A Functional Census of p53 Cancer and Suppressor Mutants

描述(由申请人提供)：转录因子P53是一种控制DNA修复、细胞周期停滞和细胞凋亡的中心肿瘤抑制蛋白。大约一半的人类癌症存在P53突变，晚期肿瘤恢复P53功能会导致肿瘤退化。值得注意的是，这些肿瘤中的绝大多数产生全长的P53蛋白，由于单一氨基酸的变化而失去了肿瘤抑制功能。因此，一种有吸引力的全身性癌症治疗的新方法是对p53癌突变体进行药物再激活。重新激活p53癌症突变体是可行的，因为我们和其他人已经证明，引入额外的突变(第二位点抑制突变)可以恢复原本不活跃的p53癌症突变体的活性。此外，据报道，一些有希望的小分子药物先导作用机制未知，可以重新激活p53癌症突变体。挑战在于了解导致p53癌症突变体重新激活的结构变化，并通过小分子诱导这种变化。这是一个复杂的问题，因为在p53癌症突变体中发现了临床上相关的单一氨基酸变化的多样性。我们提出了高通量生物策略支持的基于机器学习的计算方法。我们使用新的饱和突变方法来对在人类癌症中发现的50个最相关的p53突变的拯救突变进行分类。在主动学习方案中使用遗传数据来训练计算分类器，该计算分类器基于建模的原子级结构特征，以预测哪些结构变化导致任何给定的p53癌症突变的重新激活。我们进一步建议应用这种改进的计算分类器来发现引起类似结构变化的小分子，并在生物学试验中测试这些预先选择的化合物对p53癌突变重新激活。从这些活体实验中获得的数据将被用来进一步改进对小分子的计算预测。总之，我们使用遗传功能数据来训练基于结构的分类器，以基于建模的结构变化的内部表示来预测P53活性。然后，该分类器将用于预测小分子对p53癌症突变体的重新激活，目的是识别抗癌药物的先导。这项拟议的研究对生物医学研究和公共卫生具有很高的影响。美国每年约有25万人死于具有全长但突变和不活跃的p53的肿瘤。这项研究的长期目标是一种重新激活突变的p53的药物，可以防止或推迟这些死亡。 公共卫生相关性：肿瘤抑制蛋白P53是预防癌症的最重要的单一蛋白质，人类所有肿瘤中有一半会产生缺陷的P53蛋白。仅在美国，有缺陷的P53蛋白的药理学重新激活就有可能防止或推迟每年超过25万人死于癌症。这项提议使用遗传策略和计算方法来预测最终可能发展为抗癌药物的小分子对P53的重新激活。

DESCRIPTION (provided by applicant): The transcription factor p53 is a central tumor suppressor protein that controls DNA repair, cell cycle arrest, and apoptosis. About half of human cancers have p53 mutations, and restoring p53 function in advanced tumors leads to tumor regression. Significantly, the large majority of these tumors produce full-length p53 proteins that have lost their tumor suppressor function due to single amino acid changes. Therefore, an attractive new approach to systemic cancer therapy is pharmacological reactivation of p53 cancer mutants. Reactivation of p53 cancer mutants is feasible because we and others have shown that introducing additional mutations (second-site suppressor mutations) can restore activity to the otherwise inactive p53 cancer mutants. In addition a few promising small molecule drug leads with unknown mechanisms of action have been reported to reactivate p53 cancer mutants. The challenge is to understand structural changes that lead to reactivation of p53 cancer mutants and to induce such changes through small molecules. This is a complex problem due to the diversity of clinically relevant single amino acid changes found in p53 cancer mutants. We propose computational approaches based on machine learning that are supported by high-throughput biological strategies. We use novel saturation mutagenesis approaches to catalogue p53 rescue mutations for the 50 most relevant p53 mutants found in human cancer. The genetic data are used in an active learning scheme to train a computational classifier, that is based on modeled atom-level structural features, to predict which structural changes lead to reactivation of any given p53 cancer mutant. We further propose to apply this improved computational classifier to discover small molecules that induce similar structural changes and test these pre-selected compounds in a biological assay for p53 cancer mutant reactivation. Data obtained from these in vivo experiments will be used to further improve the computational predictions for small molecules. In summary, we use genetic functional data to train a structure-based classifier to predict p53 activity based on an internal representation of modeled structural changes. The classifier will then be used to predict reactivation of p53 cancer mutants by small molecules with the aim to identify cancer drug leads. The proposed research has high impact on biomedical research and public health. About 250,000 US deaths yearly are due to tumors with full length but mutated and inactive p53. The long-term goal of this research, a drug that reactivates mutant p53, could prevent or delay these deaths. PUBLIC HEALTH RELEVANCE: The tumor suppressor protein p53 is the single most important protein to prevent cancer, and half of all human tumors produce a defective p53 protein. Pharmacological reactivation of the defective p53 proteins could potentially prevent or delay over 250,000 cancer deaths yearly in the US alone. This proposal uses genetic strategies and computational approaches to predict p53 reactivation by small molecules that can eventually develop into cancer drugs.