MiRNAs are influenced by both copy number alteration and genomic location

We examined the TCGA ovarian cancer datasets, representing 487 tumors profiled for miRNA expression, for patterns of correlation between the miRNAs and other molecular features, to see whether the overall trends observed would fit our initial expectations. To begin with, we considered that miRNAs with expression levels frequently altered by changes in DNA copy number may reveal a subset of miRNAs under clonal selection in the tumors; such miRNAs would be of potential interest as candidate oncomiRs or tumor suppressive miRs. We therefore systematically analyzed miRNAs for both loss and gain of DNA copy number associated with a concordant change in mature miRNA expression level (Figure 1A, Dataset S1). This analysis revealed several miRNAs in focally amplified and deleted genomic regions. In particular, let-7b was the most frequently deleted miRNA having both recurrent hemizygous genomic loss (86% of samples) and homozygous deletion (7.2%). Another deleted miRNA, miR-31, was recently found by our group to suppress ovarian cancer cell proliferation [10]. Four members of the miR-30 family were among the most frequently amplified miRNAs. Interestingly, these members were encoded at two different focally amplified loci (8q24 and 1p34) and all four miRNAs showed strong concordant change in mature miRNA expression.

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Figure 1. In ovarian tumors, expression patterns of miRNAs (miRs) are influenced by both copy number alteration (CNA) and genomic location.

(A) MiRNAs correlated with CNA. Left plot shows hemizygous loss (N< = 1 copies) versus homozygous deletion (N = 0) for miRNA precursors. Correlation (Pearson's) between precursor DNA copy number and mature miRNA expression is indicated by symbol size, and only miRNAs with a minimum correlation (R>0.3) are included here; cytoband regions in bold represent focal CNA events. Similarly, right plot shows gain (N> = 3) versus amplification (N> = 4) for miRNAs having R>0.3 for precursor copy versus mature expression. (B) miRNAs are frequently coexpressed with neighboring miRNAs. Plot shows relationship between the distance separating miRNA loci and their coordinate expression. Each miRNA was paired with each of the others lying in the same orientation on the same chromosome. For each pair, the distance between the two loci (right axis) was ranked, and the correlation coefficient for their expression was plotted according this rank (left axis). (C) miRNAs are frequently coexpressed with host genes. For each of 188 miRNA-host gene pairs (same orientation), the correlation was computed both between miRNA and host gene expression; pair orderings are the same (“X”, no corresponding mRNA or gene copy data; p-values by two-sided t-statistic).

https://doi.org/10.1371/journal.pone.0034546.g001

In our data, we also found miRNAs to be frequently coexpressed with neighboring miRNAs as anticipated. Previously, when examining miRNA expression profiles in a small dataset of 24 normal human tissues, Baskerville and Bartel found evidence that proximal pairs of miRNAs are generally coexpressed (suggesting that they are processed from polycistronic primary transcripts), and that intronic miRNAs are usually coexpressed with their host gene mRNA (suggesting that they both derive from a common transcript) [15]. To extend these preliminary observations to ovarian cancer (thereby reinforcing current notions of miRNA biology as well as the integrity of our TCGA data), we made pairwise comparisons for each chromosome between the expression profiles of all miRNAs oriented in the same direction, calculating for each pair a correlation coefficient; the results showed that most miRNA genes within 50–100 kb of each other had highly correlated expression patterns (Figure 1B). Notably, at distances beyond 100 kb (exceeding the length of most human genes), the correlation between pairs dropped dramatically to zero. While DNA copy number alterations (CNA) undoubtedly influence gene and miRNA expression in cancer [10], [16], pairwise correlations in copy number levels between proximal miRNAs showed a very different pattern from the pairwise expression correlations; high proximal correlations for copy number extended for >1 Mb in length, with no dramatic drop (Figure S1A).

Approximately 177 of the 558 mature human miRNAs profiled are located in the genome within the introns of host genes, and we found miRNAs to be frequently coexpressed with these host genes in our data. For each of 188 miRNA-host gene pairs (each comprised of a miRNA located within the boundaries of a known gene, same orientation, where some mature miRNAs have multiple genomic locations), we computed the correlation between miRNA and host gene expression. MiRNA-host gene pairs tended to be strongly correlated with each other and, with 52% of the miRNA-host gene pairs with available data showing significant positive correlation (P<0.01), in agreement with previous studies [17], [18] (Figure 1C). As expected, miRNA expression was also correlated with host gene copy number, though the correlations were not as strong as for gene expression (Figure S1B).

