Gene Expression Profile of Metastatic Human Pancreatic Cancer Cells Depends on the Organ Microenvironment Toru Nakamura,1 Isaiah J

Research Article

Gene Expression Profile of Metastatic Human Pancreatic Cancer Cells Depends on the Organ Microenvironment Toru Nakamura,1 Isaiah J. Fidler,1 and Kevin R. Coombes2

Departments of 1Cancer Biology and 2Biostatistics and Applied Mathematics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas

Abstract in vitro as monolayer cultures. Although easy to accomplish, To determine the influence of the microenvironment on monolayer cultures are not subjected to any cross talk (e.g., changes in gene expression, we did microarray analysis on paracrine signaling pathways associated with growth in vivo; three variant lines of a human pancreatic cancer (FG, L3.3, ref. 5). Clinical observations of cancer patients and studies in and L3.6pl) with different metastatic potentials. The variant rodent models of cancers have concluded that certain tumors tend lines were grown in tissue culture in the subcutis (ectopic) or to metastasize to certain organs (6). The concept that metastasis pancreas (orthotopic) of nude mice. Compared with tissue results only when certain tumor cells interact with a specific culture, the number of genes of which the expression was organ microenvironment was originally proposed in Paget’s affected by the microenvironment was up-regulated in tumors venerable ‘‘seed and soil’’ hypothesis (7). Indeed, spontaneous metastasis is produced by tumors growing at orthotopic sites, growing in the subcutis and pancreas. In addition, highly metastatic L3.6pl cells growing in the pancreas expressed whereas the same tumor cells implanted into ectopic sites fail to significantly higher levels of 226 genes than did the L3.3 or produce metastasis (8). FG variant cells. Growth of the variant lines in the subcutis The purpose of this study was to determine whether expression did not yield similar results, indicating that the orthotopic of genes associated with metastatic potential of tumor cells microenvironment significantly influences gene expression in depends on the organ microenvironment. We have previously pancreatic cancer cells. These data suggest that investigations described an orthotopic model for metastasis of human pancreatic of the functional consequence of gene expression require cancer used to select variant cell lines with increasing metastatic capacity (9). We used gene expression profiles generated from accounting for experimental growth conditions. [Cancer Res microarray analysis to compare the phenotypes of three pancreatic 2007;67(1):139–48] cell lines with different metastatic potentials growing in vitro as monolayer cultures or in vivo at ectopic (s.c.) and orthotopic Introduction (pancreatic) sites. We found that the differential gene expression At the time of diagnosis, human neoplasms are heterogeneous profile in metastatic pancreatic cancer cells depends on growth in and consist of multiple subpopulations of cells with differing a biologically relevant orthotopic organ microenvironment. biological properties that include metastatic potential (1, 2). Cells with different metastatic properties have been isolated from primary neoplasms and from metastatic lesions, showing that Materials and Methods not all tumor cells have the capacity to complete the arduous Pancreatic cancer cell lines and culture conditions. The selection of metastatic process (1). Card analysis using marker chromosomes the human pancreatic cell lines with different metastatic potentials was showed that metastases originate from a single progenitor cell previously described (9). The low metastatic COLO 375 FG human (3). Isolation of cells with different metastatic potential from pancreatic cancer cell line was injected into the spleen of nude mice. Cells heterogeneous neoplasms has been accomplished by in vivo isolated from liver lesions designated as L3.3 produced liver lesions at a selection or by random cloning of cultured parental tumor cells higher incidence than the original COLO 375 FG cells did. Next, the L3.3 (1). Tumor cells with different metastatic capabilities have been cells were injected into the pancreas of nude mice. Spontaneous liver shown to differ in expression of proteins, such as collagenases, metastases were harvested and cells were established in culture. The E-cadherin, interleukin-8, basic fibroblast growth factor, and many selection cycle was repeated thrice to yield a cell line designated L3.6pl (pancreas-to-liver metastasis; ref. 9). others (4). In most cases, however, these differential expressions The variant cell lines (FG, L3.3, and L3.6pl) were maintained in Eagle’s were most evident in tumor cells growing at specific orthotopic MEM supplemented with 10% fetal bovine serum, sodium pyruvate, sites. nonessential amino acids, L-glutamine, a 2-fold vitamin solution (Life Recent advances in the development of microarrays provide a Technologies, Inc., Grand Island, NY), and a penicillin-streptomycin mixture method to assess genome-wide modifications in gene expression (Flow Laboratories, Rockville, MD). Adherent monolayer cultures were j that can differentiate between metastatic and nonmetastatic maintained on plastic and incubated at 37 C in a mixture of 5% CO2 cells isolated from a single parental neoplasm. Many biological and 95% air. The cultures were free of Mycoplasma and the following investigations of solid tumor cells often use cell lines growing pathogenic murine viruses: reovirus type 3, pneumonia virus, K virus, Theiler’s encephalitis virus, Sendai virus, minute virus, mouse adenovirus, mouse hepatitis virus, lymphocytic choriomeningitis virus, ectromelia Note: Supplementary data for this article are available at Cancer Research Online virus, and lactate dehydrogenase virus (assayed by M.A. Bioproducts, (http://cancerres.aacrjournals.org/). Walkersville, MD). The cultures were maintained for no longer than Requests for reprints: Isaiah J. Fidler, Department of Cancer Biology, Unit 173, 12 weeks after recovery from frozen stocks. The University of Texas M.D. Anderson Cancer Center, P.O. Box 302429, Houston, TX Animals and production of tumors. Male athymic BALB/c nude mice 77230-1429. Phone: 713-792-8580; Fax: 713-792-8747; E-mail: [email protected]. I2007 American Association for Cancer Research. (NCI-nu) were obtained from the Animal Production Area of the National doi:10.1158/0008-5472.CAN-06-2563 Cancer Institute Frederick Cancer Research and Development Center www.aacrjournals.org 139 Cancer Res 2007; 67: (1).January 1, 2007

Figure 1. Hierarchical clustering of 18 samples based on the most reliably expressed genes. CL, cell culture; SC, subcutaneous; PC, pancreas (orthotopic). Vertical axis, distance [(1 À q) / 2] computed from the Pearson product-moment correlation coefficient q.

