Dynamical hallmarks of cancer: Phenotypic switching in melanoma and epithelial-mesenchymal plasticity

表型可塑性 表型 生物 表型转换 癌症 可塑性 黑色素瘤 癌细胞 神经科学 遗传学 基因 物理 热力学
作者
Paras Jain,Maalavika Pillai,Atchuta Srinivas Duddu,Jason A. Somarelli,Yogesh Goyal,Mohit Kumar Jolly
出处
期刊:Seminars in Cancer Biology [Elsevier]
卷期号:96: 48-63
标识
DOI:10.1016/j.semcancer.2023.09.007
摘要

Phenotypic plasticity was recently incorporated as a hallmark of cancer. This plasticity can manifest along many interconnected axes, such as stemness and differentiation, drug-sensitive and drug-resistant states, and between epithelial and mesenchymal cell-states. Despite growing acceptance for phenotypic plasticity as a hallmark of cancer, the dynamics of this process remains poorly understood. In particular, the knowledge necessary for a predictive understanding of how individual cancer cells and populations of cells dynamically switch their phenotypes in response to the intensity and/or duration of their current and past environmental stimuli remains far from complete. Here, we present recent investigations of phenotypic plasticity from a systems-level perspective using two exemplars: epithelial-mesenchymal plasticity in carcinomas and phenotypic switching in melanoma. We highlight how an integrated computational-experimental approach has helped unravel insights into specific dynamical hallmarks of phenotypic plasticity in different cancers to address the following questions: a) how many distinct cell-states or phenotypes exist?; b) how reversible are transitions among these cell-states, and what factors control the extent of reversibility?; and c) how might cell-cell communication be able to alter rates of cell-state switching and enable diverse patterns of phenotypic heterogeneity? Understanding these dynamic features of phenotypic plasticity may be a key component in shifting the paradigm of cancer treatment from reactionary to a more predictive, proactive approach.
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
特兰克斯完成签到,获得积分20
刚刚
米斯特刘完成签到,获得积分20
1秒前
沫沫发布了新的文献求助10
1秒前
R先生发布了新的文献求助50
1秒前
通通通关注了科研通微信公众号
1秒前
snowdrift发布了新的文献求助10
1秒前
英姑应助北挽采纳,获得200
1秒前
kevindeng发布了新的文献求助20
2秒前
yx发布了新的文献求助10
2秒前
3秒前
6680668发布了新的文献求助10
3秒前
baobaonaixi完成签到,获得积分10
3秒前
3秒前
3秒前
三石完成签到 ,获得积分10
3秒前
4秒前
5秒前
5秒前
DAYTOY完成签到,获得积分10
5秒前
6秒前
6秒前
Flllllll完成签到,获得积分10
6秒前
喜悦成威完成签到,获得积分10
6秒前
酷波er应助南佳采纳,获得10
7秒前
7秒前
7秒前
Ava应助yan儿采纳,获得10
7秒前
丘比特应助纯真的莫茗采纳,获得10
7秒前
无花果应助勤恳的素阴采纳,获得10
7秒前
调皮的妙竹完成签到,获得积分10
8秒前
沫沫完成签到,获得积分10
8秒前
wzp发布了新的文献求助10
8秒前
8秒前
程程完成签到,获得积分20
8秒前
打打应助Ll采纳,获得10
8秒前
乐观发卡完成签到,获得积分20
9秒前
安详的帽子完成签到 ,获得积分10
9秒前
9秒前
9秒前
10秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527742
求助须知:如何正确求助?哪些是违规求助? 3107867
关于积分的说明 9286956
捐赠科研通 2805612
什么是DOI,文献DOI怎么找? 1540026
邀请新用户注册赠送积分活动 716884
科研通“疑难数据库(出版商)”最低求助积分说明 709762