Nuclear factor of activated T-cell activity is associated with metastatic capacity in colon cancer

Abstract

Metastatic recurrence is the leading cause of cancer-related deaths in patients with colorectal carcinoma. To capture the molecular underpinnings for metastasis and tumor progression, we performed integrative network analysis on 11 independent human colorectal cancer gene expression datasets and applied expression data from an immunocompetent mouse model of metastasis as an additional filter for this biologic process. In silico analysis of one metastasis-related coexpression module predicted nuclear factor of activated T-cell (NFAT) transcription factors as potential regulators for the module. Cells selected for invasiveness and metastatic capability expressed higher levels of NFATc1 as compared with poorly metastatic and less invasive parental cells. We found that inhibition of NFATc1 in human and mouse colon cancer cells resulted in decreased invasiveness in culture and downregulation of metastasis-related network genes. Overexpression of NFATc1 significantly increased the metastatic potential of colon cancer cells, whereas inhibition of NFATc1 reduced metastasis growth in an immunocompetent mouse model. Finally, we found that an 8-gene signature comprising genes upregulated by NFATc1 significantly correlated with worse clinical outcomes in stage II and III colorectal cancer patients. Thus, NFATc1 regulates colon cancer cell behavior and its transcriptional targets constitute a novel, biologically anchored gene expression signature for the identification of colon cancers with high risk of metastatic recurrence.

Conflict of interest statement

Conflict of interest: The authors disclose no potential conflicts of interest.

©2014 American Association for Cancer Research.

Figures

Figure 1. Identification of NFAT as a potential regulator of metastasis

(A) Cumulative distribution function (CDF) curves of semantic similarities for gene pairs in co-expression networks based on LLR cutoffs of 0.5 (CYAN), 1 (PINK), and 1.25 (GREEN), compared to gene pairs in the stand alone co-expression network derived from GSE17536 with a Pearson’s correlation coefficient cutoff of 0.74 (DOTTED PINK). CDF curves for all gene pairs and interacting protein pairs are included as negative (BLACK) and positive (RED) references, respectively. (B) The largest five co-expression modules, each depicted in a distinct color scheme and annotated with associated biological processes are shown. Red nodes indicate genes shared between modules. (C) GSEA analysis indicates enrichment of mouse model genes in the module representative of developmental processes (FDR<0.001). (D) Expression pattern for all genes in the developmental process module appearing in the mouse data, with the 63 leading edge genes identified by GSEA denoted as core genes. (E)Fisher’s exact test indicates significant enrichment of NFAT targets (21 genes, FDR=0.0008) among the 63 core genes.

Figure 2. Inhibition of NFATc1 in MC38Met cells

Effect of control RNAi (siScr) or NFATc1 specific RNAi on specific NFAT mRNA species: (A) NFATc1, (B) NFATc2 (C) NFATc3. (D) The effect of NFATc1 siRNA vs scrambled control si-Scr on NFAT family protein levels in MC38Met cells, and steady state levels in both MC38Par and MC38Met cells with Ramos cell lysate positive control in the right hand lane. (E) Relative rates of invasion for cells shown in (A–D) (F)

Figure 3. NFATc1 expression is associated with cell invasion in human colon cancer cells

(A) Western Blot showing NFATc1 levels in vector-transfected and NFATc1-transfected HT29 colon cancer cells. (B) Representative live cell measures after a 24 hour growth period using pools of HT29 cells shown in (A). (C) Relative rates of invasion through matrigel at 24hrs (*p<0.04) of cells depicted in (A). (D) Western Blot showing protein expression of NFATc1-c3 family members in HCT116 cells treated with either control RNAi (si-Scr) or NFATc1 specific RNAi (si-NFATc1).(E) Representative live cell measures of HCT116 cells shown in (D–E) (G) Relative rate of HCT116 cell invasion through matrigel shown in (D–F) (representative experiment, ** p<0.004).

Figure 4. Evidence for a dominant set of NFATc1 target genes

(A) Expression of eight putative target mRNA species significantly associated with NFATc1 expression in MC38Met cells following FK506 treatment (relative to DMSO control, n=4, significance is determined by 1-sample t-test against equivalence or ratio=1 as shown by verticle line). (B) Expression of eight mRNA species significantly associated with NFATc1 expression in HCT116 cells following treatment with NFATc1 specific RNAi (relative to siSCR, n=4, significance is determined by 1-sample t-test against equivalence or ratio=1 as shown by verticle line).(C) Quantification of NFATc1 pull-down on putative target gene specific promoter binding elements relative to IgG control (ASPN mRNA species undetectable, not shown). ns=p>0.05, *p<0.05,**p<0.005,***p<0.0005, ****p<0.00005.

Figure 5. An NFAT-driven transcriptional program is correlated with poor outcomes in Stage II and III colorectal cancer patients

(A–B) NFATc1 signature gene expression patterns in stages II & III colorectal cancer specimens in (A) Vanderbilt Medical Center (GSE 38832) and (B) Moffitt Cancer Center (GSE17536) data sets. The top three rows indicate whether or not a disease-specific death event or a recurrence event was recorded in follow up (black = no event, red=event, white=not available), respectively. The fourth row indicates the cancer stage (green=stage 2, dark blue=stage 3). (C–E) Overall, disease-specific and disease-free survival (OS, DSS, DFS) analyses based on a combined Vanderbilt and Moffitt data sets.

Figure 6. Overexpression of NFATc1 in MC38Par cells increases tumor incidence and liver metastases

(A) Western Blot showing NFAT proteins in MC38Par cells transfected with either empty vector (VEC) or NFATc1, β-actin is used as a loading control and Ramos extract as positive control. (B)Analysis of NFATc1 mRNA in MC38Par cells shown in (A).(C) Rates of trans-endothelial invasion for MC38Par cells shown in (A–B). Individual replicate wells from a representative experiment are plotted with the mean and the standard error of the mean (bars and whiskers). (D) Representative mice from splenic metastasis model (n=12–14/group) using MC38Par cells shown in (A–D). (E) Bioluminescence of mice injected with MC38Par cells shown in (A–D) at 14th days.(F) Incidence of liver metastases for mice injected at day 14.(G) Liver weight to body weight ratio in mice injected with either MC38Par shown in (A–F) cells at day 14 post-injection.*p<0.02, *** p=0.0004, **** p<0.0001.

Figure 7. Knockdown of NFATc1 in MC38Met cells decreases tumor incidence and liver metastases

(A) Western Blot showing NFAT proteins in MC38Met+shCtrl and MC38Met+shNFATc1 cell lines, β-actin is used as a loading control and Ramos cell extract as positive control. (B) Analysis of NFATc1 specific mRNA in cells shown in (A) (C) Rates of trans-endothelial invasion for cells are shown in (A–B). Individual replicate wells from a representative experiment are plotted with the mean and the standard error of the mean (bars and whiskers). (D) Representative (n=14–15 mice/group) bioluminescence image corresponding day 21. (E) Summary of bioluminescence signal quantified at day 21. (F) Summary incidence of liver metastases. (G) Liver metastases measured by liver weight to body weight ratio at 21 days post-injection.*p<0.03, *** p<0.001, **** p<0.0001.