Gene Expression Profiles of Epithelial Cells Microscopically Isolated from a Breast-Invasive Ductal Carcinoma and a Nodal Metastasis

Gene expression profiles of epithelial cells microscopically isolated from a breast-invasive ductal carcinoma and a nodal metastasis

I. Zucchi*†‡, E. Mento*†, V. A. Kuznetsov†§, M. Scotti*, V. Valsecchi*, B. Simionati¶, E. Vicinanza*, G. Valle¶, S. Pilottiʈ, R. Reinbold**, P. Vezzoni*, A. Albertini*, and R. Dulbecco††

*Istituto di Tecnologie Biomediche, Consiglio Nazionale delle Ricerche, Via F. lli Cervi 93, 20090 Segrate-Milan, Italy; §Genome Institute of Singapore, Department of Information and Math Sciences, 60 Biopolis Street, Singapore 138672; ¶Centro Ricerche Interdipartimentale Biotecnologie Innovative, University of Padua, 35121 Padua, Italy; ʈIstituto Nazionale Tumori, Via Venezian 31, 20133 Milan, Italy; **Max Planck Institute for Molecular Biomedicine, Cell and Developmental Biology, D48149 Muenster, Germany; and ††Salk Institute, 10010 North Torrey Pines Road, La Jolla, CA 92037

Contributed by R. Dulbecco, November 5, 2004 Expression profiles of breast carcinomas are difficult to interpret This work is a pilot study carried out by following this concept. when they are obtained from tissue in toto, which may contain a We performed SAGE on highly homogeneous populations of large proportion of non-cancer cells. To avoid this problem, we cells microscopically isolated from a primary invasive ductal microscopically isolated cells from a primary invasive ductal carci- carcinoma of the breast and from an axillary node harboring a noma of the breast and from an axillary node harboring a meta- metastatic breast carcinoma. A useful feature of the SAGE static breast carcinoma, to obtain pure populations of carcinoma technology is that databases can be compared directly with each cells (Ϸ500) and used them for serial analysis of gene expression. other. The purity of the cell population is shown in our SAGE The expression profiles generated from both populations of cells libraries by the absence or minimal expression of genes that are were compared with the profile of a disease-free mammary epi- markers of nonepithelial cells such as endothelial and stromal thelium. We showed that the expression profiles obtained are cells, adipose cells, B and T lymphocytes, and macrophages. exclusive of carcinoma cells with no contribution of non-epithelial Materials and Methods cells. From a total of 16,939 unique tags analyzed, we detected 559 MEDICAL SCIENCES statistically significant changes in gene expression; some of these Clinical Information and Cell Microdissection. Samples from both a genes have not been previously associated with breast cancer. We primary invasive ductal carcinoma of the breast and a nodal observed that many of the down-regulated genes are the same in metastasis were obtained at the Istituto Nazionale Tumori both cancers, whereas the up-regulated genes are completely (Milan) after patient consent. For the invasive library construc- different, suggesting that the down-regulation of a set of genes tion, Ϸ500 cancer cells (99% pure) were microscopically isolated may be the basic mechanism of cancer formation, while the from a primary estrogen- and progesterone-receptor-positive up-regulation may characterize and possibly control the state of invasive ductal carcinoma. For the metastatic library, the same evolution of individual cancers. The results obtained may help in amount of cells were also microscopically isolated from a characterizing the neoplastic process of breast cancer. metastatic lymph node derived from an estrogen- and progesterone-receptor-negative breast-invasive ductal carcinoma. Can- breast cancer ͉ serial analysis of gene expression ͉ cell microdissection ͉ cer cells were microscopically dissected from methylene blue- ␮ carcinoma stained 20- m frozen sections, kept at low temperature during the entire manipulation, by using microneedle aspiration (8); cancer cells are less attached to the connective tissue stroma and reast cancer progresses through a series of stages, starting as are preferentially released by mechanical force. Batypical duct hyperplasia, to ductal carcinoma in situ, invasive ductal carcinoma, and finally, metastatic disease. Global cDNA Preparation, Library Construction, and Sequencing. The micro- expression profiling has been extensively used to classify the dissected SAGE libraries were generated by following a protocol disease and to predict its clinical outcome. Most of these studies described (9), with modifications made necessary due to the used array-based platforms, and therefore, were limited to the small number of cells used as starting material. Total RNA was analysis of known but most likely incomplete selection of genes obtained by using the PicoPure RNA isolation kit (Arcturus). A and ESTs. More recently, global changes of gene expression have pre-SAGE linear amplification step was performed with T7 been determined by using serial analysis of gene expression RNA polymerase by using the RiboAmp kit (Arcturus). (SAGE), which does not have this limitation (1, 2). Both microarray and SAGE data suggest that there is considerable Tools for SAGE. Tags were analyzed by using SAGE2000 (www. diversity among breast tumor profiles (1–5). However, these data sagenet.org͞index.htm) and ESAGE software (10). The Na- were obtained primarily from tumor tissue, rather than from a tional Center for Biotechnology Information SAGEmap homogenous population of epithelial cancer cells. The problem (www.ncbi.nlm.nih.gov͞SAGE) and the CGAP SAGEgenie that arises is that, if in the context of a complex tissue, only a (http:͞͞cgap.nci.nih.gov͞SAGE͞AnatomicViewer) databases small proportion of cells corresponds to the cells of interest, were also used. The Gene Expression Level Probability Function many important regulatory genes, often expressed at low levels, was obtained by using the discrete Pareto-like probability func- but essential for determining the pathological cell phenotype, tion (11). The assignment of molecular function of proteins and will be undetected. In fact, purified and unpurified samples were chromosomal location of individual genes was based on the shown to produce different expression profiles (2), because contaminating nontumoral cells may have been present in different amounts. For instance, in tumor samples cancer cells may Abbreviations: SAGE, serial analysis of gene expression; INV, invasive; MET, metastatic. be present in proportions ranging from 5% to 50% of the total †I.Z., E.M., and V.A.K. contributed equally to this work. cell mass (6, 7). For these reasons, it is necessary to determine ‡To whom correspondence should be addressed. E-mail: [email protected]. the expression profile on a pure population of carcinoma cells. © 2004 by The National Academy of Sciences of the USA

