Integrative Cancer Genomics: Drivers, Pathways and Drugs

描述（由申请人提供）：癌症基因组学的出现，加上对肿瘤发生的分子基础的理解增加，激发了希望，即通过在本质上变得更具针对性和个体化来改善治疗。癌症基因组学研究确定了许多关键的癌症基因，导致了许多成功的靶向治疗（如格列卫，赫赛汀和Plexxikon）。尽管取得了这些成功，但大多数癌症没有靶向治疗，即使存在靶向治疗，反应也是高度可变的，即使在共享靶向突变和肿瘤类型的患者中也是如此。为了使癌症进入个性化治疗时代，识别每个肿瘤中驱动肿瘤进展的改变，确定连接这些畸变的网络，并识别预测靶向治疗敏感性的因素变得非常重要。随着癌症基因组图谱（TCGA）等项目以惊人的速度积累癌细胞基因组，揭示了惊人的遗传复杂性。要解释癌症基因组，一个关键的计算挑战是将小麦从谷壳中分离出来，并确定可能在功能上驱动癌症的关键改变，然后在定义这些基因后，开始确定作用机制和治疗意义。利用我们已发表的方法CONEXIC（Akavia et.al Cell 2010）和LirNet（Lee et. al，PLOS Gen 2009）中的组件，我们将开发整合癌症基因组数据的机器学习算法。我们将把我们开发的方法应用于黑色素瘤、胶质母细胞瘤、卵巢癌、乳腺癌和结肠癌，并通过实验跟进我们的计算发现，以更好地了解这些致命癌症中的每一种。该资助开发的方法将加速发现，以快速从现代基因组研究中提取最大价值，并帮助将癌症基因组学从诊断领域带到治疗领域。

DESCRIPTION (provided by applicant): The emergence of cancer genomics, combined with increased understanding of the molecular basis of oncogenesis, has stimulated hope that treatment will improve by becoming more targeted and individualized in nature. Cancer genomics studies established a number of critical cancer genes, leading to a number of successful targeted therapies (e.g. Gleevec, Herceptin and Plexxikon). Despite these successes, most cancers do not have a targeted therapy and when one exists, response is highly variable, even among patients that share the targeted mutation and tumor type. To move cancer into the era of personalized therapies, it becomes important to identify the alterations driving tumor progression in each tumor, determine the network that links these aberrations, and identify factors that predict sensitivity to targeted therapies. As projects such as The Cancer Genome Atlas (TCGA) amass cancer cell genomes at a breathtaking pace, a staggering genetic complexity is revealed. To interpret cancer genomes, a key computational challenge is to separate the wheat from the chaff and define both what are the key alterations likely to be functionally driving cancer and then, after defining such genes, begin to identify mechanisms of action and therapeutic implications. Leveraging components from our published methods, CONEXIC (Akavia et.al Cell 2010) and LirNet (Lee et.al, PLOS Gen 2009), we will develop machine-learning algorithms that integrate cancer genomic data to do just that. We will apply the methods we develop to melanoma, glioblastoma, ovarian, breast and colon cancer and experimentally follow up on our computational findings, towards a better understanding of each of these deadly cancers. The approaches developed in this grant will accelerate discovery to rapidly extract the maximal value from modern genomic studies and help carry cancer genomics from the diagnostic to the therapeutic realm.