A common approach for understanding how drugs induce their therapeutic effects is to identify the genetic determinants of drug sensitivity. Because ‘chemo-genetic profiles’ are performed in a pooled format, inference of gene function is subject to several confounding influences related to variation in growth rates between clones. In this study, we developed Method for Evaluating Death Using a Simulation-assisted Approach (MEDUSA), which uses time-resolved measurements, along with model-driven constraints, to reveal the combination of growth and death rates that generated the observed drug response. MEDUSA is uniquely effective at identifying death regulatory genes. We apply MEDUSA to characterize DNA damage-induced lethality in the presence and absence of p53. Loss of p53 switches the mechanism of DNA damage-induced death from apoptosis to a non-apoptotic death that requires high respiration. These findings demonstrate the utility of MEDUSA both for determining the genetic dependencies of lethality and for revealing opportunities to potentiate chemo-efficacy in a cancer-specific manner.

了解药物如何产生治疗效果的常见方法是确定药物敏感性的遗传决定因素。由于“化学遗传图谱”是以汇总形式进行的，因此基因功能的推断会受到与克隆之间生长速率变化相关的几种混杂影响。在这项研究中，我们开发了使用模拟辅助方法评估死亡的方法（MEDUSA），该方法使用时间分辨测量以及模型驱动的约束来揭示产生观察到的药物反应的生长率和死亡率的组合。MEDUSA 在识别死亡调控基因方面具有独特的功效。我们应用 MEDUSA 来表征 p53 存在和不存在时 DNA 损伤诱导的致死率。p53 的缺失将 DNA 损伤诱导的死亡机制从细胞凋亡转变为需要高呼吸的非细胞凋亡死亡。这些发现证明了 MEDUSA 的实用性，既可以确定致死率的遗传依赖性，也可以揭示以癌症特异性方式增强化疗功效的机会。