mRNA and microRNA Expression Profiles of the NCI-60 Integrated with Drug Activities

Introduction

Micro RNAs (microRNAs) are small noncoding RNAs, approximately 18 to 24 nucleotides in length, that regulate the levels of their target mRNAs in a highly multiplexed way (1–5). More than 700 human microRNAs have been sequenced according to the latest miRBase release (Release 14). They are abundantly present in all human cells and have been estimated to target at least ∼60% of all genes (2, 6, 7). It is also well-established that microRNAs regulate cell proliferation and apoptosis, both of which are critical processes in cancer (8, 9). microRNAs have been reported to play roles in a number of human malignancies, including leukemias and breast, lung, liver, brain, and colon cancers (1, 10–13). For example, mir-15a and mir-16-1 are frequently deleted or downregulated in chronic lymphocytic leukemia (14). More than half of the known microRNA sequences are located in chromosomal regions that are often genetically altered in human cancer, for example at fragile sites or regions of deletion or amplification (11). Overexpression of microRNAs (such as mir-155) in cancers implies their possible function as oncogenes through the negative regulation of tumor suppressor genes and/or genes that inhibit cell differentiation or apoptosis (8, 15, 16). Conversly, some microRNAs (such as let-7d and mir-127) that are underexpressed in cancers may function as tumor suppressors by inhibiting oncogenes and/or genes that control cell differentiation or apoptosis (15, 16).

To study the relationships among expression levels of the various mRNAs and microRNAs, as well as their correlations with drug activity, the National Cancer Institute (NCI)-60 panel of human tumor cancer cell lines was used (17, 18). The panel, which consists of 60 cell lines from nine tissues of origin, includes melanomas (ME), leukemias (LE), and cancers of breast (BR), kidney (RE), ovary (OV), prostate (PR), lung (LC), central nervous systems (CNS), and colon (CO) origin. Several mRNA expression profiling studies of the NCI-60 have been reported previously (17, 19–21), and microRNA profiling studies of the NCI-60 have also been done on 241 human microRNAs using stem-loop real-time PCR (22), and on 321 human microRNAs using microarray (23). Clustering analyses of mRNA and microRNA expression in those studies revealed that some cell types (leukemia, colon, CNS, and melanoma) are grouped in a manner reflecting their tissues of origin. Expression levels of mRNAs and microRNAs were also significantly associated with drug sensitivity or resistance (20, 22, 23).

To expand and improve our mRNA and microRNA expression data (24), we have profiled the NCI-60 using the Agilent Whole Human Genome Oligo Microarray (mRNA_Agilent) and Agilent Human microRNA Microarray (V2; microRNA_Agilent), which interrogate ∼21,000 genes and 723 microRNAs, respectively. Both profiles include technical replicates. This report describes the generation, quality-control, and integrative analyses of the profile data sets obtained from the two platforms, focusing on the correlations of mRNA and microRNA expression with drug activity (leaving detailed examination of the linkages between mRNAs and microRNAs for a future report). Previous molecular profiles of the NCI-60 by our research group have appeared in the Spotlight on Molecular Profiling series (17, 24–29) and elsewhere (20, 21, 30, 31). The profiling data, metadata, and SQL capabilities for querying them are freely available in our CellMiner relational database program (32), where a listing of our Spotlight series publications is kept current.

Results

Cell and tissue-of-origin correlations of mRNA and microRNA expression levels

Microarray correlation comparisons were made for the designated cell-cell combinations for mRNAs and microRNAs in Fig. 1A and C, respectively. For the mRNA microarrays, all 41,000 probes were included. For the microRNA microarrays, 365 microRNAs with detectable expression in at least 10% of the cell lines were included. These visualizations provide both assessments of the technical reproducibility and variability among disparate cell lines.

