Cell lines representative of five types of human cancer were used: HT29 and Caco-2 for colon cancer, MCF-7 and MDA-MB-468 for breast cancer, MIA PaCa-2 for pancreatic cancer, K562 for erythroblastic leukemia, and Saos-2 for osteosarcoma. These cell lines are sensitive to MTX, with IC50s of 1.67 × 10^-8 M MTX for HT29, 4.87 × 10^-8 M MTX for MDA-MB-468 and 1.16 × 10^-8 M MTX for MIA PaCa-2 cells. IC50 values were calculated using GraphPad Prism 5 version 5.0a for Macintosh (GraphPad Software, San Diego, CA, USA). Resistant cells were obtained in the laboratory upon incubation with stepwise concentrations of MTX (Lederle) as previously described 8. HT29, Caco-2 and K562 resistant cells were able to grow in 10^-5 M MTX; MIA PaCa-2, Saos-2, MCF-7 and MDA-MB-246 cells were resistant to 10^-6 M MTX.

Human cell lines were routinely grown in Ham's F12 medium supplemented with 7% fetal bovine serum (both from Gibco/Invitrogen, Grand Island, NY, USA) at 37°C in a 5% CO₂ humidified atmosphere. Resistant cells were routinely grown in selective DHFR medium lacking glycine, hypoxanthine and thymidine (-GHT medium; Gibco), the final products of dihydrofolate reductase (DHFR) activity. This medium was supplemented with 7% dialyzed fetal bovine serum (Gibco).

Gene expression was analyzed by hybridization to the GeneChip^® Human Genome U133 PLUS 2.0 from Affymetrix, containing over 54,000 transcripts and variants. Total RNA for oligo arrays was prepared from triplicate samples of every sensitive and resistant cell line using the RNAeasy Mini kit (Qiagen, Germantown, Maryland, USA) following the recommendations of the manufacturer. Labeling, hybridization and detection were carried out following the manufacturer's specifications.

Gene expression analyses were performed using three samples of both sensitive and resistant cells for each of the seven cell lines studied. These analyses were carried out with the GeneSpring GX software v 7.3.1 (Agilent Technologies, Santa Clara, CA, USA), using the latest gene annotations available (March 2009). This software package allows multi-filter comparisons using data from different experiments to perform the normalization, generation of restriction (filtered) lists and functional classifications of the differentially expressed genes. Normalization was applied in two steps: 'per chip normalization', by which each measurement was divided by the 50th percentile of all measurements in its array; and 'per gene normalization', by which all the samples were normalized against the median of the control samples (sensitive cells). The expression of each gene was reported as the ratio of the value obtained for each condition relative to the control condition after normalization of the data. Then, data were filtered using the control strength, a control value calculated using the Cross-Gene Error model on replicates 9 and based on average base/proportional value. Measurements with higher control strength are relatively more precise than measurements with lower control strength. Genes that did not reach this value were discarded. Additional filtering was performed to determine differentially expressed genes. A first filter was performed by selecting the genes that displayed a P-value corrected by false discovery rate (Benjamini and Hochberg false discovery rate) of less than 0.05. The output of this analysis was then filtered by fold expression. Thus, lists of genes differentially expressed by at least twofold were generated for each of the seven resistant cell lines.

The lists of genes differentially expressed by at least twofold with a P-value < 0.05 including multiple testing correction for each cell line were divided into two groups: overexpressed and underexpressed genes. Comparisons of lists of overexpressed genes were performed using Venn diagrams in GeneSpring GX. Lists of underexpressed genes were also compared using the same approach. All lists were compared in pairs and lists of genes in common between each pair were generated.

BANs were constructed with the aid of Pathway Architect software v3.0 (Stratagene-Agilent). Briefly, this software package generates interaction networks starting with the genes in a given list (entities) taking into account the information present in a database of known molecular interactions. The lists correspond to the collection of differentially expressed genes under specific conditions. The database of molecular interactions is composed of more than 1.6 million interactions divided into different classes (binding, regulation, promoter binding, transport, metabolism, protein metabolism and expression). The interactions are extracted from the literature using a Natural Language Processing (NLP) tool run on Medline abstracts (NLP references), plus those obtained from external curated databases like BIND (Biomolecular Interaction Network Database) 10 and MINT (Molecular INTeraction) 11. Interactions in the interaction database are scored according to five different categories: maximum, high, medium, low and minimal. Curated interactions (BIND and MINT sources) are given the maximum quality score, as are any interactions that have at least three NLP references. Pathway Architect gathers all that information to construct novel views as to how the entities in a list could be interacting with each other, even including entities not present in the original list (neighbors resulting from the expanded interaction). Customized analyses were performed to select relevant interaction networks with an associated high confidence index since such networks are likely to mirror biological significance. One-step expansion (using the expand network command) of the original set of entities with maximum score interaction were then analyzed by setting an advanced filter that included the categories of binding, expression, metabolism, promoter binding, protein modification and regulation. This procedure gives a final representation formed of a collection of nodes with different degrees of interrelationship. Some gene products from the original lists were not significantly connected with other members or neighbors and, therefore, were removed from the final view. Finally, members of the network were matched with expression levels.

