Abstract C143: Combining siRNAs targeting thymidylate synthase and thymidine kinase 1 or 2 sensitizes human tumor cells to 5FUdR and pemetrexed.
Bibliographic record
Abstract
Abstract Thymidylate synthase (TS) is the only de novo source of thymidylate (dTMP) for DNA synthesis and repair. Drugs targeting TS protein are a mainstay in cancer treatment, but off-target effects and toxicity limit their use. Cytosolic thymidine kinase (TK1) and mitochondrial thymidine kinase (TK2) contribute to an alternative dTMP-producing pathway, by salvaging thymidine from the tumor milieu, and may modulate resistance to TS-targeting drugs. Combined downregulation of these enzymes is an attractive strategy to enhance cancer therapy. We have previously shown that antisense targeting TS enhanced tumor cell sensitivity to TS-targeting drugs in vitro and in vivo. As both TS and TKs contribute to increased cellular dTMP, we hypothesized that TKs mediate resistance to the capacity of TS siRNA to sensitize tumor cells to TS-targeting anticancer drugs. We assessed the effects of targeting TK1 or TK2 with siRNA alone and in combination with siRNA targeting TS and/or TS-protein targeting drugs on tumor cell proliferation. Downregulation of TK with siRNA enhanced the capacity of TS siRNA to sensitize tumor cells to traditional TS protein-targeting drugs (5FUdR and pemetrexed). The sensitization was greater than that observed in response to any siRNA used alone, and was specific to drugs targeting TS. Upregulation of TK1 in response to combined 5FUdR and TS siRNA suggests that TK knockdown may be therapeutically useful in combination with these agents. TKs may be useful targets for cancer therapy when combined with molecules targeting TS mRNA and TS protein. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr C143.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".