-q22.31 58,747 66,395 MAP2K1, MAP2K5,CA15 
    15q24.3 73,972 75,550.99 ZNF291, HMG20A 
    15q26.1-q26.2 86,471 94,507.94 NTRK3, PLIN 
    17q24.3-q25.3 68,271 74,465.33 CDC42EP4, RAB37, CDK3, SLC9A3R1 
    18p11.21 10,116 13,628.32 TNFSF5IP1 
    18q21.1-q21.31 44,641 54,427.66 WDR7, MAPK4, NEDD4L 
    20p13-p12.3 151 6,720.456 PTPRA 
    21q22.11-q22.13 30,669 37,669.65 CLDN14, RUNX1 
    

 

 

 
Putative tumor suppressor gene
 
Losses    
    2p12 76,415 81,844.75 CTNNA2 
    4p16.1-p15.33 7,179 15,409.73 WDR1 
    4p15.31 18,039 21,064.85 SLIT2 
    4p15.1-p14 30,425 37,344.35 CENTD1, PCDH7 
    8p23.3-p22 345 17,472.8 DLC1, GATA4, PINX1, NEIL2, TUSC3 
    10q21.1-q21.2 51,823 59,928.89 PCDH15, DKK1 
    10q23.1 82,068 85,966.8 PCDH21 
    13q12.13-q12.13 23,753 24,434.49 PARP4 
    15q12 21,482 23,337.05 NDN, C15orf2, SNURF, UBE3A 
    15q13.3-q15.1 29,169 38,061.79 THBS1, TRPM1 
    15q15.3-q21.2 41,446 48249.97 TP53BP1 
    15q21.3 50,631 55,795.41 HNF-6 
    15q24.1-q24.2 70,607 74,238.08 NRG4, NEO1 
    15q25.1-q25.2 75,852 80,209.64 CRABP1 
    18p11.31-p11.22 3,502 9,469.78 PTPRM, DAL-1 
    18q11.2-q21.1 19,363 42,965.66 CDH2 
    18q21.33-q23 59,150 72,826.14 CDH7, CDH19 
    22q11.22-q13.1 19,667 36,036.96 CHEK2 
Cytogenetic bandsStart (kb)End (kb)Putative oncogene
Gains    
    2q33.1-q33.3 199,000 205,000 DPP4, FZD7 
    2q33.3-q34 207,000 210,000 CREB1, FZD5 
    2q35 216,000 222,000 WNT6,WNT10A, PTPRN, DSP 
    2q37.1 232,000 234,000 EIF4E2 
    3q27.1-q27.3 184,000 187,000 EIF4G1, ETV5, EPHB3, ABCC5, MAP3K13 
    4p16.3-p15.33 248 11,276.68 FGFR3, SNURF, RNF4, GAK, TACC3 
    4p14 39,260 41,111.01 HIP2, UCHL1 
    5p15.33 368 1,869.006 TERT 
    5q31.2-q32 136,000 144,000 FGF1,CDC25, EGR-1 
    5q33.1-q33.2 148,000 154,000 CDX1, PDGFRB, GPX3 
    5q34-q35.3 168,000 181,000 FGF18, FGFR4, FOX1, ETEA, STK10, RAB24, VEGFR-3, SRC2 
    6p21.33-p12.3 31,367 46,265.01 TNF, VEGF 
    7q11.23-q21.11 71,713 77,318.47 FZD9, CLDN4, CLDN3, PTPN12 
    7q22.1 96,873 103,000 ZFP95, SERPINE1,CLDN15 
    8p12-p11.21 33,711 423,48.47 SPFH2, BRF2, RAB11FIP1, FLJ14299, PROSC, EIF4BP1, ASH2L, LSM1, BAG4, DDHD2, HTPAP, WHSC1L1, AP3M2, IKBKB, FGFR1, EIF4EBP1, TACC1, C8ORF4, SFRP1, INDO, IKBKB, POLB, VDAC3, SLC20A2, LOC114926 
    9q31.3 107,000 109,000 KLF4, EPB41L4B 
    11p15.5-p15.3 400 12,022.4 HRAS, EIF3S5, EIF4G2, IGF2, LMO1 
    12p13.31 5,699 9,576.503 VWF 
    12p13.1-p12.3 12,857 15,916.1 EPS8, RERG, ARHGDIB 
    12q13.12-q14.1 46,343 58,299.56 CDK2, CDK4, ERBB3, SAS, GLI-1, MMP19, ITGA7,GPD1 
    12q14.2-q14.3 62,342 64,794.16 HMGA2 
    12q23.3 103,000 103,000 TRA1 
    12q24.23-q24.31 117,000 124,000 EIF2B1, CDK2AP1 
    12q24.33 127,000 132,000 PXN 
    13q34 110,000 114,000 PTPN11 
    14q11.2-q12 19,884 25,192.05 DAD1, BCL2L2, RAB2B, MMP14 
    14q23.2-q24.2 61,087 69,758.86 FUT8, EIF2S1, RAB15 
    14q24.3 72,262 75,186.99 NEK9, ENTPD5, EIF2B2 
    14q32.31-q32.33 98,912 105,000 AKT1, JAG2 
    15q11.2 20,524 21,438.85 MKRN3 
    15q14-q15.3 37,664 41,610.45 RAD51 
    15q21.3 50,631 52,066.41 HNF6 
    15q22.2-q22.31 58,747 66,395 MAP2K1, MAP2K5,CA15 
    15q24.3 73,972 75,550.99 ZNF291, HMG20A 
    15q26.1-q26.2 86,471 94,507.94 NTRK3, PLIN 
    17q24.3-q25.3 68,271 74,465.33 CDC42EP4, RAB37, CDK3, SLC9A3R1 
    18p11.21 10,116 13,628.32 TNFSF5IP1 
    18q21.1-q21.31 44,641 54,427.66 WDR7, MAPK4, NEDD4L 
    20p13-p12.3 151 6,720.456 PTPRA 
    21q22.11-q22.13 30,669 37,669.65 CLDN14, RUNX1 
    