HT29 cells (30,000) were plated in 1 ml of -GHT medium and transfection was performed 18 hours later. For each well, Lipofectamine™ 2000 (Invitrogen, Carlsbad, CA, USA) in 100 μl of serum free -GHT medium was mixed in Eppendorf tubes with 100 nM of small interfering RNA (siRNA) in 100 μl of serum free -GHT medium. The mixture was incubated at room temperature for 20 minutes before addition to the cells. MTX (2 × 10^-8 M) was added 48 hours after siRNA treatment and 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assays 12 were performed 3 days after MTX addition. Treatment of MDA-MB-468 and MIA PaCa-2 cells was performed following the same protocol but using Metafectene™ (Biontex, Martinsried, Germany). A non-related siRNA was used as negative control; it was transfected in parallel with the other siRNAs and used to normalize the results.

The siRNAs were designed using the software iRNAi v2.1. (The Netherlands Cancer Institute, Amsterdam, The Netherlands) Among the possible alternatives, sequences rich in A/T at the 3' end of the target were chosen. Then, BLAST resources in NCBI were used to assess the degree of specificity of the sequence recognition for these siRNAs. Only the siRNAs that reported the target gene as the only mRNA hit, or some family members in the case of siUGT1A, were selected. The sequences for the sense strand of all siRNAs used are available in Table 1.

A global comparison of all cell lines was performed using GeneSpring GX v 7.3.1. The triplicate samples for each condition in each of the seven cell lines (42 samples) were imported into one single experiment. Normalization was performed in two steps: 'per chip normalization' (as described above) and 'per gene normalization', by which the samples were normalized against the median of all samples. Lists of genes displaying a false discovery rate-corrected P-value < 0.05 were generated for each cell line. As a filter, these values had to appear in at least two out of the seven cell lines. A hierarchical clustering method in GeneSpring was used to group genes on the basis of similar expression patterns over all samples. The distance matrix used was Pearson correlation, and average linkage was used as clustering algorithm. The same clustering method was used to group the cell lines on the basis of similar patterns of gene expression.

Gene mRNA levels were determined by real-time RT-PCR. Total RNA was extracted from cells using Ultraspec™ RNA reagent (Biotecx, Houston, TX, USA) following the recommendations of the manufacturer. For determining gene-node mRNA levels upon siRNA treatment, cells were treated as described above and total RNA was prepared 48 hours after transfection using the same reagent. In either case, complementary DNA was synthesized in a total volume of 20 μl by mixing 500 ng of total RNA and 125 ng of random hexamers (Roche, Mannheim, Germany) in the presence of 75 mM KCl, 3 mM MgCl₂, 10 mM dithiothreitol, 20 units of RNasin (Promega, Madison, WI, USA), 0.5 mM dNTPs (AppliChem, Darmstadt, Germany), 200 units of M-MLV reverse transcriptase (Invitrogen) and 50 mM Tris-HCl buffer, pH 8.3. The reaction mixture was incubated at 37°C for 60 minutes. The cDNA product was used for subsequent real-time PCR amplification using an ABI Prism 7000 Sequence Detection System (Applied Biosystems, Foster City, CA, USA) with 25 ng of the cDNA mixture, the assays-on-demand from Applied Biosystems Hs00758822_s1 for DHFR and Hs00356991_m1 for Adenine Phosphoribosyltransferase (APRT) or the following primers: DKK1, 5'-AGTACTGCGCTAGTCCCACC-3' and 5'-CTGGAATACCCATCCAAGGTGC-3'; UGT1A, 5'-TAAGTGGCTACCCCAAAACG-3' and 5'-CTCCAGCTCCCTTAGTCTCC-3'; EEF1A1, 5'-CGTCATTGGACACGTAGATTCGGG-3' and 5'-GGAGCCCTTTCCCATCTCAGC-3'.

Genomic DNA from either sensitive or resistant cells was obtained with the Wizard™ Genomic DNA Purification Kit (Promega) following the manufacturer's recommendations. One hundred nanograms of DNA and the assays-on-demand Hs00758822_s1 for DHFR and Hs99999901_s1 for 18S were used for real-time PCR amplification.

