A survivin-driven tumour-activatable minicircle system for prostate cancer theranostics
Bibliographic record
Abstract
Abstract Gene vectors driven by tumour-specific promoters to express reporter genes and therapeutic genes are an emerging approach for improved cancer diagnosis and treatment. Minicircles (MCs) are shortened plasmids stripped of prokaryotic sequences and have potency and safety characteristics that are beneficial for clinical translation. We previously developed survivin-driven, tumour-activatable MCs for cancer detection via a secreted blood reporter assay. Here we present a novel theranostic system for prostate cancer featuring a pair of survivin-driven MCs, combining selective detection of aggressive tumours via a urinary reporter test and subsequent tumour treatment with gene-directed enzyme prodrug therapy. Methods We engineered both diagnostic and therapeutic survivin-driven MCs expressing Gaussia luciferase, a secreted reporter that is detectable in the urine, and cytosine deaminase:uracil phosphoribosyltransferase fusion, respectively. Diagnostic MCs were evaluated in mice carrying orthotopic prostate tumours with varying survivin levels, measuring reporter activity in serial urine samples. Therapeutic MCs were evaluated in mice receiving prodrug using bioluminescence imaging to assess cancer cell viability over time. Results Diagnostic MCs revealed mice with aggressive prostate tumours exhibited significantly higher urine reporter activity than mice with non-aggressive tumours and tumour-free mice. Combined with 5-fluorocytosine prodrug treatment, therapeutic MCs resulted in reduced bioluminescence signal in mice with aggressive prostate tumours compared to control mice. Conclusion Sequential use of these MCs may be used to first identify patients carrying aggressive prostate cancer by a urinary reporter test, followed by stringent treatment in stratified individuals identified to have high-risk lesions. This work serves to highlight tumour-activatable MCs as a viable platform for development of gene-based tumour-activatable theranostics.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".