实体瘤从omics到clinicomics的历程:成功案例与挑战。

3区 生物学 Q1 Biochemistry, Genetics and Molecular Biology
Sanjana Mehrotra, Sankalp Sharma, Rajeev Kumar Pandey
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引用次数: 0

摘要

癌症 "一词包含一组不同的疾病类型,其病理特征、基因改变和对疗法的反应各不相同。据世界卫生组织统计,癌症是全球第二大死因,每六例死亡中就有一例是死于癌症,因此给全球医疗保健系统带来了沉重负担。高通量 omics 技术与先进的成像工具相结合,彻底改变了我们探究肿瘤分子结构的能力,并为我们提供了前所未有的对疾病的理解。然而,从基础研究发现到将其转化为具有临床意义的疗法以改善患者护理之间还存在差距。要弥补这一差距,就需要分析来自多组学平台的大量高维数据集。多组学数据与患者病史、组织学检查和成像等临床信息的整合催生了临床组学这一新概念,并可能加快癌症从实验室到临床的转变。放射组学涉及借助深度学习和人工智能(AI)工具从医学影像数据中提取定量特征。这些特征可以捕捉到有关肿瘤形状、质地、强度和空间分布的详细信息。多组学、转化生物信息学、放射组学和临床组学等相关领域结合在一起,可以根据癌症患者的分子特征和临床特点提供循证建议。在本章中,我们将总结实体癌中的多组学研究,并特别关注乳腺癌。我们还回顾了基于机器学习和人工智能的算法及其在癌症诊断、亚型分析、预后判断以及耐药性和复发预测中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A journey from omics to clinicomics in solid cancers: Success stories and challenges.

The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease. Yet, there is a gap between basic research discoveries and their translation into clinically meaningful therapies for improving patient care. To bridge this gap, there is a need to analyse the vast amounts of high dimensional datasets from multi-omics platforms. The integration of multi-omics data with clinical information like patient history, histological examination and imaging has led to the novel concept of clinicomics and may expedite the bench-to-bedside transition in cancer. The journey from omics to clinicomics has gained momentum with development of radiomics which involves extracting quantitative features from medical imaging data with the help of deep learning and artificial intelligence (AI) tools. These features capture detailed information about the tumor's shape, texture, intensity, and spatial distribution. Together, the related fields of multiomics, translational bioinformatics, radiomics and clinicomics may provide evidence-based recommendations tailored to the individual cancer patient's molecular profile and clinical characteristics. In this chapter, we summarize multiomics studies in solid cancers with a specific focus on breast cancer. We also review machine learning and AI based algorithms and their use in cancer diagnosis, subtyping, prognosis and predicting treatment resistance and relapse.

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来源期刊
Advances in protein chemistry and structural biology
Advances in protein chemistry and structural biology BIOCHEMISTRY & MOLECULAR BIOLOGY-
CiteScore
7.40
自引率
0.00%
发文量
66
审稿时长
>12 weeks
期刊介绍: Published continuously since 1944, The Advances in Protein Chemistry and Structural Biology series has been the essential resource for protein chemists. Each volume brings forth new information about protocols and analysis of proteins. Each thematically organized volume is guest edited by leading experts in a broad range of protein-related topics.
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