Total extracts from cells, either sensitive or MTX-resistant, were used to assay DHFR protein levels. Cells were washed twice with ice-cold phosphate-buffered saline and scraped in 200 ml lysis buffer (50 mM Hepes, 500 mM NaCl, 1.5 M MgCl₂, 1 mM EGTA, 10% glycerol (v/v), 1% Triton X-100 and protease inhibitor cocktail). Cells were incubated in ice for 1 hour with vortexing every 15 minutes and then centrifuged at 14,000 rpm at 4°C for 10 minutes. Five microliters of the extract were used to determine protein concentration by the Bradford assay (Bio-Rad, Munich, Germany). The extracts were frozen in liquid N₂ and stored at -80°C. Fifty micrograms of both sensitive and resistant cell total extracts were resolved by 15% SDS-PAGE. Transference to PVDF membranes (Immobilon P, Millipore, Bedford, MA, USA) using a semidry electroblotter was followed by incubation with an antibody against DHFR (Davids Biotechnologie, Regensburg, Germany), and detection was accomplished using secondary horseradish peroxidase-conjugated antibody and enhanced chemiluminescence, as recommended by the manufacturer (Amersham/GE Healthcare, Buckinghamshire, UK). To normalize the results, blots were re-probed with an antibody against actin (Sigma, St. Louis, MO, USA).

HT29 cells, either sensitive or MTX-resistant, were seeded into six-well plates the day before transfection at a density of 2 × 10⁵ cells/well in Ham's F12 medium containing 7% fetal bovine serum. Transfection was performed using FUGENE™ HD (Roche). For each well, 6 μl of FUGENE™ HD in 100 μl of serum-free medium was incubated at room temperature for 5 minutes. The mixture was added to 1 μg of TOPFLASH (Millipore) and incubated at room temperature for 20 minutes before addition to the cells. In co-transfections, 1 μg of TOPFLASH was mixed together with 2 μg of pBATEM2-CDH before the addition of FUGENE™ HD in serum-free medium. The total amount of DNA was kept constant at 3 μg, adding empty vector when necessary. Luciferase activity was assayed 30 hours after transfection.

In all cases, cell extracts were prepared by lysing the cells with 200 μl of freshly diluted 1× Reporter Lysis Buffer (Promega). The lysate was centrifugated at 13,000 g for 2 minutes to pellet the cell debris and the supernatants were transferred to a fresh tube. A 15-μl aliquot of the extract was added to 15 μl of the luciferase assay substrate (Promega) and the luminiscence of the samples was read immediately on a Gloomax 20/20 luminometer (Promega); light production (relative light units) was measured for 10 s. Each transfection was performed in triplicate. Protein concentration was determined by the Bradford assay and used to normalize the results.

Data are presented as mean ± standard error (SE). Statistical analyses were performed using the unpaired t-test option in GraphPad InStat version 3.1a for Macintosh (GraphPad Software). P-values < 0.05 were considered to be statistically significant.

In a previous study, we analyzed the differential gene expression between sensitive and MTX-resistant cells derived from the human colon cancer cell line HT29 13. In the present work we extend the study of gene expression profiles associated with MTX resistance by including another six MTX-resistant cell lines. Together, the studied cell lines represent colon cancer (CaCo2 and HT29), breast cancer (MCF-7 and MDA-MB-468), pancreatic cancer (MIA PaCa-2), erythroblastic leukemia (K562) and osteosarcoma (SaOs-2). Total RNA was extracted for the seven pairs of sensitive and MTX-resistant cell lines, and the expression profile of the 54,700 transcripts and variants included in the HG U133 PLUS 2.0 microarray from Affymetrix was compared between each pair using GeneSpring GX software v7.3.1. Upon normalization and statistical filtering of the data, lists of genes differentially expressed by at least twofold were built as described in Methods. These lists are presented as Additional data files 1 to 7. The data discussed in this report have been deposited in the Gene Expression Omnibus (GEO) 14 and are accessible through GEO series accession number [GSE16648].

We compared the gene expression patterns of all the studied cell lines together. Lists of genes displaying a false discovery rate-corrected P-value < 0.05 were generated for each cell line. Then, hierarchical clustering in GeneSpring GX was used to construct a heat map displaying both a gene tree and a sample tree (Figure 1), as described in Methods. Two facts could be extracted from this representation. First, there is a high correlation between cell lines sharing the same tissue origin. The two colon cancer cell lines studied (HT29 and Caco-2) are more highly correlated in gene expression with each other than with all the other cell lines. The breast cancer cell lines studied (MCF-7 and MDA-MB-468) also showed similar gene expression, although the degree of correlation is slightly lower than that for the colon cancer cell lines. The other three cell lines studied (MIA PaCa-2, K562 and Saos-2) displayed different gene expression from the colon or the breast cancer cell lines, and thus cluster apart from them. Second, gene expression of the resistant cells is more closely correlated with that of their sensitive cell counterparts than with any other sample or cell line.

