A Roadmap for the Future of Systems Biology in Cancer Research

Cancer systems biology seeks to understand how cancer arises as a system of interconnected molecules, cells, and tissues, with the goal of understanding, predicting, and controlling the disease. In the last decade, the field has rapidly grown as advances in experimental, computational, and analytic technologies have improved our ability to capture and recapitulate the complexities of cancer at multiple scales. However, the field's promise to understand how specific molecular changes give rise to altered cancer outcomes remains incompletely fulfilled. Fortunately, an opportunity exists to accelerate progress by better coordinating modeling and data-gathering efforts across the cancer systems biology community. This will create the foundation for building accurate, multiscale cancer models that can better predict and identify improved therapeutic interventions. Here, we outline some of the current challenges in cancer systems biology research, how they can be addressed, and actions that the community can take to accelerate progress in the field. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .

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Publication Details

Journal
Cancer Research
Published
2025-10-15
DOI
https://doi.org/10.1158/0008-5472.can-25-0700
Primary Topic
Genetics, Bioinformatics, and Biomedical Research
Type
article
Field-Weighted Citation Impact
0.00
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article

A Roadmap for the Future of Systems Biology in Cancer Research

Hannah Newman, Ashley Clayton, Aaron S. Meyer, Jianhua Xing et al.
Cancer Research
Genetics, Bioinformatics, and Biomedical Research
article

A Roadmap for the Future of Systems Biology in Cancer Research

Hannah Newman, Ashley Clayton, Aaron S. Meyer, Jianhua Xing, Juan I. Fuxman Bass, Russell C. Rockne, Thomas E. Yankeelov, Jonathan P. Mochel, H Wiley, James C. Costello, Emek Demir, Andréi S. Rodin, Herbert M. Sauro, Jineta Banerjee, Said M. Afify, Ahmet Acar, Sandhya Prabhakaran, Carlos F. Lopez, Melanie Hayden Gephart, Maxine C. Umeh-Garcia, Yapeng Su, Randy F. Sweis, David E. Hill, Ghmkin Hassan, Mengzhou Hu, Orion GB Banks, Kimberly A. Luddy, Song Feng
article en

Abstract

Cancer systems biology seeks to understand how cancer arises as a system of interconnected molecules, cells, and tissues, with the goal of understanding, predicting, and controlling the disease. In the last decade, the field has rapidly grown as advances in experimental, computational, and analytic technologies have improved our ability to capture and recapitulate the complexities of cancer at multiple scales. However, the field's promise to understand how specific molecular changes give rise to altered cancer outcomes remains incompletely fulfilled. Fortunately, an opportunity exists to accelerate progress by better coordinating modeling and data-gathering efforts across the cancer systems biology community. This will create the foundation for building accurate, multiscale cancer models that can better predict and identify improved therapeutic interventions. Here, we outline some of the current challenges in cancer systems biology research, how they can be addressed, and actions that the community can take to accelerate progress in the field. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .

Cancer ResearchVol. 85(24)
Cañada College (US), Boston University (US), City Of Hope National Medical Center (US), Livestrong Foundation (US), Sage Bionetworks (US), Infectious Disease Research Institute (US), The University of Texas MD Anderson Cancer Center (US), Okayama University of Science (JP), Pacific Northwest National Laboratory (US), University of San Diego (US), University of California, Los Angeles (US), Okayama University (JP), Oregon Health & Science University (US), University of Georgia (US), University of Pittsburgh (US), University of California, San Francisco (US), Georgetown University (US), California Institute for Regenerative Medicine (US), University of Washington (US), Middle East Technical University (TR), Georgetown University Medical Center (US), Environmental Molecular Sciences Laboratory (US), Moffitt Cancer Center (US), University of California San Diego (US), University of Illinois Chicago (US), University of Chicago (US), University of Chicago Medical Center (US), Fred Hutch Cancer Center (US), Cancer Consortium (US), Dana-Farber Cancer Institute (US), UPMC Hillman Cancer Center (US), University of Colorado Cancer Center (US), Stanford Medicine (US), Altos Labs, Beckman Research Institute, OHSU Knight Cancer Institute, University of Colorado Anschutz Medical Campus (US), University of California, Davis (US), The University of Texas at Austin (US), University of Colorado Denver (US), Stanford University (US)
Openalex Percentile: Top 74%
Genetics, Bioinformatics, and Biomedical Research
DOIOpen Access PDFOpenAlex
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