The Next Frontier in Quantitative Co-clinical Imaging to Advance Functional Precision Oncology
Year: 2026
Type: article
Abstract: Precision oncology seeks to transform cancer treatment into one tailored for individual patients. Genomic alterations have been the dominant drivers of patient-specific therapeutic strategies. While genomic insights can guide treatment planning, genotype-directed therapies do not guarantee response to therapy. Functional confirmation of target expression, target engagement, and downstream biological effects is essential to defining therapeutic endpoints. Translational imaging can play a central role in closing the loop between genome and function to operationalize functional precision medicine (FPM). Quantitative co-clinical imaging provides a powerful framework to link genotype with function by confirming drug delivery, target engagement, pharmacodynamic effects, tumor function and heterogeneity across animal models and patients to advance FPM. Over the past decade, the Co-Clinical Imaging Research Resource Program (CIRP) of the National Cancer Institute (NCI) has made considerable progress in advancing quantitative imaging biomarker development. However, critical gaps remain in the integration and standardization of co-clinical imaging biomarkers. The next frontier requires moving beyond quantitative endpoints toward predictive oncology. This includes validating imaging-aware models, developing quantitative imaging strategies to support new precision medicine trials, and embedding co-clinical data into computational and in silico frameworks such as radiomics, cross-species mathematical modeling, digital twins, and virtual imaging trials. Equally important is the development of a domain-specific repository that vertically integrates imaging with multi-modal data to enable reproducible, artificial intelligence-ready pipelines. These advances will accelerate the translation of co-clinical imaging biomarkers into decision models that complement genomics, improve patient stratification, and guide selection and personalization of therapies-advancing the promise of FPM.
Source: Clinical Cancer Research
Authors: Kooresh I. Shoghi, Cristian T. Badea, Donna M. Peehl, Thomas L. Chenevert, Heike E. Daldrup-Link, Douglas S. Hawkins, A. McGarry Houghton, Paul E. Kinahan, Michael T. Lewis, Cynthia X. Ma, H. Charles Manning, Robert S. Miyaoka, Yvonne M. Mowery, James P.B. O’Connor, Peter J. O’Dwyer, Brian D. Ross, Renuka Sriram, Thomas E. Yankeelov, Rong Zhou, John Kurhanewicz, David A. Mankoff
Institutions: Washington University in St. Louis, Alvin J. Siteman Cancer Center, Duke University, University of California, San Francisco, University of Michigan, Michigan Medicine, Stanford University, Seattle Children's Hospital, University of Washington, Fred Hutch Cancer Center, Baylor College of Medicine, Dan L Duncan Comprehensive Cancer Center, The University of Texas MD Anderson Cancer Center, University of Pittsburgh, UPMC Hillman Cancer Center, University of Pittsburgh Medical Center, Institute of Cancer Research, ECOG-ACRIN Cancer Research Group, University of Pennsylvania, UT Health Austin, The University of Texas at Austin, Abramson Cancer Center
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funders: Damon Runyon Cancer Research Foundation, Cancer Prevention and Research Institute of Texas, Brigham and Women's Hospital, National Institutes of Health, University of Texas MD Anderson Cancer Center, Stand Up To Cancer, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, National Institute of Dental and Craniofacial Research
Awards: Recruitment of Established Investigators, G1456844873, G1788796038, G2234118846, G2564692659, G2717438012, INTEGRATING OMICS AND QUANTITATIVE IMAGING DATA IN CO-CLINICAL TRIALS TO PREDICT TREATMENT RESPONSE IN TRIPLE NEGATIVE BREAST CANCER, Penn Quantitative MRI Resource for Pancreatic Cancer, Co-Clinical Quantitative Imaging of Small Cell Neuroendocrine Prostate Cancer Using Hyperpolarized 13C MRI, G3574825479, University of Michigan Quantitative Co-Clinical Imaging Research Resource, Children's Oncology Group Statistics and Data Center, Patient-derived Xenograft and Advanced In Vivo Models (PDX-AIM) Core Facility of Texas, The Duke Preclinical Research Resources for Quantitative Imaging Biomarkers, COG NCTN Network Group Operations Center, G7326920056, Imaging Disease Heterogeneity and Response to Therapy in Myelofibrosis, MDACC-PREDICT, Fred Hutchinson Cancer Center Lung SPORE, FFNP-PET as a predictive biomarker of response to endocrine therapy approaches in advanced breast cancer, A Quantitative PET/CT Research Resource for Co-Clinical Imaging of Lung Cancer Therapies, Preclinical Imaging XNAT-enabled Informatics (PIXI) Center, Development of a Virtual Preclinical CT Platform for Advanced Imaging and Theranostics in Head and Neck Cancer Research, Cancer Center Support Grant, PDX Trial Center for Breast Cancer Therapy, Development of quantitative bone marrow magnetic resonance imaging biomarkers to assess efficacy of novel molecularly targeted agents for myelofibrosis, Vanderbilt-Ingram Cancer Center SPORE in Gastrointestinal Cancer, Cancer Center Support Grant, Hyperpolarized MRI Technology Resource Center, Washington University Co-Clinical Imaging Research Resource, G931214414
OpenAlex ID: W7214098368