Diversity of miRNA and gene expression patterns suggestive of ovarian tumor subtypes

Previously, unsupervised clustering of miRNA expression data had suggested three general groupings or subtypes–with designations C1, C2, and C3–of high-grade serous ovarian tumors. The C1 subtype had been associated with worse patient survival as compared to the other two subtypes; in addition, the C1 subtype overlapped somewhat with the gene expression-based “proliferative” subtype, and the C2/C3 subtypes overlapped more with the gene-based “mesenchymal” subtype [8]. The average “silhouette” widths of the miRNA-based clusters had indicated them to be somewhat weakly defined with substantial within-group heterogeneity, while still reflecting patterns of biological diversity [8]. As described below, here we sought to further characterize these miRNA-based clusters, using our mRNA and miRNA expression data.

Given the role of miRNAs in repressing gene expression, we might anticipate that miRNAs and their mRNA targets in general would appear anti-correlated in expression in human tumors. A number of computational algorithms—the most well-known of these being miRanda [4] and TargetScan [7]—have been developed to predict the targeting of a given gene transcript by a specific miRNA (based on both sequence alignment of miRNA to gene 3′-UTR and on species conservation). By integration of both miRNA and gene expression patterns using previously-used approaches [10], we could define putative miRNA:mRNA functional pairs underlying the miRNA-based subtypes. Using the set of miRNAs and genes differing significantly between the groups, miRNA:mRNA pairs were defined by both predicted targeting association and anti-correlation in expression patterns (with the miRNA high and the gene low specifically in the given subtype, or vice versa) (Figure 2 and Dataset S2). MiRNAs high in C1 and C2 tumors included let-7 family members (excluding let-7b). Potential gene targets of interest included genes upregulated during epithelial-to-mesenchymal transition (EMT), such as ZEB2, MMP2, SNAI2, FN1, TWIST1, which were all high in the (mesenchymal-associated) C2 and C3 tumors; furthermore, C2 tumors had high vimentin mRNA and low E-cadherin mRNA.

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Figure 2. MiRNA correlates of molecular subtype, with associated gene expression patterns.

Unsupervised clustering of miRNA expression data had identified three subtypes (C1–C3) of high-grade serous ovarian tumors. For miRNAs and genes differing significantly between the groups (t-test P<0.01, fold change>1.5, any subtype compared to the other tumors), predicted miRNA:mRNA functional pairs were defined, based on both anti-correlation in expression and predicted miRNA targeting interaction (both miRanda and TargetScan). For each miRNA:mRNA group (e.g. miRNA high/gene low in C1 versus other tumors), expression patterns are represented as heat maps (rows, miRNAs or gene transcripts; columns, profiled samples).

https://doi.org/10.1371/journal.pone.0034546.g002

MiRNAs correlated in expression with worse patient outcome

A set of molecular correlates of patient outcome is another resource TCGA data may provide to the research community. Previously, we identified a gene transcriptional signature predictive of overall survival in ovarian cancer [8]. Here, we carried out a similar analysis to define a miRNA signature of patient prognosis. In a training subset of 228 ovarian tumors (with outcome data, TCGA batches 9–15), 34 human miRNAs were individually correlated with time to death (Figure 3A, P<0.01, univariate Cox, average signal>50 units). Each of the 253 validation samples (batches 17–24) was assigned a prognostic score, reflecting the similarity between its expression profile and the prognostic miRNA signature pattern; the signature showed statistically significant associations with survival (Figure 3A, Log rank P = 0.03, miRNA risk index>0 vs <0, and P = 0.02, univariate Cox of the prognostic score as a continuous variable). Similar attempts at defining miRNA signatures of response to platinum therapy were unsuccessful (Document S1); however, as platinum-resistance is most likely a complex and multifactorial process, a role for miRNAs in platinum-resistance cannot be ruled out. A previous study [19] identified three miRNAs being associated with outcome in ovarian cancer; two of these miRNAs, miR-337 and miR-410, were also significant (P<0.05) in our training dataset. While a previous study had described an association between Dicer and Drosha expression levels and overall survival in ovarian cancer [20], we did not observe this association in our data (Figure S2).

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Figure 3. MiRNAs correlated with patient survival in ovarian cancer.

