Harnessing Telomerase in Cancer Therapeutics
Bibliographic record
Abstract
Telomerase is an attractive target for anti-cancer therapeutics due to its requirement for cellular immortalization and expression in greater than 85% of human neoplasms. Though initially promising, strategies that inhibit telomerase with either small molecules or antisense oligonucleotides have a major limitation, namely the lag time required for telomere shortening before cellular effects are attained. As alternative approaches, immunotherapy and gene therapy have been tailored to exploit, rather than antagonize telomerase expression and/or activity. Immunotherapy requires the presence of the catalytic subunit of telomerase, hTERT, to elicit an immune response directed towards hTERT peptide-presenting cells. hTERT promoter-driven gene therapy and mutant telomerase RNA (hTR) gene therapy depend on the innate telomerase activity of cancer cells to drive the expression of pro-apoptotic genes and to synthesize mutated DNA sequences onto telomeres, respectively. In addition, we will discuss telomestatin, a G-quadruplex binding ligand that may exert anti-proliferative effects independently of telomere shortening. In this review, the progress, advantages, and limitations of these strategies in the ongoing effort to develop clinically relevant telomerase-based cancer therapeutics will be examined.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".