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本文引用的文献

1
Cancer biomarkers: closer to delivering on their promise.癌症生物标志物:更接近兑现其承诺。
Cancer Cell. 2011 Sep 13;20(3):279-80. doi: 10.1016/j.ccr.2011.08.021.
2
The 18p11.22 locus is associated with never smoker non-small cell lung cancer susceptibility in Korean populations.18p11.22 基因座与韩国人群中从不吸烟的非小细胞肺癌易感性相关。
Hum Genet. 2012 Mar;131(3):365-72. doi: 10.1007/s00439-011-1080-z. Epub 2011 Aug 25.
3
Genetics, genomics, and cancer risk assessment: State of the Art and Future Directions in the Era of Personalized Medicine.遗传学、基因组学与癌症风险评估:个性化医疗时代的现状与未来方向
CA Cancer J Clin. 2011 Sep-Oct;61(5):327-59. doi: 10.3322/caac.20128. Epub 2011 Aug 19.
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The emergence of genome-based drug repositioning.基于基因组的药物重定位的出现。
Sci Transl Med. 2011 Aug 17;3(96):96ps35. doi: 10.1126/scitranslmed.3001512.
5
Mitochondrial DNA mutations in respiratory complex-I in never-smoker lung cancer patients contribute to lung cancer progression and associated with EGFR gene mutation.非吸烟肺癌患者呼吸复合物 I 中线粒体 DNA 突变促进肺癌进展,并与 EGFR 基因突变相关。
J Cell Physiol. 2012 Jun;227(6):2451-60. doi: 10.1002/jcp.22980.
6
Genetic analysis in the Collaborative Cross breeding population.协作交叉繁殖群体中的遗传分析。
Genome Res. 2011 Aug;21(8):1223-38. doi: 10.1101/gr.113886.110. Epub 2011 Jul 6.
7
DNA secondary structures and epigenetic determinants of cancer genome evolution.DNA 二级结构和癌症基因组进化的表观遗传决定因素。
Nat Struct Mol Biol. 2011 Jul 3;18(8):950-5. doi: 10.1038/nsmb.2089.
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Reduced lung-cancer mortality with low-dose computed tomographic screening.低剂量计算机断层扫描筛查可降低肺癌死亡率。
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9
Personalized medicine: new genomics, old lessons.个体化医学:新基因组学,旧教训。
Hum Genet. 2011 Jul;130(1):3-14. doi: 10.1007/s00439-011-1028-3. Epub 2011 Jun 26.
10
Whole-genome sequencing: a step closer to personalized medicine.全基因组测序:向个性化医疗又迈进了一步。
JAMA. 2011 Apr 20;305(15):1596-7. doi: 10.1001/jama.2011.484.

将癌症“组学”转化为改善的结果。

Translating cancer 'omics' to improved outcomes.

机构信息

British Columbia Cancer Research Centre, Vancouver V5Z 1L3, Canada.

出版信息

Genome Res. 2012 Feb;22(2):188-95. doi: 10.1101/gr.124354.111.

DOI:10.1101/gr.124354.111
PMID:22301133
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3266027/
Abstract

The genomics era has yielded great advances in the understanding of cancer biology. At the same time, the immense complexity of the cancer genome has been revealed, as well as a striking heterogeneity at the whole-genome (or omics) level that exists between even histologically similar tumors. The vast accrual and public availability of multi-omics databases with associated clinical annotation including tumor histology, patient response, and outcome are a rich resource that has the potential to lead to rapid translation of high-throughput omics to improved overall survival. We focus on the unique advantages of a multidimensional approach to genomic analysis in this new high-throughput omics age and discuss the implications of the changing cancer demographic to translational omics research.

摘要

基因组学时代极大地促进了对癌症生物学的理解。与此同时,癌症基因组的巨大复杂性以及即使在组织学上相似的肿瘤之间在全基因组(或组学)水平上存在的明显异质性也已经显露出来。多组学数据库的大量积累和公开可用性,以及相关的临床注释,包括肿瘤组织学、患者反应和结果,是一个丰富的资源,有可能将高通量组学快速转化为提高整体生存率。在这个高通量组学的新时代,我们专注于基因组分析的多维方法的独特优势,并讨论癌症人群变化对转化组学研究的影响。