For the mRNA technical replicates, seen as the gray diagonal in Fig. 1A (going from top left to bottom right), the mean correlation was 0.992, with a standard deviation of 0.004 and a range of 0.977 to 0.997. For the mRNA correlations from different cell lines, the mean correlation decreased to 0.892, with a standard deviation of 0.025 and a range of 0.824 to 0.987. For the microRNA technical replicates, seen as the gray diagonal in Fig. 1C (going from top left to bottom right), the mean correlation was 0.994 with a standard deviation of 0.007, and a range of 0.952 to 0.999. For the microRNA correlations from different cell lines, the mean decreased to 0.775, with a standard deviation of 0.082 and a range of 0.469 to 0.965.

The tissue-tissue mRNA and microRNA expression correlations in Fig. 1B and D, respectively, indicate the levels of variation both within and between tissues of origin. The same probe sets are used as in Fig. 1A and C. For mRNA expression comparisons of cells within a single tissue of origin, the mean correlation was 0.928, with a standard deviation of 0.017, and a range of 0.899 to 0.946. For mRNA expression for cells from different tissues of origin, the mean correlation dropped to 0.890, with a standard deviation of 0.020 and a range of 0.854 to 0.923. For microRNA expression comparisons for cells within a single tissue of origin, the mean correlation was 0.864, with a standard deviation of 0.043 and a range of 0.792 to 0.921. For microRNA expression for cells from different tissues of origin, the mean correlation dropped to 0.768, with a standard deviation of 0.068 and a range of 0.636 to 0.851.

These correlation results indicate high technical reproducibility of the Agilent mRNA and microRNA platforms. They also show the generally coherent mRNA and microRNA expressions within groups of cell lines. The most coherent groups are the colon (CO) and CNS, and the least coherent are the leukemia (LE) and breast (BR). Greater variation was generally observed for cell lines from different tissues of origin, and greater variability of expression levels among cells for microRNAs than for mRNAs.

Clustering and distribution analyses of mRNA and microRNA expression in the NCI-60

The expression patterns and clustering of 3,032 mRNA probes selected for both high level and diverse expression across the NCI-60 are shown in Fig. 2A. Figure 3A depicts the distribution of the number of cell lines expressing each of 20,146 probes with top-quartile expression levels in at least one cell line. That distribution was bimodal. Probes with generally low expression appear as the predominantly blue vertical strips in Fig. 2A and on the left side of the histogram in Fig. 3A. Probes with generally high expression appear as the predominantly red vertical strips in Fig. 2A and on the right side of the histogram in Fig. 3A. About 10% of the probes had expression levels higher than the 75th percentile in all cell lines. Clustering of the cell lines based on patterns of mRNA expression (vertical axis in Fig. 2A) showed, for the most part, clear separation by tissue of origin.

The clustering result for the 365 microRNA set expressed in at least 10% of the cell lines is shown in Fig. 2B. Figure 3B shows the histogram of the more inclusive number of microRNA with detectable expression in at least one cell line. The distribution was bimodal. Thirty percent (217 out of 723) of the microRNAs were not detectable in any of the cell lines. Probes expressed in only a small number of cell lines appear as the predominantly blue vertical strips in Fig. 2B and on the left side in Fig. 3B. Probes expressed in most of the cell lines appear as the predominantly red vertical strips in Fig. 2B and on the right side in Fig. 3B. Clustering of the cell lines based on patterns of microRNA expression (vertical axis in Fig. 2B) showed, for the most part, clear separation by tissue of origin.

Overall, the distribution of the numbers of cell lines in which either mRNA or microRNA were expressed was bimodal, i.e., most of mRNAs and microRNAs were expressed in either low or high levels in most cell lines (Fig. 3A and B, respectively). Noticeably, cell lines from the different tissues of origin tend to cluster in separate groups in terms of both mRNA and microRNA (see annotations on the right of Fig. 2A and B, respectively). The entire set of leukemia (6 out of 6 LE) and 9 out of 10 melanoma (ME) cell lines cluster together in the mRNA clustering analysis. For the microRNA, 6 out of 6 LE and 10 out of 10 ME cluster together again, indicating their relatively high degree of coherence with respect to patterns of expression. In contrast, the lung cancer (LC) and CNS cell lines tended to cluster the least both for mRNA and microRNA, indicating their relatively genetic heterogeneity. The ovarian (OV) group of cells is somewhat intermediate with 3 out of 7 cell lines (OGROV1, OVCAR-4, and OVCAR-3) clustering together for both mRNA and microRNA.