www.pnas.org͞cgi͞doi͞10.1073͞pnas.0408260101 PNAS ͉ December 28, 2004 ͉ vol. 101 ͉ no. 52 ͉ 18147–18152 Downloaded by guest on September 29, 2021 LocusLink database (www.ncbi.nlm.nih.gov͞projects͞Locus- libraries, when compared with the Br࿝N library. Of these 559 Link͞). tags, 392 (70.1%) correspond to known genes or ESTs, and of the other 167 tags (29.9%), 152 match to multiple genes, and 15 do In Situ Hybridization. In situ hybridization was performed on human not match to any genes. A selection of the 50 most down- breast tissues as described (12). Sense and antisense riboprobes regulated genes is reported in Table 1 and a selection of the 50 were generated by in vitro transcription using T7 or Sp6 polymerase most up-regulated genes is reported in Table 2. Tables 3–5 list primers, from the cloned region of 762 nt, between primers tags down-regulated in the INV and MET libraries, as follows: CXCL6F͞CXCL6R (5Ј-TCATAAAATTGCCCAGTCTTC-3Ј 221 tags matching to known genes, including 40 ribosomal genes and 5Ј-TGTTTTTGGGCTTCTTCATCT-3Ј) of the human are listed in Table 3, 76 multiple-gene-matching tags are re- CXCL6 mRNA sequence NM࿝002993. ported in Table 4, and 3 no-matching tags are reported in Table 5. Tables 6–8 list tags up-regulated in the INV and MET Supporting Information. Tables 3–8 are published as supporting libraries, as follows: 171 tags matching to known genes, including information on the PNAS web site. 30 ribosomal genes, are listed in Table 6, 76 multiple-gene- matching tags are reported in Table 7, and 12 no-matching tags Results are reported in Table 8. Generation of SAGE Libraries from Microdissected Cells. Two SAGE libraries were obtained: one from cancer epithelial cells isolated Genes Down-Regulated in INV and MET Carcinoma Libraries. Our by microscopic dissection from a primary invasive (INV) breast findings are in part similar and in part different in relation to ductal carcinoma (the INV library, 17,306 tags), and the other previously published work (1, 2). In agreement with the earlier from cancer cells microscopically isolated from a lymph node work, we observed that the most dramatic difference in gene harboring a metastatic (MET) breast carcinoma (the MET expression, between cancer and normal cells, involves genes library, 10,363 tags). In both cases, the purified cells were down-regulated in cancer. Of 221 down-regulated genes (Table obtained in very limited amounts (Ϸ500 cells) and, due to the 3), 134 genes are down-regulated in both the INV and MET very limited amount of RNA obtained, amplification was re- libraries, 77 genes are down-regulated only in the INV library, quired. To verify that the RNA amplification preserved the and 10 genes are down-regulated only in the MET library. A original mRNA abundance, an amplification diagnosis test was large fraction of these genes are of unknown function or their carried out by performing hybridization comparison of the RNA function has not been associated to breast cancer. In previous before and after amplification, in a dot-blot analysis using work by Polyak and coworkers (1, 2), 32 genes were found to radioactive probes for 10 different genes, expressed at different be down-regulated in all breast cancer samples analyzed in levels of abundance in normal and tumoral mammary gland their study. Nineteen of these genes are also down-regulated epithelial cells. This control showed that the RNA amplification in our INV and MET libraries in a significant way, and they are did not induce any preferential amplification (data not shown). as follows: PNRC1, CEBPD, TM4SF1, ANXA1, TNFRSF10B, The purity of the cell population is shown in our SAGE libraries RASD1, CXCL1, IL8, TFF1, SCGB3A1, LIF, LAMC2, CXCL3, by the absence or minimal expression of genes that are markers CCL20, CXCL6, KRT6B, SOD2, SAT, and