Using a training dataset of TCGA miRNA expression profiles, a prognostic miRNA signature was defined (left panel) and then applied to a test dataset (right panel), each tumor being assigned a score measuring how well the tumor's expression patterns reflected those of the signature pattern. Kaplan-Meier analysis (log-rank tests) compares time to death for ovarian cancer patients showing higher risk (prognostic score>0) versus lower risk (prognostic score<0). Univariate Cox test treats the prognostic score as a continuous variable. (miR-923 was recently determined to be an rRNA fragment, according to miRBase.).

https://doi.org/10.1371/journal.pone.0034546.g003

Similar to what was done for the subtype-specific miRNAs, miRNA:mRNA predicted pairs were defined, using the larger set of miRNAs and genes correlated with outcome (P<0.05, univariate Cox) but in opposite directions to their predicted interactors (Figure S3 and Dataset S3). In the validation cohort, we compared the miRNA-based prognostic signature scores with the gene-based prognostic signature scores generated previously [8]. The correlation between the two sets of scores was statistically significant, though not high (Pearson's R = 0.2, P<0.001, Figure S4A). An additional set of scores obtained by averaging both miRNA and gene scores was significant by univariate Cox (P = 0.003), though a multivariate model combining the two scores gave indeterminate results, with each score trending towards significance (P< = 0.07 for each, Figure S4B). In a three-way analysis separating tumors with high or low scores (>0 or <0, respectively) for both gene and miRNA from the rest of the tumors, there was good separation between the groups (Log rank P = 0.01), with the mixed group (high for miRNA, low for gene, or vice versa) showing an intermediate outcome as compared to the predicted worse versus better groups (Figure S4C). In conclusion, miRNA expression patterns may complement gene expression patterns in predicting survival, with further study warranted; this issue of integrating miRNA and mRNA with survival data has also been examined elsewhere, using different analytical methods [21].

Some miRNAs correlated with disease progression could conceivably have a functional role in ovarian cancer; miRNAs correlated with better patient prognosis, for example, could be considered candidate tumor suppressors. MiR-148a, one of our better prognosis miRNAs, was recently found to inhibit proliferation in ovarian cancer cells [22]. With an aim towards uncovering new candidates for therapeutic targeting, we over-expressed miR-26b in vitro, which, while not our top significant miRNA, was correlated with better prognosis (P<0.05), as well as previously shown as inducing apoptosis in breast cancer [23]. Interestingly, miR-26b inhibited proliferation of HEYA8 but not OVCAR-8 cells (Figure S5, both cell lines being derived from ovarian cancers), though cell line-specific effects of miRNAs in ovarian cancer have been reported previously (possibly reflecting genotypic differences) [10]. One other miRNA from our signature that we tested, miR-146a, had no effect in either HEYA8 or OVCAR-8 (data not shown); no other miRNAs from this signature were tested in vitro by our group.

MiRNAs and their predicted gene targets tend to be anti-correlated within ovarian tumors

A key to studying miRNAs is identifying their gene targets. While miRNA targeting predictions made in silico (the vast majority being unvalidated) may have sizable rates of false positives and negatives, we hypothesized that considering correlations between gene and miRNA expression across a large panel of tumors could provide further support for potential miRNA:mRNA targeting relationships. To this end, we computed all possible miRNA:mRNA correlations across the 487 TCGA ovarian tumors, for the top expressed 191 miRNAs and 8547 genes. We then sorted the 191×8547 miRNA:mRNA pairs by low to high correlation, and found that among the most anti-correlated pairs, there was high enrichment for predicted miRNA:mRNA targeting interactions by miRanda algorithm (Figure 4A), where no such enrichment was observed for the positively correlated miRNAs:mRNAs. (This trend was observed when considering all other miRNAs and genes in addition to those most highly expressed, Figure S6.) In addition to validating the public target prediction databases as being enriched for true positives, this finding indicated that thousands of miRNA:mRNA targeting interactions are active in ovarian cancer and influence tumor gene expression heterogeneity.

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Figure 4. Correlations between miRNAs and genes in ovarian cancer.

