Transcriptional Targets of Hepatocyte Growth Factor Signaling and Ki-Ras Oncogene Activation in Colorectal Cancer

Oncogene (2006) 25, 91–102 & 2006 Nature Publishing Group All rights reserved 0950-9232/06 $30.00 www.nature.com/onc ORIGINAL ARTICLE Transcriptional targets of hepatocyte growth factor signaling and Ki-ras oncogene activation in colorectal cancer

IM Seiden-Long1,2, KR Brown1,2, W Shih1, DA Wigle3, N Radulovich1, I Jurisica1,2,4 and M-S Tsao1,2,5

1Ontario Cancer Institute/Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada; 2Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; 3Department of Surgery, University of Toronto, Toronto, Ontario, Canada; 4Department of Computer Science, University of Toronto, Toronto, Ontario, Canada and 5Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada

Both Ki-ras mutation and hepatocyte growth factor Introduction (HGF) receptor Met overexpression occur at high frequency in colon cancer. This study investigates the Colorectal carcinogenesis is characterized by a well- transcriptional changes induced by Ki-ras oncogene and delineated series of genetic mutations and aberrant gene HGF/Met signaling activation in colon cancer cell lines in expression events (Fearon and Vogelstein, 1990). Ki-ras vitro and in vivo. The model system used in these studies oncogene activation and the overexpression of growth included the DLD-1 colon cancer cell line with a mutated factor receptors on the cell surface have been shown to Ki-ras allele, and the DKO-4 cell line generated from play important rolesin colon cancer progression DLD-1, with its mutant Ki-ras allele inactivated by (Shirasawa et al., 1993; Fazekas et al., 2000). Ki-ras is targeted disruption. These cell lines were transduced with constitutively activated by mutations in 40–50% of cDNAs of full-length Met receptor. Microarray tran- colorectal cancers(Martinez-Garza et al., 1999; Tortola scriptional profiling was conducted on cell lines stimulated et al., 1999). Met isa receptor tyrosinekinasethat with HGF, as well as on tumor xenograft tissues. critically regulatescellular proliferation, motility, mor- Overlapping genes between in vitro and in vivo microarray phogenesis and apoptosis (Jiang et al., 1999). Met data sets were selected as a subset of HGF/Met and receptor overexpression has been reported in greater Ki-ras oncogene-regulated targets. Using the Online than 50% of primary colorectal adenocarcinomas(Liu Predicted Human Interaction Database, novel HGF/Met et al., 1992; Di Renzo et al., 1995). and Ki-ras regulated proteins with putative functional One of the key mediatorsof hepatocyte growth factor linkage were identified. Novel proteins identified included (HGF)/Met signaling is the Ras family membrane- histone acetyltransferase 1, phosphoribosyl pyrophosphate associated guanine nucleotide exchange proteins (Gra- synthetase 2, chaperonin containing TCP1, subunit 8, ziani et al., 1993; Vojtek and Der, 1998). Both the HGF/ CSE1 chromosome segregation 1-like (yeast)/cellular Met interaction and Rasoncogene may lead to apoptosis susceptibility (mammals), CCR4–NOT tran- activation of several downstream signaling cascades scription complex, subunit 8, and cyclin H. Transcript including the Raf/MEK/MAPK (Halaban et al., 1992) levels for these Met-signaling targets were correlated with and the PI3K/PKB (Graziani et al., 1991; Rodriguez- Met expression levels, and were significantly elevated in Viciana et al., 1994) pathways, both of which are both primary and metastatic human colorectal cancer capable of inducing transcriptional events. Additionally, samples compared to normal colorectal mucosa. These the Met receptor iscapable of regulating transcription genes represent novel Met and/or Ki-ras transcriptionally independent of Ras, through STAT-3 (Boccaccio et al., coregulated genes with a high degree of validation in 1998), b-catenin (Danilkovitch-Miagkova et al., 2001; human colorectal cancers. Monga et al., 2002) and through Crk/JNK (Conner Oncogene (2006) 25, 91–102. doi:10.1038/sj.onc.1209005; et al., 1999; Garcia-Guzman et al., 1999) pathways. published online 12 September 2005 Consequently, there is a potential for both Ras- dependent and -independent transcriptional events Keywords: hepatocyte growth factor; protein–protein downstream from HGF/Met signaling. interaction; microarray; real-time RT–PCR; tumor We have previously investigated the effect of com- markers; tissue array bined Ki-ras mutation and Met overexpression in the progression of colon cancer (Seiden Long et al., 2003). The model employed the colon cancer cell line DLD-1 Correspondence: Dr M-S Tsao, Department of Pathology, Ontario with an activating Ki-ras mutation, aswell asits Cancer Insititute/Princess Margaret Hospital, Room 4-302, 610 derivative cell line DKO-4 that hashad a targeted University Avenue, 9-610, Toronto, Ontario, Canada M5G 2M9. E-mail: [email protected] disruption of the mutant Ki-ras allele but retainsthe Received 23 April 2005; revised 17 June 2005; accepted 1 July 2005; remaining wild-type Ki-ras allele (Shirasawa et al., published online 12 September 2005 1993). Overexpression of Met in these lines results in Met transcriptional signaling in colorectal cancer IM Seiden-Long et al 92 spontaneous activation of the receptor. Met receptor overexpression enhanced tumorigenicity only in the cell line with Ki-ras mutation but not in itsKi- ras oncogene inactivated counterpart, thusidentifying ras oncogene as a modulator of Met signaling activity in colon cancer cells. We report here the transcriptional consequences of HGF/Met and Rassignaling in colon cancer cell lines DLD-1 and DKO-4 using a novel approach that interfacesexpressionmicroarray profiling data with a protein–protein interaction (PPI) database. The approach led to the identification of novel subset of HGF/Met and Rasregulated genes,which are elevated both in primary human colorectal cancersaswell as metastatic lesions.

