Cancer spread (metastasis) is responsible for 90% of cancer-related fatalities. Informing patient treatment to prevent metastasis, or kill all cancer cells in a patient's body before it becomes metastatic is extremely powerful. However, aggressive treatment for all non-metastatic patients is detrimental, both for quality of life concerns, and the risk of kidney or liver-related toxicity. Knowing when and where a patient has metastatic risk could revolutionize patient treatment and care. In this review, we attempt to summarize the key work of engineers and quantitative biologists in developing strategies and model systems to predict metastasis, with a particular focus on cell interactions with the extracellular matrix (ECM), as a tool to predict metastatic risk and tropism.

中文翻译：

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矩阵与转移之间的预测联系。

癌症扩散（转移）是造成90％与癌症相关的死亡的原因。在转移之前通知患者治疗以防止转移或杀死患者体内的所有癌细胞是非常有力的。但是，对于所有非转移性患者而言，积极治疗不利于生活质量，不利于肾脏或肝脏相关毒性的风险。知道患者何时何地有转移风险可能会彻底改变患者的治疗和护理。在这篇综述中，我们试图总结工程师和定量生物学家在开发策略和模型系统以预测转移的过程中的关键工作，特别侧重于细胞与细胞外基质（ECM）的相互作用，以此作为预测转移风险和向性的工具。