Abstract 3207: Telomerase inhibition affects fludarabine sensitivity in quiescent primary chronic lymphocytic leukemia lymphocytes
Bibliographic record
Abstract
Abstract B- Cell chronic leukemia (CLL) is the most common leukemia in adults. It is clinically very heterogeneous and has a highly variable clinical course. Telomere length and telomerase activity have been shown to be excellent prognostic factors in CLL. Moreover, telomere maintenance is deficient in CLL lymphocytes. These studies have prompted the assessment of the telomerase inhibitor GRN163L in a phase I-II clinical trial in CLL and lymphomas. Here we report that telomerase activity is not required for the survival of quiescent primary CLL lymphocytes. In contrast telomerase activity seems to be required for the survival of quiescent primary CLL lymphocytes after treatment with fludarabine (FLU) in vitro. Furthermore, the effect of fludarabine on telomerase activity was associated with the basal expression of the telomere shelterin protein TRF2. In summary, our results suggest that GRN163L in combination with FLU may be useful to decrease the tumor burden in CLL. To our knowledge this is the first report assessing the effect of telomerase inhibition in combination with a chemotherapeutic agent in non-stimulated primary CLL lymphocytes. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 3207.
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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) | 0.003 | 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".