Identification of Drug Targets and Their Validation in Cancer Therapy Design

成年多细胞生物体（如人类）的细胞数量受到非常严格的控制，在正常情况下，新细胞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出现故障而导致细胞过度分裂或细胞死亡减少时，就会产生癌症。 癌症治疗的一种可能方法是快速而有力地诱导癌细胞死亡，该项目旨在使用工程方法来解释特定治疗分子显着成功诱导细胞死亡背后的原理。此外，希望在此过程中，将发现并成功验证其他治疗分子。由于这项工作是由改善癌症治疗的目标驱动的，因此该项目的潜在社会效益可能是巨大的。此外，该项目将在得克萨斯州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，在那里，广泛传播研究成果，向研究生提供真正的跨学科实践教育，并以少数民族和少数民族机构为目标，这是最优先事项。癌症是与细胞周期控制丧失相关的大量疾病的总称，导致不受控制的细胞增殖和/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传导途径的功能障碍。这些故障可以以许多不同的方式发生，并且发生在特定通路中的许多不同位置。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，然后找到特别适合的药物或药物组合。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，迫切需要精确识别通路中的故障点，希望能够导致更有可能成功的更具靶向的治疗。迄今为止，许多癌症治疗主要集中在阻断细胞增殖所必需的通路。然而，通常情况下，即使药物最初成功治疗癌症，成功通常是短暂的，因为癌细胞能够激活药物不靶向的其他途径。治疗癌症的另一种方法是使用能够诱导细胞死亡的药物。靶向细胞死亡的化疗药物也显示出耐药性，当癌细胞找到逃避药物诱导细胞死亡的机制时，就会出现耐药性。然而，如果人们能够鉴定出沿着细胞死亡途径的分子，这些分子可以在确保细胞死亡中起决定性作用，而不管上游信号传导的破坏，那么用药物靶向这些分子将提供治疗癌症的强大策略。主要基于专家领域知识，在过去几年中已经确定了一种这样的分子MCL 1。这种分子在实现对多种黑色素瘤细胞系的稳健细胞杀伤方面取得了显着的成功，也已在实验中得到证实。受这一初步成功的启发，该项目的目标是在贝叶斯框架中将有关细胞死亡的先前途径信息与数据相结合，以开发能够识别细胞死亡决定性调节剂的模型。所确定的调制器的有效性也将通过实验验证。

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.