• 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

PanDrugs2:利用整合的个体多组学数据对癌症疗法进行优先级排序。

PanDrugs2: prioritizing cancer therapies using integrated individual multi-omics data.

机构信息

Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain.

CINBIO, Universidade de Vigo, Department of Computer Science, ESEI-Escuela Superior de Ingeniería Informática, 32004 Ourense, Spain.

出版信息

Nucleic Acids Res. 2023 Jul 5;51(W1):W411-W418. doi: 10.1093/nar/gkad412.

DOI:10.1093/nar/gkad412
PMID:37207338
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10320188/
Abstract

Genomics studies routinely confront researchers with long lists of tumor alterations detected in patients. Such lists are difficult to interpret since only a minority of the alterations are relevant biomarkers for diagnosis and for designing therapeutic strategies. PanDrugs is a methodology that facilitates the interpretation of tumor molecular alterations and guides the selection of personalized treatments. To do so, PanDrugs scores gene actionability and drug feasibility to provide a prioritized evidence-based list of drugs. Here, we introduce PanDrugs2, a major upgrade of PanDrugs that, in addition to somatic variant analysis, supports a new integrated multi-omics analysis which simultaneously combines somatic and germline variants, copy number variation and gene expression data. Moreover, PanDrugs2 now considers cancer genetic dependencies to extend tumor vulnerabilities providing therapeutic options for untargetable genes. Importantly, a novel intuitive report to support clinical decision-making is generated. PanDrugs database has been updated, integrating 23 primary sources that support >74K drug-gene associations obtained from 4642 genes and 14 659 unique compounds. The database has also been reimplemented to allow semi-automatic updates to facilitate maintenance and release of future versions. PanDrugs2 does not require login and is freely available at https://www.pandrugs.org/.

摘要

基因组学研究经常使研究人员面对患者中检测到的大量肿瘤改变列表。由于只有少数改变是诊断和设计治疗策略的相关生物标志物,因此这些列表很难解释。PanDrugs 是一种有助于解释肿瘤分子改变并指导个性化治疗选择的方法。为此,PanDrugs 对基因的可操作性和药物的可行性进行评分,提供了一份基于证据的优先药物清单。在这里,我们介绍 PanDrugs2,这是 PanDrugs 的一次重大升级,除了体细胞变异分析外,它还支持新的集成多组学分析,同时结合体细胞和种系变异、拷贝数变异和基因表达数据。此外,PanDrugs2 现在考虑癌症遗传依赖性,以扩展肿瘤脆弱性,为无法靶向的基因提供治疗选择。重要的是,生成了一种新颖的直观报告来支持临床决策。PanDrugs 数据库已经更新,整合了 23 个主要来源,支持从 4642 个基因和 14659 个独特化合物中获得的 >74K 个药物-基因关联。该数据库也已重新实现,允许半自动更新,以方便维护和发布未来版本。PanDrugs2 无需登录,可在 https://www.pandrugs.org/ 免费获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7063/10320188/a69e2e083efe/gkad412fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7063/10320188/0c30e95b04af/gkad412figgra1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7063/10320188/9420a2ca093e/gkad412fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7063/10320188/a0ae147eecde/gkad412fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7063/10320188/a69e2e083efe/gkad412fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7063/10320188/0c30e95b04af/gkad412figgra1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7063/10320188/9420a2ca093e/gkad412fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7063/10320188/a0ae147eecde/gkad412fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7063/10320188/a69e2e083efe/gkad412fig3.jpg

相似文献

1
PanDrugs2: prioritizing cancer therapies using integrated individual multi-omics data.PanDrugs2:利用整合的个体多组学数据对癌症疗法进行优先级排序。
Nucleic Acids Res. 2023 Jul 5;51(W1):W411-W418. doi: 10.1093/nar/gkad412.
2
PanDrugs: a novel method to prioritize anticancer drug treatments according to individual genomic data.泛药物治疗策略:一种基于个体基因组数据对癌症治疗药物进行优先级排序的新方法。
Genome Med. 2018 May 31;10(1):41. doi: 10.1186/s13073-018-0546-1.
3
The use of PanDrugs to prioritize anticancer drug treatments in a case of T-ALL based on individual genomic data.基于个体基因组数据,使用泛药物优先考虑 T-ALL 患者的抗癌药物治疗。
BMC Cancer. 2019 Oct 26;19(1):1005. doi: 10.1186/s12885-019-6209-9.
4
Whole-Exome Sequencing of Metastatic Cancer and Biomarkers of Treatment Response.转移性癌症的全外显子组测序和治疗反应的生物标志物。
JAMA Oncol. 2015 Jul;1(4):466-74. doi: 10.1001/jamaoncol.2015.1313.
5
From multi-omics data to the cancer druggable gene discovery: a novel machine learning-based approach.从多组学数据到癌症可药物化基因发现:一种基于机器学习的新方法。
Brief Bioinform. 2023 Jan 19;24(1). doi: 10.1093/bib/bbac528.
6
Integrated analysis of multi-omics data for the discovery of biomarkers and therapeutic targets for colorectal cancer.多组学数据的综合分析用于发现结直肠癌的生物标志物和治疗靶点。
Comput Biol Med. 2023 Mar;155:106639. doi: 10.1016/j.compbiomed.2023.106639. Epub 2023 Feb 11.
7
Identifying subpathway signatures for individualized anticancer drug response by integrating multi-omics data.通过整合多组学数据,为个体化抗癌药物反应鉴定亚途径特征。
J Transl Med. 2019 Aug 6;17(1):255. doi: 10.1186/s12967-019-2010-4.
8
DGMP: Identifying Cancer Driver Genes by Jointing DGCN and MLP from Multi-omics Genomic Data.DGMP:通过结合多组学基因组数据的 DGCN 和 MLP 识别癌症驱动基因。
Genomics Proteomics Bioinformatics. 2022 Oct;20(5):928-938. doi: 10.1016/j.gpb.2022.11.004. Epub 2022 Dec 1.
9
An integrated framework for reporting clinically relevant biomarkers from paired tumor/normal genomic and transcriptomic sequencing data in support of clinical trials in personalized medicine.一个用于报告来自配对肿瘤/正常基因组和转录组测序数据的临床相关生物标志物的综合框架,以支持个性化医学中的临床试验。
Pac Symp Biocomput. 2015:56-67.
10
GMIEC: a shiny application for the identification of gene-targeted drugs for precision medicine.GMIEC:一个用于精准医学中基因靶向药物识别的闪亮应用。
BMC Genomics. 2020 Sep 10;21(1):619. doi: 10.1186/s12864-020-06996-y.

