Identification of Drug Targets and Their Validation in Cancer Therapy Design
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The number of cells in an adult multicellular organism such as a human being is under very tight control and, under normal circumstances, there is some kind of a balance between new cell production and cell death. Roughly speaking, cancer results when there is excessive cell division or reduced cell death due to some malfunctioning in the cell number control system. A possible approach to cancer therapy is to quickly and robustly induce the death of cancer cells and this project seeks to use an engineering approach to explain the rationale behind the dramatically successful induction of cell death by a particular therapeutic molecule. In addition, the hope is that in the process, additional therapeutic molecules will be discovered and successfully validated. Since the work is driven by the goal of improving cancer treatment, the potential societal benefits of this project could be enormous. In addition, the project will be carried out at the newly formed Center for Bioinformatics and Genomic Systems Engineering (CBGSE) at Texas A & M University, where widespread dissemination of the research results, imparting truly interdisciplinary hands-on education to graduate students, and beneficially targeting minorities and minority institutions, are top priorities.Cancer is an umbrella term for a large number of diseases that are associated with loss of cell-cycle control, leading to uncontrolled cell proliferation and/or reduced apoptosis. This loss of cell-cycle control usually manifests itself as malfunction(s) in the cellular signaling pathways. These malfunctions can occur in many different ways and at many different locations in a particular pathway. As a result, a proper design of cancer therapy should first attempt to identify the location and type of malfunction in the pathway and then arrive at a drug or drug combination that is particularly well suited for it. Unfortunately, the current approach to cancer therapy does not follow such a systematic procedure. Thus, for the vast majority of cancers, there is a critical need for precisely identifying the failure point(s) in the pathway, hopefully leading to a more targeted therapy with a better likelihood of success.Many of the cancer therapies to date have mostly focused on blocking the pathways essential to cell proliferation. However, more often than not, even if the drugs are initially successful in treating the cancer, the success is usually short lived as the cancer cell is able to activate some other pathways not targeted by the drug. An alternative approach to treat cancer would be to use drugs that are capable of inducing cell death. Chemotherapeutic drugs targeting cell death also display drug resistance which occurs when the cancer cells figure out mechanisms to evade the cell death inducing activity of the drug. If, however, one could identify molecules along the cell death pathway that can play a decisive role in ensuring cell death, regardless of the upstream signaling breakdown(s), then targeting such molecules with drugs would provide a robust strategy for treating cancer. Based mainly on expert domain knowledge, one such molecule MCL1 has been identified over the last couple of years. This molecule has remarkable success in achieving robust cell killing across a diverse panel of melanoma cell lines has also been experimentally demonstrated. Motivated by this preliminary success, the goal of this project is to combine prior pathway information concerning cell death along with data, in a Bayesian framework, to develop models that would allow the identification of decisive modulators of cell death. The effectiveness of the modulators identified will also be experimentally validated.