Results

Expression profiling of cell lines in vitro Spotted microarrayscontaining 1718 cDNA clones (http://www.microarray.ca) were used to track the transcriptional changes induced by HGF stimulation of both the DLD-1 (Ki-rasG13D) cell line and itsmutant Ki-ras inactivated clone DKO-4. The cellswere serum starved 24 h prior to the start of all experimental time points (0 h), and total RNA was serially isolated from cells stimulated by HGF over a 48-h time course. RNA of samples from all time points were then referenced against those isolated at 0 h. Control RNA was also isolated from serum-starved cells at the equivalent 6 h time point (30 h post serum starvation). For both cell lines, the sample from the 6 h control time point demonstrated no significant changes from the 0 h sample. However, by 12 h, the serum-starved control samples began to show significant changes, which complicated the interpretation of growth factor effects in the latter part of the time course (data not shown). Thus, results from analyses using only data from less than 6 h post-HGF stimulation are reported here. Following data filtration (see Materials and methods section), the retained 177 out of 1718 genes clustered into two major groupsof genes(Figure 1a). The largest cluster included genes that were downregulated in both Figure 1 Comparative gene expression profiling between in vitro DLD-1 and DKO-4 by HGF stimulation, but the and in vivo sample sources. (a) Gene expression changes in DLD-1 and DKO-4 cellsstimulatedby HGF from cDNA microarray data. changeswere lesspronounced in DLD-1 cellstreated Cells were grown to 50% confluence and serum starved for 24 h, by HGF. The smaller cluster contained genes that were and then stimulated by HGF. Total RNA was extracted at 0, 15, upregulated by HGF stimulation in both cell lines, as 30 min, 1, 3 and 6 h after growth factor addition. DLD-1 and well asgenesthat were unchanged in the DLD-1 cells DKO-4 24 h serum-starved samples were used for reference RNA. but were substantially upregulated by HGF in the Serum starvation controls were carried out for each cell line at 0 min and 6 h in the time course. Genes (n ¼ 177) have been DKO-4 cells. Overall, the genes transcriptionally regu- hierarchically clustered with the Eisen Cluster and TreeView lated by HGF in both DLD-1 and DKO-4 cellswere programs and displayed on a color scale, with red representing similar, but the magnitude of changes appeared greater positive values (upregulated) and green representing negative in DKO-4 cellswith functional wild-type Ki- ras (downregulated) log2 converted values. Gray represents areas with no data. (b) Gene expression changes in DLD-1, DLD-1 Met, genotype. DKO-4 and DKO-4 Met tumor xenograft tissues from Affymetrix array data. All samples have been referenced to DLD-1. Data have been expressed as log2 ratioswith respectto reference. Genes Expression profiling of tumor tissues in vivo (n ¼ 1190) have been hierarchically clustered and displayed as in Using the U133A Affymetrix oligonucleotide micro- (a). (c) Component planes from BTSVQ cluster analysis using arrayscontaining approximately 14 500 unique tran- transcript expression levels in DLD-1, DLD-1 Met, DKO-4 and scripts, global transcriptional profiling was performed DKO-4 Met tumor xenograft tissues from Affymetrix array data on xenograft tumor tissues formed by the DLD-1 and for all present genes.

Oncogene Met transcriptional signaling in colorectal cancer IM Seiden-Long et al 93 DKO-4 cell lineswith and without forced Met over- Common transcripts regulated by HGF/Met signaling expression. All expression levels were referenced to To select genes for further analysis and validation, the DLD-1 control tissue and filtered to include only genes filtered gene lists from both in vitro and in vivo data sets that were altered at least two-fold across the data set. were analyzed for intersection using their Unigene ID. There were 1190 unique transcripts that survived this The rationale for choosing this method of focused data filtration, 93 of which showed four-fold or greater analysis stemmed from the similar trends observed in variability in one of the cell line xenograft systems both data sets in terms of global transcriptional changes. (Supplementary Table 1). Hierarchical clustering of the Of the 177 transcripts found significantly altered by 1190 transcripts showed that expression changes be- HGF stimulation in vitro and the 1190 transcripts tween DLD-1 compared to DKO-4 tumorswere similar altered by at least two-fold in vivo, 20 unique transcripts to those induced by Met overexpression in the DLD-1 were found to overlap (Table 1 and Figure 2a). Among or DKO-4 samples (Figure 1b). DKO-4 tumors also these, eight had published information with respect demonstrated transcriptional changes similar to both to potential interactionswith other proteins.These DLD-1 and DKO-4 tumors. The global expression data included adenylyl cyclase-associated protein (CAP), were analysed using a Binary Tree-Structured Vector Cyclin H (CCNH), chaperonin containing TCP1, Quantization (BTSVQ) algorithm, and the results were subunit 8 (CCT8), CCR4–NOT transcription complex, visualized using self-organizing map (SOM) component subunit 8 (CNOT8), CSE1 chromosome segregation planes(Sultan et al., 2002). This also demonstrated that 1-like (yeast)/cellular apoptosis susceptibility (mammals) the differencesin expressionprofilesbetween the DLD-1 (CSE1L/CAS), histone acetyltransferase 1 (HAT1), and DKO-4 tumorswere more pronounced than phosphoribosyl pyrophosphate synthetase 2 (PRPS2) between these lines and their corresponding Met-over- and a member of the RAS family (RAB11A). Using a expressing derivatives. The results indicate that expres- consolidated database of PPIs that integrates publicly sion changes induced by Ki-ras oncogenic activation are available protein interaction data sets from Mus greater than those induced by Met overexpression in musculus, Caenorhabditis elegans, Drosophia melanoga- tumorsformed by thesecolon cancer lines(Figure 1c). ster, Saccharomyces cerevisiae and Homo sapiens However, similar to the in vitro findings(Figure 1a), the sources and mapping them to human protein orthologs magnitude of transcriptional changes consequent to Met (http://ophid.utoronto.ca), proteinsthat potentially overexpression in DKO-4 tumors appeared exaggerated interacted with the eight transcripts were identified compared to that in DLD-1 tumors. Overall, there were (Supplementary Table 2). Interestingly, six of the far greater transcriptional upregulation than down- eight target genesof Met activation demonstrate regulation events in response to Met signaling in vivo. potential functional connection with each other through This finding was in contrast to the results of cell line intermediate interacting proteins(Supplementary Table cDNA microarray profiling results, which primarily 2 and Figure 2b). These six proteins have roles in showed the majority of genes being transcriptionally transcription, genome maintenance and metabolism. downregulated in response to HGF stimulation. The intermediate connecting proteinshave similaras

Table 1 Genes whose expression is altered in vitro and in vivo following activation of HGF/Met signaling Affymetrix probe ID Unigene ID Gene name

203138_at Hs.13340 Histone acetyltransferase 1: HAT1 201735_s_at Hs.174139 Chloride channel 3: CLCN3 208310_s_at Hs.433622 Follistatin-like 1: FSTL1 201687_s_at Hs.227913 Apoptosis inhibitor 5: API5 200015_s_at 200778_s_at Hs.155595 Neural precursor cell expressed, developmentally downregulated 5: NEDD5 203401_at Hs.104123 Phosphoribosyl pyrophosphate synthetase 2: PRPS2 202351_at Hs.295726 Vitronectin receptor a subunit precursor (INTEGRIN ALPHA-V): CD51 200873_s_at Hs.416211 Chaperonin containing TCP1, subunit 8 (y): CCT8 203087_s_at Hs.113319 Kinesin heavy-chain member 2:KIF2 202163_s_at Hs.26703 CCR4–NOT transcription complex, subunit 8: CNOT8 214016_s_at Hs.180610 Splicing factor proline/glutamine rich: SFPQ 200625_s_at 213798_s_at Hs.104125 Adenylyl cyclase-associated protein: CAP 200723_s_at Hs.278672 Membrane component, chromosome 11, surface marker 1: M11S1 203196_at Hs.139336 ATP-binding cassette, subfamily C (CFTR/MRP), member 4: ABCC4 201601_x_at 214022_s_at Hs.366 Interferon-induced transmembrane protein 1 (9–27): IFITM1 201111_at 201112_s_at Hs.90073 Chromosome segregation 1-like (yeast): CSE1L or CAS 217758_s_at Hs.8203 SM-11044-binding protein: SMBP 200864_s_at Hs.75618 Member RAS oncogene family: RAB11A 213397_x_at Hs.283749 Ribonuclease, RNase A family, 4: RNASE4 204093_at Hs.514 Cyclin H: CCNH

