Genomic profiling for clinical decision making in lymphoid neoplasms

计算生物学 疾病 表观遗传学 免疫分型 基因组学 生物 生物信息学 基因组 医学 病理 遗传学 基因 流式细胞术
作者
Laurence de Leval,Ash A. Alizadeh,P. Leif Bergsagel,Elı́as Campo,Andrew Davies,Ahmet Doǧan,Jude Fitzgibbon,Steven M. Horwitz,Ari Melnick,William G. Morice,Ryan D. Morin,Bertrand Nadel,Stefano Pileri,Richard Rosenquist,Davide Rossi,Itziar Salaverría,Christian Steidl,Steven P. Treon,Andrew D. Zelenetz,Ranjana H. Advani
出处
期刊:Blood [Elsevier BV]
卷期号:140 (21): 2193-2227 被引量:158
标识
DOI:10.1182/blood.2022015854
摘要

With the introduction of large-scale molecular profiling methods and high-throughput sequencing technologies, the genomic features of most lymphoid neoplasms have been characterized at an unprecedented scale. Although the principles for the classification and diagnosis of these disorders, founded on a multidimensional definition of disease entities, have been consolidated over the past 25 years, novel genomic data have markedly enhanced our understanding of lymphomagenesis and enriched the description of disease entities at the molecular level. Yet, the current diagnosis of lymphoid tumors is largely based on morphological assessment and immunophenotyping, with only few entities being defined by genomic criteria. This paper, which accompanies the International Consensus Classification of mature lymphoid neoplasms, will address how established assays and newly developed technologies for molecular testing already complement clinical diagnoses and provide a novel lens on disease classification. More specifically, their contributions to diagnosis refinement, risk stratification, and therapy prediction will be considered for the main categories of lymphoid neoplasms. The potential of whole-genome sequencing, circulating tumor DNA analyses, single-cell analyses, and epigenetic profiling will be discussed because these will likely become important future tools for implementing precision medicine approaches in clinical decision making for patients with lymphoid malignancies.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
香蕉觅云应助LegendThree采纳,获得10
1秒前
小趴菜发布了新的文献求助10
1秒前
小二郎应助小车采纳,获得10
1秒前
徐妮发布了新的文献求助10
2秒前
13223456发布了新的文献求助10
2秒前
万能图书馆应助Babyblue采纳,获得10
3秒前
研友_VZG7GZ应助jimmy采纳,获得10
3秒前
xyu发布了新的文献求助10
4秒前
5秒前
传统的盼曼完成签到,获得积分10
6秒前
tooty完成签到,获得积分10
6秒前
yongp发布了新的文献求助10
6秒前
854fycchjh完成签到,获得积分10
6秒前
9秒前
不安千万完成签到,获得积分10
10秒前
舒服的嚓茶完成签到,获得积分10
10秒前
qiqi完成签到,获得积分10
12秒前
小韩完成签到,获得积分10
13秒前
大模型应助cnm采纳,获得10
13秒前
无极微光应助迷人念柏采纳,获得20
13秒前
13秒前
问筠完成签到,获得积分10
14秒前
14秒前
sdfwsdfsd完成签到,获得积分10
14秒前
Akim应助xyu采纳,获得10
14秒前
14秒前
Owen应助科研人采纳,获得10
15秒前
FashionBoy应助虚幻凡柔采纳,获得10
15秒前
李忆梦完成签到 ,获得积分10
15秒前
虚拟的如容完成签到,获得积分10
16秒前
科研小白发布了新的文献求助10
16秒前
16秒前
晨曦完成签到,获得积分10
16秒前
molihuakai应助duanhahaha采纳,获得10
17秒前
wanci应助yongp采纳,获得10
17秒前
17秒前
18秒前
seven完成签到,获得积分10
18秒前
19秒前
顺利凌兰发布了新的文献求助10
20秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
48V Low-voltage Power Distribution Network (PDN) Architecture Industry Report, 2024 800
Fundamentals of Pharmaceutical and Biologics Regulations: A Global Perspective, Second Edition 700
Matrix Methods in Data Mining and Pattern Recognition Second Edition 610
适配Micro-LED色转换的高兼容性量子点负性光刻胶制备与工艺研究 500
Direct and Iterative Linear System Solvers 500
Vander's Renal Physiology第10版 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7309991
求助须知:如何正确求助?哪些是违规求助? 8926936
关于积分的说明 18920247
捐赠科研通 6972065
什么是DOI,文献DOI怎么找? 3213087
关于科研通互助平台的介绍 2381440
邀请新用户注册赠送积分活动 2191228