Application of contemporary molecular biology techniques to clinical samples in oncology resulted in the accumulation of unprecedented experimental data. These "omics" data are mined for discovery of therapeutic target combinations and diagnostic biomarkers. It is less appreciated that omics resources could also revolutionize development of the mechanistic models informing clinical pharmacology quantitative decisions about dose amount, timing, and sequence. We discuss the integration of omics data to inform mechanistic models supporting drug development in immuno-oncology. To illustrate our arguments, we present a minimal clinical model of the Cancer Immunity Cycle (CIC), calibrated for non-small cell lung carcinoma using tumor microenvironment composition inferred from transcriptomics of clinical samples. We review omics data resources, which can be integrated to parameterize mechanistic models of the CIC. We propose that virtual trial simulations with clinical Quantitative Systems Pharmacology platforms informed by omics data will be making increasing impact in the development of cancer immunotherapies.

中文翻译：

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整合Omics数据源以指导免疫肿瘤治疗的机理模型：临床药理学家指南。

当代分子生物学技术在肿瘤学临床样品中的应用导致了前所未有的实验数据的积累。挖掘这些“组学”数据可发现治疗靶标组合和诊断性生物标志物。人们几乎不了解，组学资源还可以彻底改变机理模型的开发，从而通知临床药理学有关剂量，时间和顺序的定量决策。我们讨论了组学数据的整合，以为支持免疫肿瘤学中药物开发的机理模型提供信息。为了说明我们的论点，我们介绍了使用从临床样本转录组学推断出的肿瘤微环境组成对非小细胞肺癌进行校准的癌症免疫周期（CIC）的最小临床模型。我们回顾了组学数据资源，可以集成这些参数化CIC的机械模型。我们建议，由组学数据提供的具有临床定量系统药理学平台的虚拟试验模拟将对癌症免疫疗法的发展产生越来越大的影响。