Oncogene Met transcriptional signaling in colorectal cancer IM Seiden-Long et al 94

Figure 2 PPIsfor transcriptional targetsoverlapping between in vitro and in vivo data sets. (a) Venn diagram demonstrating overlap between in vitro and in vivo data sets. (b) Ball-and-stick schematic of PPIs found in OPHID for genes from overlapping data set (8/20). Original targetsare highlighted in red, and each line representsa putative protein interaction. Interacting protein functionsare classified by color, using GO terms. Abbreviations used: histone acetyltransferase 1 (HAT1), phosphoribosyl pyrophosphate synthetase 2 (PRPS2), CCR4–NOT transcription complex, subunit 8 (CNOT8), adenylyl cyclase-associated protein (CAP), RAS oncogene family member RAB11A (RAB11A), chaperonin containing TCP1, subunit 8 (CCT8), CSE1 chromosome segregation 1-like (yeast)/cellular apoptosis susceptibility (mammals) (CSE1L/CAS) and Cyclin H (CCNH). (c) PPI network combining OPHID data with published literature sources. Targets identified as interacting by OPHID in (b) have been further evaluated by PathwayAssist in order to find interactions with existing published data.

Oncogene Met transcriptional signaling in colorectal cancer IM Seiden-Long et al 95 well as additional putative roles in translation, transport Furthermore, CCNH associates with MYC (Eberhardy and cellular organization. and Farnham, 2001), which bindsto the helicase RUVBL2/Tip49 (Wood et al., 2000; Dugan et al., 2002). RUVBL2 wasidentified by Online Predicted Met-associated molecular interaction network Human Interaction Database (OPHID) as interacting To investigate if these putative PPIs could be integrated with CCT8 and CNOT8. Integration of thisadditional further into a Met-regulated ‘molecular interaction s published data may lead to a more comprehensive and network’, PathwayAssist software was used to query integrated molecular interaction network (Figure 2c). any additional PPIsthat theseMet target geneshave previously been associated with. PathwayAssists software uses a curated database compiled from published Validation of transcriptional changes literature to generate putative interaction networks. The six interacting genes identified from microarray Similar to HAT1, CCNH and CNOT8 have been data were selected for further validation by real-time functionally linked to the histone modification pathway quantitative RT–PCR. For in vivo samples, RNA from through variousintermediaries(Kaufman et al., 1995; eight independent xenograft tumor samples was utilized Verreault et al., 1996; Bhattacharjee et al., 2001). for each group (representative samples Figure 3a and b).

Figure 3 Validation of microarray data by real-time quantitative RT–PCR and fluorescence immunohistochemistry. (a, b) Real-time PCR data for N ¼ 8 tumor tissue RNA samples, with each reaction conducted in replicate for DLD-1, DLD-1 Met, DKO-4 and DKO- 4 Met tumor tissues. Values have been expressed as fold change with respect to DLD-1 average value. Significant differences between samples are indicated above where applicable (Mann–Whitney Po0.05). Sample mediansare indicated. ( c) Real-time quantitative RT– PCR data for DLD-1 cell lines serum starved for 24 h and then stimulated by HGF for 3 h. Each reaction has been repeated N ¼ 4 times and average fold change is expressed as the ratio of 0-min average values. Data are shown as mean7s.e.m., all differences between 0 min control and 3 h samples are significant by both Student’s t-test (Po0.05) and Mann–Whitney test (Po0.05). (d, e). Quantification of CAS and HAT1 levels in tumor tissues by fluorescence immunohistochemistry. The sections were immunostained with primary antibodiesto CAS and HAT1, followed by Cy3-conjugated secondaryantibodiesand DAPI. IOD valuesfor N ¼ 20 quantified, background subtracted Â 20 images for either HAT1 or CAS expressed as a ratio to average DLD-1 tissue IOD. Statistical significance is indicated where appropriate (Mann–Whitney Po0.05) and median valuesare indicated in each column.

Oncogene Met transcriptional signaling in colorectal cancer IM Seiden-Long et al 96 Since the Affymetrix analyses used pooled tumor RNA evaluated in 30 normal human tissues, 63 primary samples, it was anticipated that sample heterogeneity colorectal cancer and 30 metastatic lesions. RNA levels could become apparent when the original three aswell from all six transcriptional targets identified by micro- asan additional five sampleswere individually analyzed. array analysis are significantly elevated between normal Thisform of validation allowsfor the expansionof and primary carcinoma tissues (Figure 4 and Table 2). sample number for statistical comparison between data Transcript levels are additionally elevated between sets. For HAT1, CAS, CCT8 and CCNH, significant primary carcinoma and metastases, indicating that more differencesin transcriptional changeswere observed aggressive lesions are associated with greater expression between DLD-1, DLD-1 Met and DKO-4 Met samples. levelsof thesetranscripts(Figure 4b–d). The pattern of For PRPS2, the only significant difference was between transcript expression correlates strongly with the ex- DKO-4 and DKO-4 Met. For CNOT8, the addition of pression of Met receptor in these tissues, indicating the extra samples resulted in a loss of significant differences potential for a single mechanism of transcriptional detected by microarray study. Validation by real-time regulation for all these targets. RT–PCR was also conducted on RNA samples that In addition to primary transcriptional targets identi- were originally used for cDNA microarray experiments. fied by microarray analysis, several other transcripts For DLD-1 cells, HAT1, PRPS2, CAS, CNOT8, CCT8 (PSMC3, IPO4, RUVBL2 and IDH3A – Figure 2c) were and CCNH transcript levels were all significantly also chosen for validation. These other transcripts were upregulated within 3 h after HGF stimulation only identified by PPI interactionsin OPHID and (Figure 3c). Overall, the findingsdemonstrate a con- PathwayAssist. Remarkably, three of these targets also cordance between in vitro and in vivo data sets. showed correlation with Met mRNA expression levels (Figure 4e), aswell asupregulation in colorectal primary and metastatic tumors compared to the normal mucosa Altered HAT1 and CAS protein expression in xenograft (Figure 4f). tumors The targetschosenfor validation at the protein level were contingent upon the availability of specific anti- Altered CAS and HAT1 protein levels in human bodies. Using quantitative image analysis, the induction colorectal cancer samples of CAS and HAT1 by Met overexpression was validated Human tissue arrays were evaluated for CAS and HAT1 on xenograft tumor tissue specimens. Tumor histology protein expression levels by immunohistochemistry. indicated large, central necrotic regions of tissue Nuclear and cytoplasmic staining of both CAS and surrounded by viable tumor cells containing very little HAT1 are seen in human tissues. In normal colon, both interspersed stromal component (data not shown). The HAT1 and CAS staining was strongest at the crypt base, tumor cells were distinguished in the 4,6-diamidino-2- where there isthe greatestamount of cellular prolifera- phenylindole (DAPI) channel by their nuclear morphol- tion. In tumors, both HAT1 and CAS staining was ogy on a low-resolution Â 5 scan of the tumor section. diffuse throughout the tissue. For CAS, both nuclear Using the Â 5 scan as a map to identify viable tumor and cytoplasmic staining was increased in primary regions, five representative high-resolution ( Â 20) colorectal cancer samples and metastases (Figure 5b imagesin the Cy3 (antibody of interest) channel were and e) compared to the normal colonic epithelium captured for each tumor and four tumors per sample set (Figure 5a and e). HAT1 nuclear staining was similar (20 imagesin total). For HAT1 and CAS, the images between normal and primary tumors, but was decreased were quantified by thresholding the staining intensity to in metastases. HAT1 cytoplasmic staining showed capture the strongest signal. CAS demonstrated pri- dramatic increase in primary and metastatic tumors marily nuclear localization in xenograft tissues, while (Figure 5d and e) compared to the normal mucosa HAT1 wasboth nuclear and, to a lesserextent, (Figure 5c and e). Analysis of all four sample sets by cytoplasmic. In samples expressing HAT1 and CAS at Fisher’s exact test (Po0.0001) indicatesthat there isan high levels, >95% of tumor cells in any captured field extremely low probability that the patternsobservedare demonstrated nuclear staining. This indicates that random. HAT1 and CAS are coexpressed in these tissues (data not shown). HAT1 protein levels were significantly increased in Ki-ras knockout DKO-4 tumors, but Met- Discussion overexpression alone did not seem to cause significant increases in either Ki-ras mutant or Ki-ras knockout Using a global gene expression profiling strategy, we tissue (Figure 3d). For CAS, increases in protein levels have shown that, in the DLD-1 colon cancer cell line, were observed in both Met overexpressing and Ki-ras the primary signaling pathway that mediates transcrip- knockout cells(Figure 3e), indicating that CAS levels tional regulation of gene expression downstream from were both Met and Ki-ras dependent in these tissues. the Met receptor isthe Raspathway. Met overexpression or HGF stimulation did not appear to induce a Transcriptional targets of HGF/Met and Ki-ras signaling large number of unique transcriptional changes that in colorectal carcinogenesis differed significantly from those induced by Ki-ras The relative levelsof mRNA transcriptsfor HAT1, oncogene alone. Rather, the effectswere broadly over- CAS, PRPS2, CNOT8, CCT8 and CCNH were lapping (Figure 1a–c). Our findings also demonstrate