STC2; the remain- of nonepithelial cells, such as endothelial and stromal cells, ing 13 are also down-regulated in our INV and MET libraries, adipose cells, B and T lymphocytes, and macrophages (data not but are not reported in Tables 1 and 3 because, according to shown). our parameters, they are not down-regulated significantly (P Ͼ 0.05). In agreement with Porter et al.’s results (1, 2), a large SAGE Library Comparison. Our INV library was matched against fraction of genes down-regulated in the INV and MET libraries the tag list of the SAGE library Br࿝N (library name: ‘‘N2’’), encodes secreted proteins such as chemokines CXCL1, obtained by Porter et al. (1), from a disease-free mammary CXCL2, CXCL3, CXCL6 and CCL20; cytokines IL6, IL8, and epithelium through immunomagnetic purification, using an anti- LIF; and the protein LOC118430 (small breast epithelial Ber-Ep4 antibody coupled to magnetic beads (1). We compared mucin). the tag abundance distribution of our INV library with that of the Additional genes down-regulated in Table 1 are not asso- Br࿝N SAGE library and observed that the abundance distribu- ciated to breast cancer but are described to be important in the tion of distinct SAGE tags in the two libraries was similar (data genesis of other tumor types. They include the following: the not shown). However, the Br࿝N library is different in size in tumor suppressor genes: MSF, DOC1R (a potential human respect to our INV library, and this fact may affect the results of tumor suppressor gene), SUI1 (a putative translation initiation the statistical comparison of the gene expression levels in the factor), and ST13 (also called SNC6) (14–17); genes involved libraries (13). To avoid this problem, we constructed similar size in the apoptotic pathway are as follows: GADD45B and sublibraries of 10,082 tags each, by choosing tags randomly PDCD5 (18, 19); genes controlling cell proliferation are: LIF, without replacement from the Br࿝N library and from our INV which regulates the growth of normal human breast epithelial library. cells, and SEMA3B, a mediator of p53 tumor-suppressor The comparison of the two sublibraries showed a greater activity (20–23). abundance of rare transcripts in our INV sublibrary with respect Other genes in Table 1 include PD-LIM1, which has a role in to the normal Br࿝N sublibrary. Also, in our INV sublibrary, the cytoskeletal organization, cell structure and shape, cell migra- fraction of rarely expressed transcripts is statistically higher (P Ͻ tion, cell polarity, and cytokinesis (24); PFN1, that, when over- 0.001, data not shown). Thus, we could expect to observe not expressed, reduces the migration of invasive breast cancer cells only a more specific gene expression profile but also novel genes (25); CLDN4 and KRT7, whose expression was detected in in our INV library. We in fact detected 29 tags matching to ESTs normal estrogen-responsive cells (26), suggesting that they may or mRNAs encoding for hypothetical proteins, and 11 no- encode factors important in the autocrine͞paracrine estrogen- matching tags that had not been detected previously (see Tables signaling pathway regulating normal mammary epithelium; 6 and 8). SCGB1D2 and CST3, which are both of unknown function. We also found down-regulation of mitochondrial genes, such as TAG Analysis. From a total of 37,751 tags analyzed, 16,939 unique SOD2 (1, 2), and down-regulation of the ribosomal genes S18, tags were detected. By using a P value of Ͻ0.05 and a ratio of S29, L13, L35, L36, and L36a (see Tables 1 and 3). It is reported difference in expression of Ͼ1.5-fold, 559 tags were identified as that these ribosomal genes may act as tumor suppressors, differentially expressed in a significant way in the INV and MET because loss-of-function mutations in these genes result in tumor

