Neural Tumor-Initiating Cells Have Distinct Telomere Maintenance and Can be Safely Targeted for Telomerase Inhibition
Bibliographic record
Abstract
PURPOSE: Cancer recurrence is one of the major setbacks in oncology. Maintaining telomeres is essential for sustaining the limitless replicative potential of such cancers. Because telomerase is thought to be active in all tumor cells and normal stem cells, telomerase inhibition may be nonspecific and have detrimental effects on tissue maintenance and development by affecting normal stem cell self-renewal. METHODS: We examined telomerase activity, telomere maintenance, and stem cell maturation in tumor subpopulations from freshly resected gliomas, long-term, primary, neural tumor-initiating cells (TIC) and corresponding normal stem cell lines. We then tested the efficacy of the telomerase inhibitor Imetelstat on propagation and self-renewal capacity of TIC and normal stem cells in vitro and in vivo. RESULTS: Telomerase was undetectable in the majority of tumor cells and specific to the TIC subpopulation that possessed critically short telomeres. In contrast, normal tissue stem cells had longer telomeres and undetectable telomerase activity and were insensitive to telomerase inhibition, which results in proliferation arrest, cell maturation, and DNA damage in neural TIC. Significant survival benefit and late tumor growth arrest of neuroblastoma TIC were observed in a xenograft model (P = 0.02). Furthermore, neural TIC exhibited irreversible loss of self-renewal and stem cell capabilities even after cessation of treatment in vitro and in vivo. CONCLUSIONS: TIC exhaustion with telomerase inhibition and lack of telomerase dependency in normal stem cells add new dimensions to the telomere hypothesis and suggest that targeting TIC with telomerase inhibitors may represent a specific and safe therapeutic approach for tumors of neural origin.
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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) | 0.001 | 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".