NSMCE2, a novel super-enhancer-regulated gene, is linked to poor prognosis and therapy resistance in breast cancer

Abstract

Background: Despite today's advances in the treatment of cancer, breast cancer-related mortality remains high, in part due to the lack of effective targeted therapies against breast tumor types that do not respond to standard treatments. Therefore, identifying additional breast cancer molecular targets is urgently needed. Super-enhancers are large regions of open chromatin involved in the overactivation of oncogenes. Thus, inhibition of super-enhancers has become a focus in clinical trials for its therapeutic potential. Here, we aimed to identify novel super-enhancer dysregulated genes highly associated with breast cancer patients' poor prognosis and negative response to treatment.

Methods: Using existing datasets containing super-enhancer-associated genes identified in breast tumors and public databases comprising genomic and clinical information for breast cancer patients, we investigated whether highly expressed super-enhancer-associated genes correlate to breast cancer patients' poor prognosis and to patients' poor response to therapy. Our computational findings were experimentally confirmed in breast cancer cells by pharmacological SE disruption and gene silencing techniques.

Results: We bioinformatically identified two novel super-enhancer-associated genes - NSMCE2 and MAL2 - highly upregulated in breast tumors, for which high RNA levels significantly and specifically correlate with breast cancer patients' poor prognosis. Through in-vitro pharmacological super-enhancer disruption assays, we confirmed that super-enhancers upregulate NSMCE2 and MAL2 transcriptionally, and, through bioinformatics, we found that high levels of NSMCE2 strongly associate with patients' poor response to chemotherapy, especially for patients diagnosed with aggressive triple negative and HER2 positive tumor types. Finally, we showed that decreasing NSMCE2 gene expression increases breast cancer cells' sensitivity to chemotherapy treatment.

Conclusions: Our results indicate that moderating the transcript levels of NSMCE2 could improve patients' response to standard chemotherapy consequently improving disease outcome. Our approach offers a new avenue to identify a signature of tumor specific genes that are not frequently mutated but dysregulated by super-enhancers. As a result, this strategy can lead to the discovery of potential and novel pharmacological targets for improving targeted therapy and the treatment of breast cancer.

Keywords: Breast cancer; MAL2; NSMCE2; Super-enhancers; Therapy resistance.

Fig. 1

Identification and characterization of candidate SE associated genes in breast cancer. A Venn diagram showing the distribution of DNA regions identified as SEs across the 4 breast PDX samples examined. 26 SE regions were found to be common to all samples. B Pie charts for SEs enrichment based on chromosome location for the 4 breast PDX samples analyzed reveal that most SE regions (including those that are common to all the breast cancer samples analyzed) are enriched in chromosome 8 after normalization. B.I Shows enrichment of all DNA regions identified as SEs. B.II Shows enrichment of all DNA regions identified as SEs normalized by chromosome gene number. B.III Shows enrichment of SE-associated genes common to all samples. B.IV Shows enrichment of SE-associated genes common to all samples normalized by chromosome gene number. C Heatmap showing gene expression correlations for the SE-associated genes found common to the 4 breast cancers analyzed, reveals 4 clusters of highly associated genes. This analysis was generated by using RNA-Seq expression data from TCGA breast primary tumors (n = 1097)

Fig. 2

High NSMCE2 and MAL2 RNA expression in tumors is linked to breast cancer patients’ poor prognosis. A Box plots showing that gene expression levels are significantly higher in breast tumor samples when compared to normal samples for 9 SE-associated genes. Expression levels information for normal samples (Normal Tissue GTEX and Solid Tissue Normal TCGA) was obtained from GTEX and TCGA RNA-Seq datasets, respectively. Expression levels from primary breast tumors (Primary Tumor TCGA) were obtained from TCGA RNA-Seq datasets. Gene expression levels were compared by ANOVA. B Univariate Kaplan–Meier plots showing breast cancer patients' survival probability over time based on gene expression for each NSMCE2, MAL2, or EIF3H. These analyses were performed using breast primary tumors RNA-Seq data mined from two independent datasets, TCGA (n = 1097) or METABRIC (n = 1904). Red and blue curves represent samples that show high and low gene expression levels, respectively, relative to the median expression value for each gene. High levels of either NSMCE2 or MAL2 were found to be significantly correlated with breast cancer patients' poor prognosis in the two datasets. Plots were analyzed using the Log-rank test. OS = overall survival, RFS = relapse free survival. C Multivariate Cox model of overall survival for NSMCE2 and MAL2 RNA expression in TCGA breast primary tumors including age, stage and tumor subtype as covariates show that high levels of either NSMCE2 or MAL2 are significantly correlated with patients' poor prognosis