18148 ͉ www.pnas.org͞cgi͞doi͞10.1073͞pnas.0408260101 Zucchi et al. Downloaded by guest on September 29, 2021 Table 1. Selection of the 50 genes most down-regulated in the INV and͞or MET libraries with respect to the Br࿝N library from normal mammary epithelium Gene symbol Br࿝N INV MET Unigene ID Genetic map

Transcription factors͞chromatin͞nuclear proteins NFKBLA 20 0 1 81328 14q13 LMNA 9 1 1 436441 1q21.2-q21.3 JUNB 8 0 0 400124 19p13.2 HMGN1 7 1 1 356285 21q22.3 PNRC1 6 0 0 75969 6q16.1 JUND 5 0 0 2780 19p13.2 DDB1 5 0 1 290758 11q12-q13 DDX5 12 4 1 279806 17q21 BAP1 4 0 1* 106674 3p21.31-p21.2 Cell cycle͞apoptosis͞cell growth MSF 6 0 0 288094 17q25 GADD45B 6 1 0 110571 19p13.3 PPM1G 6 1 1 17883 2p23.3 AREG 5 0 0 270833 4q13-q21 LGALS3 5 0 0 411701 14q21-q22 DOC-1R 4 0 0 379039 11q13 PDCD5 4 0 0 443831 19q12-q13.1 Membrane proteins͞antigens͞receptors͞adhesion TM4SF1 16 0 0 351316 3q21-q25 ANXA1 9 1 1 287558 9q12-q21.2 ZYX 7 0 0 75873 7q32 CLDN4 4 0 0 5372 7q11.23 Signal transduction TACSTD2 12 1 0 23582 1p32-p31 ARHA 8 0 0 77273 3p21.3 MEDICAL SCIENCES STRN4 5 0 0 406918 19q13.2 RASD1 4 0 0 25829 17p11.2 YWHAE 7 2 1 79474 17p13.3 Secreted and ECM proteins LOC118430 60 1 0 348419 12q CXCL1 45 0 0 789 4q21 IL8 44 0 0 624 4q13-q21 CXCL2 22 0 0 75765 4q21 LIF 10 0 0 2250 22q12.2 SCGB1D2 7 0 0 204096 11q13 CXCL3 5 0 0 89690 4q21 CCL20 4 0 0 75498 2q33-q37 CXCL6 4 0 0 164021 4q21 IL6 4 0 0 512234 7p21 SEMA3B 4 0 0 82222 3p21.3 CST3 6 0 2* 304682 20p11.21 Cytoskeleton ACTG1 21 1 0 14376 17q25 KRT6B 8 0 0 432677 12q12-q13 PDLIM1 8 0 0 75807 10q22-q26.3 KRT7 14 2 2 23881 12q12-q13 PFN1 20 6 4 408943 17p13.3 Protein synthesis, transport, and degradation EIF4A1 11 0 2 129673 17p13 SEC61B 8 0 0 191887 9q22.32-q31.3 SUI1 10 2 3 150580 17q21.31 EIF3S4 4 0 0 28081 19p13.2 PICALM 4 0 0 39252 11q14 ST13 4 0 1* 377199 22q13.2 Metabolism SOD2 26 0 9 384944 6q25.3 SAT 10 0 0 28491 Xp22.1

The frequency for each tag is given for a 10,000-tag library. Asterisks refer to tags whose P value is not statistically signiﬁcant in one of the libraries. The complete data are published in Tables 3–8.

formation at high frequency in zebrafish cell lines (27). We morphogenesis of the normal mammary epithelium (2). This observed that many of the genes, the expression of which was finding suggests that loss of specific functions, including the found down-regulated in our SAGE libraries, play important ability to differentiate, or the loss of the epithelial phenotype, roles in the regulation of cell growth, differentiation, and may have an essential role in tumorigenesis.

