Intermetastatic and Intrametastatic Heterogeneity Shapes Adaptive Therapy Cycling Dynamics.
Intermetastatic and Intrametastatic Heterogeneity Shapes Adaptive Therapy Cycling Dynamics.
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DOI：
10.1158/0008-5472.can-22-2558
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发表时间：
2023-08-15
期刊：
影响因子：
11.2
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中科院分区：
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在适应性治疗期间，多尺度数学建模与生物标记物动力学相结合，有助于识别转移性癌症的潜在特征，为治疗决策提供信息。在药物应用和非药物假期之间交替进行的适应性疗法可以利用敏感和耐药细胞之间的竞争，以最大限度地延长进展时间。然而，最佳的给药方案取决于转移瘤的性质，而转移瘤的性质在临床实践中往往是无法直接测量的。在这里，我们提出了一个框架，通过在第一个适应性治疗周期中的肿瘤反应动力学来估计转移的特征。对16例经雄激素去势治疗的转移性去势耐药前列腺癌患者的纵向前列腺特异性抗原(PSA)水平进行了分析，以探讨周期动力学与临床变量的关系，如Gleason评分、一个周期内转移数目的变化以及整个疗程中的总周期数。适应性治疗的第一个周期包括响应期(应用治疗，直到PSA下降50%)和再生长期(停止治疗，直到达到最初的PSA水平)，描绘了计算转移系统的几个特征：较大的转移瘤有更长的周期；更高比例的耐药细胞减缓了周期；更快的细胞周转率加快了药物反应时间，减缓了再生长时间。转移的数目不影响周期时间，因为反应动力学是由最大的肿瘤主导的，而不是集合。此外，转移间异质性越高的系统对持续治疗的反应越好，并且与Gleason评分高或低的患者的动力学相关。相反，转移内异质性较高的系统对适应性治疗的反应更好，并且与Gleason评分中等的患者的动力学相关。在适应性治疗期间，多尺度数学建模与生物标记物动力学相结合，有助于识别转移性癌症的潜在特征，为治疗决策提供信息。
Multiscale mathematical modeling combined with biomarker dynamics during adaptive therapy helps identify underlying features of metastatic cancer to inform treatment decisions. Adaptive therapies that alternate between drug applications and drug-free vacations can exploit competition between sensitive and resistant cells to maximize the time to progression. However, optimal dosing schedules depend on the properties of metastases, which are often not directly measurable in clinical practice. Here, we proposed a framework for estimating features of metastases through tumor response dynamics during the first adaptive therapy treatment cycle. Longitudinal prostate-specific antigen (PSA) levels in 16 patients with metastatic castration-resistant prostate cancer undergoing adaptive androgen deprivation treatment were analyzed to investigate relationships between cycle dynamics and clinical variables such as Gleason score, the change in the number of metastases over a cycle, and the total number of cycles over the course of treatment. The first cycle of adaptive therapy, which consists of a response period (applying therapy until 50% PSA reduction), and a regrowth period (removing treatment until reaching initial PSA levels), delineated several features of the computational metastatic system: larger metastases had longer cycles; a higher proportion of drug-resistant cells slowed the cycles; and a faster cell turnover rate sped up drug response time and slowed regrowth time. The number of metastases did not affect cycle times, as response dynamics were dominated by the largest tumors rather than the aggregate. In addition, systems with higher intermetastasis heterogeneity responded better to continuous therapy and correlated with dynamics from patients with high or low Gleason scores. Conversely, systems with higher intrametastasis heterogeneity responded better to adaptive therapy and correlated with dynamics from patients with intermediate Gleason scores. Multiscale mathematical modeling combined with biomarker dynamics during adaptive therapy helps identify underlying features of metastatic cancer to inform treatment decisions.