A number of factors, generally studied in isolation in previous studies, have been shown to influence E-cad expression (4
). In the present study, we assessed regulation of E-cad expression levels in the NCI-60 cell panel by eight of the potential effectors ( and ) using (i) our E-cad promoter DNA methylation data; (ii) data from six different microarray platforms; and (iii) data from pharmacological assays employing RNAi and 5-AC treatment. These factors were assessed in combination, yielding a coherent picture of multi-factorial network regulation of E-cad expression. Included were the effects of DNA methylation of the E-cad promoter region, DNA copy number, and expression levels of the transcriptional repressors SNAI1, SNAI2, TCF3, TCF8, TWIST1, and ZFHX1B. In addition, levels of E-cad were shown to be associated with cell-cell adhesion and to correlate to the potencies of alkylating agents, topoisomerase 1 inhibitors, and a DNA-antimetabolite.
Since transcript and protein levels often do not correlate over diverse cell types (32
), it was not obvious that they would in this case. However, we found that the E-cad transcript and protein expression data do correlate with each at statistically significant levels (mean r = 0.83; p-values all <0.05). There was no correlation for DNA copy number; hence no evidence that copy number regulates E-cad expression.
Next, we found that E-cad expression correlates negatively with E-cad DNA promoter region methylation in a consistent and significant manner (correlation coefficient of the mean is −.47, p <.05). In modeling the influences on E-cad expression, we treated methylation as an independent variable because of two lines of evidence. First, there are multiple prior examples of transcription factor binding being blocked by the presence of DNA methylation, (41
). Second, our data from multiple microarray platforms, as well as the branched DNA assay, indicated that E-cad is not expressed in any of the NCI-60 if the methylation level is greater than approximately 30% (18
). That is, DNA methylation (but not the other factors studied) appeared sufficient by itself for down-regulation of E-cad. We, therefore, define a “non-permissive level” of methylation as >30% for purposes of the regulatory and statistical modeling. Down-regulation of E-cad in cancer by promoter region methylation (but not the threshold at which it occurs) has been well documented, (3
Three additional factors found to be associated with and statistically predict E-cad expression in the NCI-60, were SNAI2, TCF8, and ZFHX1B. All showed significant negative correlations across the panel (p<0.05, two-tailed, no multiple comparisons correction) with the five measurements of E-cad expression (). Additionally, show that high expression levels of SNAI2, TCF8, or ZFHX1B are associated with low levels of E-cad expression. Although high levels of TWIST1 were also associated with low E-cad expression (p=0.01), they were not independently predictive once the confounding effects of methylation, TCF8, SNAI2, and ZFHX1B had been removed. The bracketed expression ranges of SNAI2, TCF8, and ZFHX1B (, respectively) associated with low E-cad expression all show “exception” cell lines with detectable E-cad expression (MALME-3M and UACC-257 for SNAI2 and ZFHX1B; EKVX for TCF8), indicating that those ranges do not absolutely prevent E-cad expression. SNAI1 and TCF3 expression were uncorrelated with E-cad expression.
The correlations shown in between the six E-cad measurements and the six transcriptional repressor expression levels for the NCI-60 and the eight tissues of origin (excluding prostate for which there are only two lines) provides additional information that both confirms prior and indicates novel relationships. The significant correlations with E-cad expression for SNAI2 in breast and TCF8 in non-small cell lung cancer support prior findings (46
). The correlations between E-cad expression and TCF8 in breast and ovarian lines and TWIST1 in melanomas are novel. The correlations with SNAI1, TCF3, and ZFHX1B were not consistently statistically significant for any of the tissue-of-origin types.
Based on this bioinformatic and statistical analysis, we propose for the regulatory factors analyzed that the top three levels of influence on E-cad expression in the NCI-60 are (i) E-cad promoter methylation, (ii) expression of TCF8, and (iii) expression of ZFHX1B or SNAI2 (whose profiles are not linearly separable statistically). E-cad is not expressed at E-cad methylation levels >30%, and is repressed at SNAI2, ZFHX1B and TCF8 HG-U133 intensity levels >5.16, 3.30, and 5.18, respectively (, respectively). In this model, DNA methylation is sufficient for down-regulation of E-cad, whereas the transcriptional repressors are conditionally sufficient (i.e., sufficient in some cell types but not in others).
To test this regulatory ranking and also assess our ability to predict treatment combinations that would result in E-cad up-regulation, we designed functional assays ( and data for SNAI2 not shown) in which we manipulated the four statistically predictive factors. In each case, the optimal test case for E-cad up-regulation was a cell line with that repressor in its proposed repressive range, levels of the other three repressive factors that were not, and undetectable levels of E-cad expression (i.e., <5.40 by HG-U133 array in ). Quantitation of RNA expression was provided by the branched DNA assay, which proved to be more sensitive than the HG-U133 microarrays.
