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免疫代谢抗肿瘤药物靶点的发现鉴定出雌激素相关受体α。

Discovery of Targets for Immune-Metabolic Antitumor Drugs Identifies Estrogen-Related Receptor Alpha.

机构信息

Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.

Department of Obstetrics and Gynecology, University of Colorado School of Medicine, Aurora, Colorado.

出版信息

Cancer Discov. 2023 Mar 1;13(3):672-701. doi: 10.1158/2159-8290.CD-22-0244.

DOI:10.1158/2159-8290.CD-22-0244
PMID:36745048
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9975674/
Abstract

UNLABELLED

Drugs that kill tumors through multiple mechanisms have the potential for broad clinical benefits. Here, we first developed an in silico multiomics approach (BipotentR) to find cancer cell-specific regulators that simultaneously modulate tumor immunity and another oncogenic pathway and then used it to identify 38 candidate immune-metabolic regulators. We show the tumor activities of these regulators stratify patients with melanoma by their response to anti-PD-1 using machine learning and deep neural approaches, which improve the predictive power of current biomarkers. The topmost identified regulator, ESRRA, is activated in immunotherapy-resistant tumors. Its inhibition killed tumors by suppressing energy metabolism and activating two immune mechanisms: (i) cytokine induction, causing proinflammatory macrophage polarization, and (ii) antigen-presentation stimulation, recruiting CD8+ T cells into tumors. We also demonstrate a wide utility of BipotentR by applying it to angiogenesis and growth suppressor evasion pathways. BipotentR (http://bipotentr.dfci.harvard.edu/) provides a resource for evaluating patient response and discovering drug targets that act simultaneously through multiple mechanisms.

SIGNIFICANCE

BipotentR presents resources for evaluating patient response and identifying targets for drugs that can kill tumors through multiple mechanisms concurrently. Inhibition of the topmost candidate target killed tumors by suppressing energy metabolism and effects on two immune mechanisms. This article is highlighted in the In This Issue feature, p. 517.

摘要

未加标签

通过多种机制杀死肿瘤的药物具有广泛的临床获益潜力。在这里,我们首先开发了一种计算多组学方法(BipotentR),以找到同时调节肿瘤免疫和另一种致癌途径的癌细胞特异性调节剂,然后用它来鉴定 38 种候选免疫代谢调节剂。我们通过机器学习和深度神经网络方法显示,这些调节剂的肿瘤活性通过抗 PD-1 治疗对黑色素瘤患者进行分层,从而提高了当前生物标志物的预测能力。识别出的最主要调节剂 ESRRA 在免疫治疗耐药肿瘤中被激活。其抑制作用通过抑制能量代谢和激活两种免疫机制来杀死肿瘤:(i)细胞因子诱导,导致促炎性巨噬细胞极化;(ii)抗原呈递刺激,将 CD8+T 细胞募集到肿瘤中。我们还通过将其应用于血管生成和生长抑制因子逃逸途径来证明 BipotentR 的广泛适用性。BipotentR(http://bipotentr.dfci.harvard.edu/)为评估患者反应和发现同时通过多种机制发挥作用的药物靶点提供了资源。

意义

BipotentR 为评估患者反应和识别可通过多种机制同时杀死肿瘤的药物靶点提供了资源。抑制排名最高的候选靶点通过抑制能量代谢和对两种免疫机制的影响来杀死肿瘤。本文在本期特色文章中进行了重点介绍,第 517 页。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d0a/9975674/b66005abd35a/672fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d0a/9975674/906d373e8a16/672fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d0a/9975674/76731615376c/672fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d0a/9975674/124894a31ddb/672fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d0a/9975674/523b6f317734/672fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d0a/9975674/4d28f1776d04/672fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d0a/9975674/b66005abd35a/672fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d0a/9975674/906d373e8a16/672fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d0a/9975674/76731615376c/672fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d0a/9975674/124894a31ddb/672fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d0a/9975674/523b6f317734/672fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d0a/9975674/4d28f1776d04/672fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d0a/9975674/b66005abd35a/672fig6.jpg

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