Identification and validation of microRNAs that synergize with miR-34a - a basis for combinatorial microRNA therapeutics

Abstract

Efforts to search for better treatment options for cancer have been a priority, and due to these efforts, new alternative therapies have emerged. For instance, clinically relevant tumor-suppressive microRNAs that target key oncogenic drivers have been identified as potential anti-cancer therapeutics. MicroRNAs are small non-coding RNAs that negatively regulate gene expression at the posttranscriptional level. Aberrant microRNA expression, through misexpression of microRNA target genes, can have profound cellular effects leading to a variety of diseases, including cancer. While altered microRNA expression contributes to a cancerous state, restoration of microRNA expression has therapeutic benefits. For example, ectopic expression of microRNA-34a (miR-34a), a tumor suppressor gene that is a direct transcriptional target of p53 and thus is reduced in p53 mutant tumors, has clear effects on cell proliferation and survival in murine models of cancer. MicroRNA replacement therapies have recently been tested in combination with other agents, including other microRNAs, to simultaneously target multiple pathways to improve the therapeutic response. Thus, we reasoned that other microRNA combinations could collaborate to further improve treatment. To test this hypothesis miR-34a was used in an unbiased cell-based approach to identify combinatorial microRNA pairs with enhanced efficacy over miR-34a alone. This approach identified a subset of microRNAs that was able to enhance the miR-34a antiproliferative activity. These microRNA combinatorial therapeutics could offer superior tumor-suppressive abilities to suppress oncogenic properties compared to a monotherapeutic approach. Collectively these studies aim to address an unmet need of identifying, characterizing, and therapeutically targeting microRNAs for the treatment of cancer.

Keywords: Mirna-34; combinatorial therapeutics; miR-34; miR-34a; miRNA therapeutics; synergism.

Figure 1.

Screening approach. (a) Use of a dual luciferase expression vector to evaluate transfection efficiency and cell number. The schematic depicts a model in which firefly luciferase is used as a measurement of the transfection efficiency when cells are co-transfected with a siRNA against firefly luciferase (siLuc2) and a miRNA. Renilla activity serves as a proxy for cell number and its activity is only affected when a miRNA that alters cell proliferation is transfected. (b) A linear response between cell number and Renilla and Firefly luciferase activity (Error bars: mean ± s.d., n = 6). C) Firefly luciferase repression upon transfection with siLuc2. Error bars: mean ± s.d. Each experiment corresponds to n = 6 per treatment. (d) miR-34a dose response in H441-pmiR cells. Error bars: mean ± s.d. Each experiment corresponds to n = 8 per treatment. Statistical analysis performed with two-way ANOVA with post hoc Bonferroni correction (***, P < 0.001). (e) Firefly and (f) Renilla activity following transfection with miR-34a or PremiR-NC2 in presence of siLuc2. Error bars: mean ± s.d. Each experiment corresponds to n = 6 per treatment. Statistical analysis for B, C, D, E and F performed with a one-way ANOVA with post hoc Bonferroni correction (*, P < 0.05; **, P < 0.01; ****, P < 0.0001). RLU: relative light units.

Figure 2.

miRNAs alter miR-34a antiproliferative potential. (a) miRNA mimics ranked according to effect, normalized to negative control (NC) transfected cells. Red dashed line = 3 s.d. of the effect of miR-34a alone. Black dashed line = effect of NC treated cells. Blue shaded box = 333 miRNA mimics that were moved forward in B. n = 6. Each dot represents the mean effect of a miRNA combination. (b) Validation of 333 miRNA mimics represented as the effect of the individual miRNA (E_single) minus the effect when combined with miR-34a (E_pair). Values <0 indicate that the combination has a better antiproliferative activity than the miRNA by itself. n = 6. Each dot represents the mean effect of a miRNA combination. Blue shaded box = 111 miRNA mimics with apparent combinatorial activity. The five miRNAs subsequently determined to have synergistic activity with miR-34a are indicated.

Figure 3.

Validation of a combinatorial effect of the 111 miRNA mimics in combination with miR-34a in a panel of NSCLC cell lines. (a) Validation of sulforhodamine B (SRB) method for secondary verification of screen hits. Absorbance of SRB as a linear response to H441-pmiR cell number. Error bars: mean ± s.d., n = 6. (b) Response of H441-pmiR to miR-34a measured by SRB. Error bars: mean ± s.d., n = 6. (c) miRNA combinations evaluated in a panel of six NSCLC cell lines. miRNAs included in this comparison had a CI lower than 1 (potential synergism), p < 0.05 and cell proliferation of the single miRNA was <1.

Figure 4.

Evaluation of synergism of positive hits. The possibility of synergism with miR-34a was evaluated for (a) miR-6715b, (b) miR-4664-3p, (c) miR-3157-3p, (d) miR-5100 and (e) let-7a-2-3p. (For A-E. red lines: miR-34a alone, blue lines: putative synergistic miRNA alone, green lines: combination) miRNA combinations have a stronger effect than each miRNA on its own. (f) Evaluation of synergism using the (CI)-isobologram equation. The equation helps to gain a better understanding of drug interactions, where CI < 1 indicate synergism. Statistical analysis performed with one-way ANOVA with post hoc Bonferroni correction (***, P < 0.001; ****, P < 0.0001).

Figure 5.

Pathway enrichment analysis of miR-34a-synergistic miRNA target genes. Predicted targets for the five synergistic miRNAs were determined and used to identify pathways that the target genes are enriched in. Cancer-related pathways/diseases are in bold text with corresponding bars depicted in grey.