 

 

 
Putative tumor suppressor gene
 
Losses    
    2p12 76,415 81,844.75 CTNNA2 
    4p16.1-p15.33 7,179 15,409.73 WDR1 
    4p15.31 18,039 21,064.85 SLIT2 
    4p15.1-p14 30,425 37,344.35 CENTD1, PCDH7 
    8p23.3-p22 345 17,472.8 DLC1, GATA4, PINX1, NEIL2, TUSC3 
    10q21.1-q21.2 51,823 59,928.89 PCDH15, DKK1 
    10q23.1 82,068 85,966.8 PCDH21 
    13q12.13-q12.13 23,753 24,434.49 PARP4 
    15q12 21,482 23,337.05 NDN, C15orf2, SNURF, UBE3A 
    15q13.3-q15.1 29,169 38,061.79 THBS1, TRPM1 
    15q15.3-q21.2 41,446 48249.97 TP53BP1 
    15q21.3 50,631 55,795.41 HNF-6 
    15q24.1-q24.2 70,607 74,238.08 NRG4, NEO1 
    15q25.1-q25.2 75,852 80,209.64 CRABP1 
    18p11.31-p11.22 3,502 9,469.78 PTPRM, DAL-1 
    18q11.2-q21.1 19,363 42,965.66 CDH2 
    18q21.33-q23 59,150 72,826.14 CDH7, CDH19 
    22q11.22-q13.1 19,667 36,036.96 CHEK2 

Validation of aCGH

High-resolution CGH. To validate the results of aCGH, all cases were subjected to high-resolution CGH (HR-CGH). Genomic alterations were identified in all cases. The number of chromosomal arms with aberrations ranged from 3 to 15 per case (median, six changes / case). The number of gains ranged from one to nine gains / case (median, three gains / case) and losses ranged from one to seven (median, four losses / case; Fig. 1B; Supplementary Table S2). The most frequent recurrent gains comprised 1q (11 cases), 8q (6 cases), and 8p and 19p (both, 3 cases), whereas the most frequent recurrent losses were 16q (12 cases), and 11q, 13q, 17q, and 18 p (all, 4 cases). Concurrent gain of 1q and loss of 16q, regarded as a hallmark feature of low grade ductal carcinomas and lobular carcinomas, were seen in 12 of 13 cases (76.9%). The agreement between chromosomal arm changes detected by aCGH and HR-CGH ranged from 47% to 100% (median, 88%; mean, 80%), with aCGH showing a markedly increased resolution and sensitivity (Fig. 1; Supplementary Fig. S1).