Oncogene Met transcriptional signaling in colorectal cancer IM Seiden-Long et al 97

Figure 4 Validation of target genes that are transcriptionally regulated by HGF/Met signaling in normal and malignant colorectal tissues. (a) Correlation between transcript levels of Met mRNA and six target genes identiﬁed by microarray experiments. Ct refersto sample threshold intensity cutoff value; (b–d) mRNA expression levels of Met, CCNH and CNOT8 genesin normal colorectal mucosa, primary carcinomas and metastases. All values expressed as ratio of fold change with respect to mean normal mucosa; (e) correlation between mRNA transcript levels of Met and four genes identiﬁed by PPI; (f) mRNA expression levels of Importin 4 (IPO4) in normal colorectal mucosa, primary carcinomas and metastases.

Table 2 Validation of transcript level increases in human tissues that a potent oncogene such as Ki-ras may lower the Gene Normal o Normal o Tumor o Correlation global transcriptional activity of cancer cells and tumora metastasisa metastasisa with Metb insulate them from large transcriptional alterations induced by growth factor stimulation. Nevertheless, Met o0.0001 o0.0001 o0.0001 N/A these cells remain receptive to transcriptional activation Identified by microarray induced by the HGF/Met signaling, and such modest CCT8 o0.0001 o0.0001 0.6462 0.74 amount of transcriptional alterations may still impact HAT1 o0.0001 o0.0001 o0.0001 0.85 on enhancing the tumorigenicity of cellswith Ki- ras CCNH 0.0016 o0.0001 0.0004 0.73 mutation, but not of cellswith wild-type Ki- ras CNOT8 o0.0001 o0.0001 o0.0001 0.79 genotype (Seiden-Long et al., 2003). In thisstudy, in CSEIL/CAS o0.0001 o0.0001 0.0007 0.88 PRPS2 o0.0001 o0.0001 0.0715 0.76 vivo growth appearsto strongly influence transcriptional profiling of the colorectal cancer cells, as is demon- Identified by protein–protein interactions strated by the strong contrast between upregulation and PSMC3 o0.0001 o0.0001 0.1263 0.75 downregulation eventsbetween in vitro and in vivo data RUVBL2 o0.0001 o0.0001 0.0155 0.81 IDH3A 0.2094 0.0045 0.0799 0.62 sets. It has been shown that the extracellular matrix IPO4 o0.0001 o0.0001 0.0057 0.83 (ECM) upon which cellsare growing can significantly alter transcriptional profiles (Yarwood and Woodgett, aMann–Whitney P-values. bSpearman’scorrelation coefficient. 2001). There isincreasingevidence that HGF/Met does

Oncogene Met transcriptional signaling in colorectal cancer IM Seiden-Long et al 98 Table 3 Genesthat overlap with previouslypublished microarray proﬁling studies on HGF-induced gene expression changes in MDCK and HUVEC cells Gene symbol Unigene Gene name

MDCK cellsa CYCS Hs.437060 Cytochrome c, somatic DDX3 Hs.380774 DEAD (Asp–Glu–Ala–Asp) box polypeptide 3 EIF4E Hs.79306 Eukaryotic translation initiation factor 4E ETF1 Hs.77324 Eukaryotic translation termination factor 1 FBXW2 Hs.444354 F-box and WD-40 domain protein 2 HSPA9B Hs.184233 Heat shock 70 kDa protein 9B ITGA6 Hs.212296 Integrin, a 6 KLF5 Hs.84728 Kruppel-like factor 5 MAP3K5 Hs.151988 Mitogen-activated protein kinase kinase kinase 5 OGT Hs.405410 O-linked N-acetylglucosamine (GlcNAc) transferase PFDN4 Hs.91161 Prefoldin 4 PPAP2B Hs.432840 Phosphatidic acid phosphatase type 2B PPID Hs.143482 Peptidylprolyl isomerase D SCHIP1 Hs.61490 Schwannomin interacting protein 1 SLC12A2 Hs.110736 Solute carrier family 12, member 2 YWHAZ Hs.386834 Tryptophan 5-monooxygenase activation protein ZNF216 Hs.406096 Zinc-ﬁnger protein 216

