Identification of Drug Targets and Their Validation in Cancer Therapy Design

像人类这样的成年多细胞生物体中的细胞数量受到非常严格的控制，在正常情况下，新细胞的产生和细胞死亡之间存在某种平衡。粗略地说，当细胞数量控制系统中的某些故障导致细胞过度分裂或细胞死亡减少时，癌症就会发生。一种可能的癌症治疗方法是快速而有力地诱导癌细胞死亡，该项目试图使用工程方法来解释特定治疗分子显著成功地诱导细胞死亡背后的原理。此外，希望在这个过程中，更多的治疗分子将被发现并成功验证。由于这项工作是由改善癌症治疗的目标推动的，这个项目的潜在社会效益可能是巨大的。此外，该项目将在德克萨斯农工大学新成立的生物信息学和基因组系统工程中心(CBGSE)进行，在该中心，广泛传播研究成果，向研究生传授真正的跨学科动手教育，并受益于少数族裔和少数族裔机构，是首要任务。癌症是大量疾病的总称，这些疾病与细胞周期控制的丧失有关，导致细胞增殖失控和/或细胞凋亡减少。这种细胞周期控制的丧失通常表现为细胞信号通路的故障(S)。这些故障可能以许多不同的方式发生，并且发生在特定路径中的许多不同位置。因此，正确的癌症治疗设计应该首先尝试确定通路中故障的位置和类型，然后得出特别适合它的药物或药物组合。不幸的是，目前的癌症治疗方法并没有遵循这样一个系统的程序。因此，对于绝大多数癌症来说，迫切需要准确地识别通路中的失败点(S)，希望能导致更有针对性的治疗，更有可能成功。迄今为止，许多癌症治疗主要集中在阻断对细胞增殖至关重要的通路。然而，通常情况下，即使这些药物最初在治疗癌症方面取得了成功，这种成功通常也是短暂的，因为癌细胞能够激活一些不是药物靶向的其他途径。治疗癌症的另一种方法是使用能够诱导细胞死亡的药物。针对细胞死亡的化疗药物也表现出耐药性，当癌细胞找到逃避药物诱导细胞死亡活性的机制时，就会出现耐药性。然而，如果人们能够识别出细胞死亡途径上能够在确保细胞死亡方面发挥决定性作用的分子，而不考虑上游信号的破坏(S)，那么用药物来靶向这些分子将为治疗癌症提供一个强有力的策略。主要基于专家领域的知识，在过去的几年里已经发现了这样一个分子MCL1。这种分子在实现对多种黑色素瘤细胞系的强大杀伤力方面取得了显着的成功，这一点也已在实验中得到证明。在这一初步成功的推动下，该项目的目标是在贝叶斯框架内将有关细胞死亡的先前途径信息与数据结合起来，以开发能够识别细胞死亡的决定性调控因素的模型。确定的调制器的有效性也将得到实验验证。

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.