Mechanistic models, when combined with pertinent data, can improve our knowledge regarding important molecular and cellular mechanisms found in cancer. These models make the prediction of tissue-level response to drug treatment possible, which can lead to new therapies and improved patient outcomes. Here we present a data-driven multiscale modeling framework to study molecular interactions between cancer, stromal, and immune cells found in the tumor microenvironment. We also develop methods to use molecular data available in The Cancer Genome Atlas to generate sample-specific models of cancer.

中文翻译：

---

一种可推广的数据驱动的胰腺导管腺癌多细胞模型。

机制模型与相关数据相结合，可以提高我们对癌症中发现的重要分子和细胞机制的了解。这些模型使得对药物治疗的组织水平反应的预测成为可能，这可以导致新的治疗方法并改善患者的治疗效果。在这里，我们提出了一个数据驱动的多尺度建模框架，以研究在肿瘤微环境中发现的癌症、基质和免疫细胞之间的分子相互作用。我们还开发了使用癌症基因组图谱中可用的分子数据生成癌症样本特定模型的方法。