Telomerase- and Alternative Telomere Lengthening–Independent Telomere Stabilization in a Metastasis-Derived Human Non–Small Cell Lung Cancer Cell Line: Effect of Ectopic hTERT
Bibliographic record
Abstract
In the majority of human malignancies, maintenance of telomeres is achieved by reactivation of telomerase, whereas a smaller fraction uses an alternative telomere lengthening (ALT) mechanism. Here, we used 16 non-small cell lung cancer (NSCLC) cell lines to investigate telomere stabilization mechanisms and their effect on tumor aggressiveness. Three of 16 NSCLC cell lines (VL-9, SK-LU-1, and VL-7) lacked telomerase activity, correlating with significantly reduced tumorigenicity in vitro and in vivo. Of the three telomerase-negative cell lines, only SK-LU-1 displayed characteristics of an ALT mechanism (i.e., highly heterogeneous telomeres and ALT-associated promyelocytic leukemia bodies). VL-9 cells gained telomerase during in vitro propagation, indicating incomplete immortalization in vivo. In contrast, NSCLC metastasis-derived VL-7 cells remained telomerase and ALT negative up to high passage numbers and following transplantation in severe combined immunodeficient mice. Telomeres of VL-7 cells were homogeneously short, and chromosomal instability (CIN) was comparable with most telomerase-positive cell lines. This indicates the presence of an efficient telomere stabilization mechanism different from telomerase and ALT in VL-7 cells. To test the effect of ectopic telomerase reverse transcriptase (hTERT) in these unique ALT- and telomerase-negative tumor backgrounds, hTERT was transfected into VL-7 cells. The activation of telomerase led to an excessively rapid gain of telomeric sequences resulting in very long ( approximately 14 kb), uniform telomeres. Additionally, hTERT expression induced a more aggressive growth behavior in vitro and in vivo without altering the level of CIN. These data provide further evidence for a direct oncogenic activity of hTERT not based on the inhibition of CIN.
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) | 0.000 | 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 | 0.001 |
| 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".