Combined antiproliferative effects of cytolytic vaccinia virus and 111In- and 177Lu-DOTATOC on somatostatin-receptor (SSTR2)-positive cells
Bibliographic record
Abstract
2313 Objectives Cytolytic viruses are promising new cancer treatments that could be combined with targeted radiotherapy. Our aim was to study the combined effect of recombinant double-deleted vaccinia virus (VV-SSTR) encoding SSTR2, 111In-DOTATOC emitting Auger electrons and 177Lu-DOTATOC emitting β-radiation on the survival of cells expressing SSTR2 in monolayer or as spheroids. Methods DOTATOC was labeled with 111In or 177Lu ( 6-8 MBq/µg specific activity). HEK-293 cells transfected with the SSTR2 gene were exposed to 0.3-20 ng of 111In- or 177Lu-DOTATOC. In combination therapy, cells were exposed for 24h to VV-SSTR at Multiplicity of infection of 1, then treated with 20ng of 111In- or 177Lu-DOTATOC. Cell growth was measured by WST-1 assay directly or after trypsinization of spheroids and replating of cells after 5d of treatment or 8d in combination therapy. Results 111In- and 177Lu-DOTATOC (20 ng) decreased cell growth by 27 ± 3% and 46 ± 1% respectively. 177Lu-DOTATOC diminished spheroid growth by 67 ± 14% at 2.5ng and 89% (single data) at 20 ng. 111In-DOTATOC (2.5ng) had no effect on spheroid growth but 20 ng decreased proliferation by 89 ± 4%. Virus alone decreased cell growth by 27 ± 3% while combination with 111In-DOTATOC or177Lu-DOTATOC decreased growth by 75 ± 13% and 94 ± 3% respectively Conclusions We conclude that combining targeted radiotherapy with 111In- or 177Lu-DOTATOC with cytolytic virus therapy with VV-SSTR enhanced the killing of cells expressing SSTR2. 177Lu-DOTATOC was more effective than 111In-DOTATOC.
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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) | 0.001 | 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".