Correlation of drug activity with mRNA and microRNA expression

The CIM in Fig. 4A relates activity patterns for the previously described 1,429-drug set (35) to the expression patterns of 2,564 mRNA probes selected on the basis of high correlation to drug activities in the NCI-60. Supplementary Fig. S2 shows the same approach for the set of 121 drugs of known mechanism of action (many of them clinically used). The CIMs in Fig. 4B and Supplementary Fig. S3 relate the drug activity patterns of the same 1,429-drug and 121-drug sets to the expression patterns of the 365 microRNA set in the NCI-60. A positive correlation (red color in Fig. 4A or B and Supplementary Figs. S2 and S3) indicates that when mRNA or microRNA expression level increases, the activity level of the corresponding drug will increase and therefore the cells are more sensitive. Similarly, a negative correlation (blue color in Fig. 4A or B and Supplementary Figs. S2 and S3) indicates that when mRNA or microRNA expression levels increase, the activity level of the corresponding drug will decrease, and therefore, the cells are more resistant. The broad patterns of positive and negative correlation between mRNA or microRNA expression and drug activity reflect the existence of coherent blocks within which mRNA or microRNA levels are consistently correlated either positively or negatively to drug activities.

The histograms of the distribution of the ratios (see the Data Analysis section) for all 41,000 mRNA probes and the 365 microRNAs, in Supplementary Fig. S1A and B, respectively, support the visual observation of a predominant unidirectionality of those correlations found in Fig. 4. The mean ratio value for mRNA (in Supplementary Fig. S1A) was 0.83, indicating that 83% of the drugs have significant correlation with an mRNA, on average, in a unidirectional fashion. The mean ratio value for microRNA (in Supplementary Fig. S1B) was 0.86. Note that the selection of mRNA probes on the basis of high correlation in Fig. 4A is biasing toward the generation of patterns that might not be seen, or be as strong, if the probes were not selected in that way. However, the consistency and robustness of the dichotomous patterns seen were also supported by the histogram shown in Supplementary Fig. S1A, which was based on all mRNA probes.

The correlation analysis of mRNA and microRNA expression with drug activity shows that most drugs have a coherent relationship to substantial blocks of both transcript and microRNA expression levels, indicated by the coherent positive or negative mRNA and microRNA correlations across drugs.

Figure 5 compares the clustering results for the set of 121 drugs with defined mechanism of action purely on the basis of drug activities (Fig. 5A), on the basis of the cross-correlations of drug activities and mRNA expression levels (Fig. 5B), and on the cross-correlations of the drug activities with microRNA expression levels (Fig. 5C). In all three cases, drugs were generally clustered according to their mechanism of action classes.

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Figure 5.

Dendograms of the set of 121 drugs with putatively known mechanisms of action. A, clustering on the basis of the drug activity data alone. B, clustering on the basis of correlations with the expression levels of 2,564 mRNAs from Fig. 4A. C, clustering on the basis of correlations with the expression levels of 365 microRNAs from Fig. 4B. Color coding is by drug mechanism of action. Included are A2, A6, and A7 alkylating agents (alkylating at the N-2 position of guanine, alkylating at the O-6 position of guanine, and alkylating at the N-7 position of guanine, respectively), tubulin-active antimitotic agents (Tu), topoisomerase 1 inhibitors (T1), topoisomerase 2 inhibitors (T2), DNA antimetobolites (DA), RNA/DNA antimetobolites (RD).

Discussion

The NCI-60 cell lines have been profiled more comprehensively at the DNA, RNA, protein, and pharmacological levels than any other set of cells in existence. The resulting molecular databases, therefore, constitute a uniquely valuable information resource for understanding cancer biology, assessing molecular pharmacology, and developing new approaches to analyze and interpret high-throughput molecular data. The profiling studies reported here for mRNA and microRNA expression using the recent Agilent microarrays add two important new platforms of high quality data for mRNA and microRNA expression (42).

