BackgroundThe aim of this study is to determine anti-cancer effect of Icariside II purified from the root of Epimedium koreanum Nakai on human acute myeloid leukemia (AML) cell line U937.Methodology/Principal FindingsIcariside II blocked the growth U937 cells in a dose- and time-dependent manner. In this anti-proliferation process, this herb compound rendered the cells susceptible to apoptosis, manifested by enhanced accumulation of sub-G1 cell population and increased the terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL)-positive cells. Icariside II was able to activate caspase-3 and cleaved poly (ADP-ribose) polymerase (PARP) in a time-dependent manner. Concurrently, the anti-apoptotic proteins, such as bcl-xL and survivin in U937 cells, were downregulated by Icariside II. In addition, Icariside II could inhibit STAT3 phosphorylation and function and subsequently suppress the activation of Janus activated kinase 2 (JAK2), the upstream activators of STAT3, in a dose- and time-dependent manner. Icariside II also enhanced the expression of protein tyrosine phosphatase (PTP) SH2 domain-containing phosphatase (SHP)-1, and the addition of sodium pervanadate (a PTP inhibitor) prevented Icariside II-induced apoptosis as well as STAT3 inactivation in STAT3 positive U937 cells. Furthermore, silencing SHP-1 using its specific siRNA significantly blocked STAT3 inactivation and apoptosis induced by Icariside II in U937 cells.Conclusions/SignificanceOur results demonstrated that via targeting STAT3-related signaling, Icariside II sensitizes U937 cells to apoptosis and perhaps serves as a potent chemotherapeutic agent for AML.
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.
Even when targets responsible for chemoresistance are identified, drug development is often hampered due to the poor druggability of these proteins. We systematically analyzed therapy-resistance with a large-scale cancer cell transcriptome and drug-response datasets and predicted the candidate drugs based on the gene expression profile. Our results implicated the epithelial–mesenchymal transition as a common mechanism underlying resistance to chemotherapeutic drugs. Notably, we identified ITGB3, whose expression was abundant in both drug resistance and mesenchymal status, as a promising target to overcome chemoresistance. We also confirmed that depletion of ITGB3 sensitized cancer cells to conventional chemotherapeutic drugs by modulating the NF-κB signaling pathway. Considering the poor druggability of ITGB3 and the lack of feasible drugs to directly inhibit this protein, we took an in silico screening for drugs mimicking the transcriptome-level changes caused by knockdown of ITGB3. This approach successfully identified atorvastatin as a novel candidate for drug repurposing, paving an alternative path to drug screening that is applicable to undruggable targets.Electronic supplementary materialThe online version of this article (10.1186/s12943-018-0924-8) contains supplementary material, which is available to authorized users.
SummaryThe selective survival advantage of culture-adapted human embryonic stem cells (hESCs) is a serious safety concern for their clinical application. With a set of hESCs with various passage numbers, we observed that a subpopulation of hESCs at late passage numbers was highly resistant to various cell death stimuli, such as YM155, a survivin inhibitor. Transcriptome analysis from YM155-sensitive (YM155S) and YM155-resistant (YM155R) hESCs demonstrated that BCL2L1 was highly expressed in YM155R hESCs. By matching the gene signature of YM155R hESCs with the Cancer Therapeutics Response Portal dataset, BH3 mimetics were predicted to selectively ablate these cells. Indeed, short-course treatment with a sub-optimal dose of BH3 mimetics induced the spontaneous death of YM155R, but not YM155S hESCs by disrupting the mitochondrial membrane potential. YM155S hESCs remained pluripotent following BH3 mimetics treatment. Therefore, the use of BH3 mimetics is a promising strategy to specifically eliminate hESCs with a selective survival advantage.
Although many molecular targets for cancer therapy have been discovered, they often show poor druggability, which is a major obstacle to develop targeted drugs. As an alternative route to drug discovery, we adopted an in silico drug repositioning (in silico DR) approach based on large-scale gene expression signatures, with the goal of identifying inhibitors of lung cancer metastasis. Our analysis of clinicogenomic data identified GALNT14, an enzyme involved in O-linked N-acetyl galactosamine glycosylation, as a putative driver of lung cancer metastasis leading to poor survival. To overcome the poor druggability of GALNT14, we leveraged Connectivity Map approach, an in silico screening for drugs that are likely to revert the metastatic expression patterns. It leads to identification of bortezomib (BTZ) as a potent metastatic inhibitor, bypassing direct inhibition of poorly druggable target, GALNT14. The antimetastatic effect of BTZ was verified in vitro and in vivo. Notably, both BTZ treatment and GALNT14 knockdown attenuated TGFβ-mediated gene expression and suppressed TGFβ-dependent metastatic genes, suggesting that BTZ acts by modulating TGFβ signalingTaken together, these results demonstrate that our in silico DR approach is a viable strategy to identify a candidate drug for undruggable targets, and to uncover its underlying mechanisms.
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