New molecular tools for precision medicine in pituitary neuroendocrine tumors.

IF 2.5 Q3 ENDOCRINOLOGY & METABOLISM Minerva endocrinology Pub Date : 2024-09-01 Epub 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
{"title":"New molecular tools for precision medicine in pituitary neuroendocrine tumors.","authors":"Montserrat Marques-Pamies, Joan Gil, Elena Valassi, Laura Pons, Cristina Carrato, Mireia Jordà, Manel Puig-Domingo","doi":"10.23736/S2724-6507.23.04063-0","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":18690,"journal":{"name":"Minerva endocrinology","volume":" ","pages":"300-320"},"PeriodicalIF":2.5000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Minerva endocrinology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23736/S2724-6507.23.04063-0","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/23 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
引用次数: 0

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.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
4.60
自引率
0.00%
发文量
146
期刊最新文献
Diet quality in patients with treatment-resistant schizophrenia: time for improving nutritional recommendations. Zebrafish model in the relentless race to tyrosine kinase inhibitors for neuroendocrine neoplasms. Assessing the impact of a dedicated referral and management algorithm in maternal hypothyroidism. Divulging the overlooked condition: diabetic ketoacidosis as an imminent risk with sodium-glucose co-transporter-2 inhibitors treatment in type 2 diabetes mellitus. Obesity prevention across the lifespan: assessing the efficacy of intervention studies and discussing future challenges.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1