Retzlaff Jimmy, Lai Xin, Berking Carola, Vera Julio
Laboratory of Systems Tumor Immunology, Department of Dermatology, Universitätsklinikum Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Deutsches Zentrum Immuntherapie, Erlangen, Germany.
Comput Struct Biotechnol J. 2023 Mar 4;21:1930-1941. doi: 10.1016/j.csbj.2023.02.014. eCollection 2023.
Recent progress in our understanding of cancer mostly relies on the systematic profiling of patient samples with high-throughput techniques like transcriptomics. With this approach, one can find gene signatures and networks underlying cancer aggressiveness and therapy resistance. However, omics data alone cannot generate insights into the spatiotemporal aspects of tumor progression. Here, multi-level computational modeling is a promising approach that would benefit from protocols to integrate the data generated by the high-throughput profiling of patient samples. We present a computational workflow to integrate transcriptomics data from tumor patients into hybrid, multi-scale cancer models. In the method, we conduct transcriptomics analysis to select key differentially regulated pathways in therapy responders and non-responders and link them to agent-based model parameters. We then determine global and local sensitivity through systematic model simulations that assess the relevance of parameter variations in triggering therapy resistance. We illustrate the methodology with a generated agent-based model accounting for the interplay between tumor and immune cells in a melanoma micrometastasis. The application of the workflow identifies three distinct scenarios of therapy resistance.
我们对癌症理解的最新进展主要依赖于通过转录组学等高通量技术对患者样本进行系统分析。通过这种方法，可以找到癌症侵袭性和治疗抗性背后的基因特征和网络。然而，仅组学数据无法深入了解肿瘤进展的时空方面。在这里，多层次计算建模是一种很有前景的方法，它将受益于整合患者样本高通量分析所生成数据的方案。我们提出了一种计算工作流程，将肿瘤患者的转录组学数据整合到混合多尺度癌症模型中。在该方法中，我们进行转录组学分析，以选择治疗反应者和无反应者中关键的差异调节通路，并将它们与基于主体的模型参数联系起来。然后，我们通过系统的模型模拟来确定全局和局部敏感性，评估参数变化在引发治疗抗性中的相关性。我们用一个生成的基于主体的模型来说明该方法，该模型考虑了黑色素瘤微转移中肿瘤细胞与免疫细胞之间的相互作用。该工作流程的应用识别出三种不同的治疗抗性情况。
