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基于影像学的基因组学预测肺癌放疗疗效。

Radiogenomics predicting tumor responses to radiotherapy in lung cancer.

机构信息

The University of Texas Southwestern Medical Center, The Hamon Center for Therapeutic Oncology Research, Dallas, TX 75390-8593, USA.

出版信息

Semin Radiat Oncol. 2010 Jul;20(3):149-55. doi: 10.1016/j.semradonc.2010.01.002.

Abstract

The recently developed ability to interrogate genome-wide data arrays has provided invaluable insights into the molecular pathogenesis of lung cancer. These data have also provided information for developing targeted therapy in lung cancer patients based on the identification of cancer-specific vulnerabilities and set the stage for molecular biomarkers that provide information on clinical outcome and response to treatment. In addition, there are now large panels of lung cancer cell lines, both non-small-cell lung cancer and small-cell lung cancer, that have distinct chemotherapy and radiation response phenotypes. We anticipate that the integration of molecular data with therapy response data will allow for the generation of biomarker signatures that predict response to therapy. These signatures will need to be validated in clinical studies, at first retrospective analyses and then prospective clinical trials, to show that the use of these biomarkers can aid in predicting patient outcomes (eg, in the case of radiation therapy for local control and survival). This review highlights recent advances in molecular profiling of tumor responses to radiotherapy and identifies challenges and opportunities in developing molecular biomarker signatures for predicting radiation response for individual patients with lung cancer.

摘要

最近发展起来的全基因组数据阵列检测能力为肺癌的分子发病机制提供了宝贵的见解。这些数据还为基于鉴定癌症特异性弱点的肺癌患者的靶向治疗提供了信息,并为分子生物标志物奠定了基础,这些标志物提供了有关临床结果和治疗反应的信息。此外,现在有大量的肺癌细胞系面板,包括非小细胞肺癌和小细胞肺癌,它们具有不同的化疗和放射反应表型。我们预计,将分子数据与治疗反应数据相结合,将生成能够预测治疗反应的生物标志物特征。这些特征需要在临床研究中进行验证,首先是回顾性分析,然后是前瞻性临床试验,以表明使用这些生物标志物可以帮助预测患者的预后(例如,在放射治疗局部控制和生存的情况下)。这篇综述强调了肿瘤对放射治疗反应的分子分析的最新进展,并确定了为预测肺癌患者的放射反应开发分子生物标志物特征的挑战和机遇。

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本文引用的文献

2
Systems biology modeling of the radiation sensitivity network: a biomarker discovery platform.
Int J Radiat Oncol Biol Phys. 2009 Oct 1;75(2):497-505. doi: 10.1016/j.ijrobp.2009.05.056.
3
Integration of molecular profiling into the lung cancer clinic.
Clin Cancer Res. 2009 Sep 1;15(17):5317-22. doi: 10.1158/1078-0432.CCR-09-0913. Epub 2009 Aug 25.
4
Cancer stem cells and tumor response to therapy: current problems and future prospects.
Semin Radiat Oncol. 2009 Apr;19(2):96-105. doi: 10.1016/j.semradonc.2008.11.004.
6
Biomarkers for the lung cancer diagnosis and their advances in proteomics.
BMB Rep. 2008 Sep 30;41(9):615-25. doi: 10.5483/bmbrep.2008.41.9.615.
7
Radiotherapy for small-cell lung cancer-Where are we heading?
Lung Cancer. 2009 Mar;63(3):307-14. doi: 10.1016/j.lungcan.2008.06.013. Epub 2008 Aug 3.
8
Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study.
Nat Med. 2008 Aug;14(8):822-7. doi: 10.1038/nm.1790. Epub 2008 Jul 20.
10
Advances in anti-VEGF and anti-EGFR therapy for advanced non-small cell lung cancer.
Lung Cancer. 2009 Jan;63(1):1-9. doi: 10.1016/j.lungcan.2008.05.015. Epub 2008 Jun 25.

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