A New Era of Data-Driven Cancer Research and Care: Opportunities and Challenges

Felicia Gomez^#¹, Arpad M Danos^#¹, Guilherme Del Fiol², Anant Madabhushi^{3

4}, Pallavi Tiwari^{5

6}, Joshua F McMichael¹, Spyridon Bakas^{7

8

9

10

11}, Jiang Bian¹², Christos Davatzikos¹³, Elana J Fertig¹⁴, Jayashree Kalpathy-Cramer¹⁵, Johanna Kenney¹⁶, Guergana K Savova^{17

18}, Meliha Yetisgen¹⁹, Eliezer M Van Allen^{20

21

22}, Jeremy L Warner^{23

24}, Fred Prior²⁵, Malachi Griffith¹, Obi L Griffith¹

Affiliations

¹ Department of Medicine, Washington University School of Medicine, St Louis, Missouri.
² Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah.
³ Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia.
⁴ Atlanta Veterans Affairs (VA) Medical Center, Decatur, Georgia.
⁵ Department of Radiology and Biomedical Engineering, University of Wisconsin, Madison, Wisconsin.
⁶ William S. Middleton Memorial Veterans Affairs (VA) Healthcare, Madison, Wisconsin.
⁷ Departments of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, Indiana.
⁸ Departments of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana.
⁹ Departments of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana.
¹⁰ Departments of Neurological Surgery, Indiana University School of Medicine, Indianapolis, Indiana.
¹¹ Department of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University, Indianapolis, Indiana.
¹² Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida.
¹³ Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania.
¹⁴ Department of Oncology and Applied Mathematics & Statistics, Johns Hopkins Medicine, Baltimore, Massachusetts.
¹⁵ Department of Ophthalmology, University of Colorado School of Medicine, Aurora, Colorado.
¹⁶ Technology Research Advocacy Partnership, National Cancer Institute, Bethesda, Maryland.
¹⁷ Department of Pediatrics, Harvard Medical School, Boston, Massachusetts.
¹⁸ Boston Children's Hospital, Boston, Massachusetts.
¹⁹ Department of Biomedical and Health Informatics, University of Washington, Seattle, Western Australia.
²⁰ Department of Medicine, Dana-Farber Cancer Institute, Harvard School of Medicine, Boston, Massachusetts.
²¹ Broad Institute, Cambridge, Massachusetts.
²² Parker Institute for Cancer Immunotherapy, San Francisco, California.
²³ Departments of Medicine and Biostatistics, Brown University, Providence, Rhode Island.
²⁴ Lifespan Cancer Institute, Rhode Island Hospital, Providence, Rhode Island.
²⁵ Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas.

^# Contributed equally.

PMID: 39363742
PMCID: PMC11463721
DOI: 10.1158/2159-8290.CD-24-1130

A New Era of Data-Driven Cancer Research and Care: Opportunities and Challenges

Felicia Gomez et al. Cancer Discov. 2024.

. 2024 Oct 4;14(10):1774-1778.

doi: 10.1158/2159-8290.CD-24-1130.

Authors

Affiliations

¹ Department of Medicine, Washington University School of Medicine, St Louis, Missouri.
² Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah.
³ Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia.
⁴ Atlanta Veterans Affairs (VA) Medical Center, Decatur, Georgia.
⁵ Department of Radiology and Biomedical Engineering, University of Wisconsin, Madison, Wisconsin.
⁶ William S. Middleton Memorial Veterans Affairs (VA) Healthcare, Madison, Wisconsin.
⁷ Departments of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, Indiana.
⁸ Departments of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana.
⁹ Departments of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana.
¹⁰ Departments of Neurological Surgery, Indiana University School of Medicine, Indianapolis, Indiana.
¹¹ Department of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University, Indianapolis, Indiana.
¹² Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, Florida.
¹³ Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania.
¹⁴ Department of Oncology and Applied Mathematics & Statistics, Johns Hopkins Medicine, Baltimore, Massachusetts.
¹⁵ Department of Ophthalmology, University of Colorado School of Medicine, Aurora, Colorado.
¹⁶ Technology Research Advocacy Partnership, National Cancer Institute, Bethesda, Maryland.
¹⁷ Department of Pediatrics, Harvard Medical School, Boston, Massachusetts.
¹⁸ Boston Children's Hospital, Boston, Massachusetts.
¹⁹ Department of Biomedical and Health Informatics, University of Washington, Seattle, Western Australia.
²⁰ Department of Medicine, Dana-Farber Cancer Institute, Harvard School of Medicine, Boston, Massachusetts.
²¹ Broad Institute, Cambridge, Massachusetts.
²² Parker Institute for Cancer Immunotherapy, San Francisco, California.
²³ Departments of Medicine and Biostatistics, Brown University, Providence, Rhode Island.
²⁴ Lifespan Cancer Institute, Rhode Island Hospital, Providence, Rhode Island.
²⁵ Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas.