HUVEC cellsb PPAP2B Hs.432840 Phosphatidic acid phosphatase type 2B TIMP1 Hs.446641 Tissue inhibitor of metalloproteinase 1

aGeneswith changesthat were two-fold or greater are included (Balkovetz et al., 2004). bGeneswith changesthat were 1.5-fold and with Po0.1 were included (Gerritsen et al., 2003). Figure 5 Tissue array immunohistochemistry for CAS and HAT1 in human normal colonic mucosa, primary colorectal carcinoma and metastases. Representative CAS staining in normal mucosa (a) and primary colorectal carcinoma (b), and of HAT1 staining Met-regulated genes. The paucity of overlap genes with in normal mucosa (c) and primary colorectal carcinoma (d). the studies performed on HUVEC cells is likely due to (e) Relative percentage of cores that show positive nuclear (N) or the incomplete list of HGF-induced genes that was cytoplasmic (C) staining for CAS and HAT1. The tissue array is published (Gerritsen et al., 2003). Divergent gene composed of up to 41 normal colorectal mucosa tissue, 158 primary adenocarcinoma and 73 metastatic colorectal cancers in the liver or expression changes induced by the same factor could lung, which are representative of those from 30 normal, 63 primary also be cell type or cell line dependent. and 30 metastatic patient tissue blocks, respectively. For all four Microarray data sets were integrated with OPHID in sample sets (nuclear or cytoplasmic for both CAS and HAT1), an attempt to further expand the scope of our under- Po0.0001, Fisher’s exact test. standing of expression changes induced by specific genes. Application of the limited in vitro and in vivo overlapped genesto thisanalytical approach identified not exist in solitary confinement on the cell surface, but six genes that belong to a hypothetical network of rather asa component of a larger, multi-component, function at the protein level. The transcriptional signaling complex. In fact, HGF/Met signaling is upregulation of these six target genes by HGF/Met/ intimately linked with that of other ECM-associated Ki-ras signaling was confirmed using quantitative real- signaling proteins on the cell surface, such as integrins, time RT–PCR, both in vitro and in vivo (Figure 3). It is plexinsand CD44 (Bertotti and Comoglio, 2003). notable that the transcriptional patterns for HAT1 and We identified 1190 transcripts that were differentially CAS in Figure 3a–b differ from the protein expression expressed two-fold or more in vivo following Met pattern in Figure 3d–e. Thisunderscorestheimportance overexpression of DLD-1 and DKO-4 cell lines. of validation of targetsby quantification of protein Comparison of our in vivo (Affymetrix derived) differ- levelsaswell asRNA levels.However, thistype of entially expressed transcripts/genes with two previously analysis is also limited by the availability of antibodies published lists of microarray-based HGF-induced gene suitable for immunohistochemistry. expression profiles (Gerritsen et al., 2003) revealed at Transcriptional regulation was also validated in least 19 genes that overlapped between the three studies primary human colorectal tissue samples (Table 2). It (Table 3) (Balkovetz et al., 2004). These genes that were was even more exciting to discover that several (three of revealed in multiple independent studies using different four evaluated) of the interacting proteinsthat linked cell linesare likely to representadditional true HGF/ the six direct targets of HGF/Met/RAS signaling were

Oncogene Met transcriptional signaling in colorectal cancer IM Seiden-Long et al 99 also transcriptionally correlated with the expression formation mediated by CAS and E-cadherin may have a levelsof Met receptor, and were significantly upregu- role in promoting metastases in human tissues. In lated during colorectal carcinogenesis (Table 2). This human tumor tissues, there is an elevation in both would indicate that microarray data can be further nuclear and cytoplasmic CAS staining (Figure 5); thus, enriched by PPI information and that PPI isan CAS may have more than one function in the invaluable hypothesis-generating tool to expand the progression of colorectal carcinoma. scope of information offered by microarrays alone. The relationship between upregulation of these genes None of these genes/proteins has previously been and the tumor biology could be different in xenograft reported as associated with either HGF/Met signaling; models and human tumor tissue samples. In DLD-1 and thus, they represent novel downstream targets of both DKO-4 tumors, differences in primary tumor growth HGF/Met and Ras signaling cascades, especially during rate are observed, which are Ki-ras and Met dependent. human colorectal carcinogenesis. These transcripts are more strongly elevated in tumors, Networking of target genesby OPHID and which show reduced growth rates in vivo. However, the PathwayAssists allowsusto identify similarly regulated impact of Ki-ras and Met on tumor metastasis cannot proteins that have related, but otherwise subtly con- be observed in a subcutaneous model. In contrast to nected, roles in cellular processes. The relationships tumor xenograft data, an increase in all six identified between these proteins and their connection with transcripts is observed for human primary tumor and transcriptional regulation can be quite complex, and metastatic tissues (Table 2). Between primary and not immediately obviousfrom initial observation. The metastatic tissues, there is a marked increase in existence of multiple interacting proteins that intercon- transcript levels, indicating a correlation between nect these target genes at a functional level provides metastasis and transcript levels. This pattern correlates mechanistic insight for relating transcriptional networks with Met expression levels in these tissues (Table 2), to biological consequences. Novel targets have reported indicating that, in human samples, there is a strong roles ranging from chromatin assembly and nuclear relationship between upregulation of Met and asso- import/export to direct association with the cellular ciated transcriptional targets which does not require transcription complexes. Ki-ras mutation (samples were all randomly selected Cytoplasmic HAT1 acetylates newly synthesized and Ki-ras status is unknown). The immunohistochem- histone H4 and to a lesser extent H2A, putatively istry data from tissue arrays for HAT1 and CAS quite promoting their transportation into the nucleus, deposi- closely reiterate the observed transcriptional patterns in tion onto newly replicated DNA and the assembly of human tissue samples. Future studies will focus on chromatin (Roth et al., 2001). The relationship between determining if elevation of these transcripts is a HAT1, Rasand Met/HGF signaling providesone causative factor in promoting colon cancer metastasis, important link between chromatin assembly or remode- or a marker of malignant transformation. For the ling and oncogenesis. The HAT1 gene has a putative former scenario, identification of a whole group of bipartite nuclear localization signal, and has been shown transcripts, which are regulated in similar manner, may to localize in the nucleusof S-phasecells(Verreault provide important prognostic information. et al., 1998). The HAT1 holoenzyme also includes the In summary, this study has provided new insight into retinoblastoma protein-binding protein 7 (RBBP7 or the convergence of transcriptional signaling pathways in RBap46), a putative negative regulator of Rasfunction tumor cells and has further emphasized the significant (Qian and Lee, 1995), and another target identified by impact a single mutant Ki-ras allele can exert in termsof PPI. Tissue array staining patterns indicate that, in regulating the overall levelsof transcriptswithinthe cell. tumor tissues and metastases, HAT1 is increased in the We have demonstrated that the analysis of common cytoplasm rather than the nucleus (Figure 5); thus, in geneswith in vitro and in vivo systems has several key thiscontext, may have dominant role in cytoplasmic advantageswhen prioritizing targetsderived from histone acetylation. genome-wide experiments for future studies. The novel Another protein that ispredicted to interact with targetsof HGF/Met and Ki- ras signaling identified both HAT1 isIPO4, which, together with importin- b, binds in model systems and in human tumor tissues in this to the nuclear localization signal (NLS) of many study all have some link to transcriptional activation, important proteinsto shufflethem into the nucleus which providesinformation that may help to explain (Behrens et al., 2003). CAS together with Ran-GTP global transcriptional patterns observed in array pro- regulate the re-export of IPO4 from the nucleus, and the files. The selection of targets for validation based on overexpression of CAS prompts the retention of PPIsprovidesa compelling way for identifying target imported proteinsin the nucleus.The CAS gene is genesthat may be linked at functional level. located on chromosome 20q13, a locus often amplified in colon and breast cancers (Brinkmann et al., 1996). In addition to itsrole in transcription, an alternative role in Materials and methods intracellular junction formation for CAS hasbeen reported in HT29 cells(Jiang et al., 2002). Thisprocess Reagents, cell lines and patient tissues isdependent on E-cadherin and regulatescellular Human recombinant HGF waspurchasedfrom R&D polarity and inhibitscell growth in vitro (Jiang et al., (Minneapolis, MN). DLD-1 and DKO-4 colon epithelial cells 2002; Jiang and Liao, 2004). The intracellular junction were routinely cultured in Dulbecco’smodified Eagle’s