Fig. 3

NSMCE2 and MAL2 RNA expression is regulated by SEs in breast cancer cells. Blocking BRD4 binding to SEs with BET inhibitors reduces gene expression for NSMCE2 and MAL2 starting at 6 h in most breast cancer cell lines tested. MCF7, HCC1954, MDA-MB-231, Hs578T and BT-549 breast cancer cells were treated with vehicle (DMSO), 1 μM JQ1 or 1 μM IBET-151 for 6 and 24 h. After treatment, changes in gene expression levels were analyzed by qPCR. ANOVA followed by Dunnett’s multiple comparison test was performed, *P < 0.05, **P < 0.01, ***P < 0.001, ****P <  0.0001

Fig. 4

High NSMCE2 expression correlates to breast cancer patients' poor response to chemotherapy. Left panels are box plots showing NSMCE2 gene expression levels in non-responder versus responder patients. Right panels are receiver operating characteristic (ROC) plots showing the specificity and sensitivity of NSMCE2 gene expression as a predictor for response to chemotherapy in: A breast cancer (n = 507), B) Grade III breast cancer (n = 194), C) Grade III TN breast cancer (n = 96), and D) Grade III HER2 + breast cancer (n = 47). In the four cases, non-responder patients have significantly higher NSMCE2 expression levels compared to the responder group and the AUC values indicate that NSMCE2 gene expression level is capable of distinguishing between responder and non-responder patients. Analyses shown in this figure were performed using ROC Plotter, Mann–Whitney U test and Receiver Operating Characteristic test. pCR = pathological complete response

Fig. 5

Lowering NSMCE2 transcript levels sensitizes breast cancer cells to chemotherapeutic agents. A SE blockade and doxorubicin synergize to induce apoptosis in breast cancer cells. BT549 and Hs578T were preincubated with vehicle (DMSO) or 1 μM JQ1 for 24 h, after which culture medium was replaced by fresh medium containing vehicle or 1 μM JQ1 ± 1 μM doxorubicin. After 48 h, apoptotic cells were quantified by Annexin V staining. Two Way ANOVA followed by Bonferroni’s multiple comparison test was performed, **P <  0.01, ***P <  0.001. B SE blockade and paclitaxel combination does not induce apoptosis in breast cancer cells. BT549 and Hs578T were preincubated with vehicle or 1 μM JQ1 for 24 h, after which culture medium was replaced by fresh medium containing vehicle or 1 μM JQ1 ± 5 nM paclitaxel. After 48 h, apoptotic cells were quantified by Annexin V staining. Two Way ANOVA followed by Bonferroni’s multiple comparison test was performed, **P <  0.01, ***P <  0.001. C Knocking down NSMCE2 sensitizes breast cancer cells to chemotherapeutic drugs. BT549 and Hs578T transduced with shControl or shNSMCE2 were incubated with 0.5 μM doxorubicin for 48 h or 5 nM paclitaxel for 72 h. After treatment, viable cells were quantified by MTT assay. Plots show the percentage of cell viability for the treatment relative to vehicle. Two Way ANOVA followed by Bonferroni’s multiple comparison test was performed, *P <  0.05, **P <  0.01, ***P <  0.001. D NSMCE2 RNA expression increases upon doxorubicin treatment but not upon paclitaxel in BT549 and Hs578T. Cells were treated with 1 μM doxorubicin or 5 nM paclitaxel for 24 h and NSMCE2 levels were quantified by qPCR. ANOVA followed by Dunnett’s multiple comparison test was performed, *P <  0.05; **P <  0.01. E) NSMCE2 RNA upregulation by doxorubicin is mediated by SEs. BT549 and Hs578T were treated with 1 μM JQ1 and/or 1 μM doxorubicin for 24 h and NSMCE2 RNA expression was determined by qPCR. Two Way ANOVA followed by Bonferroni’s multiple comparison test was performed, **P <  0.01