Takaki Mitsuaki, Haeno Hiroshi
Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan.
Front Oncol. 2021 Oct 13;11:743328. doi: 10.3389/fonc.2021.743328. eCollection 2021.
Locoregional recurrence after surgery is a major unresolved issue in cancer treatment. Premalignant lesions are considered a cause of cancer recurrence. A study showed that premalignant lesions surrounding the primary tumor drove a high local cancer recurrence rate after surgery in head and neck cancer. Based on the multistage theory of carcinogenesis, cells harboring an intermediate number of mutations are not cancer cells yet but have a higher risk of becoming cancer than normal cells. This study constructed a mathematical model for cancer initiation and recurrence by combining the Moran and branching processes in which cells require two specific mutations to become malignant. There are three populations in this model: (i) normal cells with no mutation, (ii) premalignant cells with one mutation, and (iii) cancer cells with two mutations. The total number of healthy tissue is kept constant to represent homeostasis, and there is a rare chance of mutation every time a cell divides. If a cancer cell with two mutations arises, the cancer population proliferates, violating the homeostatic balance of the tissue. Once the number of cancer cells reaches a certain size, we conduct computational resection and remove the cancer cell population, keeping the ratio of normal and premalignant cells in the tissue unchanged. After surgery, we considered tissue dynamics and eventually observed the second appearance of cancer cells as recurrence. Consequently, we computationally revealed the conditions where the time to recurrence became short by parameter sensitivity analysis. Particularly, when the premalignant cells' fitness is higher than normal cells, the proportion of premalignant cells becomes large after the surgical resection. Moreover, the mathematical model was fitted to clinical data on disease-free survival of 1,087 patients in 23 cancer types from the TCGA database. Finally, parameter values of tissue dynamics are estimated for each cancer type, where the likelihood of recurrence can be elucidated. Thus, our approach provides insights into the concept to identify the patients likely to experience recurrence as early as possible.
手术后的局部区域复发是癌症治疗中一个主要的未解决问题。癌前病变被认为是癌症复发的一个原因。一项研究表明，原发性肿瘤周围的癌前病变导致头颈部癌手术后局部癌症复发率很高。基于癌症发生的多阶段理论，携带中等数量突变的细胞还不是癌细胞，但比正常细胞有更高的癌变风险。本研究通过结合莫兰过程和分支过程构建了一个癌症起始和复发的数学模型，其中细胞需要两个特定突变才能变成恶性。该模型中有三个群体：（i）无突变的正常细胞，（ii）有一个突变的癌前细胞，以及（iii）有两个突变的癌细胞。健康组织的总数保持不变以代表内稳态，并且每次细胞分裂时都有罕见的突变机会。如果出现了有两个突变的癌细胞，癌症群体就会增殖，破坏组织的内稳态平衡。一旦癌细胞数量达到一定规模，我们进行计算切除并去除癌细胞群体，保持组织中正常细胞和癌前细胞的比例不变。手术后，我们考虑组织动态变化，最终将癌细胞的再次出现视为复发。因此，我们通过参数敏感性分析在计算上揭示了复发时间变短的条件。特别地，当癌前细胞的适应性高于正常细胞时，手术切除后癌前细胞的比例会变大。此外，该数学模型与来自TCGA数据库的23种癌症类型的1087例患者的无病生存临床数据进行了拟合。最后，估计了每种癌症类型的组织动态参数值，据此可以阐明复发的可能性。因此，我们的方法为尽早识别可能经历复发的患者这一概念提供了见解。
