Signalling pathway for RKIP and Let-7 regulates and predicts metastatic breast cancer

Abstract

Tumour metastasis suppressors are inhibitors of metastasis but their mechanisms of action are generally not understood. We previously showed that the suppressor Raf kinase inhibitory protein (RKIP) inhibits breast tumour metastasis in part via let-7. Here, we demonstrate an integrated approach combining statistical analysis of breast tumour gene expression data and experimental validation to extend the signalling pathway for RKIP. We show that RKIP inhibits let-7 targets (HMGA2, BACH1) that in turn upregulate bone metastasis genes (MMP1, OPN, CXCR4). Our results reveal BACH1 as a novel let-7-regulated transcription factor that induces matrix metalloproteinase1 (MMP1) expression and promotes metastasis. An RKIP pathway metastasis signature (designated RPMS) derived from the complete signalling cascade predicts high metastatic risk better than the individual genes. These results highlight a powerful approach for identifying signalling pathways downstream of a key metastasis suppressor and indicate that analysis of genes in the context of their signalling environment is critical for understanding their predictive and therapeutic potential.

Figure 1

Identification of an RKIP/let-7 metastasis pathway regulating BACH1, a novel let-7 target. (A) RKIP pathway relationships were trained on the BrCa443 data set (Supplementary Figure S1A) and validated using the BrCa871 data set. The top panels show GSA results for the indicated steps in the RKIP pathway. Meta-genes comprised of let-7 or BACH1 target genes were used as readouts for let-7 (LET7-TG) or BACH1 (BACH1-TG), respectively. The red curve indicates the distribution of gene scores for each gene set shown on the right side of the pathway arrow in response to RKIP (first and fourth panels), LET7-TG (second panel), or BACH1-TG (third panel). The black curve is a null distribution. The P-values for all results are P<0.001. For BACH1-TG, MMP1 was omitted from the GSA to prevent bias. In the bottom panel, a multivariable random forest (RF) model was used to determine how well individual genes in each gene set shown on the right side of the pathway arrow account for the variation in upstream pathway genes shown on the left side of the pathway arrow. Genes are ranked by an importance score, which measures the contribution of each gene in the gene set in accounting for the observed variation (higher scores are better). Shown are barplots of the importance scores along with Monte Carlo standard deviations (for presentation purposes, genes with importance scores below the bottom 10% are not shown). Overall model fit is measured by the indicated pseudo-R-squared (see Supplementary data). (B) RKIP or let-7 downregulate transcripts for predicted let-7 targets. 1833 cells transduced with control vector, wt RKIP, or S153E RKIP (upper panel) or transfected with pre-miR let-7a or pre-miR let-7g (lower panel) were analysed by qRT–PCR for predicted let-7 targets BACH1, HMGA2, IMP2, IMP3, MAP4K4, and GOLT1B (mean±s.d., n=3, ^*P<0.05). (C) RKIP or let-7g inhibits BACH1 and HMGA2 expression in 1833 cells. 1833 cells expressing vector, wt or S153E RKIP (upper panel) or let-7g (lower panel) were immunoblotted for BACH1, HMGA2, or α-tubulin antibodies. (D) Schematic representation of BACH1 3′UTR with two putative let-7-binding sites. (E) BACH1 is a direct target of let-7. Pre-miR let-7a and g inhibit wt BACH1 3′UTR (BACH13′UTRwt) reporter activity but not mutant BACH1 3′UTR (BACH13′UTRmt). Luciferase activity was measured from 1833 cells transfected with pre-miR let-7a and g and normalized by Renilla luciferase activity 48 h after transfection (mean±s.d., n=3, ^**P<0.005).

Figure 2

RKIP, let-7, BACH1, and HMGA2 regulate BMS genes. (A) RKIP or let-7 downregulate transcripts for bone metastasis gene signatures (BMS). 1833 cells expressing control vector, wt RKIP, S153E RKIP, let-7a, or let-7g were analysed by qRT–PCR (mean±s.d., n=3) for BMS genes MMP1, CXCR4, OPN, IL-11, and CTGF. (B) RKIP and let-7 inhibit MMP1, CXCR4, and OPN protein expression. 1833 cells expressing vector, wt or S153E RKIP were immunoblotted for MMP1, CXCR4, OPN, RKIP, or α-tubulin. (C) Knockdown of BACH1 or/and HMGA2 in 1833 cells decreases MMP1, CXCR4, and OPN expression. 1833 cells expressing a control vector, shRNA for BACH1 or/and shRNA for HMGA2 were assayed for BMS genes (MMP1, CXCR4, OPN, CTGF, or IL-11) by qRT–PCR (mean±s.d., n=3) or (D) were immunoblotted for the indicated proteins.