(Frederick, MD). The mice were maintained under specific pathogen- Adams, Parsippany, NJ). The animals tolerated the surgical procedure well free conditions in facilities approved by the American Association for and no anesthesia-related deaths occurred. Accreditation of Laboratory Animal Care and in accordance with current S.c. injections. Tumor cells (1 Â 106 per 50 AL HBSS) were injected into regulations and standards of the U.S. Department of Agriculture, the subcutis of the lateral flank. Tumors were harvested when they reached Department of Health and Human Services, and NIH. The mice were 7 to 9 mm in diameter. Half of each tumor was snap frozen in liquid used in accordance with institutional guidelines when they were 8 to nitrogen for mRNA extraction; the other half was fixed in formalin and 12 weeks old. embedded in paraffin. Tumor cell injection techniques. For in vivo injection, cells were RNA sample preparation. Total RNA was extracted from cultured cells harvested from subconfluent cultures by a brief exposure to 0.25% trypsin or tumor tissues by using Trizol reagent (Invitrogen, Carlsbad, CA). The and 0.02% EDTA. Trypsinization was stopped with medium containing extracted RNA was passed through an RNeasy spin column (Qiagen, 10% fetal bovine serum and then the cells were washed once in serum- Valencia, CA) with on-column DNase I treatment to remove any free medium and resuspended in HBSS. Only single-cell suspensions of contaminating genomic DNA according to the manufacturer’s protocol. >90% viability (assessed by trypan blue exclusion assay) were used for Affymetrix GeneChip hybridization. Human Genome U133 Plus 2.0 injection. GeneChip arrays (Affymetrix, Santa Clara, CA) were used for microarray Orthotopic injection. The mice were anesthetized with pentobarbital hybridizations. This GeneChip carries 54,675 probe sets. For microarray sodium administered by i.m. injection. A small left abdominal flank incision hybridization, we followed the protocol described in the manufacturer’s was made and the spleen exteriorized. Tumor cells (1 Â 106 per 50 AL HBSS) eukaryotic one-cycle target preparation protocol. In short, 10 Ag of total were injected subcapsularly in a region of the pancreas just beneath the RNA were used to prepare antisense biotinylated RNA and single-stranded spleen. We used a 30-gauge needle with a 1-mL disposable syringe. A cDNA was synthesized using a T7-Oligo(dT) promoter primer followed by successful subcapsular intrapancreatic injection of tumor cells was RNase H–facilitated second-strand cDNA synthesis, which was purified and identified by the appearance of a fluid bleb without i.p. leakage. To prevent served as a template in the subsequent in vitro transcription. The in vitro leakage, a cotton swab was held for over the site of injection for 1 min. One transcription reaction was carried out in the presence of T7 RNA layer of the abdominal wound was closed with wound clips (Auto-clip, Clay polymerase and a biotinylated nucleotide analogue/ribonucleotide mix

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Downloaded from cancerres.aacrjournals.org on September 26, 2021. © 2007 American Association for Cancer Research. Gene Expression by Metastatic Cells for cRNA. The biotinylated cRNA targets were then cleaned up and fragmented. The fragmented cRNA was used for hybridization to the U133 Plus 2.0 chip at 42jC for 16 h. The chips were washed and stained with Affymetrix GeneChip Fluid, then scanned and visualized with a GeneArray Scanner (Hewlett-Packard, Palo Alto, CA). Statistical analysis. Perfect match and mismatch features on the scanned microarray images were quantified using the Affymetrix Micro- array Suite 5.0 software (MAS 5.0). After quantification, quality control was done using the Affymetrix quality control metrics. Two arrays that failed quality control (i.e., percent present calls V40% when the median percent present call was 51.8%) were repeated with fresh extractions of RNA. The qualified data sets were then analyzed with DNA-Chip Analyzer software (dChip3; ref. 10) to produce probe set expression measurements, which were exported for analysis using the R Statistical Programming Language4 (11). Expression measurements were transformed by computing the base- two logarithm before further analysis. We filtered the genes to do hierarchical clustering using the most reliably measured genes. More precisely, we retained genes that were called present in at least 20% of genes and had an expression value >500 in at least half of the samples. We did a separate two-way ANOVA for each probe set on the Affymetrix array using the linear model Yijk = l + ai + bj + cij + Eijk (Eqn. 1), where Yijk is the observed expression of the gene in replicate k of cell line i under growth condition j; l is the overall mean expression of the gene; ai b represents the effect due to the cell line (i = FG, L3.3, or L3.6pl); j represents the effect due to the growth condition (j = culture, s.c., or orthotopic); cij represents an interaction between cell line and growth f j condition; and Eijk N (0, ) is the residual errors, which are assumed to be independent and identically distributed. We did an F test for each probe set to determine whether the model fit the observed data better than the null model Yijk = l + Eijk and computed the corresponding (unadjusted) P values. To account for multiple testing, we modeled the collection of all 54,675 P values using a h-uniform mixture model (12). We then used the h-uniform mixture model to estimate the false discovery rate (13). Pattern identification. All genes that were found to be significant by ANOVA were clustered, using unsupervised hierarchical clustering in R, to identify patterns of gene expression across the sample set. Select genes that are up-regulated in vitro, in vivo,andin metastasis. To select the 50 most highly up-regulated genes in each of these three categories, we focused on genes with significant ANOVA results. These genes were filtered to identify those expressed at a higher level in orthotopic tumors than in culture (in vivo set), higher in culture than orthotopically (in vitro set), or higher in L3.6pl than in FG (metastasis set). The filtered genes were ranked by the P values for the significance of the term in the model related to growth condition (in vivo and in vitro sets) or to cell lines (metastasis set). Gene Ontology analysis. Patterns that were represented in the top 50 lists were analyzed to identify functional categories using the Database for Annotation, Visualization and Integrated Discovery (DAVID).5 We used the Functional Annotation Tool program and reported only GOTERM-BP (Biological Process) that had corrected P values of <0.05.

