Department of Mathematics and Statistics, The College of New Jersey, Ewing, NJ, USA.
Datacor, Inc., Florham Park, NJ, USA.
NPJ Syst Biol Appl. 2022 Sep 8;8(1):32. doi: 10.1038/s41540-022-00244-7.
The promise of precision medicine has been limited by the pervasive resistance to many targeted therapies for cancer. Inferring the timing (i.e., pre-existing or acquired) and mechanism (i.e., drug-induced) of such resistance is crucial for designing effective new therapeutics. This paper studies cetuximab resistance in head and neck squamous cell carcinoma (HNSCC) using tumor volume data obtained from patient-derived tumor xenografts. We ask if resistance mechanisms can be determined from this data alone, and if not, what data would be needed to deduce the underlying mode(s) of resistance. To answer these questions, we propose a family of mathematical models, with each member of the family assuming a different timing and mechanism of resistance. We present a method for fitting these models to individual volumetric data, and utilize model selection and parameter sensitivity analyses to ask: which member(s) of the family of models best describes HNSCC response to cetuximab, and what does that tell us about the timing and mechanisms driving resistance? We find that along with time-course volumetric data to a single dose of cetuximab, the initial resistance fraction and, in some instances, dose escalation volumetric data are required to distinguish among the family of models and thereby infer the mechanisms of resistance. These findings can inform future experimental design so that we can best leverage the synergy of wet laboratory experimentation and mathematical modeling in the study of novel targeted cancer therapeutics.
精准医学的前景受到了广泛的阻碍，这些阻碍使得许多针对癌症的靶向疗法无法实施。推断这种耐药性的发生时间（即预先存在或获得）和机制（即药物诱导）对于设计有效的新治疗方法至关重要。本文使用从患者来源的肿瘤异种移植中获得的肿瘤体积数据研究了头颈部鳞状细胞癌（HNSCC）中的西妥昔单抗耐药性。我们询问仅从这些数据是否可以确定耐药机制，如果不能，需要哪些数据来推断潜在的耐药模式。为了回答这些问题，我们提出了一系列数学模型，每个模型都假设了不同的耐药发生时间和机制。我们提出了一种将这些模型拟合到个体体积数据的方法，并利用模型选择和参数敏感性分析来询问：模型家族中的哪个（哪些）成员最能描述西妥昔单抗治疗 HNSCC 的反应，这能告诉我们耐药的发生时间和机制吗？我们发现，除了单次西妥昔单抗剂量的时间过程体积数据外，还需要初始耐药分数，在某些情况下，还需要剂量递增体积数据，才能区分模型家族并推断耐药机制。这些发现可以为未来的实验设计提供信息，以便我们能够在研究新型靶向癌症治疗方法时，最好地利用湿实验室实验和数学建模的协同作用。