As DHFR is the target for MTX, and was upregulated in MTX-resistant cells, we validated its overexpression in all cell lines studied. Real-time RT-PCR was used to quantify the mRNA levels, and DHFR protein levels were determined by western blotting in both sensitive and MTX-resistant cell lines (Table 2). Copy number determination revealed dhfr amplification only in HT29, Caco-2, MCF-7 and MIA PACA-2 resistant cells (Table 2).

Lists of genes differentially expressed by at least twofold between sensitive and resistant cell lines were generated for each cell line. Each list was split in two, one group including the overexpressed genes and the other including the underexpressed genes. Then, Venn diagrams were used to compare the lists of overexpressed and underexpressed genes between HT29 and Caco-2 cell lines, between MCF-7 and MDA-MB-468 cell lines, and among MIA PaCa-2, K562 and Saos-2 cell lines (Tables 3, 4 and 5, respectively). This approach allowed us to identify differentially expressed genes with a common trend in expression among the cell lines compared.

BANs were constructed using the Pathway Architect software as described in Methods starting with the lists of genes differentially expressed in common between both colon cancer cell lines, both breast cancer cell lines and among the pancreas cancer, leukemia and osteosarcoma cell lines (Figure 2a,b,c, respectively). In the BANs generated, DKK1 is a highly interconnected node in the colon cancer cell lines, UGT1A family members formed a network of genes differentially expressed in breast cancer, and EEF1A1 was commonly overexpressed in pancreatic cancer, leukemia and osteosarcoma. A BAN including all the genes of the three lists of differentially expressed genes was constructed (Figure 3). DKK1, UGT1A and EEF1A1 all seemed to be important nodes of this newly constructed network, and thus were selected for further study.

Given that the node genes DKK1, UGT1As and EEF1A1 were overexpressed in cells resistant to MTX (Table 6), we investigated the effect of decreasing their mRNA levels by means of siRNAs on the sensitivity to this chemotherapeutic agent. We also performed treatments with siDHFR in order to assess the role of DHFR in MTX resistance. HT29 and MDA-MB-468 cell lines were used as models of colon and breast cancer, respectively, and MIA PaCa-2 cells were selected as the model for the other three cell lines. Previously, it was confirmed that the mRNA levels of the three genes were decreased 48 hours after siRNA treatment (Figure 4a-c). Cells were pre-incubated with individual siRNAs for 48 hours before the addition of methotrexate. The presence of 100 nM of either siDKK1 or siDHFR caused increases in MTX cytotoxicity in HT29 cells of 50% and 65%, respectively, compared to MTX alone (Figure 4d). Incubation with 100 nM of either siUGT1A or siDHFR in MDA-MB-468 cells caused increases in cytotoxicity of 36% and 50%, respectively, compared to MTX alone (Figure 4e). Treatment of MIA PaCa-2 cells with 30 nM of either siEEF1A1 or siDHFR resulted in increased cytotoxicity of 35% and 65%, respectively, compared to MTX alone (Figure 4f). The same approach was conducted in the resistant counterparts of the HT29, MDA-MB-468 and MIA PaCa-2 cell lines, but no significant changes in cytotoxicity were observed (P > 0.05; data not shown).

Given that the UGT1A family is involved in the metabolism of other drugs, we also performed combination treatments with siUGT1A and SN38, the active metabolite of the anticancer drug irinotecan. Transfection of the siRNA was performed as described above, and 1 nM SN38 was added 48 hours after siRNA treatment. These incubations led to a significant (P < 0.01) increase of 46% in SN38 sensitivity.

A non-related siRNA against the luciferase gene was used as a negative control in all experiments. Transfection of this siRNA was performed in parallel with the other siRNAs, and was used to normalize the results.

As DKK1 is known to be transcriptionally regulated by the Wnt pathway, we investigated the degree of activation of this signaling pathway in MTX-resistant HT29 cells compared with their sensitive counterpart. Cells were transiently transfected with the reporter plasmid TOPFLASH, bearing three T-cell factor (TCF) binding sites. A transcriptional activation of 26-fold resulted from the transfection of TOPFLASH in the resistant cells, while no significant activation was observed upon transfection in the sensitive cells (Figure 5). Additionally, co-transfections of TOPFLASH with an expression plasmid for E-cadherin (pBATEM2-CDH) were performed in both cell lines. As shown in Figure 5, overexpression of E-cadherin in the resistant cells led to a marked decrease in TOPFLASH activity, down to basal activity. No significant changes in transcriptional activity were observed when these co-transfections were performed in the sensitive cells.