• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

肿瘤后基因组时代的联合药物治疗。

Combinatorial drug therapy for cancer in the post-genomic era.

机构信息

Cancer Research UK Cancer Therapeutics Unit, Division of Cancer Therapeutics, The Institute of Cancer Research, Haddow Laboratories, Sutton, UK.

出版信息

Nat Biotechnol. 2012 Jul 10;30(7):679-92. doi: 10.1038/nbt.2284.

DOI:10.1038/nbt.2284
PMID:22781697
Abstract

Over the past decade, whole genome sequencing and other 'omics' technologies have defined pathogenic driver mutations to which tumor cells are addicted. Such addictions, synthetic lethalities and other tumor vulnerabilities have yielded novel targets for a new generation of cancer drugs to treat discrete, genetically defined patient subgroups. This personalized cancer medicine strategy could eventually replace the conventional one-size-fits-all cytotoxic chemotherapy approach. However, the extraordinary intratumor genetic heterogeneity in cancers revealed by deep sequencing explains why de novo and acquired resistance arise with molecularly targeted drugs and cytotoxic chemotherapy, limiting their utility. One solution to the enduring challenge of polygenic cancer drug resistance is rational combinatorial targeted therapy.

摘要

在过去的十年中,全基因组测序和其他“组学”技术已经确定了肿瘤细胞成瘾的致病驱动突变。这些成瘾、合成致死性和其他肿瘤脆弱性为新一代癌症药物提供了新的靶点,以治疗具有明确遗传定义的患者亚群。这种个性化癌症治疗策略最终可能会取代传统的一刀切的细胞毒性化疗方法。然而,深度测序揭示的癌症中非同寻常的肿瘤内遗传异质性解释了为什么新出现的和获得性的耐药性会随着分子靶向药物和细胞毒性化疗而出现,限制了它们的应用。解决多基因癌症药物耐药性这一持久挑战的方法之一是合理的组合靶向治疗。

相似文献

1
Combinatorial drug therapy for cancer in the post-genomic era.肿瘤后基因组时代的联合药物治疗。
Nat Biotechnol. 2012 Jul 10;30(7):679-92. doi: 10.1038/nbt.2284.
2
The possibility of clinical sequencing in the management of cancer.临床测序在癌症管理中的可能性。
Jpn J Clin Oncol. 2016 May;46(5):399-406. doi: 10.1093/jjco/hyw018. Epub 2016 Feb 24.
3
Ushering in the next generation of precision trials for pediatric cancer.为儿科癌症的下一代精准试验开辟道路。
Science. 2019 Mar 15;363(6432):1175-1181. doi: 10.1126/science.aaw4153.
4
Cancer Therapy Directed by Comprehensive Genomic Profiling: A Single Center Study.基于全面基因组分析的癌症治疗:单中心研究。
Cancer Res. 2016 Jul 1;76(13):3690-701. doi: 10.1158/0008-5472.CAN-15-3043. Epub 2016 May 18.
5
New tools for old drugs: Functional genetic screens to optimize current chemotherapy.旧药新用的工具:功能遗传学筛选以优化现有化疗药物。
Drug Resist Updat. 2018 Jan;36:30-46. doi: 10.1016/j.drup.2018.01.001. Epub 2018 Jan 12.
6
Exploiting the cancer genome: strategies for the discovery and clinical development of targeted molecular therapeutics.利用癌症基因组:发现和临床开发靶向分子治疗的策略。
Annu Rev Pharmacol Toxicol. 2012;52:549-73. doi: 10.1146/annurev-pharmtox-010611-134532.
7
Update on targeted cancer therapies, single or in combination, and their fine tuning for precision medicine.靶向癌症疗法(单药或联合用药)的最新进展及其在精准医学中的精细调整。
Biomed Pharmacother. 2020 May;125:110009. doi: 10.1016/j.biopha.2020.110009. Epub 2020 Feb 25.
8
[Precision medicine: A major step forward in specific situations, a myth in refractory cancers?].[精准医学:在特定情况下向前迈出的一大步,在难治性癌症中是神话吗?]
Bull Cancer. 2018 Apr;105(4):375-396. doi: 10.1016/j.bulcan.2018.01.009. Epub 2018 Mar 1.
9
Support of a molecular tumour board by an evidence-based decision management system for precision oncology.基于证据的决策管理系统为精准肿瘤学提供分子肿瘤委员会的支持。
Eur J Cancer. 2020 Mar;127:41-51. doi: 10.1016/j.ejca.2019.12.017. Epub 2020 Jan 23.
10
Disparity between Inter-Patient Molecular Heterogeneity and Repertoires of Target Drugs Used for Different Types of Cancer in Clinical Oncology.临床肿瘤学中不同类型癌症使用的靶向药物与患者间分子异质性和药物库之间的差距。
Int J Mol Sci. 2020 Feb 26;21(5):1580. doi: 10.3390/ijms21051580.

引用本文的文献

1
Fine tuning a logical model of cancer cells to predict drug synergies: combining manual curation and automated parameterization.微调癌细胞逻辑模型以预测药物协同作用:结合人工整理与自动参数化
Front Syst Biol. 2023 Nov 20;3:1252961. doi: 10.3389/fsysb.2023.1252961. eCollection 2023.
2
SynProtX: a large-scale proteomics-based deep learning model for predicting synergistic anticancer drug combinations.SynProtX:一种基于大规模蛋白质组学的深度学习模型,用于预测协同抗癌药物组合。
Gigascience. 2025 Jan 6;14. doi: 10.1093/gigascience/giaf080.
3
Leveraging synthetic genetic array screening to identify therapeutic targets and inhibitors for combatting azole resistance in .

