Alice Soragni, Erik S. Knudsen, Thomas N. O’Connor, Cristina E. Tognon, Jeffrey W. Tyner, Beatrice Gini, Donghwa Kim, Trever G. Bivona, Xingxing Zang, Agnieszka K. Witkiewicz, David W. Goodrich, Dadi Jiang, Seth T. Gammon, Christopher D. Willey, Paul C. Boutros, Vlad C. Sandulache, Abdullah A. Osman, Jeffrey N. Myers, Kamiya Mehla, Pankaj K. Singh, Keith S. Chan, Hongbo Gao, Himangi Marathe, on behalf of National Cancer Institute (NCI) Acquired Resistance to Therapy Network (ARTNet)
{"title":"Acquired resistance in cancer: towards targeted therapeutic strategies","authors":"Alice Soragni, Erik S. Knudsen, Thomas N. O’Connor, Cristina E. Tognon, Jeffrey W. Tyner, Beatrice Gini, Donghwa Kim, Trever G. Bivona, Xingxing Zang, Agnieszka K. Witkiewicz, David W. Goodrich, Dadi Jiang, Seth T. Gammon, Christopher D. Willey, Paul C. Boutros, Vlad C. Sandulache, Abdullah A. Osman, Jeffrey N. Myers, Kamiya Mehla, Pankaj K. Singh, Keith S. Chan, Hongbo Gao, Himangi Marathe, on behalf of National Cancer Institute (NCI) Acquired Resistance to Therapy Network (ARTNet)","doi":"10.1038/s41568-025-00824-9","DOIUrl":null,"url":null,"abstract":"Development of acquired therapeutic resistance limits the efficacy of cancer treatments and accounts for therapeutic failure in most patients. How resistance arises, varies across cancer types and differs depending on therapeutic modalities is incompletely understood. Novel strategies that address and overcome the various and complex resistance mechanisms necessitate a deep understanding of the underlying dynamics. We are at a crucial time when innovative technologies applied to patient-relevant tumour models have the potential to bridge the gap between fundamental research into mechanisms and timing of acquired resistance and clinical applications that translate these findings into actionable strategies to extend therapy efficacy. Unprecedented spatial and time-resolved high-throughput platforms generate vast amounts of data, from which increasingly complex information can be extracted and analysed through artificial intelligence and machine learning-based approaches. This Roadmap outlines key mechanisms that underlie the acquisition of therapeutic resistance in cancer and explores diverse modelling strategies. Clinically relevant, tractable models of disease and biomarker-driven precision approaches are poised to transform the landscape of acquired therapy resistance in cancer and its clinical management. Here, we propose an integrated strategy that leverages next-generation technologies to dissect the complexities of therapy resistance, shifting the paradigm from reactive management to predictive and proactive prevention. Acquired therapeutic resistance is a key contributor to cancer treatment failure, requiring new approaches to address its complex mechanisms. In this Roadmap, Soragni, Knudsen and colleagues discuss the mechanisms of acquired resistance and the models to better study it. Finally, they promote integration of biomarker-driven strategies and cutting-edge technologies to advance predictive and proactive prevention in cancer therapy.","PeriodicalId":19055,"journal":{"name":"Nature Reviews Cancer","volume":"25 8","pages":"613-633"},"PeriodicalIF":66.8000,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Reviews Cancer","FirstCategoryId":"3","ListUrlMain":"https://www.nature.com/articles/s41568-025-00824-9","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
Abstract
Development of acquired therapeutic resistance limits the efficacy of cancer treatments and accounts for therapeutic failure in most patients. How resistance arises, varies across cancer types and differs depending on therapeutic modalities is incompletely understood. Novel strategies that address and overcome the various and complex resistance mechanisms necessitate a deep understanding of the underlying dynamics. We are at a crucial time when innovative technologies applied to patient-relevant tumour models have the potential to bridge the gap between fundamental research into mechanisms and timing of acquired resistance and clinical applications that translate these findings into actionable strategies to extend therapy efficacy. Unprecedented spatial and time-resolved high-throughput platforms generate vast amounts of data, from which increasingly complex information can be extracted and analysed through artificial intelligence and machine learning-based approaches. This Roadmap outlines key mechanisms that underlie the acquisition of therapeutic resistance in cancer and explores diverse modelling strategies. Clinically relevant, tractable models of disease and biomarker-driven precision approaches are poised to transform the landscape of acquired therapy resistance in cancer and its clinical management. Here, we propose an integrated strategy that leverages next-generation technologies to dissect the complexities of therapy resistance, shifting the paradigm from reactive management to predictive and proactive prevention. Acquired therapeutic resistance is a key contributor to cancer treatment failure, requiring new approaches to address its complex mechanisms. In this Roadmap, Soragni, Knudsen and colleagues discuss the mechanisms of acquired resistance and the models to better study it. Finally, they promote integration of biomarker-driven strategies and cutting-edge technologies to advance predictive and proactive prevention in cancer therapy.
期刊介绍:
Nature Reviews Cancer, a part of the Nature Reviews portfolio of journals, aims to be the premier source of reviews and commentaries for the scientific communities it serves. The correct abbreviation for abstracting and indexing purposes is Nat. Rev. Cancer. The international standard serial numbers (ISSN) for Nature Reviews Cancer are 1474-175X (print) and 1474-1768 (online). Unlike other journals, Nature Reviews Cancer does not have an external editorial board. Instead, all editorial decisions are made by a team of full-time professional editors who are PhD-level scientists. The journal publishes Research Highlights, Comments, Reviews, and Perspectives relevant to cancer researchers, ensuring that the articles reach the widest possible audience due to their broad scope.