UCSC-Buck Specialized Genomic Data Analysis Center for the Genomic Data Analysis Network

“UCSC-Buck基因组数据分析网络基因组数据分析中心”将开发最先进的方法，用于整合各种类型的数据，以发现遗传途径，微环境，起源细胞以及驱动肿瘤发生和发展的致癌过程。该项目的长期目标是识别高度准确的模型，详细描述患者肿瘤的每个亚克隆中工作的错误遗传电路，以及通过支持癌症微环境而充当帮凶的任何“正常”细胞。最终目标是编码计算机算法，可以搜索患者的个体路径图，以获得最佳干预组合，从而消除每个肿瘤细胞，同时保持体内每个正常细胞的健康。将开发综合途径分析方法，以揭示来自泛癌症和外部数据集的肿瘤亚型的特征。将建立新技术，以揭示为个体患者量身定制的网络模型。这些工具将作为积极合作的一部分部署，以支持基因组数据分析网络的具体项目。新的概率图形模型将被用来推断中断的信号。将从正常细胞、癌细胞系模型和泛癌症研究的分析中收集细胞特征。将建立由通路机制指导的新型机器学习方法，以识别异质患者样本中的细胞状态特征。这项工作将揭示驱动转移性转化的罕见突变，这些突变目前意义不明。关于促进对治疗的反应和抵抗的遗传电路的新线索将被建立。最后，将一种类型的肿瘤与不同类型的肿瘤联系起来的跨肿瘤联系将为治疗提供新的途径。

The “UCSC-Buck Genome Data Analysis Center for the Genomic Data Analysis Network” will develop state-of-the-art methods for integrating various types of data to discover the genetic pathways, the microenvironment, the originating cells, and the oncogenic processes driving the initiation and progression of tumors. The long term goals of the project are to identify highly accurate models detailing the faulty genetic circuitry at work in each subclone of a patient’s tumor, as well as any “normal” cells acting as accomplices by supporting the cancer microenvironment. The ultimate objective is to encode computer algorithms that can search a patient’s individual pathway diagram for the best combination of interventions to eliminate every tumor cell, while preserving the health of every normal cell, in their body. Integrative pathway analysis methods will be developed to reveal signatures of tumor subtypes from Pan-Cancer and external datasets. New technologies will be established for uncovering network models tailored to individual patients. The tools will be deployed as part of an active collaboration to support the specific projects of the Genome Data Analysis Network. Novel probabilistic graphical models will be used to infer disrupted signaling. Cellular signatures will be collected from the analysis of normal cells, cancer cell line models, and Pan-Cancer investigations. Novel machine-learning methods, guided by pathway mechanisms, will be established to identify cell state signatures in heterogeneous patient samples. This work will reveal rare mutations driving metastatic transformation that are currently of unknown significance. New clues about the genetic circuitry promoting response and resistance to treatment will be established. Finally, cross-tumor connections that relate tumors of one type to a different type will suggest new avenues for treatment.