Results We did 18 microarray experiments using human U133 Plus 2.0 GeneChip microarrays, which contained 54,675 probe sets. Samples came from several mouse xenograft models of human pancreatic cancer, in which three different cell lines were grown in culture or implanted s.c. or orthotopically. The experiment followed a complete 3 Â 3 factorial design with two replicates of each combination. To determine differentially expressed genes between

Figure 2. Two-way hierarchical clustering of samples and significant genes. Red, higher expression; green, lower expression. Samples were clustered based on all 8,686 reliably expressed genes among the total of 54,675. The 3 Freely available at http://www.dchip.org/. 5,495 significant genes selected by ANOVA were standardized and clustered on 4 Freely available at http://cran.r-project.org/. the basis of their expression patterns. The horizontal purple lines separate 5 http://niaid.abcc.ncifcrf.gov/. clusters obtained by cutting the dendrogram to produce 30 clusters. www.aacrjournals.org 141 Cancer Res 2007; 67: (1).January 1, 2007

Figure 3. Expression patterns of clusters of significant genes (false discovery rate = 0.001). N, number of genes (of 5,495) in this cluster. Bright red, overexpressed; bright green, underexpressed; and black, mean expression level. The pattern numbers are the same as those in Fig. 2. tumor models (cell culture, s.c., or orthotopic) and between cell sets on the U133 Plus 2.0 GeneChip. h-Uniform mixture analysis lines based on their metastatic potential (L3.6pl, high; L3.3, low; indicated that substantial numbers of genes change expression FG, none), we used both hierarchical clustering and gene-by-gene between cell lines and in response to growth conditions ANOVA. (Supplementary Fig. S2). Overall, we found 5,495 statistically Hierarchical clustering. A total of 8,686 genes passed the filter significant probe sets when false discovery rate <0.001, which (see Materials and Methods) and we did hierarchical clustering of corresponded to a cutoff of P < 0.00046 on the unadjusted P values the samples using the Pearson product-moment correlation (see Materials and Methods). A complete list of the significant coefficient to define distances and Ward’s linkage rule (Fig. 1). All probe sets is available on our website.6 replicate pairs clustered as nearest neighbors, increasing our Using a global F test to compare the complete linear model confidence in the reproducibility of the results. The results showed (Eqn. 1) to the null model avoids additional multiple testing, but it that the six experiments done on cell cultures clustered as a block, requires an additional step to determine which factors drive the confirming that gene expression in vitro differs from the expression differential expression of individual genes. TSF2o address this issue, pattern of the same cells growing in vivo. At the next level, it we clustered all 5,495 significant probe sets based on their seemed that clustering was driven more by the cell line standardized expression profiles in the 18 samples using the (equivalently, by metastatic potential) than by the growth Pearson product-moment correlation coefficient to define distan- conditions of the tumor model. Reproducibility of the clusters ces and average linkage (Fig. 2). We cut the dendrogram to produce was tested using a bootstrap cluster test (14); at the very least, 30 different clusters; using 30 for the number of clusters was an the four main branches were robust to perturbations of the data arbitrary choice but it seems to be large enough to identify all the (Supplementary Fig. S1). distinct patterns in Fig. 2. ANOVA. To examine the two main effects (tumor model type and cell line or metastatic potential) along with their interactions, we did a separate two-way ANOVA for each of the 54,675 probe 6 http://bioinformatics.mdanderson.org/supplements.html.

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To relate the cluster patterns of Fig. 2 to the factors in the two- significantly lower levels in all three cell lines when they were way balanced design of the experiment, we computed the average growing in vitro and were expressed at higher levels when growing expression pattern of the genes in each cluster and displayed in vivo. In the same way, patterns 20, 21, 28, and 29 seem to each resulting pattern in a 3 Â 3 layout (Fig. 3). Differences that represent an in vitro pattern. are attributable to the local environment show up in these graphs We examined the top 50 genes (among the 5,495 significant ones) as horizontal bands. For example, patterns 1, 2, 7, 13, and 14 seem ranked by the significance of the ‘‘growth condition’’ term in the to represent in vivo patterns. These genes were expressed at ANOVA model (see Materials and Methods) for the contrast

Table 1. Up-regulated genes of cells in in vitro growth (top 50)

