Therapeutic potential of antisense oligodeoxynucleotides to down-regulate thymidylate synthase in mesothelioma
Bibliographic record
Abstract
Malignant mesothelioma is an aggressive tumor of the serosal surfaces of the lungs, heart, and abdomen. Survival rates are poor and effective treatments are not available. However, recent therapeutic regimens targeting thymidylate synthase (TS) in malignant mesothelioma patients have shown promise. We have reported the use of an antisense oligodeoxynucleotide targeting TS mRNA (antisense TS ODN 83) to inhibit growth of human tumor cells. To test the potential for antisense targeting of TS mRNA in treatment of malignant mesothelioma, we assessed and compared the effects of antisense TS ODN 83 on three human malignant mesothelioma cell lines (211H, H2052, and H28) and human nonmalignant mesothelioma cells (HT29 colorectal adenocarcinoma, HeLa cervical carcinoma, and MCF7 breast tumor cell lines). We report that ODN 83 applied as a single agent effectively reduced TS mRNA and protein in malignant mesothelioma cell lines. Furthermore, it inhibited malignant mesothelioma growth significantly more effectively than it inhibited growth of nonmalignant mesothelioma human tumor cell lines: a difference in susceptibility was not observed in response to treatment with TS protein-targeting drugs. In malignant mesothelioma cells, antisense TS both induced apoptotic cell death and reduced proliferation. In nonmalignant mesothelioma cells, only reduced proliferation was observed. Thus, antisense TS-mediated induction of apoptosis may be the basis for the high malignant mesothelioma sensitivity to antisense targeting of TS. Further preclinical and clinical study of TS antisense oligodeoxynucleotides, alone and in combination with TS-targeting chemotherapy drugs, in mesothelioma is warranted.
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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 | 0.000 | 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 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".