Department of Biostatistics, University of Michigan, Ann Arbor, MI.
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX.
JCO Clin Cancer Inform. 2020 May;4:399-411. doi: 10.1200/CCI.19.00140.
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.
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.
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 (456 654 days of median overall survival; = .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.
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.
在不同癌症类型的临床和体外模型系统中进行个性化网络推断，可以用于描绘特定的调控机制、发现药物靶点和途径，并在癌症中开发个体化预测模型。
我们开发了 TransPRECISE（个性化癌症特异性综合网络估计模型），这是一个多尺度贝叶斯网络建模框架，用于分析泛癌症患者和细胞系相互作用组，以识别差异和保守的通路活性，全面评估细胞系作为患者的代表性模型，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大量患者样本（>7700 个）在≥30 种肿瘤类型中的泛癌症通路活性，来自 MD 安德森细胞系项目的 640 种癌症细胞系跨越 16 个谱系，以及≥250 种细胞系对>400 种药物的反应。
TransPRECISE 捕获了多个患者和细胞系谱系之间的差异和保守的蛋白质组网络拓扑和通路电路：卵巢和肾脏癌症在两个模型系统中分别在激素受体和受体酪氨酸激酶通路中具有高度的连通性。我们的肿瘤分层方法发现了不同的临床亚型，这些亚型由不同的细胞系代表：头颈部肿瘤患者分为两个不同的亚型，由头颈部和食管细胞系代表，具有不同的预后模式（中位总生存期 456654 天；=0.02）。使用基于贝叶斯加法回归树模型和 TransPRECISE 通路评分，在多种药物中观察到细胞系药物敏感性的高预测准确性（中位数接受者操作特征曲线下面积>0.8）。
我们的研究提供了一个可推广的分析框架，用于评估临床前模型系统的转化潜力，并指导基于通路的个性化医疗决策，整合跨模型系统的基因组和分子数据。
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