Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, UK.
Departments of Translational Hematology and Oncology Research and Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA.
Syst Biol. 2020 Jul 1;69(4):623-637. doi: 10.1093/sysbio/syz070.
We use a computational modeling approach to explore whether it is possible to infer a solid tumor's cellular proliferative hierarchy under the assumptions of the cancer stem cell hypothesis and neutral evolution. We work towards inferring the symmetric division probability for cancer stem cells, since this is believed to be a key driver of progression and therapeutic response. Motivated by the advent of multiregion sampling and resulting opportunities to infer tumor evolutionary history, we focus on a suite of statistical measures of the phylogenetic trees resulting from the tumor's evolution in different regions of parameter space and through time. We find strikingly different patterns in these measures for changing symmetric division probability which hinge on the inclusion of spatial constraints. These results give us a starting point to begin stratifying tumors by this biological parameter and also generate a number of actionable clinical and biological hypotheses regarding changes during therapy, and through tumor evolutionary time. [Cancer; evolution; phylogenetics.].
我们使用计算建模方法来探索在癌症干细胞假说和中性进化的假设下，是否有可能推断出实体瘤的细胞增殖层次结构。我们致力于推断癌症干细胞的对称分裂概率，因为这被认为是进展和治疗反应的关键驱动因素。受多区域采样的出现以及由此产生的推断肿瘤进化历史的机会的推动，我们专注于从肿瘤在不同区域和随时间在参数空间中的进化中产生的系统发育树的一系列统计度量。我们发现，对于对称分裂概率的变化，这些度量中存在明显不同的模式，这取决于空间约束的包含。这些结果为我们提供了一个起点，开始根据这个生物学参数对肿瘤进行分层，并针对治疗期间和肿瘤进化过程中的变化产生了一些可行的临床和生物学假设。[癌症；进化；系统发生学。]。
