RBMS1 Suppresses Colon Cancer Metastasis through Targeted Stabilization of Its mRNA Regulon - PubMed (original) (raw)

. 2020 Sep;10(9):1410-1423.

doi: 10.1158/2159-8290.CD-19-1375. Epub 2020 Jun 8.

Johnny Yu # 1 2 3, Albertas Navickas # 1 2 3, Hosseinali Asgharian # 1 2 3, Bruce Culbertson 1 2 3, Lisa Fish 1 2 3, Kristle Garcia 1 2 3, John Paolo Olegario 1 2 3, Martin Dodel 4, Benjamin Hänisch 1 2 3, Yikai Luo 1 2 3, Ethan M Weinberg 5, Rodrigo Dienstmann 6, Robert S Warren 3 7, Faraz K Mardakheh 4, Hani Goodarzi 8 2 3

Affiliations

RBMS1 Suppresses Colon Cancer Metastasis through Targeted Stabilization of Its mRNA Regulon

Johnny Yu et al. Cancer Discov. 2020 Sep.

Abstract

Identifying master regulators that drive pathologic gene expression is a key challenge in precision oncology. Here, we have developed an analytic framework, named PRADA, that identifies oncogenic RNA-binding proteins through the systematic detection of coordinated changes in their target regulons. Application of this approach to data collected from clinical samples, patient-derived xenografts, and cell line models of colon cancer metastasis revealed the RNA-binding protein RBMS1 as a suppressor of colon cancer progression. We observed that silencing RBMS1 results in increased metastatic capacity in xenograft mouse models, and that restoring its expression blunts metastatic liver colonization. We have found that RBMS1 functions as a posttranscriptional regulator of RNA stability by directly binding its target mRNAs. Together, our findings establish a role for RBMS1 as a previously unknown regulator of RNA stability and as a suppressor of colon cancer metastasis with clinical utility for risk stratification of patients. SIGNIFICANCE: By applying a new analytic approach to transcriptomic data from clinical samples and models of colon cancer progression, we have identified RBMS1 as a suppressor of metastasis and as a post-transcriptional regulator of RNA stability. Notably, RBMS1 silencing and downregulation of its targets are negatively associated with patient survival.See related commentary by Carter, p. 1261.This article is highlighted in the In This Issue feature, p. 1241.

©2020 American Association for Cancer Research.

PubMed Disclaimer

Conflict of interest statement

Disclosure of Potential Conflicts of Interest:

No potential conflicts of interest are declared by the authors.

Figures

Figure 1.

Figure 1.. RBMS1 silencing in metastatic cells is associated with lower expression of RBMS1 targets.

(A) Regression coefficients set by PRADA as a function of the _l1_-norm of the coefficient vector. Each line is associated with an RNA-binding protein and the magnitude of its coefficient is a measure of its strength as a putative regulator of gene expression. Here, the first ten non-zero coefficients are shown as a function of _l1_-norm (i.e. sum of the magnitude of all coefficients). (B) Analysis of RBMS1 recognition sites across gene expression changes between poorly and highly metastatic colon cancer lines. In this analysis, transcripts are first ordered based on their log-fold changes from left (lower expression in metastatic cells) to right (higher expression) and then partitioned into equally populated bins (~1000 transcripts per expression bin). The red bars on the black background show the range of values in each bin (with the minimum and maximum values, i.e. −1.5 and 2, presented on the left). As shown here, genes that are expressed at a lower level in highly metastatic cells were significantly enriched for the RBMS1 binding motif (KAUAUAS) (38). In this heatmap, gold represents overrepresentation of putative RBMS1 targets while blue indicates underrepresentation. Enrichment and depletions that are statistically significant (based on hypergeometric distribution) are marked with red and dark blue borders, respectively. Also included are the logo representation of the RBMS1 binding motif, its mutual information (MI) value, the associated z-score and the Bayes factor (BF) (for details of this analysis see (28)). (C) RBMS1 expression in colon cancer lines grouped based on their metastatic capacity (Supplementary Fig. 1A). _P_-value calculated using a two-tailed Mann-Whitney _U_-test. (D) Linear regression analysis of RBMS1 expression versus average normalized expression of its putative regulon in TCGA-COAD dataset (cbioportal; N = 382). Shown are the Spearman correlation coefficient and the associated _p_-value. (E) Enrichment and depletion patterns of the RBMS1 regulon in PDX models of CRC liver metastasis. For this analysis, log-fold changes between parental (CLR-Par) and liver metastatic (CLR-LvM) were averaged across three independent PDX models, CLR4, CLR27, and CLR32. D00198.001 is the unique identifier containing the binding site information of human RBMS1 obtained from DeepBind (8) analysis. The distribution of RBMS1 targets was then assessed using mutual information, its associated _z_-score and Bayes factor. The enrichment and depletion patterns were visualized as described in (B). (F) Relative RBMS1 levels in matched poorly metastatic (Par) and highly liver metastatic (LvM) derivatives for three PDX models (CLR4, CLR32, and CLR27). _P_-value was calculated using DESeq2 (39). (G-H) Expression of RBMS1 and RBMS1 targets in matched primary tumor and liver metastases from two patients (S1029 and S567). The results are presented as described in (B). The _P-_value for RBMS1 silencing was calculated using DESeq2 (controlled for genetic background).

