van der Vegt B, de Bock G H, Hollema H, Wesseling J
Department of Pathology and Laboratory Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Crit Rev Oncol Hematol. 2009 Apr;70(1):1-11. doi: 10.1016/j.critrevonc.2008.09.001. Epub 2008 Oct 9.
65-80% of the patients with breast cancer might not benefit from the adjuvant therapy they receive based on 'classical' markers used for the selection for adjuvant therapy. Therefore it is necessary to develop new markers that are able to tailor treatment for an individual patient. A number of microarray methods have been developed in recent years to accommodate this search for new factors that determine breast cancer progression. We give an overview of the most commonly used microarray methods to identify tumour progression markers (oligo- or cDNA arrays, CGH arrays, PCR arrays, and tissue microarrays). Their applications will be illustrated using the most influential examples from literature. The potentials, limitations and the related statistical analyses of each method are discussed. We conclude that microarray studies have led to an increase in the understanding of the complexity and diversity of breast carcinoma and have provided clinical relevant subgroups of breast cancer that may benefit from patient tailored treatment. Still, more extensive external validation and long-term follow-up will be necessary before such assays can be implemented into routine clinical practice. Most likely, these novel prognostic indicators will be complementary to the already available classical prognostic factors.
基于用于辅助治疗选择的“经典”标志物,65%至80%的乳腺癌患者可能无法从其接受的辅助治疗中获益。因此,有必要开发能够为个体患者量身定制治疗方案的新标志物。近年来已开发出多种微阵列方法,以满足对确定乳腺癌进展的新因素的探索需求。我们将概述用于识别肿瘤进展标志物的最常用微阵列方法(寡核苷酸或cDNA阵列、比较基因组杂交阵列、PCR阵列和组织微阵列)。将通过文献中最具影响力的实例来说明它们的应用。讨论了每种方法的潜力、局限性及相关统计分析。我们得出结论,微阵列研究增进了对乳腺癌复杂性和多样性的理解,并提供了可能从患者量身定制治疗中获益的临床相关乳腺癌亚组。不过,在将此类检测方法应用于常规临床实践之前,仍需要更广泛的外部验证和长期随访。很可能,这些新的预后指标将与现有的经典预后因素互补。