High-Throughput Functional Genomics to Guide Precision Oncology in Gastrointestinal Tumors

摘要： 这是一份K22奖的申请书，申请人是约翰·保罗·沈博士，他是一位医学肿瘤学家，目前在 加州大学圣地亚哥分校。沈博士正在建立自己作为一个年轻的翻译研究者在 癌症基因组学领域。这个K22奖项将为沈博士提供资源和培训， 以下目标：（1）在基因组规模数据集上实现先进的计算方法（2）成为 功能基因组学专家，（3）熟练进行小鼠癌症模型实验，（4） 成功管理独立实验室。为了实现这些目标，在接受教师职位后， 博士沈将组建一个多元化的咨询委员会，包括生物信息学、实验癌症 生物学和临床肿瘤学 许多人提出，对肿瘤基因组进行测序的能力，现在已经成为可能， 一代测序，将带来一个精确肿瘤学的新时代，在这个时代，化疗的选择将 个体化以匹配单个肿瘤和患者。然而，在临床实践中使用基因组信息 但是，由于目前很少有突变与对特定药物的反应有关，因此，这种研究仍然受到限制。这 在胃肠道（GI）恶性肿瘤中尤其如此，其中几乎没有靶向治疗选择， 有效的生物标志物有助于指导化疗选择。沈博士寻求解决这一迫切需要， 采用高通量功能基因组学方法来鉴定肿瘤特异性脆弱性， 治疗上的剥削 认识到从一个肿瘤到下一个肿瘤，即使在同一个肿瘤内， 癌症类型，这里创建的功能基因组数据将与系统生物学方法相结合， 如何利用临床医生容易获得的信息来预测每个独特肿瘤的脆弱性 肿瘤学家使用基于网络的机器学习方法应用于化学基因组活力数据， 分子表征细胞系，预期预测生物标志物将被鉴定为两种新的 靶向药物和目前使用的化疗药物。这将允许肿瘤学家设计个性化的 化疗方案。

Abstract: This is an application for a K22 award for Dr. John Paul Shen, a medical oncologist currently at the University of California, San Diego. Dr. Shen is establishing himself as a young translational investigator in the field of cancer genomics. This K22 award will provide Dr. Shen with the resources and training to accomplish the following objectives; (1) implement advanced computational methods on genome scale datasets (2) become an expert in functional genomics, (3) achieve proficiency experimenting in mouse models of cancer, (4) successfully manage an independent laboratory. To achieve these objectives, after accepting a faculty position Dr. Shen will assemble a diverse advisory committee including experts in bioinformatics, experimental cancer biology, and clinical oncology. It was proposed by many that the ability to sequence a tumor genome, now made possible by next- generation sequencing, would bring about a new era of precision oncology in which chemotherapy choices would be individualized to match a single tumor and patient. However, the use genomic information in clinical practice remains limited by the fact that currently very few mutations are associated with response to a specific drug. This is particularly true in Gastrointestinal (GI) malignancies, where there are few targeted therapy options and few effective biomarkers help guide chemotherapy selection. Dr. Shen seeks to address this pressing need by employing high-throughput functional genomic methods to identify tumor specific vulnerabilities that could be exploited therapeutically. Recognizing that there will be great heterogeneity from one tumor to the next, even within the same cancer type, the functional genomic data created here will be combined with systems biology methods to identify how the vulnerabilities of each unique tumor can be predicted with information readily available to a clinical oncologist. Using network-based machine learning methods applied to chemo-genomic viability data in molecularly characterize cell lines it is expected that predictive biomarkers will be identified for both novel targeted agents and currently used chemotherapy drugs. This will allow oncologists to design individualized chemotherapy regimens for each patient.