In Vivo Imaging of Intraprostatic-Specific Gene Transcription by PET
Bibliographic record
Abstract
UNLABELLED: Better intraprostatic cancer imaging techniques are needed to guide clinicians in prostate cancer treatment decisions. Because many genes are specifically overexpressed in cancer cells, one strategy to improve prostate cancer detection is to image intraprostatic cancer-specific transcriptional activity. Because of the obstacles of weak cancer- or tissue-specific promoter activity and bladder clearance of many PET tracers, intraprostatic PET of gene transcriptional activity has not been previously reported. METHODS: The two-step transcriptional amplification (TSTA) system that amplifies the prostate-specific antigen promoter activity was used for PET imaging of the reporter gene herpes simplex virus type-1 sr39 thymidine kinase (HSV1-sr39tk). The TSTA-sr39tk system was injected directly into prostates or prostatic tumors as a replication-incompetent adenovirus (AdTSTA-sr39tk) and imaged using PET. RESULTS: AdTSTA-sr39tk was able to image prostate-specific antigen promoter transcriptional activity by 9-(4-(18)F-fluoro-3-[hydroxymethyl]butyl)guanine PET, in both mouse and canine prostates in vivo. Ex vivo small-animal PET images, scintigraphic counts, and sr39tk expression analysis confirmed the specificity of the observed signal. CONCLUSION: Here, by combining the TSTA-amplified signal with a protocol for tracer administration, we show that in vivo PET detection of transcriptional activity is possible in both mouse and immunocompetent canine prostates. These results suggest that imaging applications using transcription-based tumor-specific promoters should be pursued to better visualize cancer foci that escape detection by conventional biopsies.
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How this classification was reachedexpand
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) | 0.002 | 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 teacher head, 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".