Personalized Network Modeling of the Pan-Cancer Patient and Cell Line Interactome
Personalized Network Modeling of the Pan-Cancer Patient and Cell Line Interactome
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DOI：
10.1200/cci.19.00140
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发表时间：
2020-05-06
影响因子：
4.2
通讯作者：
Baladandayuthapani, Veerabhadran
中科院分区：
文献类型：
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作者：
Bhattacharyya, Rupam;Ha, Min Jin;Baladandayuthapani, Veerabhadran
目的通过对不同肿瘤类型的不同临床和体外模型系统进行个性化的网络推理，可以描述特定的调控机制，发现药物靶点和途径，开发个性化的癌症预测模型。方法我们开发了一个多尺度贝叶斯网络建模框架TransPRECISE(Personalized Carbon-Special Integrated Network Estiment Model)，用于分析泛癌患者和细胞系的相互作用，以识别差异和保守的途径内活动，全面评估具有代表性的细胞系作为患者的模型，并开发药物敏感性预测模型。我们评估了来自癌症蛋白质组图谱的一大批患者样本(7,700例)的泛癌通路活性，这些样本来自横跨30种肿瘤类型的癌症蛋白质组图谱，来自MD Anderson细胞系项目的一组跨越16个谱系的640个癌细胞系，以及=250个细胞系的反应。结果TransPRECISE捕获了多个患者和细胞系之间的差异和保守的蛋白质组网络拓扑和路径电路：卵巢癌和肾癌分别在两个模型系统之间的激素受体和受体酪氨酸激酶通路中具有高水平的连通性。我们的肿瘤分层方法发现由不同的细胞系代表的患者的不同临床亚型：头颈部肿瘤患者被分为两个不同的亚型，以头颈部和食道细胞系为代表，具有不同的预后模式(中位总生存期456vs654天；P=0.02)。在多种药物的细胞系中观察到较高的药物敏感性预测准确率(接受者工作特性曲线下的中位数区域)。0.8)使用带有TransPRECISE路径评分的贝叶斯加性回归树模型。结论我们的研究提供了一个可推广的分析框架，以评估临床前模型系统的翻译潜力，并指导基于路径的个性化医疗决策，跨模型系统集成基因组和分子数据。(C)2020年美国临床肿瘤学会
PURPOSE Personalized network inference on diverse clinical and in vitro model systems across cancer types can be used to delineate specific regulatory mechanisms, uncover drug targets and pathways, and develop individualized predictive models in cancer.METHODS We developed TransPRECISE (personalized cancer-specific integrated network estimation model), a multiscale Bayesian network modeling framework, to analyze the pan-cancer patient and cell line interactome to identify differential and conserved intrapathway activities, to globally assess cell lines as representative models for patients, and to develop drug sensitivity prediction models. We assessed pan-cancer pathway activities for a large cohort of patient samples (> 7,700) from the Cancer Proteome Atlas across >= 30 tumor types, a set of 640 cancer cell lines from the MD Anderson Cell Lines Project spanning 16 lineages, and >= 250 cell lines' response to. 400 drugs.RESULTS TransPRECISE captured differential and conserved proteomic network topologies and pathway circuitry between multiple patient and cell line lineages: ovarian and kidney cancers shared high levels of connectivity in the hormone receptor and receptor tyrosine kinase pathways, respectively, between the two model systems. Our tumor stratification approach found distinct clinical subtypes of the patients represented by different sets of cell lines: patients with head and neck tumors were classified into two different subtypes that are represented by head and neck and esophagus cell lines and had different prognostic patterns (456 v 654 days of median overall survival; P = .02). High predictive accuracy was observed for drug sensitivities in cell lines across multiple drugs (median area under the receiver operating characteristic curve. 0.8) using Bayesian additive regression tree models with TransPRECISE pathway scores.CONCLUSION Our study provides a generalizable analytic framework to assess the translational potential of preclinical model systems and to guide pathway-based personalized medical decision making, integrating genomic and molecular data across model systems. (C) 2020 by American Society of Clinical Oncology