New molecular tools for precision medicine in pituitary neuroendocrine tumors.

IF 2.5 Q3 ENDOCRINOLOGY & METABOLISM Minerva endocrinology Pub Date : 2024-01-23 DOI:10.23736/S2724-6507.23.04063-0
Montserrat Marques-Pamies, Joan Gil, Elena Valassi, Laura Pons, Cristina Carrato, Mireia Jordà, Manel Puig-Domingo
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Abstract

Precision, personalized, or individualized medicine in pituitary neuroendocrine tumors (PitNETs) has become a major topic in the last few years. It is based on the use of biomarkers that predictively segregate patients and give answers to clinically relevant questions that help us in the individualization of their management. It allows us to make early diagnosis, predict response to medical treatments, predict surgical outcomes and investigate new targets for therapeutic molecules. So far, substantial progress has been made in this field, although there are still not enough precise tools that can be implemented in clinical practice. One of the main reasons is the excess overlap among clustered patients, with an error probability that is not currently acceptable for clinical practice. This overlap is due to the high heterogeneity of PitNETs, which is too complex to be overcome by the classical biomarker investigation approach. A systems biology approach based on artificial intelligence techniques seems to be able to give answers to each patient individually by building mathematical models through the interaction of multiple factors, including those of omics sciences. Integrated studies of different molecular omics techniques, as well as radiomics and clinical data are necessary to understand the whole system and to finally achieve the key to obtain precise biomarkers and implement personalized medicine. In this review we have focused on describing the current advances in the area of PitNETs based on the omics sciences, that are clearly going to be the new tool for precision medicine.

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垂体神经内分泌肿瘤精准医疗的新分子工具。
垂体神经内分泌肿瘤(PitNET)的精准化、个性化或个体化医疗在过去几年中已成为一个重要话题。它基于生物标志物的使用,这些生物标志物可预测性地分离患者,并回答临床相关问题,帮助我们对患者进行个体化管理。它使我们能够进行早期诊断、预测对药物治疗的反应、预测手术效果并研究治疗分子的新靶点。迄今为止,这一领域已取得了长足的进步,但仍没有足够的精确工具可用于临床实践。其中一个主要原因是聚类患者之间的过度重叠,其误差概率目前还不能为临床实践所接受。这种重叠是由于 PitNET 的高度异质性造成的,这种异质性过于复杂,传统的生物标志物调查方法无法克服。以人工智能技术为基础的系统生物学方法似乎可以通过多种因素(包括全息科学因素)的相互作用建立数学模型,从而为每位患者单独提供答案。有必要对不同的分子全息技术以及放射组学和临床数据进行综合研究,以了解整个系统,并最终获得精确的生物标志物和实施个性化医疗的关键。在这篇综述中,我们重点介绍了目前基于全息科学的 PitNET 领域的研究进展,这些研究显然将成为精准医疗的新工具。
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CiteScore
4.60
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发文量
146
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