A Molecular Imaging System Based on Both Transcriptional and Genomic Amplification to Detect Prostate Cancer Cells In Vivo
Bibliographic record
Abstract
An imaging modality that can accurately discern prostate cancer (PCa) foci would be useful to detect PCa early or guide treatment. We have engineered numerous adenoviral vectors (Ads) to carry out reporter gene-based imaging using specific promoters to express a potent transcriptional activator, which in turn activates the reporter gene in PCa. This two-step transcriptional amplification (TSTA) method can boost promoters' activity, while maintaining cell specificity. Here, we examined a dual TSTA (DTSTA) approach, which utilizes TSTA not only to express the imaging reporter, but also to direct viral genome replication of a conditionally replicating Ad (CRAd) to further augment the expression levels of the reporter gene by genomic amplification supported in trans by coadministered CRAd. In vitro studies showed up to 50-fold increase of the reporter genome by DTSTA. Compared with TSTA reporter alone, DTSTA application exhibited a 25-fold increase in imaging signal in PCa xenografts. DTSTA approach is also beneficial for a combination of two TSTA Ads with distinct promoters, although amplification is observed only when TSTA-CRAd can replicate. Consequently, the DTSTA approach is a hybrid method of transcriptional and genomic augmentation that can provide higher level reporter gene expression potentially with a lower dose of viral administration. An imaging modality that can accurately discern prostate cancer (PCa) foci would be useful to detect PCa early or guide treatment. We have engineered numerous adenoviral vectors (Ads) to carry out reporter gene-based imaging using specific promoters to express a potent transcriptional activator, which in turn activates the reporter gene in PCa. This two-step transcriptional amplification (TSTA) method can boost promoters' activity, while maintaining cell specificity. Here, we examined a dual TSTA (DTSTA) approach, which utilizes TSTA not only to express the imaging reporter, but also to direct viral genome replication of a conditionally replicating Ad (CRAd) to further augment the expression levels of the reporter gene by genomic amplification supported in trans by coadministered CRAd. In vitro studies showed up to 50-fold increase of the reporter genome by DTSTA. Compared with TSTA reporter alone, DTSTA application exhibited a 25-fold increase in imaging signal in PCa xenografts. DTSTA approach is also beneficial for a combination of two TSTA Ads with distinct promoters, although amplification is observed only when TSTA-CRAd can replicate. Consequently, the DTSTA approach is a hybrid method of transcriptional and genomic augmentation that can provide higher level reporter gene expression potentially with a lower dose of viral administration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".