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肿瘤-基因组学:多组学水平数据的整合以实现准确的表型预测。

Onco-proteogenomics: Multi-omics level data integration for accurate phenotype prediction.

机构信息

a Department of Laboratory Medicine and Pathobiology , University of Toronto , Toronto , ON , Canada.

b Department of Pathology and Laboratory Medicine , Mount Sinai Hospital, Joseph and Wolf Lebovic Health Complex , Toronto , ON , Canada.

出版信息

Crit Rev Clin Lab Sci. 2017 Sep;54(6):414-432. doi: 10.1080/10408363.2017.1384446. Epub 2017 Oct 12.

Abstract

The overall goal of translational oncology is to identify molecular alterations indicative of cancer or of responsiveness to specific therapeutic regimens. While next-generation sequencing has played a pioneering role in this quest, the latest advances in proteomic technologies promise to provide a holistic approach to the further elucidation of tumor biology. Genetic information may be written in DNA and flow from DNA to RNA to protein, according to the central dogma of molecular biology, but the observed phenotype is dictated predominantly by the DNA protein coding region-derived proteotype. Proteomics holds the potential to bridge the gap between genotype and phenotype, because the powerful analytical tool of mass spectrometry has reached a point of maturity to serve this purpose effectively. This integration of "omics" data has given birth to the novel field of onco-proteogenomics, which has much to offer to precision medicine and personalized patient management. Here, we review briefly how each "omics" technology has individually contributed to cancer research, discuss technological and computational advances that have contributed to the realization of onco-proteogenomics, and summarize current and future translational applications.

摘要

肿瘤转化医学的总体目标是确定分子改变,这些改变提示癌症或对特定治疗方案的反应性。虽然下一代测序在这一探索中发挥了开创性作用,但蛋白质组学技术的最新进展有望为进一步阐明肿瘤生物学提供一种整体方法。根据分子生物学的中心法则,遗传信息可以写在 DNA 中,并从 DNA 流向 RNA 再流向蛋白质,但观察到的表型主要由 DNA 蛋白编码区衍生的蛋白质组决定。蛋白质组学有可能弥合基因型和表型之间的差距,因为强大的分析工具质谱已达到有效实现这一目标的成熟阶段。这种“组学”数据的整合产生了新的肿瘤蛋白质组学领域,它为精准医学和个性化患者管理提供了很多帮助。在这里,我们简要回顾一下每种“组学”技术如何单独为癌症研究做出贡献,讨论有助于实现肿瘤蛋白质组学的技术和计算进展,并总结当前和未来的转化应用。

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