Imaging CAR-NK cells targeted to HER2 ovarian cancer with human sodium-iodide symporter-based positron emission tomography
Bibliographic record
Abstract
Abstract Chimeric antigen receptor (CAR) cell therapies utilize CARs to redirect immune cells towards cancer cells expressing specific antigens like human epidermal growth factor receptor 2 (HER2). Despite their potential, CAR T cell therapies exhibit variable response rates and adverse effects in some patients. Non-invasive molecular imaging can aid in predicting patient outcomes by tracking infused cells post-administration. CAR-T cells are typically autologous, increasing manufacturing complexity and costs. An alternative approach involves developing CAR natural killer (CAR-NK) cells as an off-the-shelf allogeneic product. In this study, we engineered HER2-targeted CAR-NK cells co-expressing the positron emission tomography (PET) reporter gene human sodium-iodide symporter (NIS) and assessed their therapeutic efficacy and PET imaging capability in a HER2 ovarian cancer mouse model. NK-92 cells were genetically modified to express a HER2-targeted CAR, the bioluminescence imaging reporter Antares, and NIS. HER2-expressing ovarian cancer cells were engineered to express the bioluminescence reporter Firefly luciferase (Fluc). Co-culture experiments demonstrated significantly enhanced cytotoxicity of CAR-NK cells compared to naive NK cells. In vivo studies involving mice with Fluc-expressing tumors revealed that those treated with CAR-NK cells exhibited reduced tumor burden and prolonged survival compared to controls. Longitudinal bioluminescence imaging demonstrated stable signals from CAR-NK cells over time. PET imaging using the NIS-targeted tracer 18F-tetrafluoroborate ([18F]TFB) showed significantly higher PET signals in mice treated with NIS-expressing CAR-NK cells. Overall, our study showcases the therapeutic potential of HER2-targeted CAR-NK cells in an aggressive ovarian cancer model and underscores the feasibility of using human-derived PET reporter gene imaging to monitor these cells non-invasively in patients.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".