Correlation of Somatostatin Receptor-2 Expression with Gallium-68-DOTA-TATE Uptake in Neuroblastoma Xenograft Models
Bibliographic record
Abstract
Peptide-receptor imaging and therapy with radiolabeled somatostatin analogs such as 68Ga-DOTA-TATE and 177Lu-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 68Ga-DOTA-TATE uptake and 177Lu-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 68Ga-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 68Ga-DOTA-TATE uptake in CHLA-15 tumors. With a low dose of 177Lu-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 131I-MIBG therapy, low 123I-MIBG uptake was observed in all selected NB xenografts. In conclusion, SSTR2 expression levels are associated with 68Ga-DOTA-TATE uptake and antitumor efficacy of 177Lu-DOTA-TATE. 68Ga-DOTA-TATE PET is superior to 123I-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 177Lu-DOTA-TATE therapy.
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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".