Probe set Uni gene Symbol Name Pattern Location P

218516_s_at Hs.438689 IMPA3 myo-inositol monophosphatase A3 21 4.37eÀ10 209642_at Hs.469649 BUB1 BUB1 budding uninhibited by benzimidazoles 1 homologue (yeast) 21 8.68eÀ10 223062_s_at Hs.494261 PSAT1 phosphoserine aminotransferase 1 28 9.43eÀ10 201475_x_at Hs.355867 MARS methionine-tRNA synthetase 28 1.04eÀ09 208778_s_at Hs.487054 TCP1 t-complex 1 21 1.30eÀ09 208079_s_at Hs.250822 STK6 serine/threonine kinase 6 21 2.61eÀ09 201263_at Hs.481860 TARS threonyl-tRNA synthetase 28 3.15eÀ09 202737_s_at Hs.515255 LSM4 LSM4 homologue, U6 small nuclear RNA associated (Saccharomyces cerevisiae) 28 3.38eÀ09 205047_s_at Hs.489207 ASNS asparagine synthetase 28 7.21eÀ09 216052_x_at Hs.194689 ARTN artemin 20 8.46eÀ09 204744_s_at Hs.445403 IARS isoleucine-tRNA synthetase 21 9.30eÀ09 221059_s_at Hs.289092 COTL1 coactosin-like 1 (Dictyostelium) 21 1.26eÀ08 214452_at Hs.438993 BCAT1 branched chain aminotransferase 1, cytosolic 28 1.93eÀ08 203209_at Hs.506989 RFC5 replication factor C (activator 1) 5, 36.5 kDa 28 2.48eÀ08 213008_at Hs.513126 FLJ10719 hypothetical protein FLJ10719 28 2.53eÀ08 203145_at Hs.514033 SPAG5 sperm associated antigen 5 21 2.64eÀ08 202481_at Hs.289347 DHRS3 dehydrogenase/reductase (SDR family) member 3 15 2.78eÀ08 209408_at Hs.69360 KIF2C kinesin family member 2C 21 2.80eÀ08 217829_s_at Hs.469173 USP39 ubiquitin specific protease 39 21 2.84eÀ08 213671_s_at Hs.355867 MARS methionine-tRNA synthetase 28 3.38eÀ08 225300_at Hs.525796 C15orf23 chromosome 15 open reading frame 23 21 3.77eÀ08 228400_at Hs.432504 — transcribed locus, strongly similar to NP_065910.1 Shroom-related protein; 28 3.81eÀ08 likely orthologue of mouse Shroom; F-actin-binding protein [Homo sapiens] 209545_s_at Hs.103755 RIPK2 receptor-interacting serine-threonine kinase 2 28 4.12eÀ08 217853_at Hs.520814 TENS1 tensin-like SH2 domain containing 1 28 5.44eÀ08 205194_at Hs.512656 PSPH phosphoserine phosphatase 28 6.30eÀ08 204092_s_at Hs.250822 STK6 serine/threonine kinase 6 21 6.39eÀ08 203405_at Hs.473838 DSCR2 Down syndrome critical region gene 2 21 8.09eÀ08 214794_at Hs.478044 PA2G4 proliferation-associated 2G4, 38 kDa 21 8.94eÀ08 220181_x_at Hs.482363 SLC30A5 solute carrier family 30 (zinc transporter), member 5 28 1.14eÀ07 208969_at Hs.75227 NDUFA9 NADH dehydrogenase (ubiquinone) 1a subcomplex, 9, 39 kDa 28 1.19eÀ07 226517_at Hs.438993 BCAT1 branched chain aminotransferase 1, cytosolic 21 1.26eÀ07 207528_s_at Hs.6682 SLC7A11 solute carrier family 7, member 11 21 1.32eÀ07 203968_s_at Hs.405958 CDC6 CDC6 cell division cycle 6 homologue (S. cerevisiae) 28 1.40eÀ07 225687_at Hs.472716 C20orf129 chromosome 20 open reading frame 129 21 1.46eÀ07 208511_at Hs.521097 PTTG3 pituitary tumor-transforming 3 28 1.60eÀ07 201266_at Hs.434367 TXNRD1 thioredoxin reductase 1 28 1.66eÀ07 205401_at Hs.516543 AGPS alkylglycerone phosphate synthase 28 1.66eÀ07 217080_s_at Hs.93564 HOMER2 homer homologue 2 (Drosophila) 20 1.73eÀ07 202533_s_at Hs.83765 DHFR dihydrofolate reductase 21 1.81eÀ07 204033_at Hs.436187 TRIP13 thyroid hormone receptor interactor 13 21 1.93eÀ07 206085_s_at Hs.19904 CTH cystathionase (cystathionine c-lyase) 15 2.02eÀ07 226001_at Hs.272251 KLHL5 kelch-like 5 (Drosophila) 28 2.07eÀ07 202870_s_at Hs.524947 CDC20 CDC20 cell division cycle 20 homologue (S. cerevisiae) 21 2.10eÀ07 202705_at Hs.194698 CCNB2 cyclin B2 21 2.13eÀ07 204023_at Hs.518475 RFC4 replication factor C (activator 1) 4, 37 kDa 21 2.20eÀ07 226661_at Hs.33366 CDCA2 cell division cycle associated 2 28 2.40eÀ07 218073_s_at Hs.476525 TMEM48 transmembrane protein 48 21 2.45eÀ07 1555427_s_at Hs.472056 SYNCRIP synaptotagmin binding, cytoplasmic RNA interacting protein 21 2.57eÀ07 225285_at Hs.438993 BCAT1 branched chain aminotransferase 1, cytosolic 21 2.65eÀ07 202402_s_at Hs.274873 CARS cysteinyl-tRNA synthetase 20 3.13eÀ07

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Downloaded from cancerres.aacrjournals.org on September 26, 2021. © 2007 American Association for Cancer Research. Cancer Research between growth in vitro and in vivo. Most of the 50 genes up- statistically significant functional categories using Gene Ontology regulated in cells growing in culture came from patterns 21 and 28. (Table 3). Multiple categories were statistically significant. The On the other hand, genes expressed in cells growing in vivo came patterns up-regulated in vitro were enriched for cell cycle primarily from patterns 1, 2, and 7 (Tables 1 and 2). To better (pattern 21), metabolism (patterns 21 and 28), and translation understand the functional differences among those patterns, we (pattern 28). The patterns up-regulated in vivo were enriched placed the genes from each pattern into DAVID, which identified for regulation of transcription (pattern 1), antigen processing

Table 2. Up-regulated genes of cells in in vivo growth (top 50)

