Multidrug-resistant Cancer Cells Facilitate E1-independent Adenoviral Replication
Bibliographic record
Abstract
Resistance to chemotherapy is responsible for a failure of current treatment regimens in cancer patients. We have reported previously that the Y-box protein YB-1 regulates expression of the P-glycoprotein gene mdr1, which plays a major role in the development of a multidrug resistant-tumor phenotype. YB-1 predicts drug resistance and patient outcome in breast cancer. Thus, YB-1 is a promising target for new therapeutic approaches to defeat multidrug resistance. In drug-resistant cancer cells and in adenovirus-infected cells YB-1 is found in the nucleus. Nuclear accumulation of YB-1 in adenovirus-infected cells is a function of the E1 region, and we have shown that YB-1 facilitates adenovirus replication. Here we report that E1A-deleted or mutant adenovirus vectors, such as Ad312 and Ad520, replicate efficiently in multidrug-resistant (MDR) cancer cells and induce an adenovirus cytopathic effect resulting in host cell lysis. Thus, replication-defective adenoviruses are a previously unrecognized vector system for a selective elimination of MDR cancer cells. Our work forms the basis for the development of novel oncolytic adenovirus vectors for the treatment of MDR malignant diseases in the clinical setting.
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 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".