Department of Applied Mathematics, University of Washington, Lewis Hall 201, Box 353925, Seattle, WA, 98195, USA.
Herbold Computational Biology Program, Fred Hutchinson Cancer Center, 1241 Eastlake Ave E, Seattle, WA, 98102, USA.
Bull Math Biol. 2024 Oct 29;86(12):142. doi: 10.1007/s11538-024-01372-3.
Cancers are typically fueled by sequential accumulation of driver mutations in a previously healthy cell. Some of these mutations, such as inactivation of the first copy of a tumor suppressor gene, can be neutral, and some, like those resulting in activation of oncogenes, may provide cells with a selective growth advantage. We study a multi-type branching process that starts with healthy tissue in homeostasis and models accumulation of neutral and advantageous mutations on the way to cancer. We provide results regarding the sizes of premalignant populations and the waiting times to the first cell with a particular combination of mutations, including the waiting time to malignancy. Finally, we apply our results to two specific biological settings: initiation of colorectal cancer and age incidence of chronic myeloid leukemia. Our model allows for any order of neutral and advantageous mutations and can be applied to other evolutionary settings.
癌症通常是由健康细胞中驱动突变的顺序积累引起的。这些突变中的一些，如肿瘤抑制基因的第一个拷贝失活，可能是中性的，而另一些，如导致癌基因激活的突变，可能为细胞提供选择性生长优势。我们研究了一种多类型分支过程，该过程从稳态中的健康组织开始，并对中性和有利突变的积累进行建模，直到癌症发生。我们提供了关于前癌种群大小和到达具有特定突变组合的第一个细胞的等待时间的结果，包括恶变的等待时间。最后，我们将我们的结果应用于两种特定的生物学环境：结直肠癌的起始和慢性髓性白血病的年龄发病率。我们的模型允许中性和有利突变的任何顺序，并可应用于其他进化环境。