^# Contributed equally.

PMID: 39363742
PMCID: PMC11463721
DOI: 10.1158/2159-8290.CD-24-1130

Abstract

People diagnosed with cancer and their formal and informal caregivers are increasingly faced with a deluge of complex information, thanks to rapid advancements in the type and volume of diagnostic, prognostic, and treatment data. This commentary discusses the opportunities and challenges that the society faces as we integrate large volumes of data into regular cancer care.

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Conflict of interest statement

A. Madabhushi reports other support from Picture Health, Inspirata Inc., and Elucid Bioimaging and personal fees from SimBioSys during the conduct of the study. P. Tiwari reports other support from LivAi Inc. and personal fees from Johnson & Johnson outside the submitted work. S. Bakas reports grants from NIH/NCI during the conduct of the study; grants from NIH/NCI outside the submitted work. C. Davatzikos reports grants from R01-NS-042645 Imaging signatures of Genetic Mutation Brain tumor and grants from R01CA269948 ReSPOND—Generalizable quantitative imaging during the conduct of the study. E.J. Fertig reports grants from NIH/NCI during the conduct of the study; grants from Break Through Cancer, NIH/NIA, Lustgarten Foundation, Roche/Genentech, and resistanceBio and personal fees from Merck outside the submitted work. J. Kalpathy-Cramer reports other support from Siloam Vision and grants from GE and Genentech outside the submitted work. E.M. Van Allen reports personal fees from Tango Therapeutics, Genome Medical, Genomic Life, Monte Rosa Therapeutics, Manifold Bio, Enara, Serinus Biosciences, Foaley & Hoag, TracerDx, and Riva Therapeutics and grants from Novartis, BMS, Janssen, Sanofi, and NextPoint outside the submitted work; in addition, E.M. Van Allen has a patent for Institutional patents filed on chromatin mutations and immunotherapy response, and methods for clinical interpretation pending and issued. J.L. Warner reports grants from NIH/NCI during the conduct of the study; grants from NIH/NCI, AACR, and Brown Physician’s Incorporated, personal fees from Westat and The Lewin Group, and other support from HemOnc.org LLC outside the submitted work. O.L. Griffith reports grants from NIH during the conduct of the study; personal fees from Jaime Leandro Foundation outside the submitted work; and Board of Directors of the Cancer Genomics Consortium. F. Gomez reports grants from NCI/NIH during the conduct of this study. No disclosures were reported by the other authors.

Figures

**Figure 1.**
The comprehensive journey of cancer care: from prevention to personalized treatment in the era of integrated data analysis. The journey of a person with cancer begins with preventive and screening measures to avoid the development of cancer and to detect cancer at an early stage when it is more treatable. If screening detects cancer, the disease is further diagnosed and assessed *via* laboratory tests, genomic analysis, and imaging. Following diagnosis, a course of treatment and monitoring is proposed. Cancer treatment and management has a range of outcomes and often involves iterative rounds of analysis and monitoring. As the patient journey continues, outcomes that can be experienced include remission, cure, relapse, or palliative care when treatments are no longer effective. Data collected from people with cancer in each stage of the journey: prevention, screening, diagnosis, treatment, disease management, and outcome, comprise the landscape of data that are increasingly integrated into medical health records and analyzed with AI tools. The goal of the data integration is to optimize treatment to address the specific disease biology experienced by the person with cancer in a framework of personalized medicine. However, this integrated data-driven approach presents new equity, education, and privacy challenges that must be addressed in order to maximize benefits for society.

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A New Era of Data-Driven Cancer Research and Care: Opportunities and Challenges

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A New Era of Data-Driven Cancer Research and Care: Opportunities and Challenges

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Conflict of interest statement

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