The present data analyses show a high degree of reproducibility for both the mRNA and microRNA microarrays, as depicted by the close to 1 Pearson correlation coefficient in diagonal cells in the Fig. 1A and C CIMs. As indicated in Fig. 1A and C, and in tabular form in Fig. 1B and D, the within-tissue-of-origin correlations tend to be higher than those between tissues of different origin for both the mRNAs and the microRNAs. This is true for all of the mRNA comparisons, and all but two of the microRNA comparisons (the exceptions being the LC-PR, and OV-PR, as compared with LC-LC, and OV-OV, respectively). Those results indicate that the tissue-of-origin signature remains for both the mRNA and microRNA transcriptomes, despite adaptation of the cell lines to in vitro culture. That observation is consistent with prior results (20, 22, 23). An apparent example of disease subtyping within a tissue of origin is provided by the high correlation (r = 0.955) for mRNA between CCRF-CEM and MOLT-4 (Fig. 1A), both of which are acute lymphocytic leukemias. LOXIMVI, which is unlike the other melanoma lines by both mRNA and microRNA expression, has previously been noted to be highly differentiated and amelanotic, i.e., it lacks melanin and other melanoma signature markers (43).

CIMs of the NCI-60 based on mRNA and microRNA expression levels (Fig. 2) also indicate residual tissue-of-origin signatures that are generally consistent with those seen in prior profiling studies (20, 22, 23). Overall, there were four tissue types (i.e., leukemia, colon, renal, and melanoma) in which cell lines tend to be well clustered by both mRNA and microRNA according to their tissues of origin. The leukemias were the most clearly separated from other tissue-of-origin lines (as indicated by the height of the cluster branches in both figures). Otherwise, notable exceptions were HCC-2998 (CO) and LOXIMVI (ME) for the mRNA-based clustering, and HCT116 (CO), SN12C (RE), ACHN (RE), and LOXIMVI (ME) for the microRNA-based clustering (Fig. 2B). Those cell lines have also tended to group with different tissue-of-origin lines in prior profiling studies (20, 22, 23).

The other five tissue types were more variable. The CNS cell lines clustered well for mRNAs but poorly for microRNAs. Breast, lung, ovarian, and prostate lines formed small and/or widely distributed groups. Some cell pairs known to be similar, or to have been reported to cluster together in previous profiling studies (20, 22, 23), also clustered together here. Examples include two hormone-dependent estrogen receptor-positive breast cancer cell lines, MCF7 and T47D, OVCAR8 and its drug resistant derivative NCI/ADR-RES, and the CNS cancer cell lines U251 and SNB-19, which probably originated from the same patient (25). We identify two additional robust pairs, LC:HOP92/BR:MDA-MB-231, and OV:OVCAR3/OV:OVCAR4, which have clustered together in both the current and prior mRNA and microRNA profiling studies (20, 22, 23).

The bimodal distribution seen in Fig. 3A indicates that there are relatively large groups of mRNAs that are either expressed at high levels in a small number of cancer lines (left side of Fig. 3A and also the predominant blue strips in Fig. 2A), or expressed at high levels ubiquitously across all cell lines (right side of Fig. 3A and also the red strips in Fig. 2A). Similarly, the bimodal distribution seen in Fig. 3B indicates that there are relatively large groups of microRNAs that either have detectable expression in a small number of cell lines or have detectable expression across all cell lines. The bimodal distributions may reflect some mRNA, and microRNAs have critical housekeeping functionalities in cells whereas some have tissue- or cell line-specific functionalities.

CIM visualizations for the correlations of drug activities with expression levels of mRNA in Fig. 4A and Supplementary Fig. S2 as well as the histogram in Supplementary Fig. S1A indicate the presence of large unidirectional blocks (i.e., with either positive or negative correlation). The 1,429 drugs formed two well-separated clusters: cluster I, which consists of 146 drugs, and cluster II, which consists of the remaining 1,283 drugs. There are also two well-organized mRNA clusters. The 871 mRNAs in cluster A are predominantly negatively correlated with the drugs from cluster I, and positively correlated with the drugs from cluster II. The 1,251 mRNAs in cluster B are predominantly positively correlated with the drugs in cluster I, and negatively correlated with the drugs in cluster II. Functional analysis using KEGG (44) indicates that mRNAs in cluster A are enriched significantly with ribosome proteins, and mRNAs in cluster B are enriched significantly with focal adhesion pathway proteins. Based on UniProtKB, the remaining mRNAs (i.e., those in cluster C) are enriched with glycoproteins. The histogram in Supplementary Fig. S1A reinforces the unidirectional preponderance of the correlations, as depicted by the increase in the number of mRNAs on the right side of the histogram.

