Chemotherapy-Induced Upregulation of Somatostatin Receptor-2 Increases the Uptake and Efficacy of 177Lu-DOTA-Octreotate in Neuroendocrine Tumor Cells
Bibliographic record
Abstract
Lu-DOTA-octreotate (LuTate) is recommended for different types of neuroendocrine tumors (NETs) which overexpress somatostatin receptors (SSTR). A combination with chemotherapy improves objective response to LuTate in NET patients and here we characterized chemotherapy-induced upregulation of SSTR2 receptors as a cause for this improved response to LuTate. The NET cell lines with low (BON-1) or relatively high (NCI-H727) SSTR2-expression levels, and non-NET cancer and normal cells were treated with chemotherapeutic drugs and assessed for upregulation of SSTR2. We report that an exposure to low or high doses of drugs, such as temozolomide for 24 h or 5 day results in upregulation of SSTR2 between 3-7 days, increased LuTate uptake and decreased rate of cell proliferation. This effect is at the level of SSTR2-mRNA and is more pronounced in low SSTR2 expressing BON-1 than in high SSTR2 expressing NCI-H727 or non-NET cancer or normal cells. Thus, a properly timed pre-treatment with low-dose chemotherapy could not only improve therapeutic efficacy of LuTate in NET patients who are presently eligible for PRRT, but also allow PRRT to be administered to patients with low SSTR-expressing NETs, who would otherwise not respond to this modality because of insufficient radiation delivery.
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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".