Fluorescent and chromogenic in situ hybridization. To further validate specific gains of genomic material identified by aCGH, in situ hybridization was done in two sets of samples: five cases with gains of 8p12-p11.2 and five cases with gains of 11q13. The results were confirmed in all cases (Fig. 2). In these experiments, adjacent stromal cells (fibroblasts, lymphocytes, and macrophages) and residual normal breast lobules and ducts were used as internal controls, and showed normal copy numbers, ruling out possible copy number polymorphisms.

Fig. 2.

CLCs with 8p12-p11.2 and 11q13 copy number gains. A, representative aCGH plots for three CLCs with focal copy number gain at 8p12-p11.2 (red arrowheads) and 11q13 (green arrowheads). Median absolute deviation–centered log2 ratios are plotted on the Y-axis against each clone according to genomic location on the X-axis. The centromere is represented by a vertical dotted line. Horizontal dashed lines correspond to median absolute deviation–centered log2 ratios of 0.8 and −0.8. B, matched representative dual-color fluorescent in situ hybridization for FGFR1 (green) and CCND1 (red). Note more than five copies of each probe/nucleus. In LC51, stromal cells showed only two copies of each probe. Inset, note the presence of more than five signals of each probe in the nuclei of neoplastic cells. C, immunohistochemistry for FGFR1. Note the strong membrane and cytoplasmic positivity in neoplastic cells.

Fig. 2.

CLCs with 8p12-p11.2 and 11q13 copy number gains. A, representative aCGH plots for three CLCs with focal copy number gain at 8p12-p11.2 (red arrowheads) and 11q13 (green arrowheads). Median absolute deviation–centered log2 ratios are plotted on the Y-axis against each clone according to genomic location on the X-axis. The centromere is represented by a vertical dotted line. Horizontal dashed lines correspond to median absolute deviation–centered log2 ratios of 0.8 and −0.8. B, matched representative dual-color fluorescent in situ hybridization for FGFR1 (green) and CCND1 (red). Note more than five copies of each probe/nucleus. In LC51, stromal cells showed only two copies of each probe. Inset, note the presence of more than five signals of each probe in the nuclei of neoplastic cells. C, immunohistochemistry for FGFR1. Note the strong membrane and cytoplasmic positivity in neoplastic cells.

Close modal

E-cadherin is down-regulated in CLCs by a combination of genetic and epigenetic mechanisms

By aCGH, deletion (physical loss) of 16q22.1 was observed in all cases, 12 of them including the genomic locus of the CDH1 gene (16q22.1-67.3 Mb, see below). A number of different mechanisms of CDH1 gene silencing were observed (Table 1). In two cases, CDH1 gene was silenced by a combination of physical loss and CDH1 frameshift truncating mutations (6). In five cases, CDH1 gene was inactivated by a combination of physical loss and CDH1 gene promoter methylation. Out of the remaining six cases, physical loss of 16q22.1 was detected in five cases. In one case (LC32), a partial deletion of 16q was detected by HR-CGH, however, the telomeric boundary of the deleted region could not be well defined. Interestingly, no LOH on 16q21-q22 or deletion on 16q22.1, as defined by aCGH, was found. This case exemplifies the greater accuracy of aCGH in mapping regions harboring deletions (12, 13, 21). The use of aCGH and LOH proved invaluable to detect losses on 16q22.1: two cases that were not informative by LOH showed loss of 16q22.1. In addition, aCGH and chromosomal HR-CGH revealed loss of 16q22.1 in two out of three cases that showed no LOH at this region. As previously described, aCGH seemed to be more sensitive than chromosomal HR-CGH for detecting loss of genomic material in the presence of normal cell contaminants (21). For instance, in case LC71, aCGH revealed loss of 16q22.1, whereas LOH analysis was not informative and chromosomal CGH showed only a trend for loss of 16q.

