对澳大利亚卵巢癌研究中的 791 名高级别浆液性卵巢癌 (HGSOC) 患者进行了血清癌抗原 125 (CA-125) 水平的时间序列分析，以评估化疗耐药性的发展和对治疗的反应。为了研究化疗耐药性并更好地预测治疗效果，我们检查了两个特征：耐药性（定义为患者接受治疗时 CA-125 变化率）和侵袭性（定义为患者未接受治疗时 CA-125 变化率） ）。我们发现，随着治疗线数量的增加，基于数据的阻力增加（CA-125 衰减率降低）。我们使用两种不同的癌细胞类型（治疗敏感细胞和治疗抵抗细胞）的数学模型来估计个体患者这两种特征的价值和演变。通过拟合个体患者 HGSOC 数据，我们的模型成功捕获了 CA-125 水平的动态。从数学模型估计的参数显示，推断治疗敏感细胞和治疗抵抗细胞生长率低（模型估计侵袭性低）和治疗抵抗细胞死亡率高（模型估计抵抗力低）的患者具有较高的死亡率。完成二线治疗后生存时间更长。这些发现表明，数学模型可以表征个体患者的耐药性和侵袭性程度，这提高了我们对化疗耐药发展的理解，并可以预测 HGSOC 患者的治疗效果。版权所有：© 2024 Jitmana 等人。这是一篇根据知识共享署名许可条款分发的开放获取文章，允许在任何媒体上不受限制地使用、分发和复制，前提是注明原始作者和来源。

A time-series analysis of serum Cancer Antigen 125 (CA-125)levels was performed in 791 patients with high-grade serous ovarian cancer (HGSOC) from the Australian Ovarian Cancer Study to evaluate the development of chemoresistance and response to therapy. To investigate chemoresistance and better predict the treatment effectiveness, we examined two traits: resistance (defined as the rate of CA-125 change when patients were treated with therapy) and aggressiveness (defined as the rate of CA-125 change when patients were not treated). We found that as the number of treatment lines increases, the data-based resistance increases (a decreased rate of CA-125 decay). We use mathematical models of two distinct cancer cell types, treatment-sensitive cells and treatment-resistant cells, to estimate the values and evolution of the two traits in individual patients. By fitting to individual patient HGSOC data, our models successfully capture the dynamics of the CA-125 level. The parameters estimated from the mathematical models show that patients with inferred low growth rates of treatment-sensitive cells and treatment-resistant cells (low model-estimated aggressiveness) and a high death rate of treatment-resistant cells (low model-estimated resistance) have longer survival time after completing their second-line of therapy. These findings show that mathematical models can characterize the degree of resistance and aggressiveness in individual patients, which improves our understanding of chemoresistance development and could predict treatment effectiveness in HGSOC patients.Copyright: © 2024 Jitmana et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.