Novel free-access platform for evaluating antitumor agents
Several previous analyses, including the C-map, have shown that genome-wide gene expression analysis is effective in predicting modes of action of chemical compounds.[3, 4, 32-34] In the present report, we describe the development of a comprehensive gene expression dataset specializing in the analysis of standard antitumor agents. This open-to-the-public database is available for evaluating the likely mechanisms of action of new anticancer compounds.
In our clustering analysis, drugs with similar mechanisms of action such as genotoxic drugs, proteasome inhibitors, HDAC inhibitors, and ER stress inducers were clustered together (Fig. 1). These results strongly suggest that our gene expression data accurately reflect the mode of action of the agents. The KEGG pathway analysis confirmed that the ER stress-related gene set was actually induced by the ER stressors (Table S4), which is consistent with our clustering results.
We acquired our gene expression data using human colon cancer HT-29 cells, whereas the C-map data mainly consisted of data that were obtained using human breast cancer MCF7 cells or human prostate cancer PC3 cells. It is noteworthy that our signature data of compounds in HT-29 cells were closely related to those in the C-map (Table 3). Moreover, we obtained gene expression data in human myeloma RPMI8226 cells and found that the data were closely related with the data obtained in HT-29 cells (Fig. S2). These results indicate that our signature data could be applicable to data that are obtained in other types of cancer.
To obtain gene expression data that reflect the mode of action of agents, the exposure time of cells to drugs is an important issue. We basically chose a short exposure time (6 h) and, in most of the agents, significant gene expression changes were observed (Table 2) and the data were clustered in a mechanism of action-dependent manner (Fig. 1). These results indicate that the exposure time would be basically suitable for many agents. By contrast, for some agents that did not show dramatic effects on gene expression in the 6-h treatment, we tested a longer exposure time (16 h) (Table 1). However, we found that the 16-h treatment data tended to cluster together, even though the agents had different modes of action (Fig. 1). These observations suggest that longer exposure time might not necessarily be better than shorter exposure time even though gene expression changes are increasingly dramatic. Thus, the drug treatment regime must be carefully chosen for gene signature acquisition and subsequent mechanism analysis.
Mining cryptic linkages and gaps between ER stress and related agents
As reported previously, ER stress is closely related with tumor microenvironment conditions as well as the effect of several antitumor agents. Therefore, we included well-known ER stress-inducing agents in our compound panel. We found that the tubulin binding agents, proteasome inhibitors, and Hsp90 inhibitors were clustered together with the ER stress inducers in a group different from classical genotoxic agents (Fig. 1). This observation indicates that these drugs could have unique modes of action.
It has been reported that proteasome inhibitors induce ER stress as well as suppressing nuclear factor-κB activation by interfering with the degradation of I-κB. In our clustering analysis, the protease inhibitors formed a cluster with the ER stress inducers (Fig. 1) and the analysis with our program predicted that the inhibitors induce ER stress (Table S3). Moreover, KEGG pathway analysis revealed that these agents modulate the expression of ER stress-related genes (indicated as “protein processing in endoplasmic reticulum” in Table S4). These data support the notion that ER stress could play an essential role in the mode of action of proteasome inhibitors.
Our gene signature analysis further revealed that the proteasome inhibitors induce an atypical type of ER stress. In particular, we found that the inhibitors induce a subset of ER stress-related genes (class 1 genes) while only marginally inducing the other genes (class 2 genes) (Figs. 2, S3). It is still unclear what causes this atypical gene expression pattern. Presumably there is a negative feedback mechanism that selectively suppresses class 2 gene expression. It is noteworthy that the analysis with our program was able to detect the mechanistic difference between the proteasome inhibitors and the well-known ER stress-inducing agents. Namely, when we entered the ER signature gene set in our program, the ER stress inducers ranked significantly higher than the proteasome inhibitors (Table 5). Thus, our program could potentially predict detailed mechanisms of action of anticancer drugs.
The Hsp90 inhibitors did not typically induce the ER stress-related genes (Fig. 2, Table 5), although they were clustered with the ER stress inducers (Fig. 1). To determine the connection between the inhibitors and ER stress, we carried out our connectivity scoring analysis. We found that the signature of the Hsp90 inhibitors weakly correlated with those of some ER stress inducers, such as thapsigargin or tunicamycin (Table S5), suggesting that the inhibitors would not be typical ER stressors but could marginally induce ER stress. It was reported that some Hsp90 inhibitors induce ER stress, but the level of ER stress induction differs among the inhibitors. These data suggest that ER stress induction by Hsp90 inhibitors could depend both on compound types and on cell types.
As described above, we developed in-house R programs for calculating scores for ranking gene expression changes, and for searching statistically significant pathways from the KEGG database. The program for searching pathways enables us to easily produce simple charts for each pathway analyzed and give graphical information of the location of genes in each pathway. The programs and database developed in this study will be made available on our website. Our database included some compounds that are not present in the C-map database. Therefore, unanticipated characteristics of a novel compound might be obtained by using our database.
In summary, we have developed a publicly available gene expression database of standard anticancer agents as well as some related application programs. Our gene expression database is specialized in antitumor agents, and our datasets include some anticancer agents not contained in other databases, such as C-map. To establish a more comprehensive database, we plan to add new antitumor agents and update our database. Thus, our database would be suitable for primary characterization of new candidate compounds in comparison with known anticancer agents. We have also acquired data concerning differential sensitivity of human cancer cell lines to anticancer agents and established a “sensitivity-based” signature database.[33-36] Further trials are planned to develop an integrated database of antitumor agents that include both gene expression-based and sensitivity-based signatures. Our public database and related programs will be helpful for evaluating candidate compounds as novel antitumor agents.