CLCs frequently harbor complex genetic aberrations on 8p

The short arm of chromosome 8 showed an intriguing pattern of genomic changes. Loss of 8p was seen in four cases, including one with loss of the whole arm, and three cases harboring partial deletions of 8p. The minimal deleted region encompassed 8p23.3-8p22, the genomic location of several putative tumor suppressor genes, including deleted in liver cancer-1 (DLC-1), which is reported to be deleted in up to 40% of all breast carcinomas, GATA-binding protein 4 (GATA4), and tumor suppressor candidate 3 (N33/TUSC3; ref. 26). In these cases, the deleted regions were followed by high-level gains of 8p12-p11.23, suggesting that the “break-fusion-bridge” may be the underlying genetic mechanism for this amplicon (2628). Interestingly, gains of 11q13 followed by losses of 11q14.1-qter were also found in these samples (Fig. 2). Gains of 8p were detected in three additional cases, and in these six cases, the minimal region of genomic gain spanned 2 Mb (36.9-38.9 Mb). This amplicon encompasses multiple genes reported to play roles in breast cancer progression (16, 2837), such as fibroblast growth factor receptor 1 (FGFR1; refs. 16, 28, 3135), transforming acidic coiled coil gene 1 (TACC1; ref. 36), and eukaryotic translation initiation factor 4E-binding protein 1 (EIF4EBP1; ref. 37; Table 2).

Correlations between chromosomal abnormalities

Figure 1C shows the association between different gains and losses of genomic material in this series of lobular carcinomas. Despite the limited sample size, significant associations were observed. For instance, gain of 1q showed strong correlations with gain of 16p and loss of 16q. Briefly, concurrent large gains of 1q and losses of 16q were detected in 12 cases, with gains of 16p found in 8 of these 12 cases. These findings might suggest the presence of the unbalanced chromosomal translocation t(1;16)/der(1;16) (q10;p10) in these eight cases (61.5%), which is reported to be found in up to 75% of CLCs (38, 39). An association between gains of 8p12-p11.23 and 11q13.3 were also observed, which was confirmed by dual-color FISH (Fig. 2).

MDA-MB-134 as a putative model for the study of CLCs with 8p12-p11.2 and 11q13 coamplifications

To investigate whether any breast cancer cell lines had genetic changes characteristic of CLC, we examined a database of the molecular genetic profiles of 28 breast cancer cell lines generated with aCGH (40).8

8

A. Mackay, manuscript in preparation.

Like CLCs, the cell line MDA-MB-134 is positive for hormone receptors, lacks HER2 amplification and overexpression, and harbored physical loss of 16q- and coamplification of 8p12-p11.21 and 11q13, and deletions of 8pter-p12 and 11q13.3-qter (refs. 28, 4143; Fig. 3; Supplementary Table S3). Through a combination of immunohistochemistry, expression profile analysis (Supplemental Table S4), and aCGH, we confirmed previous results that this cell line expresses estrogen receptor and progesterone receptor, shows reduced levels of β-catenin, and lacks HER2 and E-cadherin expression (data not shown). In addition, the underlying genetic mechanism of CDH1 inactivation is homozygous deletion of CDH1 gene (ref. 43; data not shown). Thus, although MDA-MB-134 is reported to be derived from an “invasive ductal carcinoma”, our observations suggest that MDA-MB-134 has the hallmarks of molecular genetic and expression profiles of CLC. The cell line ZR-75-1 also harbors loss of 16q, high-level gains on 8p12-p11.2 and 11q13.3, deletion of 11q13.3-qter, but no changes on 8p12-pter (refs. 28, 41; Fig. 3) and is positive for estrogen receptor (44). However, despite hemizygous loss of 16q, E-cadherin is expressed at normal levels (Supplementary Table S4).

Fig. 3.

