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神经内分泌肿瘤生物标志物:从单一分析物到转录本及算法

Neuroendocrine tumor biomarkers: From monoanalytes to transcripts and algorithms.

作者信息

Modlin Irvin M, Bodei Lisa, Kidd Mark

机构信息

Emeritus Professor Gastroenterological Surgery, Yale University, School of Medicine, USA.

Division of Nuclear Medicine, European Institute of Oncology, Milan, Italy.

出版信息

Best Pract Res Clin Endocrinol Metab. 2016 Jan;30(1):59-77. doi: 10.1016/j.beem.2016.01.002. Epub 2016 Jan 18.

Abstract

The management of neuroendocrine neoplasia remains a perplexing problem because of the lack of knowledge of the biology of the disease, its late presentation, the relative insensitivity of imaging modalities and a paucity of predictably effective treatment options. A critical limitation is posed by the lack of accurate biomarkers to guide management, monitor the efficacy of therapy and provide a prognostic assessment of disease progress. Currently utilized monoanalyte biomarkers (e.g. chromogranin, serotonin, pancreastatin etc.) exhibit variable metrics, poor sensitivity, specificity, and predictive ability and are rarely used to guide clinical decision making. A National Cancer Institute Neuroendocrine Tumor summit conference held in 2007 noted biomarker limitations to be a crucial unmet need in the management of neuroendocrine tumors. Nevertheless little progress has been made in this field until recently with the consideration of blood transcript analysis, circulating tumor cells and miRNA measurement. Given the complexity and multidimensionality of the neoplastic process itself, the heterogeneity of neuroendocrine tumors (NET) as well as the interaction of the tumor microenvironment, it is not unexpected that no single (monoanalyte) biomarker has proven to be effective. This deduction reflects the growing recognition that use of a monoanalyte to define a multidimensional disease process has inherent flaws. Logic dictates that no single measured parameter can capture the pathobiological diversity of neoplasia and monoanalytes cannot define the multiple variables (proliferation, metabolic activity, invasive potential and metastatic propensity) that constitute tumor growth. Thus far, most biomarkers whether in tissue or blood/urine have been single analytes with varying degrees of sensitivity and specificity and in general have failed to exhibit robust metrics or lacked methodological rigor. Neuroendocrine (NE) disease represents an area of biomarker paucity since the individual biomarkers (gastrin, insulin etc) are not widely applicable to the diverse types of NE neoplasia (NEN). Broad spectrum markers such as CgA have limitations in sensitivity, specificity and reproducibility. This review serves to provide a general background of the evolution of NET biomarkers. It provides an assessment of their current and past usage and limitations in assessing their diagnostic, pathologic and prognostic aspects in respect of NET. It provides a view of the changing methodology of biomarker development and the application of biomathematical analyses to redefining detection and treatment. Finally, it presents a description and consensus on current advances in transcript analysis, miRNA measurement and circulating tumor cell identification.

摘要

神经内分泌肿瘤的管理仍然是一个令人困惑的问题,原因在于对该疾病生物学特性了解不足、其出现较晚、成像方式相对不敏感以及可预测的有效治疗选择匮乏。缺乏准确的生物标志物来指导管理、监测治疗效果以及对疾病进展进行预后评估构成了一个关键限制。目前使用的单一分析物生物标志物(如嗜铬粒蛋白、血清素、胰抑制素等)表现出不同的指标、较差的敏感性、特异性和预测能力,很少用于指导临床决策。2007年举行的一次美国国立癌症研究所神经内分泌肿瘤峰会指出,生物标志物的局限性是神经内分泌肿瘤管理中一个关键的未满足需求。然而,直到最近考虑血液转录本分析、循环肿瘤细胞和微小RNA测量之前,该领域进展甚微。鉴于肿瘤形成过程本身的复杂性和多维度性、神经内分泌肿瘤(NET)的异质性以及肿瘤微环境的相互作用,没有单一(单一分析物)生物标志物被证明有效也就不足为奇了。这一推断反映出越来越多的人认识到,使用单一分析物来定义一个多维度的疾病过程存在固有缺陷。逻辑表明,没有单一测量参数能够捕捉肿瘤形成的病理生物学多样性,单一分析物无法定义构成肿瘤生长的多个变量(增殖、代谢活性、侵袭潜能和转移倾向)。到目前为止,大多数生物标志物,无论是组织中的还是血液/尿液中的,都是单一分析物,具有不同程度的敏感性和特异性,总体上未能表现出可靠的指标或缺乏方法学严谨性。神经内分泌(NE)疾病代表了生物标志物匮乏的领域,因为个体生物标志物(胃泌素、胰岛素等)并不广泛适用于各种类型的NE肿瘤(NEN)。广谱标志物如嗜铬粒蛋白A在敏感性、特异性和可重复性方面存在局限性。本综述旨在提供NET生物标志物演变的一般背景。它评估了它们在评估NET的诊断、病理和预后方面的当前和过去的使用情况及局限性。它展示了生物标志物开发方法的变化以及生物数学分析在重新定义检测和治疗方面的应用。最后,它介绍并就转录本分析、微小RNA测量和循环肿瘤细胞鉴定方面的当前进展达成共识。

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