本文引用的文献

1
Development of therapeutic combinations targeting major cancer signaling pathways.针对主要癌症信号通路的治疗组合的开发。
J Clin Oncol. 2013 Apr 20;31(12):1592-605. doi: 10.1200/JCO.2011.37.6418. Epub 2013 Mar 18.
2
Global cancer transitions according to the Human Development Index (2008-2030): a population-based study.全球癌症发病趋势与人类发展指数(2008-2030 年):基于人群的研究。
Lancet Oncol. 2012 Aug;13(8):790-801. doi: 10.1016/S1470-2045(12)70211-5. Epub 2012 Jun 1.
3
Efficacy and safety of regorafenib in patients with metastatic and/or unresectable GI stromal tumor after failure of imatinib and sunitinib: a multicenter phase II trial.
利用合成基因阵列筛选来确定用于对抗……中唑类耐药性的治疗靶点和抑制剂。 (原文句末不完整)
Microbiol Spectr. 2025 Sep 2;13(9):e0252224. doi: 10.1128/spectrum.02522-24. Epub 2025 Aug 11.
4
A highly annotated drug combination resource for catalyzing precision combinatorial therapy.一个用于催化精准联合治疗的高度注释的药物组合资源。
Sci Data. 2025 Jul 23;12(1):1284. doi: 10.1038/s41597-025-05630-4.
5
GPerturb: Gaussian process modelling of single-cell perturbation data.GPerturb:单细胞扰动数据的高斯过程建模
Nat Commun. 2025 Jul 1;16(1):5423. doi: 10.1038/s41467-025-61165-7.
6
SNAI2 cooperates with MEK1/2 and HDACs to suppress BIM- and BMF-dependent apoptosis in TERT promoter mutant cancers.SNAI2与MEK1/2和组蛋白去乙酰化酶协同作用,抑制端粒酶逆转录酶(TERT)启动子突变型癌症中BIM和BMF依赖性凋亡。
PLoS One. 2025 Jun 25;20(6):e0322961. doi: 10.1371/journal.pone.0322961. eCollection 2025.
7
Advancing therapeutics in small-cell lung cancer.小细胞肺癌治疗方法的进展
Nat Cancer. 2025 Jun 16. doi: 10.1038/s43018-025-00996-1.
8
Pisces: A multi-modal data augmentation approach for drug combination synergy prediction.双鱼座:一种用于药物组合协同作用预测的多模态数据增强方法。
Cell Genom. 2025 Jul 9;5(7):100892. doi: 10.1016/j.xgen.2025.100892. Epub 2025 Jun 3.
9
MADSP: predicting anti-cancer drug synergy through multi-source integration and attention-based representation learning.MADSP:通过多源整合和基于注意力的表征学习预测抗癌药物协同作用
Bioinformatics. 2025 Jun 2;41(6). doi: 10.1093/bioinformatics/btaf326.
10
Building a unified model for drug synergy analysis powered by large language models.构建一个由大语言模型驱动的药物协同作用分析统一模型。
Nat Commun. 2025 May 15;16(1):4537. doi: 10.1038/s41467-025-59822-y.
regorafenib 在伊马替尼和舒尼替尼治疗失败的转移性和/或不可切除的胃肠道间质瘤患者中的疗效和安全性:一项多中心 II 期试验。
J Clin Oncol. 2012 Jul 1;30(19):2401-7. doi: 10.1200/JCO.2011.39.9394. Epub 2012 May 21.
4
Genetically engineered mouse models: closing the gap between preclinical data and trial outcomes.基因工程小鼠模型:弥合临床前数据与试验结果之间的差距。
Cancer Res. 2012 Jun 1;72(11):2695-700. doi: 10.1158/0008-5472.CAN-11-2786. Epub 2012 May 16.
5
Circumventing cancer drug resistance in the era of personalized medicine.在个性化医疗时代规避癌症药物耐药性。
Cancer Discov. 2012 Mar;2(3):214-26. doi: 10.1158/2159-8290.CD-12-0012. Epub 2012 Feb 28.
6
Sequential application of anticancer drugs enhances cell death by rewiring apoptotic signaling networks.抗癌药物的序贯应用通过重新布线细胞凋亡信号网络增强细胞死亡。
Cell. 2012 May 11;149(4):780-94. doi: 10.1016/j.cell.2012.03.031.
7
From an ethics of rationing to an ethics of waste avoidance.从资源分配伦理到避免浪费伦理。
N Engl J Med. 2012 May 24;366(21):1949-51. doi: 10.1056/NEJMp1203365. Epub 2012 May 2.
8
Chemical genomics identifies small-molecule MCL1 repressors and BCL-xL as a predictor of MCL1 dependency.化学生物学鉴定小分子 MCL1 抑制剂和 BCL-xL 作为 MCL1 依赖性的预测因子。
Cancer Cell. 2012 Apr 17;21(4):547-62. doi: 10.1016/j.ccr.2012.02.028.
9
Personalized medicine: patient-predictive panel power.个体化医疗:预测性患者面板的力量。
Cancer Cell. 2012 Apr 17;21(4):455-8. doi: 10.1016/j.ccr.2012.03.030.
10
Dynamic reprogramming of the kinome in response to targeted MEK inhibition in triple-negative breast cancer.针对三阴性乳腺癌中靶向 MEK 抑制的激酶组的动态重编程。
Cell. 2012 Apr 13;149(2):307-21. doi: 10.1016/j.cell.2012.02.053.