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基因资料库在预测肺癌成效不如预期

来源:医源世界 作者:Allison Gandey 2007-6-20
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摘要: 研究者表示,较容易进行且较便宜的基因表现资料库,并未胜过标准病理检验,这项以微阵列为基础的肺癌临床预后预测试验的回顾性文章发现,这项新的技术实际上可能较不实用,且最好留给传统技术与工具不易判断的病例。 明尼苏达州罗彻斯特梅约诊所Zhifu Sun医师向Medscape表示,我们通常假设新的技术会比目前的好,但是我......


  December 6, 2006 – 研究者表示,较容易进行且较便宜的基因表现资料库,并未胜过标准病理检验,这项以微阵列为基础的肺癌临床预后预测试验的回顾性文章发现,这项新的技术实际上可能较不实用,且最好留给传统技术与工具不易判断的病例;该试验结果发表于11月号的癌症流行病学生物标记与预防期刊上。
  
  明尼苏达州罗彻斯特梅约诊所Zhifu Sun医师向Medscape表示,我们通常假设新的技术会比目前的好,但是我们的研究显示,基因表现资料库在预测预后上并不具更多优势。
  
  在一项有关该试验的记者会中,研究者指出,当基因表现资料库已经被成功应用于分类不同肿瘤,并且评估肿瘤分级、转移、以及病患存活率,但最近的研究结果证实了这项新技术在预测预后上并未提供更多的好处。
  
  梅约诊所的资深作者Ping Yang医师向主播表示,越来越多的证据显示,以基因为基础的预测是不稳定,且目前并不清楚基因表现资料库相较于临床与病理检验在预测预后的能力上是否有差异。
  
  【肿瘤的快照】
  研究者在论文中表示,就像病理学检验,基因表现资料库是肿瘤生长某个过程的快照,但仅在其分子阶段;如果有对肿瘤转移扮演关键角色的基因,它们可能被明显组织表现型,例如肿瘤细胞种类、分级有关的过度基因表现压制,且这些基因的表现并不容易以常见的分析方法侦测;研究者表示,这些基因可能以低于目前DNA微阵列技术能够侦测的程度表现。
  
  目前这项研究由国家卫生研究院、国家癌症机构、与梅约诊所赞助,整理了目前最新的以微阵列为基础的肺癌临床预后预测文献,研究者也针对样本数目够大、有足够临床资讯的试验进行次组分析。
  
  研究者发现,不同试验之间以基因表现为基础的预后差异很大,且他们所观察到的大部分缺乏独立确效;他们发现,当传统的预后因子,包括年龄、性别、分期、细胞种类、与肿瘤分级一起考虑时,基因表现资料库的表现就相对较差。
  
  我们的结论是,由目前分析方法选择的基因表现签名可以透过已知的传统预后因子取代,他们表示,特别是病理检验亚型与分化等级。

Gene Profiling Less Effective in Predicting Lung Cancer

By Allison Gandey
Medscape Medical News

December 6, 2006 ??Researchers report that gene-expression profiling fails to outperform standard histologic examinations, which are less expensive and easily conducted. Their findings, a review of microarray-based lung cancer clinical-outcome prediction studies, show that the new technique may be less useful in practice and may be best reserved for cases that challenge conventional methods and tools. The results appear in the November issue of Cancer Epidemiology Biomarkers & Prevention.




"We often assume that new approaches are better than current ones," lead author Zhifu Sun, MD, from the Mayo Clinic in Rochester, Minnesota, told Medscape. "But our study demonstrates that gene-expression profiling does not provide much of an advantage in outcome prediction."



In a news release about the study, the researchers point out that while gene-expression profiling has been successfully used to classify various tumors and assess tumor stage, metastasis, and patient survival rates, recent data have shown that gene-based prediction for lung cancer is not yet consistent.



?rowing evidence suggests that gene-based prediction is not stable, and little is known about the prediction power of a gene-expression profile as compared with well-known clinical and pathologic predictors,??senior author Ping Yang, MD, also from the Mayo Clinic, told reporters.



A Snapshot of a Tumor



Like histologic examination, gene-expression profiling is a snapshot of a tumor at certain point of its growth, only it is at the molecular level, the researchers explain in their paper. If there are genes critical for metastasis, they are likely overwhelmed by highly expressed genes responsible for an obvious histologic phenotype such as tumor-cell type or grade and are not easily detectable by common analytical approaches. These genes, the researchers say, may be expressed at levels that are below the detection limit of current DNA microarray technology.



The current study, funded by the National Institutes of Health, the National Cancer Institute, and the Mayo Clinic, summarized up-to-date publications in microarray-based lung cancer clinical-outcome prediction. The investigators also conducted secondary analyses of the studies with sufficient sample sizes and associated clinical information.



The researchers found that the accuracy of gene-expression?ased outcome varied greatly among studies, and they observed that most lacked independent validation. They found that when conventional predictors of age, gender, stage, cell type, and tumor grade are considered collectively, the predictive advantage of the gene-expression profile diminishes.



"We conclude that outcome prediction from gene-expression signatures selected by current analytical approaches can be mostly explained by well-known conventional predictors," they write, "particularly histologic subtype and grade of differentiation."



Cancer Epidemiol Biomarkers Prev. 2006;15:2063-2068.


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