Oncogene Met transcriptional signaling in colorectal cancer IM Seiden-Long et al 100 medium (DMEM) supplemented with 10% fetal bovine serum clone sets. Arrays were pre-hybridized for 1 h at 371Cin (FBS) (Gibco-BRL, Grand Island, NY). DLD-1, DLD-1-Met, DIGeasy hybridization buffer (Roche, Laval, QC) mixed with DKO-4 and DKO-4-Met mouse tumor xenograft tissues were 0.5 mg/ml yeast tRNA, 0.5 mg/ml salmon sperm DNA and 1% generated as described previously (Seiden Long et al., 2003). bovine serum albumin in humid hybridization chambers. HAT1 and CAS antibodieswere purchasedfrom Santa-Cruz Slideswere washedwith double-deionized water and then Biotechnology (Santa Cruz, CA), and anti-goat-HRP/anti- dried with isopropanol. Probes were resuspended in DIGeasy goat-Cy3 from Jackson Immunological (West Grove, PA). hybridization buffer (Roche, Laval, QC) for 5 min at 651C, Patient tissue samples were obtained from the University then hybridized to glass arrays overnight at 371C in humid Health Network (UHN) Tissue Bank, following approval of hybridization chambers. Slides were washed the next day six this project by the UHN Research Ethics Board. All tissues timesfor 5 min at room temperature in 0.1 Â SSC, 0.1% SDS, were collected within 30 min of resection and snap-frozen in and then two timeswith 0.1 Â SSC. Slideswere dried by liquid nitrogen; their qualitieshave been verified by histology. centrifugation prior to scanning. All experimental samples Tissues included 30 normal colorectal mucosa, 63 primary were reciprocally labeled and hybridized to arraysin duplicate. colorectal cancers, and 30 metastatic colorectal cancers. The tumor samples were from 38 Dukes’ B and 25 Dukes’ C patients, and the normal mucosa was from the corresponding Scanning and analysis of data tissue of a subset of these patients. Metastatic tumors were Slides were scanned using a Genepix 4000 scanner (Axon from liver (22 cases) and lung (8 cases) metastasectomy Instruments Inc., Union City, USA), and Tiff files analyzed specimens. Tissue arrays were constructed from paraffin using Quantarray image analysis software (Perkin-Elmer, blocks of formalin-fixed tissues that were taken for quality Ontario, Canada) Cy3 and Cy5 channelswere normalized by control of the banked tissue, and thus were representative of sub-array intensity, and ratios were calculated as test sample the frozen tissues. Minimums of two cores were taken from divided by reference sample on each array. Ratio values are each block. shown as an average of four spots: two spots for each cDNA on a single array, and two arrays for each test sample (with Growth factor stimulation and cell lysis Cy3 and Cy5 reversed for replicates). DLD-1 and DKO-4 cells Subconfluent (30–50%) cultureswere washedtwice with at 0 min self–self (control) hybridization ratios were analyzed Hanks’ buffered saline solution (HBSS) and incubated in by frequency distribution, demonstrating that the mean ratio 7 serum-free or supplement-free medium overnight. At 2 h prior for control data wasapproximately 1, and that 2 standard to the experiment, the cellswere once more washedwith deviationsfrom the mean wasin the range of 0.70–1.3 (data HBSS, and the media replaced by fresh serum-free medium. not shown). Clustering was performed using the Eisen Cluster The experiment wasinitiated by adding HGF (10 ng/ml) to the and TreeView software package (Eisen et al., 1998). All media, and total cellular RNA wasisolatedby guanidine selected DLD-1 or DKO-4 gene ratios were log2 transformed isothiocyanate–phenol extraction at 15, 30 min, 1, 3, 6, 12, 24 and filtered in the following fashion. Data for the DLD-1 and and 48 h after growth factor addition (Tsao et al., 1998). RNA DKO-4 growth factor time course were filtered to remove any was also isolated from serum-free control cultures at 6, 12, 24, genes that did not show at least one time point with an up- or and 48 h. downregulation of at least four-fold with respect to reference and an absolute range of four-fold change across the gene. These filters left 177 out of 1718 genes to be clustered for the Probe preparation for cDNA microarray experiments growth factor stimulations. All groups were clustered using an Total cellular RNA (10–20 mg) was reverse transcribed in the unsupervised hierarchical algorithm. Data are displayed by presence of Amino-Allyl dUTP (Sigma, Oakville, ON) using color representation of log2 ratio data, with green representing the manufacturer’srecommended protocol for SuperscriptII gene downregulation with respect to reference RNA and red reverse transcriptase (Invitrogen Inc., Ontario, Canada). representing gene upregulation with respect to reference RNA. Reactionswere purified usingMicrocon-30 columns(Milli- Unsupervised clustering was also performed using a BTSVQ pore, Bedford, USA). Sampleswere dried usinga Speedvac algorithm (Sultan et al., 2002; Liu et al., 2004; Barrios-Rodiles concentrator, resuspended in 0.1 M sodium bicarbonate et al., 2005). BTSVQ combinesSOMsand iterative k-means (pH ¼ 9.0), and divided for reciprocal labeling. Cy3 and Cy5 clustering in a complementary fashion. The algorithm parti- dyes (Pharmacia, Piscataway, NJ) resuspended in DMSO were tionsdata usinga k-means algorithm in sample space, where k added to each 4.5 ml aliquot of cDNA. For one set of aliquots iskept constantat 2. Iteratively applying the algorithm and the DLD-1 or DKO-4 reference waslabeled with Cy5 dye, using evaluation of variance as a stopping criterion, it while test samples were labeled with Cy3 dye, and for the other generatesa binary tree. For each level of the binary tree, the set of aliquots DLD-1 or DKO-4 reference was labeled with genesare ranked both with respectto the likelihood of the gene Cy3 dye, while test samples were labeled with Cy5 dye. being differentially expressed across all the samples in the same Coupling wasperformed at room temperature in darknessfor cluster and t-statistics. This provides an accurate method of 45 min. Reactionswere quenched by addition of 4 M hydro- excluding genes with variable expression across samples, as xylamine and incubated for 15 min at room temperature in the well as genes with low significance, and thus enables selection dark. Cy3 and Cy5 reactionswere then combined for each set of the most differentiating genes between clusters. The SOM of reference and test samples, and unincorporated nucleotides algorithm is then used to cluster the gene space, to produce were removed using a High Pure PCR purification kit (Roche, pseudocolor component planes for visualization (see Laval, QC). Probeswere dried usinga Speedvac concentrator Figure 1c). During training, SOM algorithm generatesbest- prior to resuspension. matching vectorsfor the data and randomly distributesthem on the 2D map. The SOM neighborhood function brings cDNA microarray hybridization similar groups spatially close to each other. We have used a Glass 1.7K3 cDNA human arrays were purchased from the Gaussian neighborhood function with medium radius. The Ontario Cancer Institute microarray facility (http://www. cluster structure in gene space is then visualized using microarrays.ca/). These arrays represent 1718 cDNA EST component planesof the already computed SOM. Thus,the sequences of known genes derived from IMAGE consortium gene expression is visualized as color intensity.

