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. 2016 Mar 8;11(3):e0150460.
doi: 10.1371/journal.pone.0150460. eCollection 2016.

Drug Repositioning for Cancer Therapy Based on Large-Scale Drug-Induced Transcriptional Signatures

Haeseung Lee  1 , Seungmin Kang  1 , Wankyu Kim  1
Affiliations

Drug Repositioning for Cancer Therapy Based on Large-Scale Drug-Induced Transcriptional Signatures

Haeseung Lee et al. PLoS One. .

Abstract

An in silico chemical genomics approach is developed to predict drug repositioning (DR) candidates for three types of cancer: glioblastoma, lung cancer, and breast cancer. It is based on a recent large-scale dataset of ~20,000 drug-induced expression profiles in multiple cancer cell lines, which provides i) a global impact of transcriptional perturbation of both known targets and unknown off-targets, and ii) rich information on drug's mode-of-action. First, the drug-induced expression profile is shown more effective than other information, such as the drug structure or known target, using multiple HTS datasets as unbiased benchmarks. Particularly, the utility of our method was robustly demonstrated in identifying novel DR candidates. Second, we predicted 14 high-scoring DR candidates solely based on expression signatures. Eight of the fourteen drugs showed significant anti-proliferative activity against glioblastoma; i.e., ivermectin, trifluridine, astemizole, amlodipine, maprotiline, apomorphine, mometasone, and nortriptyline. Our DR score strongly correlated with that of cell-based experimental results; the top seven DR candidates were positive, corresponding to an approximately 20-fold enrichment compared with conventional HTS. Despite diverse original indications and known targets, the perturbed pathways of active DR candidates show five distinct patterns that form tight clusters together with one or more known cancer drugs, suggesting common transcriptome-level mechanisms of anti-proliferative activity.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Overview of the in silico DR procedure.
(A) The structural (S), target (T), and expression (E) signatures for each compound (circles on the left) and disease (squares on the right) were compared. The associations are indicated by dashed lines in three categories (S: yellow, T: green, E: red) depending on the type of compound signature. (B) In total, seven different classifiers were constructed based on the similarity between the compound and the target signature or their combinations (S, T, E, ST, SE, TE, and STE). The DR scores were calculated using a series of classifiers based on a logistic regression with the known drug set (KD set) used as a benchmark. (C) The performance was evaluated using three independent datasets: I) the mean AUC of 100 rounds of 3-fold cross validation, II) comparison with the 29 sets of NCI-60 DTP human tumor cell line HTS data, and III) experimental validation of anti-proliferative activities using cancer cell lines and primary cells. A pathway-level interpretation of the drug mode of action was performed for active DR candidates for glioblastoma (IV).
Fig 2
Fig 2. Evaluation of prediction performance using the known drug (KD) set as a benchmark.
The classifiers using a single type of signature (S, T, and E) and their combinations (ST, SE, TE, and STE) were evaluated based on the AUCs of the ROC curve for glioblastoma, lung cancer, and breast cancer. The AUC values were calculated by averaging 100 rounds of 3-fold cross validation.
Fig 3
Fig 3. Performance evaluations using the public anti-cancer HTS dataset as a benchmark.
The seven classifiers (S, T, E, ST, SE, TE, and STE) were evaluated based on the AUCs of the ROC curve for glioblastoma, lung cancer, and breast cancer. Only compounds in the core set were evaluated. The AUC values were calculated by averaging 100 rounds of 3-fold cross validation. (A) Typical examples of performance evaluation using the HTS data set for glioblastoma (AID45), lung cancer (AID5), and breast cancer (AID97). The AUCs were independently calculated using two distinct sets of hit compounds as a benchmark (or positives)—i) the hit compounds of known anti-cancer activity (red lines) and ii) the novel hits (green lines). The distribution of AUCs using (B) the compounds of known anti-cancer activity as a benchmark, and (C) the novel hits as a benchmark.
Fig 4
Fig 4. The high-scoring DR candidates for glioblastoma among the FDA-approved drugs that were predicted based only on the expression signatures.
(A) DR scores, (B) the fraction of significantly inhibited cells summarizing the results of (C), (C) the anti-proliferative activities (% growth inhibition) for the four glioblastoma cell lines (four cell lines of TG98, A172, U251MG, and U87MG) and the eight patient-derived primary cells (the GBLs) at 10 μM, (D) in silico prediction scores for BBB transport based on http://www.cbligand.org/BBB. The red asterisk indicates experimental support for passing the BBB according to the literature. Overall, anti-proliferative activities across glioblastoma cells strongly correlated with the rankings by the DR score. Most DR candidates were shown to be able to pass the BBB.
Fig 5
Fig 5. Pathway enrichment pattern of the eight active DR candidates for glioblastoma.
The p-values and their adjusted q-values were calculated by hypergeometric test and the Benjamini-Hochberg method, respectively.
Fig 6
Fig 6. Cluster analysis of DR candidates with other cancer drugs using their pathway enrichment patterns.
The eight DR candidates and 69 cancer drugs in the LINCS dataset were clustered using the 32 up- and the 17 down-regulated pathway enrichment patterns. The eight DR candidates (red) belong to five clusters (I ~ V) with 15 cancer drugs. The bar plot on the right side shows the number of significantly enriched cancer drugs (q-value<0.05) for the corresponding pathway. The significance of up- and down-regulation is presented in red and green, respectively.
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WK was supported by National Research Foundation (NRF) grant (NRF-2014R1A2A2A01007166) and Technology Innovation Program (10050154) funded by Ministry of Trade, industry & Energy of Korea.