Figure 3

HMGA2 and BACH1, a direct regulator of MMP1, are required for breast cancer invasion. (A) Knockdown of BACH1 or/and HMGA2 inhibit invasion of 1833 cells. Cells stably expressing shBACH1 and/or shHMGA2 were assayed for invasion (mean±s.d., n=3). (B) Knockdown of BACH1 or/and HMGA2 does not affect cell proliferation of 1833 cells. Cells from (A) were assayed for cell proliferation for the indicated times (mean±s.d., n=3). (C) HMGA2 overcomes RKIP inhibitory effect on invasion in 1833 cells. Cells stably expressing RKIP, HMGA2, or both (inset) were assayed for invasion (mean±s.d., n=3). (D) HMGA2 does not affect cell proliferation of RKIP-expressing 1833 cells. Cells from (C) were assayed for cell proliferation (mean±s.d., n=3). (E) Schematic representation of MMP1 promoter with the putative BACH1-binding sites. BACH1 induces transcription by the MMP1 promoter. TPA was used as a positive control (50 ng/ml; 12 h). The MMP1 promoter region was fused to a luciferase reporter, 1833 cells were transfected with BACH1, and cells were assayed for luciferase activity as described in Materials and methods (^*P<0.05). (F) ChIPs were carried out with anti-BACH1 antibody and anti-Jun antibody (a positive control for ChIP assay) using 1833 cells. The promoter for HO1 was used as a positive control for BACH1. ChIP was analysed by qRT–PCR with primers in the MMP1 and HO1 promoters. Results represent the mean±s.d. for three samples. (G) Scheme showing mechanism for RKIP regulation of invasion and metastasis via BACH1 and HMGA2 and its target BMS pathway. BACH1 directly enhances MMP1 transcription, BACH1 and HMGA2 induce CXCR4 expression, and HMGA2 induces OPN expression.

Figure 4

Experimental validation of the RKIP signalling pathway for metastasis. (A) BMS genes overcome the inhibitory effect of wt RKIP, shBACH1, shHMGA2, and shBACH1/shHMGA2 on bone metastasis and are more effective in combination. (Left panel) 1833 cells expressing either control vector, wt RKIP, shBACH1, shHMGA2, or shBACH1/shHMGA2 were stably transduced with control vector or target BMS genes, injected into the left cardiac ventricle of mice, and imaged for luciferase activity after 3 weeks (mean±s.d., n=5–6 for each group). (Right panel) 1833 cells expressing either control vector or wt RKIP were stably transduced with control vector or target BMS genes, injected into the left cardiac ventricle of mice, and imaged for luciferase activity after 3 weeks (mean±s.d., n=5–6 for each group). (B) BACH1 depletion does not alter tumour cell growth. Knockdown of BACH1 does not affect proliferation. (Left panel) Cells were assayed for cell proliferation as described in Materials and methods. Results represent the mean±s.d. for three independent samples. (C) BACH1 depletion does not suppress tumour growth of 1833 cells. 1833 cells expressing luciferase and either control vector (six mice), or shBACH1 (six mice) were injected into the mammary fat pad of mice. After 1 week, tumours were imaged and measured for luciferase activity once a week. Representative images show shBACH1 does not affect tumour growth. Results represent the mean±s.d. for the animals. (D) BACH1 depletion inhibits intravasation of bone-tropic tumour cells (1833). 1833 cells stably expressing control vector (five mice) or shBACH1 (five mice) were injected into the mammary fat pad of mice. After 3 weeks, cells isolated from the blood were analysed for GAPDH transcripts derived from human (tumour) or mouse (control). Results represent the mean±s.e. for the animals (P<0.009 for shBACH1 relative to control).

