Pfeffer Ulrich, Romeo Francesco, Noonan Douglas M, Albini Adriana
Functional Genomics, National Cancer Research Institute, Genoa, Italy.
Clin Exp Metastasis. 2009;26(6):547-58. doi: 10.1007/s10585-009-9254-y. Epub 2009 Mar 24.
Current concepts conceive "breast cancer" as a complex disease that comprises several very different types of neoplasms. Nonetheless, breast cancer treatment has considerably improved through early diagnosis, adjuvant chemotherapy, and endocrine treatments. The limited prognostic power of classical classifiers determines considerable over-treatment of women who either do not benefit from, or do not at all need, chemotherapy. Several gene expression based molecular classifiers (signatures) have been developed for a more reliable prognostication. Gene expression profiling identifies profound differences in breast cancers, most probably as a consequence of different cellular origin and different driving mutations and can therefore distinguish the intrinsic propensity to metastasize. Existing signatures have been shown to be useful for treatment decisions, although they have been developed using relatively small sample numbers. Major improvements are expected from the use of large datasets, subtype specific signatures and from the re-introduction of functional information. We show that molecular signatures encounter clear limitations given by the intrinsic probabilistic nature of breast cancer metastasis. Already today, signatures are, however, useful for clinical decisions in specific cases, in particular if the personal inclination of the patient towards different treatment strategies is taken into account.
当前的观念认为“乳腺癌”是一种复杂的疾病，它包含几种非常不同类型的肿瘤。尽管如此，通过早期诊断、辅助化疗和内分泌治疗，乳腺癌的治疗已经有了显著改善。传统分类器的预后能力有限，这导致对那些无法从化疗中获益或根本不需要化疗的女性进行了过度治疗。已经开发了几种基于基因表达的分子分类器（特征）用于更可靠的预后评估。基因表达谱分析揭示了乳腺癌中的深刻差异，这很可能是不同细胞起源和不同驱动突变的结果，因此可以区分转移的内在倾向。现有的特征已被证明对治疗决策有用，尽管它们是使用相对较小的样本数量开发的。使用大型数据集、亚型特异性特征以及重新引入功能信息有望带来重大改进。我们表明，鉴于乳腺癌转移的内在概率性质，分子特征存在明显的局限性。然而，即便在当下，特征对于特定病例的临床决策也是有用的，特别是如果考虑到患者对不同治疗策略的个人倾向。