MDA-MB-134 as a model for the study of CLCs with 8p12-p11.2 and 11q13 coamplifications. A and B, aCGH plots of cell lines MDA-MB-134 and ZR-75.1. Log2 ratios are plotted on the Y-axis against each clone according to genomic location on the X-axis. Vertical dotted lines, the centromeres; horizontal dashed lines, log2 ratios of 0.8 and −0.8. Note that both cell lines harbor high-level gains of 8p11.2-p12 and 11q13. C, detailed view of the amplicon on 8p11.2-8p12 (dashed-line box). Note that clones mapping to FGFR1 are amplified in MDA-MB-134, whereas the ZR-75.1 cell line harbors a microdeletion in this genomic region. D, heat map of the expression levels of genes mapping to the amplicon on 8p11.2-p12 in MDA-MB-134 and ZR-75-1 cell lines. Colors represent relative levels of gene expression: high levels of expression (brightest red) and low levels or absence of expression (green) when compared with universal human reference RNA.

Fig. 3.

MDA-MB-134 as a model for the study of CLCs with 8p12-p11.2 and 11q13 coamplifications. A and B, aCGH plots of cell lines MDA-MB-134 and ZR-75.1. Log2 ratios are plotted on the Y-axis against each clone according to genomic location on the X-axis. Vertical dotted lines, the centromeres; horizontal dashed lines, log2 ratios of 0.8 and −0.8. Note that both cell lines harbor high-level gains of 8p11.2-p12 and 11q13. C, detailed view of the amplicon on 8p11.2-8p12 (dashed-line box). Note that clones mapping to FGFR1 are amplified in MDA-MB-134, whereas the ZR-75.1 cell line harbors a microdeletion in this genomic region. D, heat map of the expression levels of genes mapping to the amplicon on 8p11.2-p12 in MDA-MB-134 and ZR-75-1 cell lines. Colors represent relative levels of gene expression: high levels of expression (brightest red) and low levels or absence of expression (green) when compared with universal human reference RNA.

Close modal

FGFR1 is overexpressed when amplified in CLCs and in the MDA-MB-134 cell line

Having established the presence of an amplicon on 8p12-p11.2 in six CLCs, we sought to identify the genes showing expression changes along with copy number changes in the cell lines MDA-MB-134 and ZR-75.1. It has been shown that the amplicon on 8p12-p11 is complex in breast cancer (28, 35, 45, 46), and that it can be subdivided into four regions (28). In MDA-MB-134, two amplicons were identified: one mapping to 8p12-p11.23 spanning the region between 36.9 and 39.2 Mb and the second mapping to 8p11.21 spanning the region between 41.5 and 42.1 Mb. In ZR-75-1, the amplicon mapping to 8p12-p11.23 encompassed the region between 36.9 and 38.0 Mb, which was followed by a deletion spanning the region between 38.0 and 39.2 Mb (Fig. 3, arrowhead). Interestingly, this deleted region maps to FGFR1 (Fig. 3, arrowhead). The deletion of the BACs encompassing this region (RP11-576M6 and RP11-551D4, Supplementary Table S3) was confirmed by FISH (data not shown). When we compared aCGH and expression profiling data in these two cell lines, the FGFR1 gene was highly expressed in MDA-MB-134, whereas it showed low expression levels in ZR-75-1 (Fig. 3). Other genes that were both amplified and up-regulated in both cell lines included FLJ14299, PROSC, RAB11FIP1, ASH2L, WHSC1L1, BLP1, and MYST3 (Fig. 3). When samples of CLC were subjected to immunohistochemical analysis, all cases (n = 6) with FGFR1 copy number gains also showed protein overexpression (Fig. 2). Taken together, these data are consistent with the notion that when FGFR1 is amplified, it is also overexpressed.

FGFR1 contributes to the survival of MDA-MB-134 cells

To determine whether FGFR1 is a potential driver of the 8p12 amplicon and contributes to the survival of MDA-MB-134 cells, we transfected MDA-MB-134 cells with siRNA directed against FGFR1 or nontargeting control siRNA (Scram). Transfection of MDA-MB-134 cells with FGFR1 siRNA reduced cell viability in comparison to Scram control (Fig. 4), suggesting that FGFR1 is required for the survival of MDA-MB-134 cells. In addition, when ZR-75-1 cells were transfected with siRNA directed against FGFR1 and nontargeting controls, no statistically significant differences in cell viability were detected (data not shown).

