寻找具有代表性的转化癌症模型系统

IF 16.6 1区 医学 Q1 ONCOLOGY Cancer research Pub Date : 2025-02-01 DOI:10.1158/0008-5472.can-24-3879
Hannah E. Trembath, Philip M. Spanheimer
{"title":"寻找具有代表性的转化癌症模型系统","authors":"Hannah E. Trembath, Philip M. Spanheimer","doi":"10.1158/0008-5472.can-24-3879","DOIUrl":null,"url":null,"abstract":"Racial disparities in cancer outcomes are well documented across tumor types. For patients with breast cancer, Black women are more likely to present with more aggressive molecular features and more likely to die from disease, even after accounting for those features. Recent efforts have been aimed at developing translational model systems for precision medicine strategies, and a major focus has been on patient-derived organoids. Organoids allow for robust in vitro experimental platforms, including drug and CRISPR screens while maintaining more complex cancer and tumor microenvironment subpopulations than cell lines. For results that are broadly translationally relevant, it is important that cancer models are derived from the spectrum of human disease and humans with disease. In this issue of Cancer Research, Madorsky Rowdo and colleagues derive breast cancer organoids from patients with African ancestry and use CRISPR-Cas9 screens to identify novel therapeutic vulnerabilities. These findings demonstrate the promise of representative cancer model systems to facilitate discoveries that are most likely to translate to improved therapy for all patients. See related article by Madorsky Rowdo et al., p. 551","PeriodicalId":9441,"journal":{"name":"Cancer research","volume":"157 1","pages":""},"PeriodicalIF":16.6000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"In Search of Representative Translational Cancer Model Systems\",\"authors\":\"Hannah E. Trembath, Philip M. Spanheimer\",\"doi\":\"10.1158/0008-5472.can-24-3879\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Racial disparities in cancer outcomes are well documented across tumor types. For patients with breast cancer, Black women are more likely to present with more aggressive molecular features and more likely to die from disease, even after accounting for those features. Recent efforts have been aimed at developing translational model systems for precision medicine strategies, and a major focus has been on patient-derived organoids. Organoids allow for robust in vitro experimental platforms, including drug and CRISPR screens while maintaining more complex cancer and tumor microenvironment subpopulations than cell lines. For results that are broadly translationally relevant, it is important that cancer models are derived from the spectrum of human disease and humans with disease. In this issue of Cancer Research, Madorsky Rowdo and colleagues derive breast cancer organoids from patients with African ancestry and use CRISPR-Cas9 screens to identify novel therapeutic vulnerabilities. These findings demonstrate the promise of representative cancer model systems to facilitate discoveries that are most likely to translate to improved therapy for all patients. See related article by Madorsky Rowdo et al., p. 551\",\"PeriodicalId\":9441,\"journal\":{\"name\":\"Cancer research\",\"volume\":\"157 1\",\"pages\":\"\"},\"PeriodicalIF\":16.6000,\"publicationDate\":\"2025-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cancer research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1158/0008-5472.can-24-3879\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1158/0008-5472.can-24-3879","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

癌症结果的种族差异在各种肿瘤类型中都有很好的记录。对于乳腺癌患者来说,黑人女性更有可能呈现出更具攻击性的分子特征,更有可能死于疾病,即使在考虑了这些特征之后。最近的努力旨在为精准医学策略开发转化模型系统,主要关注患者衍生的类器官。类器官允许强大的体外实验平台,包括药物和CRISPR筛选,同时保持比细胞系更复杂的癌症和肿瘤微环境亚群。对于与翻译广泛相关的结果,重要的是癌症模型是从人类疾病和疾病患者的谱中推导出来的。在这一期的《癌症研究》中,Madorsky Rowdo和他的同事们从非洲血统的患者身上获得了乳腺癌类器官,并使用CRISPR-Cas9筛选来识别新的治疗脆弱性。这些发现表明,具有代表性的癌症模型系统有望促进最有可能转化为改善所有患者治疗的发现。参见Madorsky Rowdo等人的相关文章,第551页
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
In Search of Representative Translational Cancer Model Systems
Racial disparities in cancer outcomes are well documented across tumor types. For patients with breast cancer, Black women are more likely to present with more aggressive molecular features and more likely to die from disease, even after accounting for those features. Recent efforts have been aimed at developing translational model systems for precision medicine strategies, and a major focus has been on patient-derived organoids. Organoids allow for robust in vitro experimental platforms, including drug and CRISPR screens while maintaining more complex cancer and tumor microenvironment subpopulations than cell lines. For results that are broadly translationally relevant, it is important that cancer models are derived from the spectrum of human disease and humans with disease. In this issue of Cancer Research, Madorsky Rowdo and colleagues derive breast cancer organoids from patients with African ancestry and use CRISPR-Cas9 screens to identify novel therapeutic vulnerabilities. These findings demonstrate the promise of representative cancer model systems to facilitate discoveries that are most likely to translate to improved therapy for all patients. See related article by Madorsky Rowdo et al., p. 551
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Cancer research
Cancer research 医学-肿瘤学
CiteScore
16.10
自引率
0.90%
发文量
7677
审稿时长
2.5 months
期刊介绍: Cancer Research, published by the American Association for Cancer Research (AACR), is a journal that focuses on impactful original studies, reviews, and opinion pieces relevant to the broad cancer research community. Manuscripts that present conceptual or technological advances leading to insights into cancer biology are particularly sought after. The journal also places emphasis on convergence science, which involves bridging multiple distinct areas of cancer research. With primary subsections including Cancer Biology, Cancer Immunology, Cancer Metabolism and Molecular Mechanisms, Translational Cancer Biology, Cancer Landscapes, and Convergence Science, Cancer Research has a comprehensive scope. It is published twice a month and has one volume per year, with a print ISSN of 0008-5472 and an online ISSN of 1538-7445. Cancer Research is abstracted and/or indexed in various databases and platforms, including BIOSIS Previews (R) Database, MEDLINE, Current Contents/Life Sciences, Current Contents/Clinical Medicine, Science Citation Index, Scopus, and Web of Science.
期刊最新文献
Arginine Deprivation of ASS1-Deficient Cancers Drives Mistranslation and Shared Neoepitope Production. PI3K Regulates Wild-type RAS Signaling to Confer Resistance to KRAS Inhibition. CellFuse enables Multi-modal Integration of Single-cell and Spatial Proteomics Data for Systems-level Analysis in Cancer. Targeting DOT1L Reactivates HERV-K to Drive Cell Autonomous and Paracrine Senescence in Adenocarcinoma of the Esophagogastric Junction. Cancer-Associated Fibroblasts Promote Epithelial-Mesenchymal Transition and Classical to Basal Subtype Shift in Pancreatic Cancer.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1