Zucchi et al. PNAS ͉ December 28, 2004 ͉ vol. 101 ͉ no. 52 ͉ 18149 Downloaded by guest on September 29, 2021 Table 2. Selection of the 50 genes most up-regulated in the INV and͞or MET libraries with respect to the Br࿝N library from normal mammary epithelium Gene symbol Br࿝N INV MET Unigene ID Genetic map Transcription factors͞chromatin͞nuclear proteins HNRPC 1 35 20 476302 14q11.2 HIF1A 0 0* 31 412416 14q21-q24 HMGA1 1 1* 26 57301 6p21 RMP 0 23 0* 7943 19q12 H2AFZ 0 1* 23 119192 4q24 SRCAP 1 19 0* 136227 16p11.2 DHX9 0 0* 17 374524 1q25 PTMA 1 1* 15 459927 2q35-q36 ZNF9 0 0* 12 2110 3q21 HMGB1 2 7* 22 434102 13q12 SFRS6 1 0* 9 6891 20q12-q13.1 NME1 0 1* 8 118638 17q21.3 ADNP 0 7 0* 448540 20q13.13 Cell cycle͞apoptosis͞cell growth CEB1 0 1* 20 26663 4q22.1-q23 TGFA 0 15 0* 170009 2p13 CTGF 0 13 0* 410037 6q23.1 CCNA2 0 5 1* 85137 4q25-q31 SPY1 3 11 0* 511956 2p23.3 Membrane proteins͞antigens͞receptors͞adhesion GAGED2 0 0* 36 112208 Xp11.22-p11.21 IFITM3 0 1* 27 374650 11p15.5 STEAP 0 0* 9 61635 7q21 Signal transduction DUSP23 1 1* 22 425801 1q23.1 GNG11 0 0* 19 83381 7q31-q32 PTPN1 0 19 0* 418004 20q13.1-q13.2 YWHAZ 0 1* 15 386834 8q23.1 GNB2L1 3 1* 30 5662 5q35.3 DGKQ 0 8 0* 99932 4p16.3 Secreted and ECM proteins IGFBP4 1 1* 12 1516 17q12-q21.1 FDC-SP 0 9 0* 320147 4q13 EDN2 1 7 0* 1407 1p34 FN1 0 1* 6 418138 2q34 Cytoskeleton CYFIP1 0 14 4 26704 15q11 FSCN1 0 1* 23 118400 7p22 CAP1 1 0* 11 104125 1p34.2 Protein synthesis, transport, and degradation SAE1 1 0* 18 32748 19q13.33 CCT5 1 2* 17 1600 5p15.31 UBA52 2 3* 31 5308 19p13.1-p12 TRAP1 0 0* 14 183803 16p13.3 RPN2 0 0* 11 406532 20q12-q13.1 PPIA 17 27* 159 356331 7p13-p11.2 YKT6 1 8 0* 296244 7p15.1 HSPA8 7 0 31 180414 11q24.1 TPT1 48 6 90 374596 13q12-q14 Metabolism LDHA 3 1* 68 2795 11p15.4 NNMT 1 0* 16 364345 11q23.1 ENO1 0 0* 11 433455 1p36.3-p36.2 AHCY 1 1* 9 388004 20cen-q13.1 Others SNCAIP 0 23 0* 24948 5q23.1-q23.3 ANKRD10 0 19 0* 164969 13q34 OBTP 1 1* 12 525899 6p21.31

The frequency for each tag is given for a 10,000-tag library. Asterisks refer to tags whose P value is not statistically signiﬁcant in one of the libraries. Values in italics indicate a P value that is statistically signiﬁcant, but with expression in the opposing direction in one of the libraries. The complete data are published in Tables 3–8.

Genes Up-Regulated in INV and MET Libraries. The genes up- genes are up-regulated in the MET libraries, with moderate-to- regulated in the INV and MET libraries are listed in Table 6. high increase of expression in respect to the normal epithelium From this list, 57 genes are up-regulated in the INV and 110 library; we found few genes up-regulated in both libraries. This

18150 ͉ www.pnas.org͞cgi͞doi͞10.1073͞pnas.0408260101 Zucchi et al. Downloaded by guest on September 29, 2021 Fig. 1. CXCL6 expression in human breast tissues. Frozen sections of normal breast tissue (A) and breast-invasive ductal carcinoma (B) were hybridized with a digitonin-labeled CXCL6 antisense riboprobe. Strong expression of CXCL6 was detected in normal breast samples (A; ϫ10 magniﬁcation) but not in the invasive breast cancer samples (B; ϫ40 magniﬁcation). The sense probe gives no signal (data not shown).

gene list includes some key regulatory genes that play crucial Confirming Gene Expression by RNA in Situ Hybridization. In situ roles in cell proliferation, invasion, and metastasis in breast hybridization, performed on normal (Fig. 1A) and tumor sam- cancer or in other tumor types; genes not yet associated to the ples of breast cells (Fig. 1B), confirmed the expression of CXCL6 genesis of cancer and several ESTs of unknown function. in the normal mammary tissue and its epithelial localization. The list of genes highly expressed in the INV library (Table 2)

