Constitutive activation of STAT3 is frequently observed and closely linked with proliferation, survival, invasion, metastasis and angiogenesis in tumor cells. In the present study, we investigated whether β‐caryophyllene oxide (CPO), a sesquiterpene isolated primarily from the essential oils of medicinal plants such as guava (Psidium guajava), and oregano (Origanum vulgare L.), can mediate its effect through interference with the STAT3 activation pathway in cancer cells. The effect of CPO on STAT3 activation, associated protein kinases and phosphatase, STAT3‐regulated gene products and apoptosis was investigated using both functional proteomics tumor pathway technology platform and different tumor cell lines. We found that CPO suppressed constitutive STAT3 activation in multiple myeloma (MM), breast and prostate cancer cell lines, with a significant dose‐ and time‐dependent effects observed in MM cells. The suppression was mediated through the inhibition of activation of upstream kinases c‐Src and JAK1/2. Also, vanadate treatment reversed CPO‐induced down‐regulation of STAT3, suggesting the involvement of a tyrosine phosphatase. Indeed, we found that CPO induced the expression of tyrosine phosphatase SHP‐1 that correlated with the down‐regulation of constitutive STAT3 activation. Interestingly, deletion of SHP‐1 gene by siRNA abolished the ability of CPO to inhibit STAT3 activation. The inhibition of STAT3 activation by CPO inhibited proliferation, induced apoptosis and abrogated the invasive potential of tumor cells. Our results suggest for the first time that CPO is a novel blocker of STAT3 signaling cascade and thus has an enormous potential for the treatment of various cancers harboring constitutively activated STAT3. © 2013 Wiley Periodicals, Inc.

Background: Akt plays a major role in insulin regulation of metabolism.Results: Akt operates at 5–22% of its dynamic range. This lacks concordance with Akt substrate phosphorylation, GLUT4 translocation, and protein synthesis.Conclusion: Akt is a demultiplexer that splits the insulin signal into discrete outputs.Significance: This study provides better understanding of the Akt pathway and has implications for the role of Akt in diseases.

We recently reported a novel interaction between Bcl-2 and Rac1 and linked that to the ability of Bcl-2 to induce a pro-oxidant state in cancer cells. To gain further insight into the functional relevance of this interaction, we utilized computer simulation based on the protein pathway dynamic network created by Cellworks Group Inc. STAT3 was identified among targets that positively correlated with Rac1 and/or Bcl-2 expression levels. Validating this, the activation level of STAT3, as marked by p-Tyr705, particularly in the mitochondria, was significantly higher in Bcl-2-overexpressing cancer cells. Bcl-2-induced STAT3 activation was a function of GTP-loaded Rac1 and NADPH oxidase (Nox)-dependent increase in intracellular superoxide (O2•−). Furthermore, ABT199, a BH-3 specific inhibitor of Bcl-2, as well as silencing of Bcl-2 blocked STAT3 phosphorylation. Interestingly, while inhibiting intracellular O2•− blocked STAT3 phosphorylation, transient overexpression of wild type STAT3 resulted in a significant increase in mitochondrial O2•− production, which was rescued by the functional mutants of STAT3 (Y705F). Notably, a strong correlation between the expression and/or phosphorylation of STAT3 and Bcl-2 was observed in primary tissues derived from patients with different sub-sets of B cell lymphoma. These data demonstrate the presence of a functional crosstalk between Bcl-2, Rac1 and activated STAT3 in promoting a permissive redox milieu for cell survival. Results also highlight the potential utility of a signature involving Bcl-2 overexpression, Rac1 activation and STAT3 phosphorylation for stratifying clinical lymphomas based on disease severity and chemoresistance.

BackgroundThe personalization of cancer treatments implies the reconsideration of a one-size-fits-all paradigm. This move has spawned increased use of next generation sequencing to understand mutations and copy number aberrations in cancer cells. Initial personalization successes have been primarily driven by drugs targeting one patient-specific oncogene (e.g., Gleevec, Xalkori, Herceptin). Unfortunately, most cancers include a multitude of aberrations, and the overall impact on cancer signaling and metabolic networks cannot be easily nullified by a single drug.MethodsWe used a novel predictive simulation approach to create an avatar of patient cancer cells using point mutations and copy number aberration data. Simulation avatars of myeloma patients were functionally screened using various molecularly targeted drugs both individually and in combination to identify drugs that are efficacious and synergistic. Repurposing of drugs that are FDA-approved or under clinical study with validated clinical safety and pharmacokinetic data can provide a rapid translational path to the clinic. High-risk multiple myeloma patients were modeled, and the simulation predictions were assessed ex vivo using patient cells.ResultsHere, we present an approach to address the key challenge of interpreting patient profiling genomic signatures into actionable clinical insights to make the personalization of cancer therapy a practical reality. Through the rational design of personalized treatments, our approach also targets multiple patient-relevant pathways to address the emergence of single therapy resistance. Our predictive platform identified drug regimens for four high-risk multiple myeloma patients. The predicted regimes were found to be effective in ex vivo analyses using patient cells.ConclusionsThese multiple validations confirm this approach and methodology for the use of big data to create personalized therapeutics using predictive simulation approaches.Electronic supplementary materialThe online version of this article (doi:10.1186/s12967-015-0399-y) contains supplementary material, which is available to authorized users.

BackgroundGlioblastoma (GBM) is an aggressive disease associated with poor survival. It is essential to account for the complexity of GBM biology to improve diagnostic and therapeutic strategies. This complexity is best represented by the increasing amounts of profiling (“omics”) data available due to advances in biotechnology. The challenge of integrating these vast genomic and proteomic data can be addressed by a comprehensive systems modeling approach.MethodsHere, we present an in silico model, where we simulate GBM tumor cells using genomic profiling data. We use this in silico tumor model to predict responses of cancer cells to targeted drugs. Initially, we probed the results from a recent hypothesis-independent, empirical study by Garnett and co-workers that analyzed the sensitivity of hundreds of profiled cancer cell lines to 130 different anticancer agents. We then used the tumor model to predict sensitivity of patient-derived GBM cell lines to different targeted therapeutic agents.ResultsAmong the drug-mutation associations reported in the Garnett study, our in silico model accurately predicted ~85% of the associations. While testing the model in a prospective manner using simulations of patient-derived GBM cell lines, we compared our simulation predictions with experimental data using the same cells in vitro. This analysis yielded a ~75% agreement of in silico drug sensitivity with in vitro experimental findings.ConclusionsThese results demonstrate a strong predictability of our simulation approach using the in silico tumor model presented here. Our ultimate goal is to use this model to stratify patients for clinical trials. By accurately predicting responses of cancer cells to targeted agents a priori, this in silico tumor model provides an innovative approach to personalizing therapy and promises to improve clinical management of cancer.