Oncogene Met transcriptional signaling in colorectal cancer IM Seiden-Long et al 101 Oligonucleotide array analysis Table 3). RNA for standard curve generation was isolated Total RNA was isolated from xenograft primary tumor tissue from subconfluent DKO-4 Met cell line grown in tissue by guanadinium/phenol extraction (Tsao et al., 1998). RNA culture. Absolute transcript concentrations were calculated was then further purified using an RNeasy kit (Qiagen Inc., using standard curves for each primer set. Relative ratios were Ontario, Canada). Quality of RNA preparation wasconfirmed then calculated for each sample set in a manner similar to using an Agilent Bioanalyzer (Agilent Technologies, Palo Alto, cDNA microarray ratiosto allow relative comparisonbetween CA). The arraysutilized in theseexperimentswere U133A data sets. For human tissue samples, ratios were calculated human oligonucleotide arrays(Affymetrix Inc., Santa Clara, using an average of normal tissue values for each primer set. CA). The U133A array contains >22 000 probe sets representing 18 400 transcripts (14 500 unique genes). Each array sample PPI analysis consists of 20 mg pooled total RNA from three separate tumor tissue samples, combined in equal concentration. Sample The OPHID of predicted human PPIswasusedto generate labeling and array hybridizationswere conducted at The protein interaction data for target genesfrom microarray Centre of Applied Genomics(Hospitalfor Sick Children, experiments(Brown and Jurisica,2005). OPHID (http:// Toronto, Canada, http://tcag.bioinfo.sickkids.on.ca/index. ophid.utoronto.ca) providesa unified resourcefor exploring the interactome by combining 19 454 known human interac- php?pagename ¼ microarray.php). Sample RNA in vitro transcription, labeling and hybridization were conducted by tionsfrom BIND, DIP, HPRD and MINT with 23 889 S. cerevisiae standard Affymetrix protocols, described in detail on their interactionspredicted by interolog mapping from , C. elegans D. melanogaster M. musculus website. (http://www.affymetrix.com/support/technical/manual/ , , and . OPHID was searched using Unigene IDs of identified target genes from expression_manual.affx). Raw array image data were analyzed s using Affymetrix MAS 5.0 software. All arrays have been microarray data. The PathwayAssist software (http:// scaled to a mean intensity of 150, and ratios have been www.ariadnegenomics.com/products/pathway.html) was generated to compare all samples to DLD-1 control array purchased from Stratagene (La Jolla, CA). The reported data. interactionswere further verified by reviewing the original publications. All DLD-1 or DKO-4 gene ratioswere log 2 transformed and filtered in the following fashion. All genes that were deemed absent by the MAS 5.0 software were removed from Immunohistochemistry and image analysis consideration. Data for the DLD-1 and DKO-4 growth factor Formalin-fixed, paraffin-embedded tumor xenograft tissue time course were filtered to remove any genes that did not sections were analyzed with HAT1 and CAS primary show an absolute range of two-fold change across the gene. antibodiesand anti-goat-Cy3-labeled secondaryantibody. These filters left 1190 out of B11 000 present transcripts to be Paraffin sections at were cut at 4 mm thickness, dried in 601C clustered. All data were clustered using an unsupervised oven, de-waxed in xylene and re-hydrated. Microwave antigen hierarchical algorithm (Eisen et al., 1998). Data are displayed retrieval wasperformed in MicroMed T/T (Hacker Instru- by color representation of log2 ratio data, with green ments, Fairfield, NJ) oven for 10 min at 1201C. Primary representing gene downregulation with respect to reference antibody incubation wasperformed with CAS (1:500) or RNA and red representing gene upregulation with respect to HAT1 (1:2000) goat polyclonal antibody for 16 h at room reference RNA. temperature. Negative control wasincubated in phosphate- buffered saline (PBS). Slides were rinsed three times in PBS. Secondary antibody incubation wasperformed with Cy3- Measurement of mRNA transcript levels by real-time RT–PCR conjugated anti-goat IgG (1:100) or anti-goat-HRP for 60 min RNA was isolated as described above for the xenograft at room temperature, and slides were rinsed three times with tumors. In all, 4 mg of total cellular RNA wasreversed PBS. For normal immunohistochemistry samples, immunor- transcribed using Superscript II reverse transcriptase (Invitro- eactivity was visualized by incubation in Nova Red substrate gen Inc., Ontario, Canada). A 10 ng equivalent of cDNA was (Vector Laboratories, Burlingame, CA) for 5 min. For used for each quantitative PCR assay performed with the ABI fluorescence applications, slides were mounted on Vectashield PRISM 7700 Sequence Detection System, using SYBR green mounting medium containing DAPI (Vector Laboratories, 2 Â master mix (Perkin-Elmer Applied Biosystems, Foster Burlingame, CA) immediately prior to viewing. City, CA). Two independent assays were performed in Four xenograft tumors per sample were analyzed in cross duplicate on cDNA from each cell line grown in tissue culture section for HAT1 and CAS levels by capturing five represen- and stimulated with HGF. For tumor xenograft tissue, N ¼ 8 tative fields per tumor in regions of the tissue containing only individual tumor samples were quantified in duplicate. For living tumor cells(20 fieldsper sampletotal). Imaging human tissues, the data set consisted of 30 normal mucosa, 63 employed the Zeiss Axiovert 200M inverted stage microscope primary colorectal cancers and 30 metastatic lesions. The with a FLUAR 20 Â /0.75 NA (DIC) objective (Carl Zeiss Inc., sequences of gene transcripts that corresponded to Tornwood, NY) illuminated by a 100 W HBO Mercury lamp. the Affymetrix probe set were obtained from GenBank acces- Images were captured at 8-bit grayscale resolution by a sion numbers listed on the Affymetrix website (http://www. Coolsnap HQ CCD camera (Roper Scientific Inc., Trenton, affymetrix.com) and intron-spanning primer sets for PCR NJ). Stroma and necrotic tissue were excluded from con- amplifications were designed to these sequences using the sideration by examining distinct nuclear morphology as seen Primer Express software (Perkin-Elmer Applied Biosystems, by DAPI staining in a low-resolution (FLUAR 5 Â /0.25 NA Foster City, CA). Predicted PCR product sequences were objective) scan of the tumor. Using Image Pro software checked using BLAST for recognition of target and nontarget (Media Cybernetics, Silver Spring, MD), images were thresh- sequences. Dissociation curves were performed as routine old gated to quantify staining by sum of integrated optical verification in order to check the primersfor amplification of a density (IOD), which is defined as average pixel intensi- single band, and template dilution standard curves (5-log ty Â area, and background fluorescence was subtracted using a range) were conducted with each primer set to verify a linear secondary antibody negative control. relationship between template concentration and CT values Tissue array cores were scored for immunostaining intensity (R2>0.9, data not shown, primer sequences in Supplementary of nuclear and cytoplasmic CAS and HAT1 in normal colonic

