Utilization of genomic signatures to identify phenotype-specific drugs

Abstract

Genetic and genomic studies highlight the substantial complexity and heterogeneity of human cancers and emphasize the general lack of therapeutics that can match this complexity. With the goal of expanding opportunities for drug discovery, we describe an approach that makes use of a phenotype-based screen combined with the use of multiple cancer cell lines. In particular, we have used the NCI-60 cancer cell line panel that includes drug sensitivity measures for over 40,000 compounds assayed on 59 independent cells lines. Targets are cancer-relevant phenotypes represented as gene expression signatures that are used to identify cells within the NCI-60 panel reflecting the signature phenotype and then connect to compounds that are selectively active against those cells. As a proof-of-concept, we show that this strategy effectively identifies compounds with selectivity to the RAS or PI3K pathways. We have then extended this strategy to identify compounds that have activity towards cells exhibiting the basal phenotype of breast cancer, a clinically-important breast cancer characterized as ER-, PR-, and Her2- that lacks viable therapeutic options. One of these compounds, Simvastatin, has previously been shown to inhibit breast cancer cell growth in vitro and importantly, has been associated with a reduction in ER-, PR- breast cancer in a clinical study. We suggest that this approach provides a novel strategy towards identification of therapeutic agents based on clinically relevant phenotypes that can augment the conventional strategies of target-based screens.

Figure 1. Strategy of a gene expression signature-based drug screen.

A gene expression signature that reflects a clinical/biological phenotype is used to profile the NCI-60 panel to identify cells that exhibit the phenotype of interest. The predicted probability for the signature is correlated against the sensitivity to over 40,000 (21,603 after filtering) compounds to identify compounds that appear to be effective in cells exhibiting the phenotype.

Figure 2. Identification of RAS or PI3K pathway-specific drugs.

A. Gene expression signatures previously developed to predict RAS or PI3K pathway activation were used to predict the status of the pathways in the NCI-60 panel. The predicted probability for each oncogene activity is shown in a heatmap (lower panels; red = high and blue = low). Samples are sorted according to the RAS activity. B. A heatmap displaying the pattern of compounds correlated with RAS or PI3K pathway status. GI50s of correlated compounds with FDR less than 0.05 are shown in a heatmap (green = less sensitive and red = more sensitive) with the heatmap of predicted probabilities for RAS or PI3K activity (red = high and blue = low). Samples and compounds are sorted according to the predicted probabilities for each oncogene activity and to the correlation coefficient, respectively. RAS is positively correlated to 3616 compounds and negatively correlated to 606. For PI3K, three compounds have positive correlation and ten have negative correlation. C. Pattern of correlation of all compounds with RAS (left) or PI3K (right) predicted probability. Correlation coefficients in Pearson correlation are shown in a heatmap (green = less sensitive and red = more sensitive). Bars adjacent to the heatmap are used to indicate the compounds with FDR less than 0.05. Hypothemycin, a MEK inhibitor, is a highly correlated compound to cells with high RAS probability (rank = 331, R = 0.4998 and FDR = 0.002639). LY294002 shows strong correlation to PI3K activity without evident statistical significance (rank = 121, R = 0.3601 and FDR = 0.1463). The correlation coefficient may suggest the “strength” of the correlation. D and E. Relation between oncogenic pathway activity and pathway specific inhibitors in NCI-60 cell lines. GI50 values were plotted in the function of the predicted probalities. P value and R² were calculated by linear regression analysis of GraphPad's Prism. D. RAS pathway and hypothemycin. E. PI3K pathway and LY294002.

Figure 3. Identification of breast cancer subtype specific compounds.

