Predictors of biochemical response to somatostatin receptor ligands in acromegaly

IF 6.1 1区 医学 Q1 ENDOCRINOLOGY & METABOLISM
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Abstract

Although predictors of response to first-generation somatostatin receptor ligands (fg-SRLs), and to a lesser extent to pasireotide, have been studied in acromegaly for many years, their use is still not recommended in clinical guidelines. Is there insufficient evidence to use them? Numerous biomarkers including various clinical, functional, radiological and molecular markers have been identified. The first ones are applicable pre-surgery, while the molecular predictors are utilized for patients not cured after surgery. In this regard, factors predicting a good response to fg-SRLs are specifically: low basal GH, a low GH nadir in the acute octreotide test, T2 MRI hypointensity, a densely granulated pattern, high immunohistochemistry staining for somatostatin receptor 2 (SSTR2), and E-cadherin. However, there is still a lack of consensus regarding which of these biomarkers is more useful or how to integrate them into clinical practice. With classical statistical methods, it is complex to define reliable and generalizable cut-off values for a single biomarker. The potential solution to the limitations of traditional methods involves combining systems biology with artificial intelligence, which is currently providing answers to such long-standing questions that may eventually be finally included into the clinical guidelines and make personalized medicine a reality. The aim of this review is to describe the current knowledge of the main fg-SRLs and pasireotide response predictors, discuss their current usefulness, and point to future directions in the research of this field.

预测肢端肥大症患者对体生长抑素受体配体的生化反应
尽管对第一代体生长抑素受体配体(fg-SRLs)反应的预测指标,以及对帕司瑞奥肽(pasireotide)反应的预测指标已研究多年,但临床指南仍未推荐使用这些指标。使用它们的证据是否不足?目前已经确定了许多生物标志物,包括各种临床、功能、放射学和分子标志物。前者适用于手术前,而分子预测指标则用于手术后未治愈的患者。在这方面,预测对 fg-SRLs 有良好反应的因素具体包括:基础 GH 低、急性奥曲肽试验中 GH 低谷值、T2 MRI 低密度、致密颗粒形态、体生长激素受体 2(SSTR2)免疫组化染色高和 E-粘连蛋白。然而,对于这些生物标记物中哪一个更有用,或如何将它们与临床实践相结合,目前仍缺乏共识。采用传统的统计方法,为单一生物标志物定义可靠且具有普遍意义的临界值非常复杂。解决传统方法局限性的潜在办法是将系统生物学与人工智能相结合,目前人工智能正在为这些长期存在的问题提供答案,这些答案最终可能被纳入临床指南,使个性化医疗成为现实。本综述旨在描述目前对主要的 fg-SRL 和帕西瑞肽反应预测指标的了解,讨论它们目前的实用性,并指出该领域未来的研究方向。
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来源期刊
CiteScore
11.90
自引率
0.00%
发文量
77
审稿时长
6-12 weeks
期刊介绍: Best Practice & Research Clinical Endocrinology & Metabolism is a serial publication that integrates the latest original research findings into evidence-based review articles. These articles aim to address key clinical issues related to diagnosis, treatment, and patient management. Each issue adopts a problem-oriented approach, focusing on key questions and clearly outlining what is known while identifying areas for future research. Practical management strategies are described to facilitate application to individual patients. The series targets physicians in practice or training.
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