我们对癌症的认识近期的进展大多依赖于高通量技术如转录组学系统性地分析患者样本。这种方法可以找到潜在的癌症侵袭和治疗抗性的基因标记和网络。但仅依靠组学数据无法深入了解肿瘤进展的时空局部性。因此，多层次的计算建模是一个很有前景的方法，可以通过集成高通量分析生成的数据来受益。我们提出了一个计算工作流，将肿瘤患者的转录组数据整合到混合、多尺度的癌症模型中。在这种方法中，我们进行了转录组分析来选择治疗应答者和非应答者中的关键差异性调节途径，并将它们与基于代理的模型参数联系起来。然后，我们通过系统模型模拟确定全局和局部敏感性，以评估参数变化在触发治疗抗性方面的关联性。我们用一种新发展的基于代理的模型来说明这种方法，该模型解释了黑色素瘤微小转移中肿瘤和免疫细胞之间的相互作用。应用这种工作流程可以确定三种不同的治疗抗性场景。© 2023 作者。

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 de novo 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.© 2023 The Authors.