Probe set Uni gene Symbol Name Pattern Location P

202310_s_at Hs.172928 COL1A1 collagen, type I, a1 1 4.58eÀ12 216470_x_at Hs.367767 PRSS1 protease, serine, 1 (trypsin 1) 3 1.29eÀ11 213089_at Hs.545578 LOC153561 hypothetical protein LOC153561 1 1.23eÀ10 217683_at Hs.117848 HBE1 Hemoglobin, epsilon 1 2 2.73eÀ10 203381_s_at Hs.515465 APOE apolipoprotein E 13 6.92eÀ10 1569491_at Hs.531872 — H. sapiens, clone IMAGE:5275957, mRNA 3 6.98eÀ10 214411_x_at Hs.74502 CTRB1 chymotrypsinogen B1 3 1.49eÀ09 233149_at Hs.132260 — CDNA clone IMAGE:4750272, containing frame-shift errors 2 5.21eÀ09 1565659_at Hs.32956 FUT6 Fucosyltransferase 6 (a (1,3) fucosyltransferase) 2 6.32eÀ09 224997_x_at Hs.551588 H19 H19, imprinted maternally expressed untranslated mRNA 7 7.95eÀ09 202403_s_at Hs.489142 COL1A2 collagen, type I, a2 1 8.08eÀ09 1561891_at Hs.385509 — H. sapiens, clone IMAGE:4137880, mRNA 2 8.39eÀ09 211699_x_at Hs.449630 HBA1, 2 hemoglobin, a1, a2 2 1.06eÀ08 216405_at Hs.445351 LGALS1 lectin, galactoside-binding, soluble, 1 (galectin 1) 2 1.40eÀ08 1557505_a_at Hs.167535 SRP54 signal recognition particle 54 kDa 1 1.41eÀ08 211745_x_at Hs.449630 HBA1, 2 hemoglobin, a1, a2 2 1.47eÀ08 217572_at Hs.551006 — transcribed locus, strongly similar to NP_032244.1 hemoglobin 2 2.04eÀ08 a, adult chain 1; a1 globin [Mus musculus] 209458_x_at Hs.449630 HBA1, 2 hemoglobin, a1, a2 2 2.20eÀ08 1570339_x_at Hs.535639 — H. sapiens, clone IMAGE:4933000, mRNA 1 2.82eÀ08 241867_at Hs.270244 PARP6 poly(ADP-ribose) polymerase family, member 6 2 2.86eÀ08 204018_x_at Hs.449630 HBA1, 2 hemoglobin, a1, a2 2 4.03eÀ08 219039_at Hs.516220 SEMA4C sema domain, immunoglobulin domain (Ig), transmembrane 2 5.13eÀ08 domain (TM) and short cytoplasmic domain (semaphorin) 4C 236582_at Hs.369606 CPSF6 cleavage and polyadenylation specific factor 6, 68 kDa 1 5.21eÀ08 1555778_a_at Hs.136348 POSTN periostin, osteoblast specific factor 1 5.37eÀ08 206446_s_at Hs.21 ELA2A elastase 2A 3 5.91eÀ08 226252_at Hs.202577 — CDNA FLJ34585 fis, clone KIDNE2008758 7 6.45eÀ08 224646_x_at Hs.551588 H19 H19, imprinted maternally expressed untranslated mRNA 7 7.20eÀ08 1562265_at Hs.378760 — mRNA; cDNA DKFZp313I1020 (from clone DKFZp313I1020) 7 7.57eÀ08 206151_x_at Hs.181289 ELA3B elastase 3B, pancreatic 3 8.87eÀ08 206598_at Hs.89832 INS insulin 3 9.72eÀ08 227423_at Hs.459507 LRRC28 leucine rich repeat containing 28 1 1.05eÀ07 205066_s_at Hs.527295 ENPP1 ectonucleotide pyrophosphatase/phosphodiesterase 1 12 1.09eÀ07 212099_at Hs.502876 RHOB ras homologue gene family, member B 7 1.19eÀ07 1557232_at Hs.547594 — H. sapiens, clone IMAGE:4797260, mRNA 1 1.21eÀ07 215076_s_at Hs.443625 COL3A1 collagen, type III, a1 2 1.28eÀ07 207463_x_at Hs.435699 PRSS3 protease, serine, 3 (mesotrypsin) 3 1.34eÀ07 215540_at Hs.74647 TRA@ T-cell receptor a locus 13 1.35eÀ07 240482_at Hs.519632 HDAC3 Histone deacetylase 3 2 1.38eÀ07 242846_at Hs.445885 KIAA1217 KIAA1217 7 1.65eÀ07 206813_at Hs.483811 CTF1 cardiotrophin 1 13 1.68eÀ07 205509_at Hs.477891 CPB1 carboxypeptidase B1 (tissue) 3 1.78eÀ07 238062_at Hs.426410 LOC338328 high density lipoprotein-binding protein 2 1.90eÀ07 1557690_x_at Hs.156832 NPAS2 neuronal PAS domain protein 2 12 2.20eÀ07 205879_x_at Hs.350321 RET ret proto-oncogene 2 2.45eÀ07 219463_at Hs.22920 C20orf103 chromosome 20 open reading frame 103 3 2.87eÀ07 210080_x_at Hs.471034 ELA3A elastase 3A, pancreatic (protease E) 3 2.96eÀ07 239373_at Hs.62578 ARFGEF2 ADP-ribosylation factor guanine nucleotide-exchange factor 2 1 2.98eÀ07 202936_s_at Hs.2316 SOX9 SRY (sex determining region Y)-box 9 12 3.18eÀ07 244778_x_at Hs.471818 M11S1 membrane component, chromosome 11, surface marker 1 7 3.23eÀ07 226061_s_at Hs.188882 NUDT3 nudix (nucleoside diphosphate linked moiety X)-type motif 3 1 3.42eÀ07

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Table 3. Functional annotation of up-regulated genes in in vitro or in vivo pattern