includes some genes already known to be linked to cancer, such Conclusions MEDICAL SCIENCES as ADNP, implicated in maintaining cell survival (28); TGFA, In this paper, we report our initial work on the profiles of gene which has a role in breast tumor growth and progression (29, 30); expression in human breast carcinoma cells compared with CTGF, which stimulates angiogenesis (31); CCNA2, an oncogene normal epithelial cells, by using the SAGE approach. We used overexpressed in liver tumors (32); SPY1, which induces cell- very pure populations of cancer cells obtained by microscopic cycle progression (33); PTPN1, an activator of the cSrc and RAS isolation from frozen sections of a primary invasive breast signaling pathway, amplified in ovarian cancer (34); and YKT6 carcinoma (N.0) and from a nodal metastasis of a breast Ϸ (also called SNARE), which is associated with invasive and carcinoma. By using this approach, we collected 500 cells metastatic phenotypes in breast cancer (35). The list of genes from each sample, and the amount of mRNA obtained from highly expressed in the INV library also includes genes not these number of cells was very small; therefore the mRNA was previously associated with the genesis of cancer, such as CYFIP1, amplified. We show that this procedure can be performed SNCAIP, and ANKRD10; 11 no-matching tags and 29 tags without altering the original mRNA abundance. This approach matching to ESTs or mRNAs encoding for hypothetical proteins. is shown to be feasible, and the analysis of the two libraries Most of the up-regulated genes were found in the MET documents the presence of changes in the expression of many library. Many of these were already recognized to be associated genes, in agreement with what has been already shown by others, but there are differences in the genes affected. As with cancer, such as DHX9, which inhibits BRCA1 (36); PTMA, previously shown, we also found that several classes of genes which prevents apoptosis (37); CEB1, which is elevated when the are down-regulated or up-regulated in both libraries. A more function of p53 and RB are compromised (38); GAGED2, which general observation can be made: many of the down-regulated is overexpressed in a variety of tumors (39); STEAP, which is genes are the same in both libraries, whereas the up-regulated associated with prostate cancer and tumor progression (40); ͞ genes are almost completely different in the two libraries. The GNB2L1 RACK1, which inhibits apoptosis (41); PPIA, which is difference in the up-regulated genes may be due to the fact that associated with prostate cancer (42); and TPT1, which is over- our two libraries were obtained from different patients, who expressed in colon cancer (43). Some other genes, like OBTP, had cancers with different characteristics, especially in estro- have no known function. The activity of some of the up-regulated gen receptor expression, and could therefore have arisen by genes (see Table 2) correlates with increased cell proliferation: different mechanisms. However, there is a more interesting four of these genes are up-regulated in the INV library (ADNP, explanation: the down-regulation of a set of genes may be the TGFA, SPY1 and PTPN1), and three are up-regulated in the basic mechanism of cancer formation, whereas the up- MET library (PTMA, CEB1 and GNB2L1). regulation may characterize and possibly control the state of Some of the genes from Table 2, such as NME1 and FN1, have evolution of individual cancers, but further work is required to been already described through different expression-profiling verify this hypothesis. This information should be useful for methods to be up-regulated in breast carcinoma (44), and the clarifying the mechanism involved in the formation and pro- genes IGFBP4 and CCNA2 were found differentially regulated gression of breast cancer. in estrogen-responsive breast cancer cells (26). Several up- This work describes, in an accurate and objective way, the regulated genes of the MET library, such as PTMA, NME1, gene expression profiles of two human breast carcinomas CEB1, GNB2L1, TRAP1, HSPA8, TPT1, LDHA, RPS5, RPS7, uncontaminated by accidental intrusions from non-cancer RPL8, RPL32, and RPL34 are known to be induced by ectopic cells, and not biased by arbitrary gene selection. This approach expression of c-MYC (45). Two genes (EDN2 and NNMT) were has the potential to be used in other systems where the cells found up-regulated in human mammary epithelial cells express- of interest are very few and the contaminating cells can be a ing high levels of ERBB2 (46). problem.

Zucchi et al. PNAS ͉ December 28, 2004 ͉ vol. 101 ͉ no. 52 ͉ 18151 Downloaded by guest on September 29, 2021 We thank Drs. N. A. Datson, V. Velculescu, and S. M. Wang for their dell’Istruzione, dell’Universita`e della Ricerca͞Fondo per gli Inves- advice on the SAGE protocol; Drs. S. Bertuzzi and P. Taglialatela for timenti della Ricerca di Base Grant RBME019J9W (to P.V. and I.Z.); their advice on the in situ hybridization protocol; Dr. G. Bertalot for a Compagnia di San Paolo Grant (to P.V.); and Progetto Strategico: assistance and suggestions; and L. Susani and B. Vergani for their Genomica Funzionale, Consiglio Nazionale delle Ricerche Grant technical assistance. This work was supported by Associazione Italiana 449͞97 (to P.V.). This manuscript is no. 80 of the Genoma 2000͞ per la Ricerca sul Cancro Grant 115͞2003 (to I.Z.); Italy-USA Project Istituto Tecnologie Biomediche Avanzate Project funded by the on Cancer Pharmacogenomics Grant N.527͞B-B7 (to I.Z.); Ministero Cariplo Foundation.

