2nd Department of Pathology, Semmelweis University, 1091 Budapest, Hungary.
Clin Exp Metastasis. 2010 Aug;27(6):371-87. doi: 10.1007/s10585-010-9307-2. Epub 2010 Feb 24.
It was expected that with the advent of genomics, oncology may defeat the deadliest forms of cancer including malignant melanoma, but the past years have indicated that this is not the case. Despite the stunning success of genomics in defining markers or gene signatures for breast cancer prognosis and predicting therapies, there is virtually no progression in malignant melanoma. This is happening when experimental oncology or metastasis research is using several rodent and human melanoma models, when our knowledge on the metastatic cascade is actually derived from these models. Our critical analysis of these studies revealed several factors which might be responsible for this failure. First, it is evident, that these studies must be based on rigorous sample collection and basic pathological considerations, where divergent histological types of melanoma cannot be analysed universally. Secondly, without following basic consideration of metastasis biology, the majority of these studies were rarely based on primary tumors but frequently on various types of regional metastases. Third, successful expression profiling studies on other tumors such as breast cancer, provided evidences that the homogeneity of the patient cohort at least by clinicopathological stage is a critical element when defining prognostic signatures. Four studies attempted to define the prognostic signature of skin melanoma but only one based the study on the primary tumor resulting in heterogenous signatures with a minimal overlap (MCM3 and NFKBIZ). Four study attempted to define the invasiveness-signature in the primary tumor based on thickness or growth pattern discrimination identifying a 9-gene overlap which proved to be different from the prognostic signatures. On the other hand, seven studies analyzed various types of metastatic tissues (rarely visceral-, mostly cutaneous or lymphatic metastases) to define the metastasis-signatures, again with minimal overlap (AQP3, LGALS7 and SFN). Using seven GEO-based melanoma datasets we have performed a meta-analysis of the metastasis-gene signatures using normalization protocols. This analysis identified a 350-gene signature, the core of which was a 17-gene signature characterizing locoregional metastases where the individual components occurred in 3 studies: several members of this signature were extensively studied before in context of melanoma metastasis including WNT5A, EGFR, BCL2A1 and OPN. These data suggest that only efficient inter-disciplinary collaboration throughout genomic analysis of human skin melanoma could lead to major advances in defining relevant gene-sets appropriate for clinical prognostication or revealing basic molecular pathways of melanoma progression.
人们曾预计,随着基因组学的出现,肿瘤学可能会攻克包括恶性黑色素瘤在内的最致命形式的癌症,但过去几年的情况表明,事实并非如此。尽管基因组学在定义乳腺癌预后的标志物或基因特征以及预测疗法方面取得了惊人的成功,但恶性黑色素瘤实际上几乎没有进展。当实验肿瘤学或转移研究使用几种啮齿动物和人类黑色素瘤模型时,这种情况就会发生,而我们对转移级联的了解实际上正是源自这些模型。我们对这些研究的批判性分析揭示了几个可能导致这种失败的因素。首先,很明显,这些研究必须基于严格的样本收集和基本的病理考虑,在这些考虑中,不能普遍分析黑色素瘤的不同组织学类型。其次,由于没有遵循转移生物学的基本考虑,这些研究中的大多数很少基于原发性肿瘤,而是经常基于各种类型的局部转移。第三,在其他肿瘤(如乳腺癌)上进行的成功表达谱研究提供了证据,即患者队列的同质性,至少通过临床病理分期,是定义预后特征时的一个关键因素。四项研究试图定义皮肤黑色素瘤的预后特征,但只有一项研究基于原发性肿瘤,导致特征存在显著差异(MCM3 和 NFKBIZ)。四项研究试图基于厚度或生长模式的差异来识别侵袭性特征,确定了 9 个基因重叠,这些基因与预后特征不同。另一方面,有七项研究分析了各种类型的转移性组织(很少是内脏转移,大部分是皮肤或淋巴转移),以定义转移特征,其重叠也很小(AQP3、LGALS7 和 SFN)。我们使用七个基于 GEO 的黑色素瘤数据集,使用标准化方案对转移基因特征进行了荟萃分析。该分析确定了一个 350 个基因的特征,其核心是一个 17 个基因的特征,该特征描述了局部区域转移,其中该特征的各个成分在 3 项研究中出现:该特征的许多成员之前在黑色素瘤转移的背景下进行了广泛研究,包括 WNT5A、EGFR、BCL2A1 和 OPN。这些数据表明,只有通过对人类皮肤黑色素瘤的基因组分析进行有效的跨学科合作,才能在定义适合临床预后或揭示黑色素瘤进展的基本分子途径的相关基因集方面取得重大进展。
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