Category Gene count P

In vitro Pattern 21 Cell cycle 36 2eÀ06 Carboxylic acid metabolism 26 2eÀ05 Amino acid metabolism 16 2eÀ04 Cell division 13 3eÀ04 tRNA aminoacylation for protein translation 7 5eÀ04 Regulation of progression through cell cycle 22 8eÀ04 Spindle organization and biogenesis 5 9eÀ04 Pattern 28 RNA metabolism 23 8eÀ05 Amino acid and derivative metabolism 17 2eÀ04 Amino acid biosynthesis 7 3eÀ04 tRNA aminoacylation for protein translation 7 3eÀ04 Acetyl-coa metabolism 6 3eÀ04 Carboxylic acid metabolism 22 3eÀ04 Cellular respiration 6 7eÀ04 In vivo Pattern 1 Regulation of transcription from RNA polymerase II promoter 5 0.026 Phosphate transport 3 0.049 Sensory perception of mechanical stimulus 3 0.062 Pattern 2 Antigen processing, endogenous antigen via MHC class I 5 9eÀ05 Defense response 15 0.021 Organismal physiologic process 25 0.024 Response to biotic stimulus 15 0.03 Muscle contraction 5 0.039 Immune response 13 0.041 Pattern 7 Regulation of transcription, DNA-dependent 104 4eÀ04 Protein modification 86 6eÀ04 Negative regulation of progression through cell cycle 15 0.004 Establishment of protein localization 36 0.004 mRNA processing 17 0.006 Protein transport 34 0.007 Secretion 18 0.012 Ubiquitin cycle 31 0.012 Protein amino acid phosphorylation 33 0.019 Regulation of progression through cell cycle 28 0.021

(pattern 2), and protein modification (pattern 7). These data results suggest that ligand production and receptor activation are suggest that gene expression of cell lines growing under in vitro more prominent in the metastatic cell lines. conditions was optimized for cell proliferation, whereas gene Many of the patterns illustrate complex effects of both the local expression of the same cell lines growing in vivo was affected by environment and the metastatic potential. Some of these effects many other factors related to the microenvironment. seem to be additive, as in pattern 7. In that case, expression is high Differences between the cell lines, which may reflect their in FG and in L3.3 cells, but only when they are growing metastatic potential, show up as vertical bands in these graphs. For in vivo. The expression is low for all three of the cell lines example, patterns 4, 5, and 6 seem to include genes that are highly in vitro and stays low for the highly metastatic cell line L3.6pl even expressed in L3.3, and patterns 22 and 26 are expressed at high when it is grown in vivo. In addition, highly metastatic L3.6pl cells levels almost exclusively in FG. Patterns 10 and 16 seem to include growing in the pancreas expressed significantly higher levels of genes that are highly expressed in L3.6pl growing both in vitro and 226 genes than did the L3.3 or FG variant cells (in patterns 10, 11, in vivo, and thus may represent a metastatic pattern. In the same 13, 17, and 19). In addition, the L3.6pl cells growing in the s.c. space way, patterns 7 and 30 represent a lower metastatic pattern expressed significantly higher levels of 98 genes than the L3.3 or because they are expressed at higher levels in FG and L3.3 than in FG variant cells (see Materials and Methods). We used DAVID to L3.6pl. examine gene patterns that were differentially expressed when We also examined the top 50 genes up-regulated in metastasis the highly metastatic L3.6pl cells growing in the pancreas ranked by the significance of the ‘‘cell line’’ term in the ANOVA (orthotopic site) were compared with the same cells growing in model (see Materials and Methods). Most of these genes came from the subcutis (ectopic site; Table 6). The patterns of genes expressed patterns 5, 10, and 18 (Table 4). We identified multiple functions for in orthotopic tumors were enriched for protein amino acid these patterns using DAVID (Table 5), including morphogenesis dephosphorylation, ion transport, ATP binding, and actin cyto- (pattern 5), signal transduction (pattern 5), protein phosphoryla- skeleton organization and biogenesis. By contrast, the patterns of tion (pattern 10), glycolysis (pattern 10), Wnt receptor signaling genes expressed ectopically were enriched for tissue development, pathway (pattern 18), and secretory pathway (pattern 18). These negative regulation of cell proliferation, regulation of cell www.aacrjournals.org 145 Cancer Res 2007; 67: (1).January 1, 2007

Table 4. Up-regulated genes in highly metastatic cell line (top 50)

