Kooresh I. Shoghi

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. Although 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, and tumor function and heterogeneity across animal models and patients to advance FPM. Over the past decade, the Co-Clinical Imaging Research Resource Program of the 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 multimodal 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.

Supplementary Fig. 1. Lightsheet. Supplementary Fig. 2. 3D serial section workflow and motivation. Supplementary Figure 3. Serial section ROIs for HT206B1 (breast). Supplementary Fig. 4. Genes associated with 3D transition ROIs in breast and prostate. Supplementary Fig. 5. Serial section ROIs for HT913Z1 (prostate). Supplementary Fig. 6. 3D proximity of TAMs to non-invasive and invasive epithelial regions in HT704B1. Supplementary Fig. 7. Expression of cohort-level transition genes. Supplementary Fig. 8. Quantification and validation of transition markers. Supplementary Fig. 9. Wound healing assays for MGP and PLAT knockdowns in breast cancer cell lines. Supplementary Fig. 10. Wound healing assays for GDF15, FASN, ALDH1A3, and ANPEP knockdowns in PC-3 prostate cancer cell line. Supplementary Fig. 11. Expression for GDF15, ANPEP, ALDH3A1, and FASN in four prostate cancer cell lines, including PC-3.

HT944B1_S2H3 whole 3D rotation.

HT704B1 CODEX PanCKred CD68cyan KRT5white.

Supplementary Mov. 11 from Spatial Mapping of the Precancer-to-Cancer Transition in Breast and Prostate

Supplementary Mov. 12 from Spatial Mapping of the Precancer-to-Cancer Transition in Breast and Prostate

Supplementary Mov. 14 from Spatial Mapping of the Precancer-to-Cancer Transition in Breast and Prostate

Supplementary Mov. 15 from Spatial Mapping of the Precancer-to-Cancer Transition in Breast and Prostate

Supplementary Mov. 13 from Spatial Mapping of the Precancer-to-Cancer Transition in Breast and Prostate

Supplementary Mov. 16 from Spatial Mapping of the Precancer-to-Cancer Transition in Breast and Prostate