(A) MiRNAs and their predicted gene targets tend to be anti-correlated within ovarian tumors. Scatter plot showing mean correlation and fraction of predicted target interactions (miRanda-mirSVR score>0.1), using bins of 10000 miRNA:mRNA pairs (total number of pairs represented: 191 miRNAs X 8547 genes). Dashed line corresponds to chance expected baseline fraction (13.2%) of predicted target interactions. Correlations were computed using both Pearson's (gray line) and a simple linear regression model to account for ‘noise’ due to DNA copy alterations (black line). (B) Hierarchical clustering matrix (with Pearson's correlation coefficient as distance metric, Ward's Linkage) of correlation coefficients for all miRNA:mRNA pairs having a strong negative correlation (regression coefficient smaller than −7.0, only negative correlations represented). For each gene cluster, enriched gene classes are indicated. *, significant anti-enrichment (P<0.001, one-sided Spearman's rank, TargetScan or miRanda) for predicted targets within miRNA:mRNA correlations.

https://doi.org/10.1371/journal.pone.0034546.g004

The impact of CNA on expression level can vary greatly between genes, conceivably introducing bias when evaluating association of miRNA and gene expression levels. Therefore, in addition to a direct Pearson's correlation between miRNA and mRNA, we applied a simple linear regression model to account for ‘noise’ due to CNA, evaluating the association between expression levels of a miRNA and mRNA, when CNA status of the gene is held fixed. Interestingly, the Pearson's model and the regression model of miRNA:mRNA correlations both gave very similar overall results in terms of predicted target enrichment (Figure 4A), with the regression model's negatively correlated pairs showing slightly greater target enrichment (Figure S7). While, in general, CNA did not represent a major confounding factor, the regression model could identify individual miRNA:mRNA correlations which were missed by the Pearson's model, including miR-29a:HARS2 (Figure S8).

As another way to globally represent miRNA:mRNA interactions in ovarian cancer, for all miRNA:mRNA pairs with the strongest negative correlation (regression coefficient <−7.0, based on the linear model), we clustered the matrix of correlation coefficients (Figure 4B, consisting of 1760 genes and 35 miRNAs, matrix data table available as Dataset S4), thereby grouping miRNAs when they are negatively correlated with same genes and vice versa. We then cut the gene dendrogram to extract 6 gene clusters (based on what appeared to be natural separations within the cluster tree), each of which was found to be uniquely enriched for different gene classes (Document S2), including a cluster with Wnt and Hedgehog pathway gene members, a cluster with cell adhesion genes, two clusters with immune response genes, and a cluster of cell cycle-related genes. For several individual miRNAs, the genes anti-correlated in expression were significantly enriched for in silico predicted targets (Figure 4B and Figure S9 and Figure S10).

Genes anti-correlated with miRNAs are enriched for miRNA seeds predominantly in the 3′-UTRs

Popular algorithms for miRNA targeting prediction, such as miRanda or TargetScan, rely on basic assumptions, including targeting within the gene 3′-UTR. While miRNAs are understood to typically bind 3′-UTRs, there have been a number of studies showing target sites in coding regions as being effective [24], [25], and others suggesting alternative 5′-UTR targeting [26], [27]. In order to determine the overall trends as indicated by our data, in terms of where miRNAs tend to bind, we examined miRNA:mRNA pairs anti-correlated in expression for the presence of miRNA seed sequences (7mers) in either the 3′-UTR, 5′-UTR, or coding sequence regions. Overall, high enrichment was found for 7mer seed sequences in the 3′-UTRs of genes anti-correlated with the corresponding miRNA (Figure 5), which was comparable to the enrichment patterns observed using miRanda or TargetScan predictions (which incorporate additional sequence features with seed sequence alignment); weaker enrichment patterns were found for seed sequences in coding regions, while no enrichment was evident for 5′-UTRs.

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Figure 5. Gene transcripts with miRNA 7mer in the 3′-UTR tend to be anti-correlated with expression of the corresponding miRNA.

Analogous to Figure 4A, scatter plot showing mean correlation versus significance of enrichment for predicted target interactions (enrichment expressed as a Fisher's exact z-score), when separately considering the following potential interactions: 7mer seed sequence in 3′-UTR (black dotted line), 7mer seed sequence in 5′-UTR (black dashed line), 7mer seed sequence in coding sequence region (“cds,” gray dotted line), miRanda prediction (black solid line), TargetScan prediction (gray solid line). Plot uses bins of 10000 miRNA:mRNA pairs (total number of pairs represented: 191 miRNAs X 8547 genes). Fisher's exact z-score of +/−2.57 corresponds to significant enrichment (nominal P<0.01) for predicted targets within miRNA:mRNA pairs.

