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  Over the last decade, scientists have been intensely investigating on TRAIL, a protein released by our immune system, to treat malignant cancers. The benefit of using TRAIL, as compared to conventional chemo- or radio- therapies, is that it specifically destroys carcinomas without affecting the surrounding normal tissues. This is done through a mechanism known as the programmed cell death or apoptosis. However, the TRAIL-based treatments so far have not been overtly successful as many carcinomas are able to ‘cheat’ the death signal, triggered by the TRAIL, into a survival signal.

  A team of scientists from the Keio University in Japan has formulated a method using a computational model and, with its aid, predicts a novel target that would in return cheat the cancer cells to switch from survival to apoptosis mode. The work of Dr. Kumar Selvarajoo and colleagues, utilizing the fundamental physical law of conservation, pinpoints an intracellular target, whose removal reroutes most of the resultant signaling flux of TRAIL into a predetermined direction along the caspases-induced death pathway. Their findings are published in Scientific Reports, a newly launched journal of the Nature Publishing Group.

  Paper: Vincent Piras, Kentaro Hayashi, Masaru Tomita, and Kumar Selvarajoo. "Enhancing apoptosis in TRAIL-resistant cancer cells using fundamental response rules", Scientific Reports, November 07, 2011. click here for link.

 

Dr. Kumar Selvarajoo

Assistant Professor, Institute for Advanced Bioscience, Keio University, Japan
* Dr. Selvarajoo is team leader for the research
 
  Cancer biologists focus either to suppress the cell survival pathways or to enhance the apoptosis mechanisms independently. We felt a systemic approach considering both processes at the same time is necessary. Our previous experience working on the Toll-like receptor signaling models, based on fundamental physical rules, helped us to identify a target at the crossroad of the survival and apoptosis pathways in TRAIL signaling.The scientists predict that a novel molecule interacting with p62 could be a crucial target for enhancing apoptosis of TRAIL-resistant cancer.
 
  Although we suggest this, experimental validation is required for final confirmation as a next step. Nevertheless, using the law of nature to address biological problems could potentially revolutionize the way we understand and treat complex diseases such as cancer and inflammation. In this respect, we feel very optimistic.
 
 

Vincent Piras

Doctorate student, Institute for Advanced Biosciences, Keio University, Japan
 
- Vincent Piras and Kentaro Hayashi, joint first authors for the study, say that the currently known signaling process of TRAIL is insufficient.
  First, we developed a computational model to match experimental data with simulations. We found that the topology we know of TRAIL’s downstream signaling needs to be adjusted in order to make trustable simulations. We modified the crosstalk very carefully with response rules and checked it with biological plausibility. In total, we had to make three major insertions. Adding this information into our model, we were able to simulate accurately the key survival and death molecules in wildtype and several mutant cancer cells. To our knowledge, this is the first time a single computational model can simulate multiple experimental conditions”, says Vincent Piras, a final-year doctoral student.
 
 

Dr. Mariko Okada

Team leader, Laboratory for Cellular Systems Modeling, RIKEN Research Center for Allergy and Immunology, Japan
 
  A detailed model and large amounts of experimental data are needed to carry out cell simulations. These data are fed into the model, so it can be adjusted bit by bit to simulate the living cells. However, this task is incredibly tedious. This new approach from Piras et al. starts from an average model, and uses precise theories to change it into a model that is more likely to be true, giving us the chance to look into unexplored reactions and cuts down the work to simulate it. My own research includes looking at the cell signaling for the cell fate, so programs like this would be invaluable. The most difficult part of research is gathering a huge amount of data, putting them into several models and comparing different models. The program from this team makes this work easier, and I think it would make simulations much more accessible for biologists working on cell reactions.
 
  I think this team's research would be useful to cell simulation and modeling, just like the K super computer is right now. I also think that by adjusting this program itself, it could be used to look at cell signals during drug screening tests.
 
 

Professor Janet Oliver

Regents' Professor and Harvey Chair, Department of Pathology, University of New Mexico
 
  Targeted strategies intended to arrest particular growth-promoting or apoptosis-defying pathways in cancer cells often fail to prevent tumor growth.
 
  A new publication from Piras et al. throws light on resistance mechanisms to therapies using TRAIL, a ligand for the so-called Death Receptors, TRAIL-R1 and TRAIL-R2 (also called DR and DR5) whose expression is often altered on cancer cells. The team developed a dynamic mathematical model to represent the TRAIL signaling network in tumor cells when different combinations of death and competing decoy receptors are expressed and when different downstream signaling molecules in TRAIL-sensitive pro-survival and pro-apoptotic pathways are perturbed. Their model helps to explain why single agent therapy so often fails and, importantly, predicts new targets in the TRAIL signaling pathways for combination therapies that may shift the flux of signals firmly towards apoptosis.
 
 

Professor Alfredo Colosimo

Department of Human Physiology and Pharmacology, Biophysics and Medical faculty, Sapienza University of Rome
 
  I had the chance to meet personally prof. Selvarajoo in the occasion of a seminar he delivered in Rome, and was impressed by both the relevance of his work and the efficacy of his presentation style.
 
  In particular, I could appreciate the novelty of his computational approach to find a rationale in the often contrasting data concerning the malignant cells resistance to many pharmacological treatments.
 
  The paper recently appeared in Scientific Reports clearly describes his strategy and results in a very significant case of therapy-resistant cancer cells; it also indicates a strategy to overcome the problem in quite general terms.
 
  In my opinion the merit of such an approach is at least twofold: i) it underlines the necessary use of simplifying assumptions and of appropriate "systemic" viewpoints in the quantitative description of complex biological systems, and ii) it shows the importance of working out realistic and flexible models, directly inspired by the current experimental information and, at the same time, easily amenable to any further improvement.
 

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