Cancer cells, like microbes, live in complex metabolic environments. Recent evidence suggests that microbial behavior across metabolic environments is well described by simple empirical growth relationships, or growth laws. Do such empirical growth relationships also exist in cancer cells? To test this question, we develop a high-throughput approach to extract quantitative measurements of cancer cell behaviors in systematically altered metabolic environments. Using this approach, we examine relationships between growth and three frequently studied cancer phenotypes: drug-treatment survival, cell migration, and lactate overflow. Drug-treatment survival follows simple linear growth relationships, which differ quantitatively between chemotherapeutics and EGFR inhibition. Cell migration follows a weak grow-and-go growth relationship, with substantial deviation in some environments. Finally, lactate overflow is mostly decoupled from growth rate and is instead determined by the cells’ ability to maintain high sugar uptake rates. Altogether, this work provides a quantitative approach for formulating empirical growth laws of cancer.

癌细胞和微生物一样，生活在复杂的代谢环境中。最近的证据表明，通过简单的经验生长关系或生长规律很好地描述了代谢环境中的微生物行为。这种经验性生长关系是否也存在于癌细胞中？为了测试这个问题，我们开发了一种高通量方法，以在系统改变的代谢环境中提取癌细胞行为的定量测量。使用这种方法，我们检查了生长与三种经常研究的癌症表型之间的关系：药物治疗存活率、细胞迁移和乳酸溢出。药物治疗生存遵循简单的线性增长关系，在化疗药物和 EGFR 抑制之间存在数量上的差异。细胞迁移遵循弱的生长和生长关系，在某些环境中有很大的偏差。最后，乳酸溢出主要与生长速率脱钩，而是由细胞维持高糖摄取率的能力决定。总之，这项工作为制定癌症的经验增长规律提供了一种定量方法。