Investigating the evolution of cancer cells through single cell genomic data.

BBSRC：Thomas Pak：BB/M011224/1肿瘤进化是肿瘤进展所固有的。细胞的内在过程，如突变积累、表观遗传修饰或翻译调节，可以诱导组成肿瘤的细胞群体的多样性，为微环境和治疗背景下的进化选择提供底物。然而，人们对如何预测恶性细胞群体的进化动态知之甚少。因此，癌症的演变通常是在选择发生后观察到的，例如在复发或转移的情况下--往往为时已晚，无法进行有效的干预。在这项研究项目中，我们考虑对癌症进展过程进行建模，以便在未受干扰和受干扰的情况下，以统计学的严谨性来预测癌细胞种群可能的生长轨迹。源自这一概念的核心悬而未决的问题仍然存在，其中包括最基本的问题：“如何计算适应度，并将其用作癌细胞生长轨迹的预测属性？”

BBSRC : Thomas Pak : BB/M011224/1Cancer evolution is inherent to tumour progression. Cell-intrinsic processes such as mutation accumulation, epigenetic modification or translation regulation induce diversity within tumour-comprising cell populations, providing a substrate for evolutionary selection in microenvironmental and therapeutic contexts. Yet, little is known about how to anticipate evolutionary dynamics of a malignant cell population. As such, evolution in cancers is often observed after selection has occurred, for example in the relapsed or metastatic settings-often too late for effective intervention. In this research project, we consider modelling cancer progression processes so as to anticipate, with statistical rigour, the likely growth trajectory of a cancer cell population in the context of unperturbed and perturbed states. Core open questions derived from this concept remain, including the most fundamental: "How can fitness be calculated and used as a predictive property in the growth trajectory of cancer cells?".