Oncogene Met transcriptional signaling in colorectal cancer IM Seiden-Long et al 102 mucosa epithelial cells and tumor cells, using the 0 to 3 scores assistance in ﬂuorescence image quantiﬁcation, and Wendy representing absent, weak, moderate or strong, respectively. Zhang for assistance in analysis of publicly available micro- Samples that showed 2 or 3 staining intensity were scored as rarray databases. The authors thank Richard Lu, David positive samples. Otasek and Baiju Devani for their help in developing OPHID database and its search engine, network visualization software Acknowledgements and web interface. We gratefully acknowledge the hardware We thank Drs S Shirasawa and T Sasazuki for making and software support from IBM Life Sciences through a available the DLD-1 and DKO-4 cell lines, James Ho for Shared University Research Grant. Grant Support: Canadian assistance in immunohistochemistry, Dr Runjan Chetty Institutes of Health Research (CIHR) grant MOP-64345; (Department of Pathology, UHN) for assistance in quality National Science and Engineering Research Council (RGPIN control of tissues used for realtime RT–PCR studies. We also 203833-02), Institute for Robotics and Intelligent Systems, thank JamesJonkman and the Advanced Optical Micro- Precarn Incorporated and IBM to IJ; CIHR Doctoral imaging Facility (AOMF) at OCI for invaluable advice and Research Award to IMS-L; Precarn Scholarship to KRB.

References

Balkovetz DF, Gerrard Jr ER, Li S, Johnson D, Lee J, Jiang MC, Liao CF. (2004). J Exp Clin Cancer Res 23: TobiasJW et al. (2004). Am J Physiol Renal Physiol 286: 325–332. F702–F710. Jiang MC, Liao CF, Tai CC. (2002). Biochem Biophys Res Barrios-Rodiles M, Brown KR, Liu Z, Donovan RS, Shinjo F, Commun 294: 900–905. Liu Y et al. (2005). Science 307: 1621–1625. Jiang W, Hiscox S, Matsumoto K, Nakamura T. (1999). Crit BehrensP, Brinkmann U, Wellmann A. (2003). Apoptosis 8: Rev Oncol Hematol 29: 209–248. 39–44. Kaufman PD, Kobayashi R, Kessler N, Stillman B. (1995). Bertotti A, Comoglio PM. (2003). Trends Biochem Sci 28: Cell 81: 1105–1114. 527–533. Liu C, Park M, Tsao MS. (1992). Oncogene 7: 181–185. Bhattacharjee RN, BanksGC, Trotter KW, Lee HL, Archer Liu J, Blackhall F, Seiden-Long I, Jurisica I, Navab R, Liu N TK. (2001). Mol Cell Biol 21: 5417–5425. et al. (2004). Oncogene 23: 6316–6324. Boccaccio C, Ando M, Tamagnone L, Bardelli A, Michieli P, Martinez-Garza SG, Nunez-Salazar A, Calderon-Garciduenas Battistini C et al. (1998). Nature 391: 285–288. AL, Bosques-Padilla FJ, Niderhauser-Garcia A, Barrera- Brinkmann U, Brinkmann E, Gallo M, Scherf U, Pastan I. Saldana HA. (1999). Digest Dis 17: 225–229. (1996). Biochemistry 35: 6891–6899. Monga SP, MarsWM, PediaditakisP, Bell A, Mule K, Bowen Brown KR, Jurisica I. (2005). Bioinformatics 21: 2076–2082. WC et al. (2002). Cancer Res 62: 2064–2071. Conner EA, Teramoto T, Wirth PJ, Kiss A, Garﬁeld S, Qian YW, Lee EY. (1995). J Biol Chem 270: 25507–25513. Thorgeirsson SS. (1999). Carcinogenesis 20: 583–590. Rodriguez-Viciana P, Warne PH, Dhand R, Vanhaesebroeck Danilkovitch-Miagkova A, Miagkov A, Skeel A, Nakaigawa B, Gout I, Fry MJ et al. (1994). Nature 370: 527–532. N, Zbar B, Leonard EJ. (2001). Mol Cell Biol 21: 5857–5868. Roth SY, Denu JM, AllisCD. (2001). Annu Rev Biochem 70: Di Renzo MF, Olivero M, Giacomini A, Porte H, Chastre E, 81–120. Mirossay L et al. (1995). Clin Cancer Res 1: 147–154. Seiden-Long I, Han K, Li M, Shirasawa S, Sasazuki T, Dugan KA, Wood MA, Cole MD. (2002). Oncogene 21: Johnston M et al. (2003). Mol Cancer Res 1: 393–401. 5835–5843. Shirasawa S, Furuse M, Yokoyama N, Sasazuki T. (1993). Eberhardy SR, Farnham PJ. (2001). J Biol Chem 276: Science 260: 85–88. 48562–48571. Sultan M, Wigle DA, Cumbaa CA, Maziarz M, Glasgow J, Eisen MB, Spellman PT, Brown PO, Botstein D. (1998). Proc Tsao MS et al. (2002). Bioinformatics 18(Suppl 1): Natl Acad Sci USA 95: 14863–14868. S111–S119. Fazekas K, Csuka O, Koves I, Raso E, Timar J. (2000). Clin Tortola S, Marcuello E, Gonzalez I, ReyesG, ArribasR, Aiza Exp Metastasis 18: 639–649. G et al. (1999). J Clin Oncol 17: 1375–1381. Fearon ER, Vogelstein B. (1990). Cell 61: 759–767. Tsao MS, Liu N, Chen JR, Pappas J, Ho J, To C et al. (1998). Garcia-Guzman M, Dolﬁ F, Zeh K, Vuori K. (1999). Lung Cancer 20: 1–16. Oncogene 18: 7775–7786. Verreault A, Kaufman PD, Kobayashi R, Stillman B. (1996). Gerritsen ME, Tomlinson JE, Zlot C, Ziman M, Hwang S. Cell 87: 95–104. (2003). Br J Pharmacol 140: 595–610. Verreault A, Kaufman PD, Kobayashi R, Stillman B. (1998). Graziani A, Gramaglia D, Cantley LC, Comoglio PM. (1991). Curr Biol 8: 96–108. J Biol Chem 266: 22087–22090. Vojtek AB, Der CJ. (1998). J Biol Chem 273: 19925–19928. Graziani A, Gramaglia D, dalla ZP, Comoglio PM. (1993). Wood MA, McMahon SB, Cole MD. (2000). Mol Cell 5: J Biol Chem 268: 9165–9168. 321–330. Halaban R, Rubin JS, Funasaka Y, Cobb M, Boulton T, Yarwood SJ, Woodgett JR. (2001). Proc Natl Acad Sci USA Faletto D et al. (1992). Oncogene 7: 2195–2206. 98: 4472–4477.

Supplementary Information accompaniesthe paper on the Oncogene website(http://www.nature.com/onc)

Oncogene