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从形态学到分子:乳腺癌的既定和新兴分子诊断。

From morphologic to molecular: established and emerging molecular diagnostics for breast carcinoma.

机构信息

Department of Molecular Pathology, Pathology and Laboratory Medicine Institute, Cleveland Clinic, Lerner College of Medicine, Cleveland, OH 44195, USA.

出版信息

N Biotechnol. 2012 Sep 15;29(6):665-81. doi: 10.1016/j.nbt.2012.03.011. Epub 2012 Apr 6.

DOI:10.1016/j.nbt.2012.03.011
PMID:22504737
Abstract

Diagnostics in the field of breast carcinoma are constantly evolving. The recent wave of molecular methodologies, both microscope and non-microscope based, have opened new ways to gain insight into this disease process and have moved clinical diagnostics closer to a 'personalized medicine' approach. In this review we highlight some of the advancements that laboratory medicine technology is making toward guiding the diagnosis, prognosis, and therapy selection for patients affected by breast carcinoma. The content of the article is largely structured by methodology, with a distinct emphasis on both microscope based and non-microscope based diagnostic formats. Where possible, we have attempted to emphasize the potential benefits as well as limitations to each of these technologies. Successful molecular diagnostics, applied in concert within the morphologic context of a patient's tumor, are what will lay the foundation for personalized therapy and allow a more sophisticated approach to clinical trial stratification. The future of breast cancer diagnostics looks challenging, but it is also a field of great opportunity. Never before have there been such a plethora of new tools available for disease investigation or candidate therapy selection.

摘要

在乳腺癌诊断领域,各种技术方法正在不断发展。最近,显微镜和非显微镜技术领域的分子方法学的发展为深入了解这一疾病过程开辟了新途径,并使临床诊断更接近“个性化医疗”方法。在这篇综述中,我们强调了实验室医学技术在指导乳腺癌患者的诊断、预后和治疗选择方面所取得的一些进展。文章的内容主要按方法学结构组织,特别强调基于显微镜和非显微镜的诊断方法。在可能的情况下,我们试图强调这些技术的潜在优势和局限性。成功的分子诊断,与患者肿瘤的形态学背景相结合,将为个性化治疗奠定基础,并为临床试验分层提供更精细的方法。乳腺癌诊断的未来充满挑战,但也是一个极具发展机遇的领域。以前从未有过如此众多的新工具可用于疾病研究或候选治疗方法的选择。

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引用本文的文献

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Survival analysis in breast cancer using proteomic data from four independent datasets.使用来自四个独立数据集的蛋白质组学数据进行乳腺癌的生存分析。
Sci Rep. 2021 Aug 18;11(1):16787. doi: 10.1038/s41598-021-96340-5.
2
The Discovery of Novel Biomarkers Improves Breast Cancer Intrinsic Subtype Prediction and Reconciles the Labels in the METABRIC Data Set.新型生物标志物的发现改善了乳腺癌内在亚型预测并协调了METABRIC数据集中的标签。
PLoS One. 2015 Jul 1;10(7):e0129711. doi: 10.1371/journal.pone.0129711. eCollection 2015.