Intra-tumour heterogeneity - going beyond genetics.

FEBS JOURNAL(2016)

引用 69|浏览18
暂无评分
摘要
Cancer patients die primarily due to disease recurrence after transient treatment responses. The emergence of therapy-resistant escape variants is fuelled by intra-tumour heterogeneity, underpinned by interference and Darwinian evolution among continuously developing sub-clones in the mutating tumour. Novel cancer cell variants build upon the pre-existing genetic landscape and tumour heterogeneity is often ascribed largely to genetic variability. While mutations are required for cancer development and studies of genetic evolution of tumours have improved our understanding of cancer biology, genetics only represents one dimension of the fitness of each cancer cell. Beyond the mutations, several non-genetic factors also add significant variability, resulting in a complex and highly dynamic tumour cell population that can drive disease under almost any condition. This viewpoint article summarizes the genetic basis of intra-tumour heterogeneity, before dissecting four major interdependent non-genetic factors we think critically contribute to the overall variability of tumour cells in all types of cancer: epigenetic regulation, cellular differentiation hierarchies, gene expression stochasticity and tumour microenvironment. We finally present the relevant technological approaches to address the combined contribution of both genetic and non-genetic factors to intra-tumour heterogeneity, focusing on genomic profiling, cellular lineage tracing and single-cell RNA sequencing technologies. This strategy will ultimately allow dissection of the full range and depth of intra-tumour heterogeneity. We thus believe that understanding how cancer genetics synergize with the emerging non-genetic factors will be key for development of therapies able to tackle tumour escape and thereby improve cancer patient survival.
更多
查看译文
关键词
cancer mutation,cancer stem cells,clonal evolution,leukaemia initiating cells,lineage tracing,next-generation sequencing,non-genetic variability,population dynamics,single-cell transcriptomics,sub-clone
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要