Probe set Uni Gene Symbol Name Pattern Metastatic P

220779_at Hs.149195 PADI3 peptidyl arginine deiminase, type III 10 2.78eÀ11 204141_at Hs.512712 TUBB2 tubulin, b2 10 1.22eÀ10 209706_at Hs.55999 NKX3-1 NK3 transcription factor related, locus 1 (Drosophila) 18 4.60eÀ10 213772_s_at Hs.460336 GGA2 golgi associated, c adaptin ear containing, ARF binding protein 2 10 6.50eÀ10 204404_at Hs.162585 SLC12A2 solute carrier family 12 (sodium/potassium/chloride transporters), member 2 5 9.46eÀ10 221796_at Hs.494312 NTRK2 neurotrophic tyrosine kinase, receptor, type 2 10 3.58eÀ09 222158_s_at Hs.498317 C1orf121 chromosome 1 open reading frame 121 5 3.96eÀ09 200702_s_at Hs.510328 DDX24 DEAD (Asp-Glu-Ala-Asp) box polypeptide 24 10 4.27eÀ09 238865_at Hs.49889 LOC132430 similar to polyadenylate-binding protein 4 [poly(A)-binding protein 4 (PABP 4); 10 5.67eÀ09 inducible poly(A)-binding protein (iPABP); activated-platelet protein-1 (APP-1)] 205466_s_at Hs.507348 HS3ST1 heparan sulfate (glucosamine) 3-O-sulfotransferase 1 10 1.94eÀ08 225257_at Hs.437497 MGC20255 hypothetical protein MGC20255 5 2.53eÀ08 206366_x_at Hs.458346 XCL2 chemokine (C motif) ligand 2 5 2.72eÀ08 209685_s_at Hs.460355 PRKCB1 protein kinase C, b1 5 4.02eÀ08 201580_s_at Hs.169358 DJ971N18.2 hypothetical protein DJ971N18.2 5 4.03eÀ08 202957_at Hs.14601 HCLS1 hematopoietic cell-specific Lyn substrate 1 19 4.76eÀ08 217691_x_at Hs.500761 SLC16A3 solute carrier family 16 (monocarboxylic acid transporters), member 3 19 6.94eÀ08 218625_at Hs.103291 NRN1 neuritin 1 18 8.98eÀ08 201721_s_at Hs.371021 LAPTM5 lysosomal associated multispanning membrane protein 5 10 1.03eÀ07 204617_s_at Hs.78019 ACD adrenocortical dysplasia homologue (mouse) 10 1.49eÀ07 228171_s_at Hs.188781 PLEKHG4 pleckstrin homology domain containing, family G (with RhoGef domain) member 4 5 1.74eÀ07 214567_s_at Hs.546295 XCL1/ L2 chemokine (C motif) ligand 1 / ligand 2 5 1.78eÀ07 202856_s_at Hs.500761 SLC16A3 solute carrier family 16 (monocarboxylic acid transporters), member 3 19 2.00eÀ07 201537_s_at Hs.181046 DUSP3 dual specificity phosphatase 3 (vaccinia virus phosphatase VH1-related) 10 2.10eÀ07 200687_s_at Hs.514435 SF3B3 splicing factor 3b, subunit 3, 130 kDa 5 2.29eÀ07 206347_at Hs.444422 PDK3 pyruvate dehydrogenase kinase, isoenzyme 3 10 2.73eÀ07 219249_s_at Hs.463035 FKBP10 FK506 binding protein 10, 65 kDa 10 2.89eÀ07 227785_at Hs.549104 SDCCAG8 serologically defined colon cancer antigen 8 18 3.47eÀ07 203530_s_at Hs.83734 STX4A syntaxin 4A (placental) 10 4.19eÀ07 204420_at Hs.283565 FOSL1 FOS-like antigen 1 10 5.41eÀ07 203773_x_at Hs.488143 BLVRA biliverdin reductase A 10 5.80eÀ07 202686_s_at Hs.466791 AXL Axl receptor tyrosine kinase 18 6.41eÀ07 207935_s_at Hs.463032 KRT13 keratin 13 10 6.50eÀ07 201462_at Hs.520740 SCRN1 secernin 1 18 8.35eÀ07 211171_s_at Hs.487129 PDE10A phosphodiesterase 10A 10 8.77eÀ07 218788_s_at Hs.546424 SMYD3 SET and MYND domain containing 3 5 8.97eÀ07 200648_s_at Hs.518525 GLUL glutamate-ammonia ligase (glutamine synthase) 18 9.55eÀ07 211729_x_at Hs.488143 BLVRA biliverdin reductase A 10 9.71eÀ07 209534_x_at Hs.459211 AKAP13 A kinase (PRKA) anchor protein 13 10 9.99eÀ07 209191_at Hs.193491 TUBB6 tubulin, b6 10 1.20eÀ06 229332_at Hs.162717 GLOXD1 glyoxalase domain containing 1 10 1.22eÀ06 206935_at Hs.19492 PCDH8 protocadherin 8 5 1.29eÀ06 219620_x_at Hs.495541 FLJ20245 hypothetical protein FLJ20245 19 1.30eÀ06 224733_at Hs.298198 CKLFSF3 chemokine-like factor super family 3 10 1.38eÀ06 200738_s_at Hs.78771 PGK1 phosphoglycerate kinase 1 10 1.43eÀ06 214857_at Hs.225084 C10orf95 chromosome 10 open reading frame 95 10 1.49eÀ06 229146_at Hs.122055 C7orf31 chromosome 7 open reading frame 31 14 1.54eÀ06 219798_s_at Hs.178011 FLJ20257 hypothetical protein FLJ20257 10 1.60eÀ06 206501_x_at Hs.22634 ETV1 ets variant gene 1 18 1.61eÀ06 213456_at Hs.25956 SOSTDC1 sclerostin domain containing 1 5 1.65eÀ06 224919_at Hs.302742 MRPS6 mitochondrial ribosomal protein S6 18 1.66eÀ06

differentiation, and negative regulation of apoptosis. (Lists of the pattern unique to metastatic human pancreatic cancer cells. Three top 50 up-regulated genes in orthotopic and s.c. L3.6pl tumors are variant lines of a single human pancreatic cancer with different provided in Supplementary Tables S2 and S3). metastatic potentials were grown in vitro, in the subcutis, and in the pancreas of nude mice. The pattern of gene expression was similar in the three lines growing in culture but not in vivo Discussion (Fig. 1). Cell cultures formed a distinct branch of the dendrogram, We determined that growth in a specific organ microenviron- but there was a clear expression signature that differentiated ment is a prerequisite for identifying a differential gene expression between the three cell lines once they were growing in vivo.

