Sun Ruping, Nikolakopoulos Athanasios N
Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota, United States of America.
Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota, United States of America.
PLoS Comput Biol. 2021 Mar 17;17(3):e1008838. doi: 10.1371/journal.pcbi.1008838. eCollection 2021 Mar.
Can metastatic-primary (M-P) genomic divergence measured from next generation sequencing reveal the natural history of metastatic dissemination? This remains an open question of utmost importance in facilitating a deeper understanding of metastatic progression, and thereby, improving its prevention. Here, we utilize mathematical and computational modeling to tackle this question as well as to provide a framework that illuminates the fundamental elements and evolutionary determinants of M-P divergence. Our framework facilitates the integration of sequencing detectability of somatic variants, and hence, paves the way towards bridging the measurable between-tumor heterogeneity with analytical modeling and interpretability. We show that the number of somatic variants of the metastatic seeding cell that are experimentally undetectable in the primary tumor, can be characterized as the path of the phylogenetic tree from the last appearing variant of the seeding cell back to the most recent detectable variant. We find that the expected length of this path is principally determined by the decay in detectability of the variants along the seeding cell's lineage; and thus, exhibits a significant dependence on the underlying tumor growth dynamics. A striking implication of this fact, is that dissemination from an advanced detectable subclone of the primary tumor can lead to an abrupt drop in the expected measurable M-P divergence, thereby breaking the previously assumed monotonic relation between seeding time and M-P divergence. This is emphatically verified by our single cell-based spatial tumor growth simulation, where we find that M-P divergence exhibits a non-monotonic relationship with seeding time when the primary tumor grows under branched and linear evolution. On the other hand, a monotonic relationship holds when we condition on the dynamics of progressive diversification, or by restricting the seeding cells to always originate from undetectable subclones. Our results highlight the fact that a precise understanding of tumor growth dynamics is the sine qua non for exploiting M-P divergence to reconstruct the chronology of metastatic dissemination. The quantitative models presented here enable further careful evaluation of M-P divergence in association with crucial evolutionary and sequencing parameters.
通过下一代测序测量的转移灶-原发灶（M-P）基因组差异能否揭示转移扩散的自然史？这仍然是一个极其重要的开放性问题，对于深入理解转移进展并进而改善其预防具有重要意义。在此，我们利用数学和计算建模来解决这个问题，并提供一个框架，阐明M-P差异的基本要素和进化决定因素。我们的框架促进了体细胞变异测序可检测性的整合，从而为通过分析建模和可解释性弥合可测量的肿瘤间异质性铺平了道路。我们表明，在原发肿瘤中实验不可检测的转移播种细胞的体细胞变异数量，可以表征为从播种细胞的最后出现变异到最近可检测变异的系统发育树路径。我们发现，这条路径的预期长度主要由沿播种细胞谱系的变异可检测性衰减决定；因此，对潜在的肿瘤生长动力学有显著依赖性。这一事实的一个显著含义是，从原发肿瘤的晚期可检测亚克隆进行的扩散可能导致预期可测量的M-P差异突然下降，从而打破先前假设的播种时间与M-P差异之间的单调关系。我们基于单细胞的空间肿瘤生长模拟有力地证实了这一点，我们发现当原发肿瘤在分支和线性进化下生长时，M-P差异与播种时间呈现非单调关系。另一方面，当我们基于渐进多样化的动力学条件，或者将播种细胞限制为始终源自不可检测的亚克隆时，单调关系成立。我们的结果突出了这样一个事实，即精确理解肿瘤生长动力学是利用M-P差异重建转移扩散时间顺序的必要条件。这里提出的定量模型能够结合关键的进化和测序参数，对M-P差异进行进一步仔细评估。