CIMs for the correlation of drug activity with the expression level of microRNA in Fig. 4B and Supplementary Fig. S3 also indicate the presence of large unidirectional blocks. As in Fig. 4A, the 1,429 drugs formed two well-separated clusters in Fig. 4A, with 115 drugs in cluster I, and the remaining 1,314 drugs in cluster II. There were 93 drugs that are common to cluster I in both mRNA and microRNA drug correlations (Fig. 4). The chemical structure and corresponding NSC numbers of some of those drugs are listed in Supplementary Fig. S4. Well-organized blocks were formed between the drugs in cluster II and the microRNAs in clusters A and B. The histogram in Supplementary Fig. S1B reinforces the unidirectional preponderance of the correlations, as depicted by the increase in the number of microRNAs on the right side of the graph.

The prior chemosensitivity study conducted by Blower and colleagues (45) provides information on three microRNAs, let-7i, mir-16, and mir-21 and their relationship to activity patterns of 8 out of the 1,429 drugs (Table 1 in ref. 45). We found that the previously described change of drug potency following either silencing or forced expression of microRNAs in the A549 cell line (45) is consistent with our correlation results (Table 1). That is, if the chemosensitivity study showed that a change in microRNA expression leads to a change in drug activity, then that same association (in the same direction) was found in our correlation analysis. For example, the correlation (in Table 1) between mir-21 expression and the −log10(GI50) value of drug NSC622700 is −0.445, which indicates that when the expression of mir-21 decreases, the drug activity increases, consistent with prior results (45).

Clustering of 121 drugs with known mechanisms of action based on: (i) the correlation between drug activity and mRNA expression (Fig. 5B and Supplementary Fig. S2) or (ii) the correlation between drug activity and microRNA expression (Fig. 5C and Supplementary Fig. S3) was largely successful in classifying the drugs by their mechanisms of action. The clustering between drug activity and mRNA was consistent with prior results (20). We find that the Top1 inhibitors and antimitotic agents generally form coherent clusters. Conversely, Top2 inhibitors form several subgroups. One consistent (Top2 inhibitor) subgroup consists of bisantrene, an anthrapyrazole derivative and deoxydoxorubicin, and another consistent subgroup contains m-AMSA, mitoxantrone, and oxanthrazole. The majority of the N-7 alkylating agents (A7) cluster into one large group, but also appeared in several smaller groups. For DNA antimetabolites, all of the purine analogues (thioguanines and thiopurines) appeared together on small branches in all clusters. There are several drug pairs consistently clustered together, including cytarabine (ara-C) and its congener cyclocytidine, mitomycin and its N-methyl derivative porfiromycin, and the two O-6 position alkylating agents CCNU and BCNU.

In summary, we have characterized the NCI-60 mRNA and microRNA data using two novel Agilent platforms, the Agilent Whole Human Genome Oligo Microarray, and the Agilent microRNA microarray Human version 2. Our analysis indicates high reproducibility for both platforms and an essential biological similarity across various cell types at a molecular level. Analyses of drug activity reveal substantial blocks of both mRNA and microRNAs whose expression levels are correlated with anticancer drug response. Future work will focus on systematic analysis of the three-way relationships of mRNA expression, microRNA expression, and drug activity by taking advantage of the high-quality profiles reported here or previously. We will also extend the approach by including new drugs recently approved by the U.S. Food and Drug Administration (FDA) to elucidate their mRNA and microRNA profiles across the NCI-60 and compare them with the “classical” drugs in the present database. Such analyses are now facilitated by free web access to our data and by the possibility of searching directly the CIM shown in Figs. 2 and 4 (46).

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.