https://doi.org/10.1371/journal.pone.0034546.g005

MiR-29a impacts anti-correlated gene targets and ovarian cancer cell viability

The above results indicate widespread effects of miRNAs on gene expression in ovarian cancer, though any putative miRNA:mRNA interactions of interest remain to be validated. There are a number of ways one could arrive at candidate miRNAs for functional studies, using any of the results presented in our study. We focused our attention here on the miR-29 family, given its strong anti-correlation with many cell cycle-related genes (Figure 4B). Members of the miR-29 family have been demonstrated to act as tumor suppressors in acute myeloid leukemia and lung cancer, in part by reverting aberrant methylation patterns through its targeting of DNA methyltransferases (DNMT) and methylation-silenced tumor suppressors [28], [29]. Top anti-correlated genes of miR-29 in ovarian cancer included DNMT3A and DNMT3B (Figure 6A), suggesting a similar role for miR-29 in high-grade serous ovarian cancer. MiR-29a was under-expressed and DNMT3A mRNA was over-expressed in the DNA methylation subtype “MC2” (Figure S11); furthermore, possible targets of DNMT3A methylation in the ovarian tumors (having DNA methylation levels correlated with DNMT3A expression) were enriched for genes showing an impact by methylation (Figure S12, Table S1).

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Figure 6. MiR-29a impacts anti-correlated gene targets and ovarian cancer cell viability.

(A) Correlation of gene expression with miR-29a expression (genes from Figure 4B, same ordering). Predicted miR-29a targets are indicated. (B) Top eight words (of all 5, 6 and 7mers) enriched in 3′-UTRs of mRNAs anti-correlated with miR-29a expression (FDR<1e-6). (C) QPCR analysis showing relative quantity of selected miR-29a anti-correlated gene targets after miR-29a overexpression in HEYA8 ovarian cancer cells (SCR, scrambled control; WT, untreated; two-sided t-test P<0.05, mir-29a vs SCR and miR-29a vs WT, each comparison, except for SAE1). (D) MTS assays demonstrating the effect of miR-29a overexpression on proliferation of HEYA8 and OVCAR-8 cells (Lipo, lipofectamine-treated alone, no miRNA; two-sided t-test P<0.001, miR-29a vs each of three control groups at both 48 h and 72 h). (E) Effect of miR-29a (72 h) on proliferation, under a range of concentrations of cisplatin treatment (at 48 h). Error bars represent standard error.

https://doi.org/10.1371/journal.pone.0034546.g006

Genes anti-correlated with miR-29a were enriched for miR-29a targets as predicted by sequence analysis (either TargetScan or miRanda, Figure 6A). However, many in silico predicted targets did not show the anticipated anti-correlation patterns, again suggesting that by factoring in expression data, we could reduce the false positive rate for target predictions. Furthermore, as additional evidence for miR-29 activity, a correlation-based sequence motif analysis found that the miR-29 seed sequence complement was the top enriched motif in 3′-UTRs of mRNAs anti-correlated with miR-29a expression (Figure 6B), further suggesting that miR-29 directly regulates expression levels of many target mRNAs in the tumors; this analysis also showed strong enrichment for non-canonical miR-29a seed motifs (i.e. motifs not following the typical pattern of nucleotides 2–7) with a bulge in position 3 of the miR-29a sequence, suggesting that target prediction methods requiring perfect base pairing in the seed region of the miRNA target duplex could miss a substantial fraction of functional miRNA target interactions.

By forcing miR-29a expression in vitro in the ovarian cancer cell line HEYA8, we confirmed that a number of the genes anti-correlated with miR-29a—DNMT3A, DNMT3B, CDC6, CBX1, MYBL2, and TIMELESS (four of which were predicted direct targets)–were repressed by miR-29a (Figure 6C), which demonstrated these gene targets as relevant in both the in vitro functional models as well as the human tumor specimens; one gene tested, SAE1, showed anti-correlations but no functional repression. While miR-29 expression was not associated with survival (P>0.05, univariate Cox), forced miR-29a expression impacted cell proliferation in OvCar-8 and HEYA8 cell lines (Figure 6D) and had an additional effect on chemotherapeutic agent cisplatin in inhibiting the growth of these lines (Figure 6E).