A. Development of a gene expression signature to distinguish basal or luminal cell type in breast cancers. Expression levels of selected genes are shown in a heatmap (high = red and low = blue). B. Validation of the “basal-luminal” signature in three independent datasets of human primary breast cancers. The predicted probability for basal (blue) or luminal (red) are shown in a heatmap with the labeling for the cell type classification by microarray (GSE1456), the immunoreactivity status for estrogen and progesterone receptor (GSE1561) or the status for basal subtype by cytokeratin expression patterns (GSE3744). C, D and E. Prediction for basal and luminal properties in in vivo tumor data sets. Predicted probabilities are plotted for the groups with the defined subtype and statistically evaluated using Mann-Whitney U test. A bar indicates mean value for each group. The predicted probability for basal or luminal is shown with the labeling for the cell type classification by microarray (C; GSE1456), the immunoreactivity status for estrogen and progesterone receptor (D; GSE1561) or the status for basal subtype by cytokeratin expression patterns (E; GSE3744). Accuracy of the prediction was also shown. To evaluate the accuracy, 0.5 was used as a cut-off value.

Figure 4. Relation between the “basal-luminal” phenotype activity and correlated drugs in NCI-60 cell lines.

A. The predicted probability of NCI-60 cells for “basal-luminal” subtype and the correlated compounds. The predicted probability of NCI-60 cells for the similarity to basal (blue) or luminal (red) is shown in a heatmap and sorted according the similarity. Note that among 5 cell lines, which were characterized by the previous study and are included in NCI-60 cells, every cell line was classified accurately (basal subtype; blue arrowheads; BT549, MDA-MB-231 and MDA-MB-435 and luminal subtype; red arrowheads; MCF7 and T47D). GI50 pattern for the compounds that correlated with the probability within 0.05 of FDR was shown in a heatmap (green = less sensitive and red = more sensitive). Luminal subtype correlated compounds include 5589, while 568 compounds showed correlation to basal subtype. B. Correlation pattern of all compounds with the predicted probability to “basal-luminal” signature. Correlation coefficient in Pearson correlation is shown in a heatmap (green = less sensitive and red = more sensitive). Bars adjacent to the heatmap are used to indicate FDR less than 0.05. Tamoxifen, an estrogen receptor inhibitor, is a highly correlated compound to cells with high luminal probability (rank = 57, R = 0.6140 and FDR = 0.0000). Among 568 compounds, which basal phenotype correlated within FDR of 0.05, 85 compounds have chemical names. Through Pubmed search of all 85 compounds, Simvastatin, Lovastatin and Peplomycin are found to be currently under clinical use (Simvastatin; rank = 204, R = 0.5050 and FDR = 0.006160, Lovastatin; rank = 442, R = 0.3890 and FDR = 0.02795 and Peplomycin; rank = 329, R = 0.3910 and FDR = 0.01478). Lovastatin is not shown in Figure 4B. C, D, E and F. Tamoxifen (C), Simvastatin (D), Lovastatin (E) and Peplomycin (F) and the “basal-luminal” phenotype activity. GI50 values were plotted in the function of the predicted probabilities. P value and R² were calculated by linear regression analysis of GraphPad's Prism.

Figure 5. Experimental validation of compounds predicted to be active on breast cancer subtype.

A. Specificity of drug sensitivity measures in breast cancer cell lines. A panel of breast cancer cell lines was classified into basal or luminal subtype based on the microarray classification (shown in Figure S1) and used for measures of sensitivity to Simvastatin (A), Peplomycin (B), and Tamoxifen (C). GI50 values were calculated after cell proliferation assays of these breast cells and averaged GI50s were plotted with p value (also shown in Table S3). A non-parametric Mann-Whitney U-test was used to evaluate the result statistically. B. Confirmation of in vivo effect of Simvastatin on a basal-type breast cancer cell line. MDA-MB-231 cells were inoculated by subcutaneous injection into mice and then the mice were treated with Simvastatin for 12 days after injections. Tumor size at day 0 was the same (see Materials and Methods in detail). Sizes of tumors were plotted as a function of days after the initiation of treatment. The unpaired t-test was used for statistical evaluation and p value is shown with the plot. Asterisks indicate statistically significant differences (p value: day 4; 0.0373, day 7; 0.0569, day 11; 0.0162, day 14; 0.0280, day 18; 0.0393, day 21; 0.0416).