Modeling unveils sex differences of signaling networks in mouse embryonic stem cells.

For a short period during early development of mammalian embryos, both X chromosomes in females are active, before dosage compensation is ensured through X-chromosome inactivation. In female mouse embryonic stem cells (mESCs), which carry two active X chromosomes, increased X-dosage affects cell signaling and impairs differentiation. The underlying mechanisms, however, remain poorly understood. To dissect X-dosage effects on the signaling network in mESCs, we combine systematic perturbation experiments with mathematical modeling. We quantify the response to a variety of inhibitors and growth factors for cells with one (XO) or two X chromosomes (XX). We then build models of the signaling networks in XX and XO cells through a semi-quantitative modeling approach based on modular response analysis. We identify a novel negative feedback in the PI3K/AKT pathway through GSK3. Moreover, the presence of a single active X makes mESCs more sensitive to the differentiation-promoting Activin A signal and leads to a stronger RAF1-mediated negative feedback in the FGF-triggered MAPK pathway. The differential response to these differentiation-promoting pathways can explain the impaired differentiation propensity of female mESCs.

Personalization of cancer treatment using predictive simulation.

BACKGROUND: The 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. METHODS: We 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. RESULTS: Here, 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. CONCLUSIONS: These multiple validations confirm this approach and methodology for the use of big data to create personalized therapeutics using predictive simulation approaches.

In silico modeling predicts drug sensitivity of patient-derived cancer cells.

BACKGROUND: Glioblastoma (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. METHODS: Here, 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. RESULTS: Among 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. CONCLUSIONS: These 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.

Predictive simulation approach for designing cancer therapeutic regimens with novel biological mechanisms.

Introduction Ursolic acid (UA) is a pentacyclic triterpene acid present in many plants, including apples, basil, cranberries, and rosemary. UA suppresses proliferation and induces apoptosis in a variety of tumor cells via inhibition of nuclear factor kappa-light-chain-enhancer of activated B cells (NFκB). Given that single agent therapy is a major clinical obstacle to overcome in the treatment of cancer, we sought to enhance the anti-cancer efficacy of UA through rational design of combinatorial therapeutic regimens that target multiple signaling pathways critical to carcinogenesis. Methodology Using a predictive simulation-based approach that models cancer disease physiology by integrating signaling and metabolic networks, we tested the effect of UA alone and in combination with 100 other agents across cell lines from colorectal cancer, non-small cell lung cancer and multiple myeloma. Our predictive results were validated in vitro using standard molecular assays. The MTT assay and flow cytometry were used to assess cellular proliferation. Western blotting was used to monitor the combinatorial effects on apoptotic and cellular signaling pathways. Synergy was analyzed using isobologram plots. Results We predictively identified c-Jun N-terminal kinase (JNK) as a pathway that may synergistically inhibit cancer growth when targeted in combination with NFκB. UA in combination with the pan-JNK inhibitor SP600125 showed maximal reduction in viability across a panel of cancer cell lines, thereby corroborating our predictive simulation assays. In HCT116 colon carcinoma cells, the combination caused a 52% reduction in viability compared with 18% and 27% for UA and SP600125 alone, respectively. In addition, isobologram plot analysis reveals synergy with lowered doses of the drugs in combination. The combination synergistically inhibited proliferation and induced apoptosis as evidenced by an increase in the percentage sub-G1 phase cells and cleavage of caspase 3 and poly ADP ribose polymerase (PARP). Combination treatment resulted in a significant reduction in the expression of cyclin D1 and c-Myc as compared with single agent treatment. Conclusions Our findings underscore the importance of targeting NFκB and JNK signaling in combination in cancer cells. These results also highlight and validate the use of predictive simulation technology to design therapeutics for targeting novel biological mechanisms using existing or novel chemistry.

PI3KCA plays a major role in multiple myeloma and its inhibition with BYL719 decreases proliferation, synergizes with other therapies and overcomes stroma-induced resistance.

The phosphatidylinositide 3-kinase (PI3K) pathway is activated and correlated with drug resistance in multiple myeloma (MM). In the present study we investigated the role of PI3KCA (PI3K-α) in the progression and drug resistance in MM. We showed that the gene expression of PI3KCA isoform was higher in MM compared to normal subjects. BYL719, a novel and specific PI3KCA inhibitor inhibited the survival of primary MM cells and cell lines but not normal peripheral blood mononuclear cells. BYL719 induced the apoptosis of MM cells and inhibited their cell cycle by causing G1 arrest. BYL719 inhibited PI3K signalling, decreased proliferation and cells cycle signalling, and induced apoptosis signalling in MM cells. Finally, BYL719 synergized with bortezomib and carfilzomib, and overcame drug resistance induced by bone marrow stroma. These results were confirmed using in silico simulation of MM cell lines, BYL719 and bortezomib, and showed similar trends in survival, proliferation, apoptosis, cell signalling and synergy with drugs. In conclusion, PI3KCA plays a major role in proliferation and drug resistance of MM cells, the effects of which were inhibited with BYL719. These results provide a preclinical basis for a future clinical trial of BYL719 in MM as a single agent or in combination with other drugs.

Insights into the effects of alpha-synuclein expression and proteasome inhibition on glutathione metabolism through a dynamic in silico model of Parkinson's disease: validation by cell culture data.

Dopaminergic neurodegeneration during Parkinson disease (PD) involves several pathways including proteasome inhibition, alpha-synuclein (alpha-syn) aggregation, mitochondrial dysfunction, and glutathione (GSH) depletion. We have utilized a systems biology approach and built a dynamic model to understand and link the various events related to PD pathophysiology. We have corroborated the modeling data by examining the effects of alpha-syn expression in the absence and presence of proteasome inhibition on GSH metabolism in dopaminergic neuronal cultures. We report here that the expression of the mutant A53T form of alpha-syn is neurotoxic and causes GSH depletion in cells after proteasome inhibition, compared to wild-type alpha-syn-expressing cells and vector control. Modeling data predicted that GSH depletion in these cells was due to ATP loss associated with mitochondrial dysfunction. ATP depletion elicited by combined A53T expression and proteasome inhibition results in decreased de novo synthesis of GSH via the rate-limiting enzyme gamma-glutamyl cysteine ligase. Based on these data and other recent reports, we propose a novel dynamic model to explain how the presence of mutated alpha-syn protein or proteasome inhibition may individually impact on mitochondrial function and in combination result in alterations in GSH metabolism via enhanced mitochondrial dysfunction.