Bioengineered viral vectors for targeting and killing prostate cancer cells
Bibliographic record
Abstract
Enabling the transduction of therapeutic gene expression exclusively in diseased sites is the key to developing more effective treatments for advanced prostate cancer using viral-based therapy. While prostate cancers that express high levels of HER-2 are resistant to the killing effects of trastuzumab, they can be targeted for selective gene expression and destruction by lentiviruses with envelope proteins engineered to bind to this therapeutic antibody. More importantly, after intravenous injection, this trastuzumab-bound lentivirus is able to target castration-resistant prostate tumor xenografts, albeit with low efficiency. This proof of principle opens up multiple possibilities for the prevention and treatment of prostate cancer using a viral-based therapy. However, to be safe and more effective, the viral vectors must target prostate cancer cells more selectively and efficiently. A higher degree of specificity and efficiency of cancer cell targeting can be achieved by engineering viral vectors to bind to a specific cell surface marker and by controlling the expression of the therapeutic payload at transcriptional level, with a tissue-specific promoter, and at the translational level, with a regulatory sequences inserted into either the 5'UTR or 3'UTR regions of the therapeutic gene(s). The latter would be designed to ensure that translation of this mRNA occurs exclusively in malignant cells. Furthermore, in order to obtain a potent anti-tumor effect, viral vectors would be engineered to express pro-apoptotic genes, intra-cellar antibodies/nucleotide aptamers to block critical proteins, or siRNAs to knockdown essential cellular mRNAs. Alternatively, controlled expression of an essential viral gene would restore replication competence to the virus and enable selective oncolysis of tumor cells. Successful delivery of such bioengineered viruses may provide a more effective way to treat advanced prostate cancer.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".