Correlation of Somatostatin Receptor-2 Expression with Gallium-68-DOTA-TATE Uptake in Neuroblastoma Xenograft Models
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Bibliographic record
Abstract
Peptide-receptor imaging and therapy with radiolabeled somatostatin analogs such as 68 Ga-DOTA-TATE and 177 Lu-DOTA-TATE have become an effective treatment option for SSTR-positive neuroendocrine tumors. The purpose of this study was to evaluate the correlation of somatostatin receptor-2 (SSTR2) expression with 68 Ga-DOTA-TATE uptake and 177 Lu-DOTA-TATE therapy in neuroblastoma (NB) xenograft models. We demonstrated variable SSTR2 expression profiles in eight NB cell lines. From micro-PET imaging and autoradiography, a higher uptake of 68 Ga-DOTA-TATE was observed in SSTR2 high-expressing NB xenografts (CHLA-15) compared to SSTR2 low-expressing NB xenografts (SK-N-BE(2)). Combined autoradiography-immunohistochemistry revealed histological colocalization of SSTR2 and 68 Ga-DOTA-TATE uptake in CHLA-15 tumors. With a low dose of 177 Lu-DOTA-TATE (20 MBq/animal), tumor growth inhibition was achieved in the CHLA-15 high SSTR2 expressing xenograft model. Although, in vitro , NB cells showed variable expression levels of norepinephrine transporter (NET), a molecular target for 131 I-MIBG therapy, low 123 I-MIBG uptake was observed in all selected NB xenografts. In conclusion, SSTR2 expression levels are associated with 68 Ga-DOTA-TATE uptake and antitumor efficacy of 177 Lu-DOTA-TATE. 68 Ga-DOTA-TATE PET is superior to 123 I-MIBG SPECT imaging in detecting NB tumors in our model. Radiolabeled DOTA-TATE can be used as an agent for NB tumor imaging to potentially discriminate tumors eligible for 177 Lu-DOTA-TATE therapy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it