1. Porter, D. A., Krop, I. E., Nasser, S., Sgroi, D., Kaelin, C. M., Marks, J. R., 25. Roy, P. & Jacobson, K. (2004) Cell Motil. Cytoskeleton 57, 84–95. Riggins, G. & Polyak, K. (2001) Cancer Res. 61, 5697–5702. 26. Vendrell, J. A., Magnino, F., Danis, E., Duchesne, M. J., Pinloche, S., Pons, M., 2. Porter, D., Lahti-Domenici, J., Keshaviah, A., Bae, Y. K., Argani, P., Marks, Birnbaum, D., Nguyen, C., Theillet, C. & Cohen, P. A. (2004) J. Mol. J., Richardson, A., Cooper, A., Strauberg, R. & Riggins, G. J. (2003) Mol. Endocrinol. 32, 397–414. Cancer Res. 1, 362–375. 27. Amsterdam, A., Sadler, K. C., Lai, K., Farrington, S., Bronson, R. T., Lees, J. A. 3. Perou, C. M., Sorlie, T., Eisen, M. B., van de Rijn, M., Jeffrey, S. S., Rees, C. A., & Hopkins, N. (2004) PLoS Biol. 2, 690–698. Pollack, J. R., Ross, D. T., Johnsen, H., Akslen, L. A., et al. (2000) Nature 406, 28. Zamostiano, R., Pinhasov, A., Gelber, E., Steingart, R. A., Seroussi, E., Giladi, 747–752. E., Bassan, M., Wollman, Y., Eyre, H. J., Mulley, J. C., et al. (2001) J. Biol. 4. van’t Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A., Mao, Chem. 276, 708–714. M., Peterse, H. L., van der Kooy, K., Marton, M. J., Witteveen, A. T., et al. 29. Jiang, D., Yang, H., Willson, J. K., Liang, J., Humphrey, L. E., Zborowska, E., (2002) Nature 415, 530–536. Wang, D., Foster, J., Fan, R. & Brattain, M. G. (1998) J. Biol. Chem. 273, 5. Sorlie, T., Tibshirani, R., Parker, J., Hastie, T., Marron, J. S., Nobel, A., Deng, 31471–31479. S., Johnsen, H., Pesich, R., Geisler, S., et al. (2003) Proc. Natl. Acad. Sci. USA 30. Umekita, Y., Ohi, Y., Sagara, Y. & Yoshida, H. (2000) Int. J. Cancer. 89, 100, 8418–8423. 484–487. 6. Iacobuzio-Donahue, C. A., Argani, P., Hempen, P. M., Jones, J. & Kern, S. E. 31. Shimo, T., Kubota, S., Kondo, S., Nakanishi, T., Sasaki, A., Mese, H., (2002) Cancer Res. 62, 5351–5357. Matsumura, T. & Takigawa, M. (2001) Cancer Lett. 174, 57–64. 7. Ronnov-Jessen, L., Peterson, O. W. & Bissel, M. (1996) Phisyol. Rev. 76, 32. Haddad, R., Morrow, A. D., Plass, C. & Held, W. A. (2000) Biochem. Biophys. 69–125. Res. Commun. 274, 188–196. 8. Franzen, B., Linder, S., Okuzawa, K., Kato, H. & Auer G. (1999) Electrophoresis 33. Porter, L. A., Dellinger, R. W., Tynan, J. A., Barnes, E. A., Kong, M., 14, 1045–1053. Lenormand, J. L. & Donoghu, D. J. (2002) J. Cell Biol. 157, 357–366. 9. Velculescu, V. E., Zhang, L., Vogelstein, B. & Kinzler, K. W. (1995) Science 34. Dube, N., Cheng, A. & Tremblay, M. L. (2004) Proc. Natl. Acad. Sci. USA 101, 270, 484–487. 1834–1839. 10. Margulies, E. H. & Innis, J. W. (2000) Bioinformatics 16, 650–651. 35. Kluger, H. M., Kluger, Y., Gilmore-Hebert, M., DiVito, K., Chang, J. T., 11. Kuznetsov, V. A., Knott, G. D. & Bonner, R. F. (2002) Genetics 161, 1321–1332. Rodov, S., Mironenko, O., Kacinski, B. M., Perkins, A. S. & Sapi, E. (2004) Lab. 12. Sambrook, J. & Russel, D. W. (2001) Molecular Cloning: A Laboratory Manual Invest. 84, 320–331. (Cold Spring Harbor Lab. Press, Plainview, NY). 