When choosing cells in which we predict that E-cad would be up-regulated by DNA demethylation, none fit our optimal test case parameters. The flaws in the three best candidates were that TK-10 expresses SNAI2 in its repressive range, and SW-620 and IGROV1 have methylation levels slightly below the 30% cutoff. Still, up-regulation of E-cad followed treatment with 5-AC (for SW-620, TK-10, and IGROV1) by 2.2-, 22-, and 6.1-fold, respectively (), providing functional evidence that (i) the 23 and 24% E-cad DNA methylation levels (in SW-620 and IGROV1, respectively), although repressive, are insufficient to silence E-cad expression completely (see , no drug); (ii) and that the association between E-cad methylation and expression might be causal. E-cad was re-expressed in TK-10 in the presence of repressive levels of SNAI2. We were unable to cause E-cad up-regulation in the presence of multiple transcriptional repressors in their repressive ranges (for A498, NCI-H460, OVCAR-8, and SK-MEL-28). E-cad is not up-regulated in the secondary affect control cell lines, A549, HCT-116 or HT29, in the presence of low (baseline) levels of methylation.
To test E-cad up-regulation following TCF8 down-regulation () we used two cells lines, A549-ATCC and DU-145, with optimal profiles. E-cad was successfully up-regulated 2.4 and 2.8-fold, respectively, by TCF8 knock-down in those lines. Those findings: (i) support our predictive criteria for identifying cells that will up-regulate E-cad expression following TCF8 down-regulation and (ii) indicate that the E-cad/TCF8 expression association is potentially causal. Our model predicted successfully that E-cad up-regulation would fail in OVCAR-8 because of high DNA methylation. E-cad expression is not re-expressed in TK-10, T47D, and HCT-116 in the absence of repressive TCF-8 levels.
There were no cell lines ideal for testing E-cad up-regulation following down-regulation of ZFHX1B. Still, by using our criteria to select “next best” cell lines, we were able to up-regulate E-cad following ZFHX1B down-regulation in MALME-3M and UACC-257 by 2.4 and 1.7 fold (mean values), respectively (). Those findings (i) support our predictive criteria for identifying cells that can re-express E-cad following ZFHX1B down-regulation and (ii) indicate that the E-cad/ZFHX1B expression association is potentially causal. E-cad is not re-expressed in ACHN, in the presence of repressive levels of TCF8.
There were also no cell lines ideal for testing E-cad up-regulation following SNAI2 down-regulation. We were unable to re-express E-cad in either of our “next best” cell lines, MALME-3M or UACC-257, following SNAI2 down-regulation in the presence of repressive levels of ZFHX1B (data not shown).
Most of the cell lines that showed no detectable E-cad by U133 array have multiple repressive factors in their proposed repressive ranges, implying that multiple interventions might be required for substantial E-cad up-regulation in many cancer cell lines. Therefore, we did an initial test of the effects of siRNA and 5-AC treatment in combination in IGROV1, a cell line that combines the presence of the top two repressive factors in our regulatory ranking, E-cad DNA methylation and TCF8 expression. The results () were increases in E-cad expression of 4.8-fold for 5-AC alone, a mean of 2.3-fold following siTCF8 down-regulation, and a mean of 12.0-fold for the combination of 5-AC with siTCF8. Those results (i) support our predictive criteria for identifying cells that can be made to re-express E-cad by down-regulating both DNA methylation and TCF8 expression; (ii) indicate that both associations are potentially causal; (iii) suggest that, in the presence of multiple repressive factors, a targeted combination of treatments is likely the optimal approach for up-regulation of E-cad expression.
Both TCF8 and ZFHX1B have previously been described as being targeted by the miR-200 family (48
). Consistent with these results, we find significant negative correlations between TCF8 expression and hsa-miR-200a and 200b of -0.49, -0.55, respectively, as well as between ZFHX1B and hsa-miR-200a, 200b, and 200c of -0.56, -0.67, and -0.34, respectively. Levels for the miRNA are from our prior study (50
) and are available at http://discover.nci.nih.gov/cellminer/queryLoad.do
In the present study, we also defined two additional important factors that correlate with E-cad expression in the NCI-60: cell-cell adhesion and the potencies of a variety of drugs. Within the subset of 54 (non-leukemic) attached cell lines, the high expressers of E-cad tend to exhibit higher levels of cell-cell adhesion (), consistent with prior reports (3
). The leukemias, with no detectable E-cad, grow as spherical detached cells and exhibit limited cell-cell adhesion (data not shown). The drug activity patterns () have significant negative correlations to E-cad expression in 9/10 drugs, for both the attached cell subset and the NCI-60.
In conclusion, we report an ‘integromic’ analysis of multiple factors with the potential, either individually or in combination, to regulate E-cad expression. We relate those factors to E-cad expression at the transcript and protein levels. Statistical analysis of this data allows the prediction of which cell lines would show up-regulation of E-cad expression in pharmacological assays using 5-AC or siRNA’s against TCF8, ZFHX1B, or SNAI2. Among those regulatory factors, methylation status is proposed to be non-permissive and sufficient by itself to down-regulate E-cad expression when above a 30% threshold (18
). TCF8 expression, SNAI2 and ZFHX1B are proposed to be conditionally sufficient, and repressive ranges are proposed for each of those factors. TWIST1 is correlated with E-cad down-regulation while not being shown to be predictive. SNAI1, TCF3, and DNA copy number show no obvious effect on E-cad. The functional assays done either confirmed or extended the proposed regulatory ranking, leading us to conjecture causality for E-cad regulation for promoter methylation, TCF8, and ZFHX1B. The data thus provide a rational basis for prospectively predicting what pharmacological combinations of DNA demethylation and down-regulation of transcriptional repressors would yield E-cad up-regulation in particular cancer cell types. The findings thus have implications for strategies to suppress cancer invasion and metastasis associated with E-cad loss.