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Table 5. Functional annotation of up-regulated genes in metastasis-related genes (15–17). A significant difference in gene metastatic pattern expression patterns was identified between orthotopic tumors and ectopic tumors of L3.6pl (Table 6). The orthotopic tumors Category Gene count P expressed 226 unique genes and the ectopic tumors expressed 98 genes. Our data showing that the growth of the variant cell Pattern 5 Organ development 11 3eÀ04 lines in the subcutis did not yield similar results indicate that the Organ morphogenesis 7 8eÀ04 orthotopic microenvironment significantly influences specific gene Regulation of cellular process 27 0.006 expression in pancreatic cancer cells. Signal transduction 21 0.046 By jointly analyzing the data from the three cell lines in multiple À Pattern 10 Protein amino acid 85e04 environments, we have been able to identify expression patterns dephosphorylation that are specific to the highly metastatic cell line L3.6pl when it is Phosphate metabolism 21 5eÀ04 Glycolysis 5 0.003 grown orthotopically. Specifically, the genes in patterns 10, 11, 13, Protein modification 26 0.022 17, and 19 have their highest expression level in orthotopically Gluconeogenesis 3 0.038 grown L3.6pl cells, and the genes in patterns 6, 22, 23, 24, 29, and Protein amino acid 12 0.038 30 have their lowest expression under the same conditions. The phosphorylation 50 most highly expressed genes of the metastatic cell line, Pyruvate metabolism 3 0.043 regardless of the microenvironment, are listed in Table 4. The Enzyme linked receptor 6 0.047 dominant pattern was pattern 10, which has its highest expression protein signaling pathway in orthotopically grown L3.6pl cells. The Gene Ontology analysis Pattern 18 Regulation of cell cycle 16 2eÀ04 Wnt receptor signaling pathway 7 9eÀ04 identified functional categories that were up-regulated in the Mitotic cell cycle 9 0.002 metastatic cell line (Table 5). Many of the genes with catalytic Protein localization 15 0.004 (enzymatic) activities are involved in several phosphorylated Nucleobase biosynthesis 3 0.004 signaling pathways. Secretory pathway 7 0.018 Some of the genes have previously been identified as metastasis- Membrane organization 4 0.021 related genes (18, 19). Tropomysin-related kinase B (Table 4) was and biogenesis overexpressed in the patients with pancreatic cancer and its expres- Heat generation 2 0.022 sion correlated with perineural invasion, a positive retroperitoneal DNA replication initiation 3 0.031 margin, and shorter latency to the development of liver metastasis Regulation of protein metabolism 7 0.032 (18). Axl receptor tyrosine kinase (Table 4) is highly expressed in a variety of tumors (19–22) and was reported to be expressed at 10-fold higher levels in a peritoneal metastatic nodule than in other normal and malignant tissues (19). Axl signaling has been shown Specifically, the pattern of gene expression associated with the to affect neovascularization in vitro and angiogenesis in vivo (23). metastatic phenotype of the highly metastatic cell line L3.6pl was Many investigators have used mRNA microarrays to study the most different from that of the L3.3 or FG cell lines when the effects of in vitro and in vivo microenvironments on gene tumor cells were growing in the pancreas. (This assertion follows expression profiles in various cancers (24–28). Sandberg and from the height of the main splits in the dendrogram of Fig. 1; it Ernberg (29) reported on a meta-analysis of gene expression is also apparent from a principal component analysis contained profiles from three laboratories comprising 60 cell lines and in Supplementary Fig. S3.) We previously showed that implanta- 311 tissues (135 normal tissues and 176 tumor tissues). They tion of tumor cells at orthotopic or ectopic sites in nude mice concluded that the genes involved in cell cycle progression, produces tumors with different metastatic potentials (1, 4, 8). macromolecule processing and turnover, and energy metabolism The orthotopic tumors are invasive and metastatic, whereas the were up-regulated in cell lines, whereas cell adhesion molecules ectopic s.c. tumors are not, suggesting that different micro- and membrane signaling proteins were down-regulated. Our data environments may differentially influence the expression of reveal the same tendency for differences between in vitro and

Table 6. Comparison between up-regulated genes in orthotopic and ectopic tumor (highly metastatic cell line L3.6pl)

Tumor site Pattern Gene count Representative Gene Ontology category P

Ectopic 4 27 Tissue development 0.011 9 25 Negative regulation of cell proliferation 0.002 12 28 Regulation of cell differentiation 0.002 16 18 Negative regulation of apoptosis 0.017 Total 98 Orthotopic 10 160 Protein amino acid dephosphorylation 5.10eÀ04 13 3 Ion transport 4.50eÀ05 17 12 Protein modification 0.033 19 51 ATP binding 0.002 Total 226

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Downloaded from cancerres.aacrjournals.org on September 26, 2021. © 2007 American Association for Cancer Research. Cancer Research in vivo microenvironments. Differences in gene expression by and immunohistochemical staining studies have shown that the tumor cells growing in vitro and in vivo were also reported by expression of various genes and proteins varies between different Camphausen et al. (30), who compared two different glioblastoma regions of a single tumor and between tumors implanted in cell lines, U87 and U251, grown in cell culture, s.c., and intra- different sites of nude mice (16, 17). Our data show that the organ cerebrally. The gene expressions in the cells grown in the three microenvironment clearly influences the pattern of gene expression environments differed for both cell lines. Unlike our data, the gene profiles of tumor cells that have different metastatic potentials. expression patterns of the U87 and U251 cells in that study were These data suggest that both the nature of tumor cells and the host more similar in the orthotopic tumors than in ectopic s.c. tumors, microenvironment contribute to a variety of gene expressions and the greatest difference between the two cell lines was found in necessary for the development of cancer metastasis. the in vitro cultures (30). A follow-up study that examined the effects of radiation on U87 and U251 cells reported few commonly affected genes in cultured cells and greater changes that were Acknowledgments detected after orthotopic radiation (31). Received 7/12/2006; revised 9/18/2006; accepted 10/25/2006. The pathogenesis of metastasis selects for tumor cells that Grant support: Cancer Center Support Core Grant CA16672 and Specialized Program of Research Excellence in Prostate Cancer Grant CA902701 from the National succeed in invasion, embolization, survival in the circulation, arrest Cancer Institute, NIH. in the capillary beds, and then extravasation and growth in the The costs of publication of this article were defrayed in part by the payment of page organ parenchyma (32). Thus, the outcome of cancer metastasis charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. depends on multiple interactions of metastatic cells with a specific We thank Karen F. Phillips, ELS, for critical editorial review, and Lola Lo´pez for organ microenvironment (32, 33). Recent in situ hybridization expert preparation of this manuscript.

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Downloaded from cancerres.aacrjournals.org on September 26, 2021. © 2007 American Association for Cancer Research. Gene Expression Profile of Metastatic Human Pancreatic Cancer Cells Depends on the Organ Microenvironment

Toru Nakamura, Isaiah J. Fidler and Kevin R. Coombes

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