The Next Frontier in Quantitative Co-Clinical Imaging to Advance Functional Precision Oncology
TL;DR
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 functional precision medicine (FPM).
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.