In treatment of cancer, there has been a significant thought in the value of personalized medicine. In general there is agreement that each cancer is slightly different and therapies need to be targeted specific for different patients. However, since each combination of therapy is unique, they have to be customized for the patients. This works well when patients have to be grouped in broad groups – say those patients expressing EGFR vs. those that do not express EGFR. However, if each patient is subtly different then how do you manage the treatment? Here is where the systems biology approaches using the network model are important.
An interesting paper published in 2012 by Yaffe’s group (Cell, 149, 780, 2012) is one of the first instances of systems biology for translational research. This is one of the critical studies that highlight the importance of time and sequence of chemotherapeutic multi-drug treatment for cancer.
Yaffe’s group had previously published a paper in 2005 that showed that JNK kinase activity inhibition in cell death is dependent on cell state prior to its activation. Thus to predict the effect of chemotherapeutic drugs, it is important to ascertain the network state of the cell. Interestingly, network activity and not EGFR expression was shown to be important.
Thus, it is important to understand how the system will respond to the drug and then develop subsequent therapies that take into account the changed state of the cellular network. Effective therapy will require dosages of different drugs at specific time intervals in specific order to cause the maximum remission of the disease.
These methods are even important for drug development wherein instead of targeting just one target for drug development, multiple drugs in different combinations and given at different times will be more effective rather than a single drug.
Clinical Trials also need to be remodeled for personalized medicine. Current randomized clinical trial (RCT) system is challenging to fit with the personalized system model since RCT aims to compare two large groups of patients that are treated as equal. However, in personalized medicine the ideal scenario is one in which each patient is treated differently. If that is true, then which group do you consider as the “untreated” group that you can compare? What happens if some people in a personalized medicine show an effect and not the others? Such problems may need to be addressed before personalized medicine becomes common.