HDACs and the Epigenetic Plasticity of Cancer Cells: Target the Complexity.

Cited 16|Views17

Abstract

Cancer cells must adapt to the hostile conditions of the microenvironment in terms of nutrition, space, and immune system attack. Mutations of DNA are the drivers of the tumorigenic process, but mutations must be able to hijack cellular functions to sustain the spread of mutant genomes. Transcriptional control is a key function in this context and is controlled by the rearrangement of the epigenome. Unlike genomic mutations, the epigenome of cancer cells can in principle be reversed. The discovery of the first epigenetic drugs triggered a contaminating enthusiasm. Unfortunately, the complexity of the epigenetic machinery has frustrated this enthusiasm. To develop efficient patient-oriented epigenetic therapies, we need to better understand the nature of this complexity. In this review, we will discuss recent advances in understanding the contribution of HDACs to the maintenance of the transformed state and the rational for their selective targeting.

Translated text

Key words

HDACs,Epigenomics,Cancer,Therapy,Immune-therapy,Inhibitors,Acetylation

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper