School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia.
Bull Math Biol. 2024 Mar 19;86(4):43. doi: 10.1007/s11538-024-01272-6.
Resistance of cancers to treatments, such as chemotherapy, largely arise due to cell mutations. These mutations allow cells to resist apoptosis and inevitably lead to recurrence and often progression to more aggressive cancer forms. Sustained-low dose therapies are being considered as an alternative over maximum tolerated dose treatments, whereby a smaller drug dosage is given over a longer period of time. However, understanding the impact that the presence of treatment-resistant clones may have on these new treatment modalities is crucial to validating them as a therapeutic avenue. In this study, a Moran process is used to capture stochastic mutations arising in cancer cells, inferring treatment resistance. The model is used to predict the probability of cancer recurrence given varying treatment modalities. The simulations predict that sustained-low dose therapies would be virtually ineffective for a cancer with a non-negligible probability of developing a sub-clone with resistance tendencies. Furthermore, calibrating the model to in vivo measurements for breast cancer treatment with Herceptin, the model suggests that standard treatment regimens are ineffective in this mouse model. Using a simple Moran model, it is possible to explore the likelihood of treatment success given a non-negligible probability of treatment resistant mutations and suggest more robust therapeutic schedules.
癌症对治疗（如化疗）的耐药性主要源于细胞突变。这些突变使细胞能够抵抗细胞凋亡，不可避免地导致复发，并经常发展为更具侵袭性的癌症形式。持续低剂量治疗被认为是替代最大耐受剂量治疗的一种方法，即长时间内给予较小的药物剂量。然而，了解治疗耐药克隆的存在可能对这些新治疗方法产生的影响对于验证它们作为治疗途径至关重要。在这项研究中，使用 Moran 过程来捕捉癌症细胞中出现的随机突变，推断出治疗耐药性。该模型用于预测不同治疗方式下癌症复发的概率。模拟预测，对于具有不可忽视的发展具有耐药倾向的亚克隆的可能性的癌症，持续低剂量治疗几乎无效。此外，将模型校准到曲妥珠单抗治疗乳腺癌的体内测量值，该模型表明标准治疗方案在这种小鼠模型中无效。使用简单的 Moran 模型，可以探索在不可忽视的治疗耐药性突变概率下治疗成功的可能性，并提出更有效的治疗方案。
