Transcriptional targeting modalities in breast cancer gene therapy using adenovirus vectors controlled by α-lactalbumin promoter
Bibliographic record
Abstract
The breast-specific antigen alpha-lactalbumin is expressed in >60% of breast cancer tissues. To evaluate the effect of gene therapy for breast cancer by controlling adenovirus replication with human alpha-lactalbumin promoter, we investigated the activity of a 762-bp human alpha-lactalbumin promoter. Alpha-lactalbumin promoter showed significantly higher activity in MDA-MB-435S and T47D breast cancer cells than in normal breast cell lines or other tumor cell lines. We then developed two novel breast cancer-restricted replicative adenoviruses, AdALAE1a and AdE1aALAE1b. In AdALAE1a, expression of adenoviral E1a gene is under the control of alpha-lactalbumin promoter, and in AdE1aALAE1b, expression of both E1a and E1b genes is under the control of a single alpha-lactalbumin promoter. Both breast cancer-restricted replicative adenoviruses showed viral replication efficiency and tumor cell-killing capability similar to wild-type adenovirus in MDA-MB-435S and T47D cells. The replication efficiency and tumor cell-killing capability of both viruses were attenuated significantly in cells that did not support alpha-lactalbumin promoter. AdE1aALAE1b showed better breast cancer-restricted replication than AdALAE1a, suggesting that a transcriptional targeting modality with alpha-lactalbumin promoter controlling both E1a and E1b gene expression is superior to alpha-lactalbumin promoter controlling only E1a gene expression. Importantly, we found that AdE1aALAE1b could be used to target hormone-independent breast tumors in vivo by inhibiting the growth of MDA-MB-435S s.c. tumors. These data showed that alpha-lactalbumin promoter could regulate the replication of adenovirus to target hormone-independent breast cancers, suggesting that alpha-lactalbumin promoter can be used to develop a novel therapeutic modality for hormone-independent breast cancer.
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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) | 0.000 | 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".