Molecular profiling datasets

The set of 487 tumors analyzed were from the original TCGA set of 489 [8] (samples TCGA-04-1536 and TCGA-61-1911 did not have quality miRNA data at the time of this study). The miRNA array normalization steps are as follows. The gMeanSignal from raw array files (“level 1”) were quantile normalized and log transformed, removing duplicate samples and control probes (“level 2”). Multiple median centering steps set the median of every batch to the median of all batches: in brief, within each batch, we first subtracted the median for each miRNA, then calculated the across batch median and added it back to all samples within that batch; the resulting data were collapsed to miRNA levels (“level 3”). The level 3 miRNA data are available at the TCGA Data Portal [32]. For gene expression analysis, we relied on the previously described “unified” dataset [8].

miRNA:mRNA correlation analysis

Differentially expressed genes and miRNAs were identified using two-sided t-test on log-transformed data. Java TreeView [33] represented expression patterns as color maps. MiRNA:mRNA targeting relationships for both subtype correlates and patient survival correlates were identified using TargetScan Human (release 5.0) [7] and miRanda (September 2008) [34]; SigTerms facilitated retrieval of putative miRNA:mRNA pairs [35]. Predicted targeting relationships for miRNA:mRNA correlations were identified using miRanda (microRNA.org, August 2010, conserved set). For estimating absolute miRNA precursor copy levels (Figure 1A), we used the cBio Cancer Genomics Portal [36]. The top 191 miRNAs X 8547 genes (Figures 4 and 5) were defined as: for miRNAs, those in the top 100 with highest signal in at least ten individual samples; for genes, with expression above the tumor sample median in at least ten samples. Using a previously-described statistical framework evaluating correlation of 3′-UTR oligonucelotide (word) occurrences and mRNA expression changes [37], we analyzed motifs enriched in 3′-UTRs of mRNAs anti-correlated with miR-29a expression, computing word association z-score with P-value for all 21,504 words of length 5–7.

Survival analysis

The definition and validation of a prognostic miRNA signature was carried out essentially as described for the previously-defined prognostic mRNA (gene) signature [8], using the previously-defined training and validation subsets with expression values normalized within each subset to standard deviations from the median. Given the miRNA signature from the training dataset, the prognostic t-score was defined for each validation profile as the two-sided t-statistic comparing, within each tumor profile, the average of the poor prognosis miRNAs with the average of the good prognosis miRNAs.

Cell cultures

OVCAR-8 cells were obtained from the NCI-Frederick Cancer DCTD Tumor/Cell Line Repository (Frederick, MD), and HEY-A8 cells were obtained from Gordon Mills (M.D. Anderson), both cell lines having been properly authenticated by their respective sources; cells were passaged in our laboratory for no more than two months after resuscitation. Cells were cultured in RPMI 1640 (Gibco) with 10% heat-inactivated fetal bovine serum (Denville Scientific) and penicillin-streptomycin (Invitrogen).

miR-29a overexpression

Cell lines were transfected in 6-well plates (6×10⁴ cells/well) using 2.5 µl/well of Lipofectamine 2000 Transfection Reagent (Invitrogen) and 10 pmol of hsa-miR29a (Ambion), according to manufacturer's instructions. Control groups of cells were treated with transfection reagent alone (mock transfection), or transfected with pre-miR negative control #1 (Ambion).

Proliferation assays

Cell were seeded (3000/well) in a 96 well plate and transfected using 0.1 µl/well of Lipofectamine 2000 Transfection Reagent (Invitrogen) and 0.5 pmol of hsa-miR29a (Ambion), with lipofectamine alone or pre-miR negative control #1 (Ambion) as controls. Cell proliferation was assayed at 24, 48, and 72 hrs post-transfection using the MTS-based CellTiter 96 cell proliferation assay (Promega, Madison, WI). The time course MTS assay experiments were run three times (separate days), each with a different set of biological quadruplicates (n = 12 per group); within each experiment run, the viability measures within each time point were centered on the mean of the WT group for the first run. For cisplatin treatment, cells were transfected as described above, and media was replaced after 24 hrs with media containing Cisplatin (Sigma) (0–7.5 µg/mL); viability was assayed 72 hrs post-transfection; experiments were run three times, each with a different biological replicate (n = 3 per group); for each run, viability measures within each concentration point were centered on the mean of values for the first run.

Quantitative real-time PCR (QPCR)

Total RNA (60 ng) was reverse transcribed in a 40 µl reaction using the TaqMan® MicroRNA Reverse Transcription Kit (ABI). Custom primer sequences are in Table S2. QPCR was performed on a StepOne Real-Time PCR System (ABI) using Power- SYBR Green PCR Master Mix (ABI) in a 20 µl reaction and human ribosomal RNA 18 s as an endogenous control (which was itself not miR-29a-regulated, data not shown). The QPCR experiments were run four times (separate days), each with independent biological samples (n = 4 per group); within each experiment run, relative expression values were normalized to standard deviations from the mean.