A preclinical PET dual-tracer imaging protocol for ER and HER2 phenotyping in breast cancer xenografts
Bibliographic record
Abstract
Abstract Background Nuclear medicine is on the constant search of precision radiopharmaceutical approaches to improve patient management. Although discordant expression of the estrogen receptor (ER) and the human epidermal growth factor receptor 2 (HER2) in breast cancer is a known dilemma for appropriate patient management, traditional tumor sampling is often difficult or impractical. While 2-deoxy-2[18F]fluoro-D-glucose (18F-FDG)-positron emission tomography (PET) is an option to detect subclinical metastases, it does not provide phenotype information. Radiolabeled antibodies are able to specifically target expressed cell surface receptors. However, their long circulating half-lives (days) require labeling with long-lived isotopes, such as89Zr, in order to allow sufficient time for tracer clearance from the blood compartment and to accumulate adequately in target tumors and, thus, generate high-quality PET images. The aim of this study was to develop a dual-tracer PET imaging approach consisting of a fast-clearing small molecule and a slow-clearing antibody. This approach was evaluated in a model consisting of mice harboring separate breast cancer xenografts with either an ER+/HER2− or ER−/HER2+ phenotype, comparable to human metastatic disease with intertumor heterogeneity. Lastly, the aim of our study was to determine the feasibility of specifically identifying these two important phenotypes in an acceptable time window. Methods Female nude mice were subcutaneously implanted on opposite shoulders with the ER+/HER2− and ER−/HER2+ MCF-7 and JIMT-1 tumor cell lines, respectively. A second model was developed consisting of mice implanted orthotopically with either MCF-7 or JIMT-1 cells. Pharmacokinetic analysis, serial PET imaging, and biodistribution were first performed for [89Zr]Zr-DFO-trastuzumab (89Zr-T) up to 8 days post-injection (p.i.) in JIMT-1 bearing mice. Region-of-interest (ROI) and biodistribution-derived uptake (% injected-activity/gram of tissue [%IA/g]) values and tumor-to-background ratios were obtained. Results were compared in order to validate ROI and identify early time points that provided high contrast tumor images. For the dual-tracer approach, cohorts of tumor-bearing mice were then subjected to sequential tracer PET imaging. On day 1, mice were administered 4-fluoro-11β-methoxy-16α-[18F]-fluoroestradiol (4FMFES) which targets ER and imaged 45 min p.i. This was immediately followed by the injection of89Zr-T. Mice were then imaged on day 3 or day 7. ROI analysis was performed, and uptake was calculated in tumors and selected healthy organs for all radiotracers. Quality of tumor targeting for all tracers was evaluated by tumor contrast visualization, tumor and normal tissue uptake, and tumor-to-background ratios. Results 89Zr-T provided sufficiently high tumor and low background uptake values that furnished high contrast tumor images by 48 h p.i. For the dual-tracer approach, 4FMFES provided tumor uptake values that were significantly increased in MCF-7 tumors. When89Zr-T-PET was combined with18F-4FMFES-PET, the entire dual-tracer sequential-imaging procedure provided specific high-quality contrast images of ER+/HER2− MCF-7 and ER−/HER2+ JIMT-1 tumors for 4FMFES and89Zr-T, respectively, as short as 72 h from start to finish. Conclusions This protocol can provide high contrast images of tumors expressing ER or HER2 within 3 days from injection of 4FMFES to final scan of89Zr-T and, hence, provides a basis for future dual-tracer combinations that include antibodies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".