Consecutive Inhibition of Telomerase and Alternative Lengthening Pathway Promotes Hodgkin’s Lymphoma Cell Death
Bibliographic record
Abstract
Telomere maintenance is key during cancer development. Malignant cells can either use telomerase or an alternative lengthening of telomere (ALT) pathway to maintain their telomere length. In Hodgkin's Lymphoma (HL), the presence of telomerase activation is established. The activation of ALT has been reported recently. Our data confirm this notion describing co-localization of the phosphorylated form of telomeric repeat-binding factor 1 (pT371-TRF1) with ALT-associated promyelocytic leukemia bodies. Surprisingly, to our knowledge, there are no published studies targeting both telomere maintenance pathways in HL. Consequently, we investigated, for the first time, the effects of both telomerase and ALT inhibition on HL cell viability: We inhibited telomerase and/or ALT, given either individually, simultaneously, or consecutively. We report that the inhibition of telomerase using BIBR1532 followed by ALT inhibition, using trabectedin, caused a decrease of greater than 90% in cell viability in three patient-derived HL cell lines. Our results suggest that HL cells are most vulnerable to the consecutive inhibition of telomerase followed by ALT inhibition.
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".