Fig. 4.

Effect of FGFR1 inhibition in MDA-MB-134 cells. A, Western blot analysis of MDA-MB-134 cells after transfection with siRNA directed against FGFR1 or nontargeting control siRNA (Scram). B, transfection of MDA-MB-134 cells with FGFR1 siRNA reduced cell viability in comparison with Scram controls. C, MDA-MB-134, ZR-75-1, MCF7, and CAL51 cell lines were treated with a specific FGFR1 tyrosine kinase inhibitor, SU5402, and anchorage-dependent survival was assessed. Note that MDA-MB-134 cells were more sensitive to SU5402 than all other cell lines lacking FGFR1 amplification and overexpression. *, P = 0.237 (ANOVA). The IC50 values for each cell line were, as follows: MDA-MB-134, 7 μmol/L; ZR-75-1, >20 μmol/L; MCF7; 16 μmol/L; and CAL51, 17 μmol/L.

Fig. 4.

Effect of FGFR1 inhibition in MDA-MB-134 cells. A, Western blot analysis of MDA-MB-134 cells after transfection with siRNA directed against FGFR1 or nontargeting control siRNA (Scram). B, transfection of MDA-MB-134 cells with FGFR1 siRNA reduced cell viability in comparison with Scram controls. C, MDA-MB-134, ZR-75-1, MCF7, and CAL51 cell lines were treated with a specific FGFR1 tyrosine kinase inhibitor, SU5402, and anchorage-dependent survival was assessed. Note that MDA-MB-134 cells were more sensitive to SU5402 than all other cell lines lacking FGFR1 amplification and overexpression. *, P = 0.237 (ANOVA). The IC50 values for each cell line were, as follows: MDA-MB-134, 7 μmol/L; ZR-75-1, >20 μmol/L; MCF7; 16 μmol/L; and CAL51, 17 μmol/L.

Close modal

To further examine the importance of FGFR1 in the survival of MDA-MB-134, we used a specific FGFR1 tyrosine kinase inhibitor, SU5402. MDA-MB-134 cells, and for comparison, breast cancer cell lines, ZR-75-1, MCF7, and CAL51, which show no FGFR1 gene copy number gains (data not shown), were treated with a range of concentrations of SU5042 and assessed for survival. MDA-MB-134 cells were significantly more sensitive to SU5402 than all other cell lines lacking FGFR1 amplification (P = 0.0237, ANOVA test; 10−5 mol/L SU5402; Fig. 4). This supports the siRNA experiments and further implicates FGFR1 signaling in the survival of MDA134 cells.

The molecular genetic features of lobular breast carcinomas have received great attention in the literature in the last few years (1, 2, 57, 913, 21). Inactivation of the CDH1 gene through physical loss of 16q associated with CDH1 gene mutation and/or promoter methylation leads to the abrogation of E-cadherin expression. Together with gains of 1q, these are currently accepted hallmark molecular features of in situ and invasive lobular carcinomas (13, 57, 913, 21). However, little is known about other genetic changes driving the pathogenesis of CLCs. The present study not only confirms the known molecular genetic features of CLCs (13, 57, 913, 21), but also shows that CLCs harbor a greater genetic complexity and a higher number of recurrent genomic changes than previously appreciated with other molecular techniques (12, 13). In fact, recurrent gains on 1q, 5p, 7q, 11p, 11q, 12q, 14q, 16p, 18p, 19p+q, and 20p+q and losses on chromosomes 11q, 13q, 16q, 18q, and Xq were observed in >30% of the CLCs analyzed.

Complex genomic changes involving 11q were identified. Gains of 11q13 specifically mapping to CCND1 were observed in 53% of all CLCs. CCND1 copy number gains and CCND1 overexpression have been reported to be a feature of low grade, estrogen receptor–positive tumors (16, 17, 34, 47), including CLCs (12, 13, 16, 17). In addition, losses of 11q14.1-q25 were observed in 53.8% of the cases. Although these gains and deletions have not been reported in chromosomal CGH analyses of CLCs, Loo et al. (12) and Shelley-Hwang et al. (13) showed frequent recurrent gains of 11q13 followed by genomic losses in more telomeric regions of 11q in CLCs.

