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本文引用的文献

1
Application of next-generation sequencing in clinical oncology to advance personalized treatment of cancer.下一代测序技术在临床肿瘤学中的应用以推动癌症的个性化治疗。
Chin J Cancer. 2012 Oct;31(10):463-70. doi: 10.5732/cjc.012.10216. Epub 2012 Sep 17.
2
Application of next generation sequencing to human gene fusion detection: computational tools, features and perspectives.下一代测序在人类基因融合检测中的应用:计算工具、特点和展望。
Brief Bioinform. 2013 Jul;14(4):506-19. doi: 10.1093/bib/bbs044. Epub 2012 Aug 9.
3
The origin and evolution of mutations in acute myeloid leukemia.急性髓细胞白血病突变的起源和演变。
Cell. 2012 Jul 20;150(2):264-78. doi: 10.1016/j.cell.2012.06.023.
4
MuSiC: identifying mutational significance in cancer genomes.MuSiC:识别癌症基因组中的突变意义。
Genome Res. 2012 Aug;22(8):1589-98. doi: 10.1101/gr.134635.111. Epub 2012 Jul 3.
5
The life history of 21 breast cancers.21 例乳腺癌的生命史。
Cell. 2012 May 25;149(5):994-1007. doi: 10.1016/j.cell.2012.04.023. Epub 2012 May 17.
6
Genome-wide copy number analysis of single cells.单细胞全基因组拷贝数分析。
Nat Protoc. 2012 May 3;7(6):1024-41. doi: 10.1038/nprot.2012.039.
7
SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing.SPAdes:一种新的基因组组装算法及其在单细胞测序中的应用
J Comput Biol. 2012 May;19(5):455-77. doi: 10.1089/cmb.2012.0021. Epub 2012 Apr 16.
8
Cancer stem cells: impact, heterogeneity, and uncertainty.癌症干细胞:影响、异质性和不确定性。
Cancer Cell. 2012 Mar 20;21(3):283-96. doi: 10.1016/j.ccr.2012.03.003.
9
Intratumor heterogeneity and branched evolution revealed by multiregion sequencing.多区域测序揭示的肿瘤内异质性和分支进化。
N Engl J Med. 2012 Mar 8;366(10):883-892. doi: 10.1056/NEJMoa1113205.
10
Opening Pandora's Box--the new biology of driver mutations and clonal evolution in cancer as revealed by next generation sequencing.打开潘多拉的盒子——下一代测序揭示的癌症中驱动突变和克隆进化的新生物学。
Curr Opin Genet Dev. 2012 Feb;22(1):3-9. doi: 10.1016/j.gde.2012.01.008. Epub 2012 Mar 1.

癌症克隆进化研究进展。

Advances for studying clonal evolution in cancer.

机构信息

Department of Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, MO 63110, USA; The Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA; Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Genetics, Washington University School of Medicine, St. Louis, MO 63108, USA.

出版信息

Cancer Lett. 2013 Nov 1;340(2):212-9. doi: 10.1016/j.canlet.2012.12.028. Epub 2013 Jan 23.

DOI:10.1016/j.canlet.2012.12.028
PMID:23353056
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3783624/
Abstract

The "clonal evolution" model of cancer emerged and "evolved" amid ongoing advances in technology, especially in recent years during which next generation sequencing instruments have provided ever higher resolution pictures of the genetic changes in cancer cells and heterogeneity in tumors. It has become increasingly clear that clonal evolution is not a single sequential process, but instead frequently involves simultaneous evolution of multiple subclones that co-exist because they are of similar fitness or are spatially separated. Co-evolution of subclones also occurs when they complement each other's survival advantages. Recent studies have also shown that clonal evolution is highly heterogeneous: different individual tumors of the same type may undergo very different paths of clonal evolution. New methodological advancements, including deep digital sequencing of a mixed tumor population, single cell sequencing, and the development of more sophisticated computational tools, will continue to shape and reshape the models of clonal evolution. In turn, these will provide both an improved framework for the understanding of cancer progression and a guide for treatment strategies aimed at the elimination of all, rather than just some, of the cancer cells within a patient.

摘要

癌症的“克隆进化”模型是在技术不断进步的背景下出现并“进化”的,尤其是近年来,下一代测序仪器为癌细胞的遗传变化和肿瘤异质性提供了越来越高的分辨率图像。越来越明显的是,克隆进化不是一个单一的顺序过程,而是经常涉及多个亚克隆的同时进化,这些亚克隆之所以共存,是因为它们具有相似的适应性,或者是空间上分离的。当亚克隆相互补充生存优势时,也会发生共同进化。最近的研究还表明,克隆进化具有高度的异质性:同一类型的不同个体肿瘤可能经历非常不同的克隆进化路径。新的方法学进展,包括混合肿瘤群体的深度数字测序、单细胞测序以及更复杂的计算工具的开发,将继续塑造和重塑克隆进化模型。反过来,这些将为理解癌症进展提供一个改进的框架,并为旨在消除患者体内所有(而不仅仅是部分)癌细胞的治疗策略提供指导。