引用本文的文献

1
TARGET-SL: precision essential gene prediction using driver prioritisation and synthetic lethality.目标:利用驱动因子优先级排序和合成致死性进行精确必需基因预测。
Brief Bioinform. 2025 May 1;26(3). doi: 10.1093/bib/bbaf255.
2
Onkopus: precise interpretation and prioritization of sequence variants for biomedical research and precision medicine.Onkopus:用于生物医学研究和精准医学的序列变异的精确解读与优先级排序
Nucleic Acids Res. 2025 Jul 7;53(W1):W431-W439. doi: 10.1093/nar/gkaf376.
3
Integrating multi-omics methods for personalized treatment of refractory chronic myelomonocytic leukemia with and mutations.

本文引用的文献

1
The coming decade in precision oncology: six riddles.精准肿瘤学的未来十年:六大谜题。
Nat Rev Cancer. 2023 Jan;23(1):43-54. doi: 10.1038/s41568-022-00529-3. Epub 2022 Nov 24.
2
Identifying Actionable Variants in Cancer - The Dual Web and Batch Processing Tool MTB-Report.识别癌症中的可操作变异——双网络和批量处理工具 MTB-Report。
Stud Health Technol Inform. 2022 Aug 17;296:73-80. doi: 10.3233/SHTI220806.
3
Advancing precision oncology with large, real-world genomics and treatment outcomes data.利用大规模真实世界基因组学和治疗结果数据推动精准肿瘤学发展。
整合多组学方法用于伴有[具体基因1]和[具体基因2]突变的难治性慢性粒-单核细胞白血病的个性化治疗
Cancer Pathog Ther. 2024 Dec 9;3(2):173-175. doi: 10.1016/j.cpt.2024.12.001. eCollection 2025 Mar.
4
Long Intergenic Non-Coding RNAs of Human Chromosome 18: Focus on Cancers.人类18号染色体的长链基因间非编码RNA:聚焦于癌症
Biomedicines. 2024 Feb 28;12(3):544. doi: 10.3390/biomedicines12030544.
Nat Med. 2022 Aug;28(8):1544-1545. doi: 10.1038/s41591-022-01904-1.
4
Whole-genome and transcriptome analysis enhances precision cancer treatment options.全基因组和转录组分析增强了癌症精准治疗选择。
Ann Oncol. 2022 Sep;33(9):939-949. doi: 10.1016/j.annonc.2022.05.522. Epub 2022 Jun 9.
5
Beacon v2 and Beacon networks: A "lingua franca" for federated data discovery in biomedical genomics, and beyond.信标v2与信标网络:生物医学基因组学及其他领域中联邦数据发现的“通用语言”
Hum Mutat. 2022 Jun;43(6):791-799. doi: 10.1002/humu.24369. Epub 2022 Apr 8.
6
Systematic illumination of druggable genes in cancer genomes.对癌症基因组中可成药基因的系统性阐释。
Cell Rep. 2022 Feb 22;38(8):110400. doi: 10.1016/j.celrep.2022.110400.
7
Patient-specific Boolean models of signalling networks guide personalised treatments.基于信号网络的个体化布尔模型指导个体化治疗。
Elife. 2022 Feb 15;11:e72626. doi: 10.7554/eLife.72626.
8
Mergeomics 2.0: a web server for multi-omics data integration to elucidate disease networks and predict therapeutics.Mergeomics 2.0:一个用于多组学数据整合的网络服务器,以阐明疾病网络并预测治疗方法。
Nucleic Acids Res. 2021 Jul 2;49(W1):W375-W387. doi: 10.1093/nar/gkab405.
9
A user guide for the online exploration and visualization of PCAWG data.用于在线探索和可视化 PCAWG 数据的用户指南。
Nat Commun. 2020 Jul 7;11(1):3400. doi: 10.1038/s41467-020-16785-6.
10
A harmonized meta-knowledgebase of clinical interpretations of somatic genomic variants in cancer.癌症体细胞基因组变异的临床解读的协调元知识库。
Nat Genet. 2020 Apr;52(4):448-457. doi: 10.1038/s41588-020-0603-8. Epub 2020 Apr 3.