Figure 2.

Figure 2.. RBMS1 post-transcriptionally regulates the stability and expression of its targets.

(A) Enrichment and depletion patterns of the RBMS1 regulon in RBMS1 knockdown cells relative to control (~2.5-fold knockdown). (B) We used our computational tool, called REMBRANDTS, to estimate changes in RNA stability upon RBMS1 silencing. These differential stability estimates were then used to assess the enrichment patterns of the RBMS1 targets across the changes in RNA decay. (C) Experimental RNA stability changes were measured using α-amanitin treatment as previously described (1). The enrichment and depletion patterns of the RBMS1 regulon was then assessed among the transcripts destabilized or stabilized upon RBMS1 knockdown. (D) We used REMBRANDTS to measure changes in RNA stability between poorly and highly metastatic PDX models from three independent PDX models (CLR27, CLR32, and CLR4). As shown here, consistent with the silencing of RBMS1 in LvM PDX models (Fig. 1F) and the down-regulation of its regulon (Fig. 1E), the RBMS1 regulon is destabilized in these three independent models of CRC metastasis.

Figure 3.

Figure 3.. RBMS1 irCLIP identifies direct RBMS1 targets in colon cancer cells.

(A) 509 RBMS1 binding sites were found using irCLIP, with a significant enrichment of binding to the last exon/3’ UTR (relative to the total length of genomic features). Last exons from LATS2, AKAP12, and SDCBP are shown as examples of RBMS1 binding patterns. (B) Enrichment of the RBMS1-bound mRNAs among those that are downregulated in highly metastatic cells (top) and those destabilized upon RBMS1 silencing (bottom). (C) qRT-PCR was used to measure changes in GFP mRNA levels upon cloning RBMS1 binding sites of the listed genes downstream of the GFP ORF. mCherry was expressed from the same bidirectional promoter as GFP, and mCherry levels were used to normalize GFP measurements. A one-sample Wilcoxon signed rank test was used to assess whether the ratios in siRBMS1 samples were significantly below 1.0. (D) Scatter plot of mass spectrometry data showing proteins that co-immunoprecipitate with RBMS1 versus control IgG in SW480 cells. Shown are the average of three replicates across all detected proteins. Proteins enriched in the RBMS1 co-IP samples are shown in pink. RBMS1, PABPC1 and ELAVL1 are highlighted in red, green and violet, respectively. (E) RBMS1, PABPC1 and ELAVL1 were detected by western blot in input and eluate samples from RBMS1 and IgG immunoprecipitations (SW480 cell lysates). For RBMS1 immunoprecipitation, the lysates were additionally treated by RNaseA. (F) Heatmap showing the enrichment of poly(U) sites in the 3’ UTRs of RBMS1-bound mRNAs. (G) Venn diagram showing the overlap between RBMS1- and ELAVL1-bound mRNA 3’ UTRs. ELAVL1 targets were determined by PAR-CLIP (13). _P-_value calculated using hypergeometric test. (H) Density plot showing the log fold-change in RBMS1 target expression (determined by RNA-seq (14)) upon ELAVL1 knockdown in SW620 cells. Median value (mu) is indicated. _P-_value calculated using one-tailed Wilcoxon signed rank test.

Figure 4.

Figure 4.. RBMS1 is a suppressor of epithelial-mesenchymal transition (EMT) and metastatic liver colonization.

(A) Bioluminescence imaging plot of liver colonization by RBMS1 knockdown or control cells; N = 5 in each cohort. Two-way ANOVA was used for statistical testing. Ex vivo liver signal was also measured and compared using a one-tailed Mann-Whitney _U_-test. Also shown are representative (median signal) mice and livers. (B) Splenic injection of LS174T highly metastatic colon cancer cells overexpressing RBMS1 and those expressing mCherry as a control. Day 21 signal (normalized to day 0) was plotted and compared for both in vivo and ex vivo signal (N = 4–5; Mann-Whitney _U_-test). (C) Downregulation of EMT(−) signature genes upon RBMS1 knockdown in SW480 cells. 160-gene EMT(−) signature set (17) was compared to the rest of the transcriptome using a Mann-Whitney _U_-test. (D) Immunofluorescence staining for E-Cadherin (red) in RBMS1 knockdown and control cells (SW480 background). Note the lower expression of E-Cadherin and the more spindle-like cellular morphology in RBMS1 knockdown (top panels show DAPI signal). ECAD intensity and maximum Feret Diameter (a measure of length of the cell) for cells in control and knockdown samples (N = 64 and 37, respectively). Two-tailed Mann-Whitney _U_-test was used to compare measurements.

Figure 5.

Figure 5.. AKAP12 and SDCBP act downstream of RBMS1 to suppress CRC metastasis.

