Natural Product Informatics Research Center, Korea Institute of Science and Technology (KIST), Gangneung 25451, Republic of Korea.
Graduate School of Analytical Science and Technology (GRAST), Chungnam National University, Daejeon 34134, Republic of Korea.
Comput Biol Med. 2023 Aug;162:107035. doi: 10.1016/j.compbiomed.2023.107035. Epub 2023 May 27.
Adaptive therapy (AT) is an evolution-based treatment strategy that exploits cell-cell competition. Acquired resistance can change the competitive nature of cancer cells in a tumor, impacting AT outcomes. We aimed to determine if adaptive therapy can still be effective with cell's acquiring resistance. We developed an agent-based model for spatial tumor growth considering three different types of acquired resistance: random genetic mutations during cell division, drug-induced reversible (plastic) phenotypic changes, and drug-induced irreversible phenotypic changes. These three resistance mechanisms lead to different spatial distributions of resistant cells. To quantify the spatial distribution, we propose an extension of Ripley's K-function, Sampled Ripley's K-function (SRKF), which calculates the non-randomness of the resistance distribution over the tumor domain. Our model predicts that the emergent spatial distribution of resistance can determine the time to progression under both adaptive and continuous therapy (CT). Notably, a high rate of random genetic mutations leads to quicker progression under AT than CT due to the emergence of many small clumps of resistant cells. Drug-induced phenotypic changes accelerate tumor progression irrespective of the treatment strategy. Low-rate switching to a sensitive state reduces the benefits of AT compared to CT. Furthermore, we also demonstrated that drug-induced resistance necessitates aggressive treatment under CT, regardless of the presence of cancer-associated fibroblasts. However, there is an optimal dose that can most effectively delay tumor relapse under AT by suppressing resistance. In conclusion, this study demonstrates that diverse resistance mechanisms can shape the distribution of resistance and thus determine the efficacy of adaptive therapy.
适应性治疗（AT）是一种基于进化的治疗策略，利用细胞间竞争。获得性耐药会改变肿瘤中癌细胞的竞争性质，从而影响 AT 结果。我们旨在确定细胞获得耐药后，适应性治疗是否仍然有效。我们开发了一种基于代理的模型来考虑三种不同类型的获得性耐药的空间肿瘤生长：细胞分裂过程中的随机遗传突变、药物诱导的可逆（塑性）表型变化和药物诱导的不可逆表型变化。这三种耐药机制导致耐药细胞的不同空间分布。为了量化耐药性的空间分布，我们提出了 Ripley K 函数的扩展，即采样 Ripley K 函数（SRKF），它计算了肿瘤区域内耐药分布的非随机性。我们的模型预测，耐药性的出现空间分布可以确定在适应性和连续治疗（CT）下的进展时间。值得注意的是，由于许多小簇耐药细胞的出现，高比率的随机遗传突变会导致 AT 下的进展速度快于 CT。药物诱导的表型变化无论治疗策略如何，都会加速肿瘤的进展。低比率切换到敏感状态会降低 AT 相对于 CT 的获益。此外，我们还表明，药物诱导的耐药性需要在 CT 下进行积极治疗，无论是否存在癌症相关成纤维细胞。然而，存在一个最佳剂量，可以通过抑制耐药性最有效地延迟 AT 下的肿瘤复发。总之，这项研究表明，不同的耐药机制可以塑造耐药性的分布，从而决定适应性治疗的疗效。
