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

UCSC-Buck基因组数据分析网络中心将开发最先进的方法来整合各种类型的数据，以发现推动肿瘤发生和发展的遗传途径、微环境、原始细胞和致癌过程。该项目的长期目标是识别高度精确的模型，详细说明患者肿瘤的每个亚克隆中有缺陷的遗传电路，以及任何通过支持癌症微环境而充当帮凶的“正常”细胞。最终目标是对计算机算法进行编码，这些算法可以搜索患者的个体路径图，寻找最佳的干预组合，以消除每个肿瘤细胞，同时保护他们体内每个正常细胞的健康。综合路径分析方法将被开发来揭示来自泛癌和外部数据集的肿瘤亚型的特征。将建立新的技术来揭示为个别患者量身定做的网络模型。这些工具将作为积极协作的一部分部署，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于推断中断的信令。细胞特征将从对正常细胞、癌细胞系模型和泛癌研究的分析中收集。在途径机制的指导下，将建立新的机器学习方法，以识别不同患者样本中的细胞状态特征。这项工作将揭示驱动转移转化的罕见突变，目前尚不清楚其意义。关于促进治疗反应和耐药性的遗传回路的新线索将被建立。最后，将一种类型的肿瘤与另一种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.