36. Schlegel, B. P., Starita, L. M. & Parvin, J. D. (2003) Oncogene 22, 983–991. 13. Kuznetsov, V. A. (2001) EURASIP J. on Applied Signal Processing 4, 285–296. 37. Jiang, X., Kim, H. E., Shu, H., Zhao, Y., Zhang, H., Kofron, J., Donnelly, J., 14. Russell, S. E. H., McIlhatton, M. A., Burrows, J. F., Donaghy, P. G., Chanduloy, Burns, D., Ng, S. C., Rosenberg, S., et al. (2003) Science 299, 223–226. S., Petty, E. M., Kalikin, L. M., Church, S. W., McIlroy, S., Harkin, D. P., et al. 38. Mitsui, K., Nakanishi, M., Ohtsuka, S., Norwood, T. H., Okabayashi, K., (2000) Cancer Res. 60, 4729–4734. Miyamoto, C., Tanaka, K., Yoshimura, A. & Ohtsubo, M. (1999) Biochem. 15. Todd, R., McBride, J., Tsuji, T., Donoff, R. B., Nagai, M., Chou, M. Y., Chiang, Biophys. Res. Commun. 266, 115–122. T. & Wong, D. T. (1995) FASEB J. 9, 1362–1370. 39. Egland, K. A., Kumar, V., Duray, P. & Pastan, I. (2002) Mol. Cancer Ther. 1, 16. Tanaka, K., Tamura, H., Tanaka, H., Katoh, M., Futamata, Y., Seki, N., 441–450. Nishimune, Y. & Hara, T. (2002) Dev. Biol. 246, 466–479. 40. Hubert, R. S., Vivanco, I., Chen, E., Rastegar, S., Leong, K., Mitchell, S. C., 17. Zhang, Y., Cai, X., Schlegelberger, B. & Zheng, S. (1998) Cytogenet. Cell Genet. Madraswala, R., Zhou, Y., Kuo, J., Raitano, A. B., et al. (1999) Proc. Natl. Acad. 83, 56–57. Sci. USA 96, 14523–14528. 18. Yoo, J., Ghiassi, M., Jirmanova, L., Balliet, A. G., Hoffman, B., Fornace, A. J., 41. Kiely, P. A., Sant, A. & O’Connor, R. (2002) J. Biol. Chem. 277, 22581–22589. Jr., Liebermann, D. A., Bottinger, E. P. & Roberts, A. B. (2003) J. Biol. Chem. 42. Cande, C., Vahsen, N., Kouranti, I., Schmitt, E., Daugas, E., Spahr, C., Luban, 278, 43001–43007. J., Kroemer, R. T., Giordanetto, F., Garrido, C., et al. (2004) Oncogene 23, 19. Chen, Y., Sun, R., Han, W., Zhang, Y., Song, Q., Di, C. & Ma, D. (2001) FEBS 1514–1521. Lett. 509, 191–196. 43. Chung, S., Kim, M., Choi, W., Chung, J. & Lee, K. (2000) Cancer Lett. 156, 20. Grant, S. L., Douglas, A. M., Goss, G. A. & Begley, C. G. (2001) Growth Factors 185–190. 19, 153–162. 44. Amatschek, S., Koenig, U., Auer, H., Steinlein, P., Pacher, M., Gruenfelder, A., 21. Ochi, K., Mori, T., Toyama, Y., Nakamura, Y. & Arakawa, H. (2002) Neoplasia Dekan, G., Vogl, S., Kubista, E., Heider, K. H., et al. (2004) Cancer Res. 64, 4, 82–87. 844–856. 22. Kuroki, T., Trapasso, F., Yendamuri, S., Matsuyama, A., Alder, H., Williams, 45. Menssen, A. & Hermeking, H. (2002) Proc. Natl. Acad. Sci. USA 99, 6274– N. N., Kaiser, L. R. & Croce, C. M. (2003) Cancer Res. 63, 3352–3355. 6279. 23. Lerman, M. I. & Minna, J. D. (2000) Cancer Res. 60, 6116–6133. 46. Mackay, A., Jones, C., Dexter, T., Silva, R. L., Bulmer, K., Jones, A., Simpson, 24. Vallenius, T. & Makela, T. P. (2002) J. Cell Sci. 115, 2067–2073. P., Harris, R. A., Jat, P. S., Neville, A. M., et al. (2003) Oncogene 22, 2680–2688.

18152 ͉ www.pnas.org͞cgi͞doi͞10.1073͞pnas.0408260101 Zucchi et al. Downloaded by guest on September 29, 2021