(A) Changes in the expression of AKAP12 and SDCBP mRNAs upon RBMS1 knockdown in SW480 cells and RBMS1 over-expression in LS174T cells. The expression of target genes was determined by qRT-PCR, normalized to HPRT internal control and shown as relative fold change over shControl or OE-Control. _P-_value was calculated using one-tailed Mann-Whitney _U_-test. (B) Changes in the stability of AKAP12 and SDCBP mRNAs upon RBMS1 knockdown in SW480 cells and RBMS1 over-expression in LS174T cells. RNA stability was calculated by comparing mRNA levels with and without treatment with α-amanitin, and shown as relative fold change over shControl or OE-Control. The relative abundance of target genes was determined by qRT-PCR, normalized to 18S RNA (RNA Pol-I transcript insensitive to α-amanitin). _P-_value was calculated using one-tailed Mann-Whitney _U-_test. (C) In vivo liver colonization assays were used to measure the impact of CRISPRi-mediated silencing of the RBMS1 targets AKAP12 and SDCBP on liver metastasis (N = 6). Also shown are representative mice from each cohort. Two-way ANOVA was used to compare cohorts to control (P = 0.01 and 0.02 for sgAKAP12 and sgSDCBP respectively). Livers were also extracted and their tumor burden was measured ex vivo. Mann-Whitney _U_-test was used to compare measurements. (D) The expression of EMT(−) and EMT(+) signature genes relative to background (BG) in AKAP12 knockdown (CRISPRi) and control cells (measurements using 3’-end RNA-seq). Shown are the ANOVA _p-_value and a Mann-Whitney comparison between EMT(−) and background genes.

Figure 6.

Figure 6.. RBMS1 and its target gene signature score are associated with colon cancer metastasis and reduced survival in CRC patients.

(A) RBMS1 qPCR (relative to HPRT) in 96 colon cancer samples stratified based on tumor stage. (B) RBMS1 qPCR (relative to HPRT) in 29 normal mucosa, 25 primary colon cancer, and 37 liver metastases. (C) RBMS1 silencing was observed in ~5% of primary CRC tumors (see methods), and these patients showed substantially lower relapse-free and overall survival. Reported are Mantel-Haenszel hazard ratios (HR) and _p_-values from Gehan-Breslow-Wilcoxon tests. (D-E) AKAP12 expression in clinical samples (similar to (A) and (B)). (F) Patient primary tumors were scored based on aggregate expression of RBMS1 signature genes and the resulting values were used to perform survival analyses similar to those in (C). As shown here, lower RBMS1 signature score was significantly associated with lower relapse-free and overall survival. Also reported are Mantel-Haenszel hazard ratios (HR) and _p_-values from Gehan-Breslow-Wilcoxon tests. (G-H) Regression analysis comparing the expression of RBMS1 (G) or ELAVL1 (H) and RBMS1 80-gene signature set in TCGA pan-cancer dataset. Shown are the Spearman correlation coefficient and the associated _p-_value.

Figure 7.

Figure 7.. HDAC-mediated promoter deacetylation results in RBMS1 silencing.

(A) RBMS1 shows dynamic expression and acetylation changes across different cell types. Also shown here is the association between RBMS1 expression and its promoter acetylation (source from ENCODE). (B) RBMS1 promoter acetylation levels were measured in SW480 and LS174T cells using H3K27Ac ChIP-qPCR. An unacetylated region ~40kb away from the promoter was used as control (N = 3). (C) HDAC1 expression in colon cancer lines stratified based on their metastatic capacity. One-tailed _U-_test was used to compare the two groups. Cell line names are listed in Supplementary Fig. 1A. (D) Quantitative PCR to compare HDAC1 levels in the highly metastatic LS174T cells (with silenced RBMS1) relative to poorly metastatic SW480 cells. (E) RT-qPCR was used to measure RBMS1 mRNA levels in LA174T cells with RNAi-mediated HDAC1 silencing. (F) A schematic model of RBMS1 silencing and its role in suppressing colon cancer metastasis. One-tailed Mann-Whitney U tests were used to assess statistical significance for all panels.

Comment in

References

    1. Goodarzi H, Zhang S, Buss CG, Fish L, Tavazoie S, Tavazoie SF. Metastasis-suppressor transcript destabilization through TARBP2 binding of mRNA hairpins. Nature. 2014;513:256–60. -PMC -PubMed
    1. Goodarzi H, Liu X, Nguyen HCB, Zhang S, Fish L, Tavazoie SF. Endogenous tRNA-Derived Fragments Suppress Breast Cancer Progression via YBX1 Displacement. Cell. 2015;161:790–802. -PMC -PubMed
    1. Goodarzi H, Nguyen HCB, Zhang S, Dill BD, Molina H, Tavazoie SF. Modulated Expression of Specific tRNAs Drives Gene Expression and Cancer Progression. Cell. 2016;165:1416–27. -PMC -PubMed
    1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA Cancer J Clin. 2018;68:7–30. -PubMed
    1. Hamada K, Monnai M, Kawai K, Nishime C, Kito C, Miyazaki N, et al. Liver metastasis models of colon cancer for evaluation of drug efficacy using NOD/Shi-scid IL2Rgammanull (NOG) mice. Int J Oncol. 2008;32:153–9. -PubMed

Publication types

MeSH terms

Substances

Grants and funding

LinkOut - more resources