There is compelling evidence to suggest that DNA amplification at 8p12-p11.2 has a remarkably complex structure (28), encompassing at least four distinct amplification cores. In the present study, FGFR1 was amplified in all lobular carcinomas harboring the 8p12-p11.2 amplicon, suggesting that tumors of different types or grades may have specific, and distinct, amplicon drivers within the 8p locus (16, 26, 34, 35, 45). Alternatively, this may indicate that tumors of different histologic grades or histologies may acquire distinct genetic changes through different molecular mechanisms (13). Although FGFR1 may not be the sole oncogene in the 8p12-p11.2 amplification observed in breast carcinomas (26, 28, 29, 35, 45, 46), in the present study, we show that this oncogene was concurrently amplified and overexpressed in 43% of CLCs. Furthermore, FGFR1 expression and activation were required for the survival of MDA-MB-134 cells. Given the striking similarities between grade 1 and 2 CLCs, and grade 1 ductal carcinomas at the genetic level (13), further studies are required to determine whether this genetic subgroup of breast carcinomas harboring 1q+, 16q−, and FGFR1 and 11q13 amplifications are characterized by a lobular phenotype or if this is a more pervasive genotype within the group of low grade breast carcinomas. In fact, there are several lines of evidence to suggest that FGFR1 may play an important role in the biology of estrogen receptor–positive breast carcinomas of both lobular and low grade ductal histologies (16, 34). In a recent study, Xian et al. (48) introduced a drug-inducible FGFR1 gene into HC11 mouse mammary epithelial cells and found that FGFR1 expression led to the reinitiation of cell proliferation, increased survival of luminal cells, and loss of cell polarity. Interestingly, FGFR1 activation induced the up-regulation of matrix metalloproteinase 3, which in turn, caused the cleavage of E-cadherin (48). Therefore, FGFR1 amplification and overexpression may constitute an additional mechanism for E-cadherin down-regulation in CLCs.

The present study also emphasizes the importance of fine mapping of amplicons and that amplicons like 8p12-p11.2 are likely to have different amplicon drivers in different settings. In studies in which amplifications mapping to 8p12-p11.2 were treated as a single amplicon (45, 49), FGFR1 was ruled out as a possible amplicon driver (45, 49). However, neither the cell lines studied by Ray et al., which lacked FGFR1 expression, harbored FGFR1 amplification (49, 50), nor did the minimally amplified region defined by Garcia et al. (45) encompass FGFR1. On the other hand, there are several lines of evidence, including the data presented here, to suggest that this tyrosine kinase receptor is amplified and overexpressed in breast cancers and breast cancer cell lines (28, 32). Our results on the correlations between gene copy numbers and expression level for MDA-MB-134 and ZR-75-1 provide further evidence for this concept: although both cell lines show gain at 8p12-p11.2, the former harbors FGFR1 amplification and overexpression, whereas the latter does not.

In conclusion, this study shows that CLCs harbor more molecular genetic changes than previously appreciated and provides comprehensive lists of putative oncogenes and tumor suppressor genes frequently altered in CLCs. Apart from recurrent gains of 1q and physical losses of 16q, a subset of CLCs harbors concurrent gains of 8p12-p11 and 11q13. Furthermore, we have identified a frequent amplification/overexpression of FGFR1 in this subset of CLCs, suggesting that small molecules or antibodies directed against FGFR1 should be investigated as novel therapeutic strategies against this tumor type. Finally, MDA-MB-134 cells may provide a useful model system for the necessary mechanistic and preclinical investigations of breast carcinomas with FGFR1 amplification and overexpression.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Note: Supplementary data for this article are available at Clinical Cancer Research Online (http://clincancerres.aacrjournals.org/).

J.S. Reis-Filho, P.T. Simpson, and N. Turner contributed equally to the present study.

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Supplementary data