Figure 5

(A) BMS genes do not alter let-7g induction. (Upper panel) 1833 cells expressing tet-inducible let-7g and either control vector or BMS genes (MMP1, CXCR4, and OPN) were treated with 2 μg/ml doxycycline for 24 and 48 h and then assayed for let-7g expression by qRT–PCR. Results represent the mean±s.d. for three samples. (Lower panel) Expression of BMS proteins in 1833 cells expressing tet-inducible let-7g. 1833 cells expressing tet-inducible let-7g and either control vector or BMS genes (MMP1, CXCR4, and OPN) were treated with 2 μg/ml doxycycline for 48 h. Cell lysates were immunoblotted with antibodies to MMP1, CXCR4, OPN, and α-tubulin. (B) BMS genes rescue the inhibitory effect of let-7 on invasion. 1833 cells from (A) were assayed for invasion as described in Materials and methods. Results represent the mean±s.d. for three independent samples. (C) Let-7 expression for 2 days does not affect cell proliferation. 1833 cells from (A) were assayed for cell proliferation as in Materials and methods for the indicated times. Results represent the mean ± s.d. for three samples. (D) BMS genes partially restore metastasis to cells expressing Let-7g. 1833 cells expressing luciferase, tetracyline-inducible let-7g and either control vector or BMS genes (MMP1, CXCR4, and OPN) were grown in the presence of 2 μg/ml doxycycline for 24 h. Cells were injected into the left ventricle of mice, and 2 days later, mice were administered with drinking water containing 4% sucrose only or 2 mg/ml doxycycline and 4% sucrose. Mice were imaged for luciferase activity after 3 weeks. Results represent the mean±s.d. for the animals.

Figure 6

Association of individual RPMS genes with clinical metastasis. (A) Schematic showing the genes comprising the RPMS. Only genes shown in black lettering are elements of the RPMS. Both the LET7-TGs and the BACH1-TGs are based on bioinformatic predictions. (B) Correlation between invasive breast cancer cell lines and the RPMS. Gene expression microarray data from 13 breast cancer cell lines were analysed for expression of genes in the RPMS. For five RPMS genes (RKIP, HMGA2, MMP1, CXCR4, and OPN), the mRNA expression of these genes is measured relative to the median. For let-7 and BACH1, the meta-gene for let-7 targets (LET7-TG) and the meta-gene for BACH1 targets (BACH1-TG) are measured relative to the median. Expression of RKIP greater than the median value or expression of other RPMS genes less than the median value are expected to inhibit invasion/metastasis and are coloured in blue. Low expression of RKIP or high expression of other RPMS genes are expected to promote invasion/metastasis and are coloured in orange. Hierarchical clustering was used to group cells by RKIP pathway genes. The percentage of invasion (mean±s.d.) is indicated above the heatmap. (C) Metastasis-free survival for patients from the BrCa871 data set is shown stratified by median cut point for the expression values for each of the indicated genes. For each gene, the corresponding probe intensity values from microarray data were used, except for let-7 and BACH1 where the meta-gene was used. Red indicates expression greater than the median and blue is less than the median. For the plot ‘RKIP low/downstream high’, red indicates patients that have RKIP expression less than median and all other downstream genes have expression greater than median, while blue indicates patients that do not meet this condition. Survival curves are determined by the Kaplan–Meier method and the indicated P-values are calculated by the log-rank test.

Figure 7

The RPMS forms a network of genes that cooperatively predict and contribute to metastasis. (A) Metastasis-free survival for patients stratified by RPMS status is shown. RKIP low (RKIP transcript expression below the median) and RKIP high (RKIP transcript expression above the median) patients in the BrCa871 cohort of patients are analysed separately. Statistical significance for the indicated pathway conditions (legend on bottom) was determined by comparison to patients with low expression of all RKIP pathway genes downstream of RKIP (none) and reported as a P-value based on the log-rank test. (B) The BrCa871 data set was used to determine cooperativity between the RPMS genes in predicting clinical metastatic risk for tumours that are RKIP high or RKIP low. An importance score, which measures the degree to which genes contribute to overall prediction accuracy (higher scores are better) was calculated for RPMS genes separately or together (Coop). The cooperative importance score was then compared with the sum of the separate importance scores for the downstream genes in the pathway (Add) and the difference (Diff) was used as a measure of cooperation. Error bars are standard deviations from 500 Monte Carlo runs. (C) RPMS genes are part of a network that contributes to clinical metastasis. Cancer-related pathways were tested for association with the RKIP pathway by GSA and by overrepresentation among let-7 and BACH1 target genes using a hypergeometric test. Each node represents a pathway that passes both test criteria. Pathways with genes enriched in let-7 target genes are shown using a blue edge and pathways with genes enriched in BACH1 targets are shown with a brown edge. A meta-gene score for each pathway shown was used along with the indicated RPMS genes (light blue) in a predictive model for metastasis using the BrCa871 data set (see Materials and methods). The contribution of each pathway to metastatic risk was measured by a relative importance score. This relative importance score is displayed in quartiles as colour-coded pathway nodes. When considering the entire network, pathways with an importance score less than zero (beige) contribute little to metastatic risk prediction. The prediction error for the entire network is 37.9±0.66%.