Synergistic Cytotoxicity against Human Tumor Cell Lines by Oncolytic Adenovirus dl1520 (ONYX-015) and Melphalan
Bibliographic record
Abstract
AIMS AND BACKGROUND: In light of the need for more selective anticancer therapy, much work has been directed at developing compounds or biological agents that target functions specific to cancer cells. To this end, numerous viruses have been engineered to exploit the dependence of cancer cells on particular anomalies that contribute to their rogue proliferative activity, such as dysfunctional p53, overactive mitogenic signaling, or a defective interferon response. The oncolytic human adenovirus dl1520 (ONYX-015) was engineered to propagate specifically in p53-deficient tumors, which comprise over half of all tumors. Based on successes in clinical trials, the full potential of dl1520 and other oncolytic viruses may be even better realized by using them in combination with conventional chemotherapy drugs. METHODS: As a model system in which to test this potential, representative cell lines from 2 common cancer types, oral squamous cell carcinoma (HN-5a) and colon adenocarcinoma (HT-29), were chosen, as well as platinum-drug-resistant variants of each. RESULTS: Following preliminary screening of virus and drug combinations, dl1520 and melphalan were found to synergistically inhibit proliferation of all the cancer cell lines. Melphalan pretreatment or cotreatment with dl1520 enhanced inhibition of proliferation by dl1520 by up to 60% and increased apoptosis by up to 25%. The tight-junction protein CAR (coxsackie and adenovirus receptor), via which adenovirus enters cells, was not upregulated by treatment with melphalan, suggesting that other mechanisms contribute to synergy. CONCLUSIONS: The synergy between melphalan and dl1520 suggests that tumor-selective cell killing by oncolytic viruses may be augmented by combining with cytotoxic drugs.
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) | 0.001 | 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) | 0.001 | 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 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".