Abstract 4291: Imetelstat, a telomerase inhibitor in phase I trials in solid tumor and hematological malignancies, has broad activity against multiple types of cancer stem cells
Bibliographic record
Abstract
Abstract Numerous studies show that multiple tumor types have rare subpopulations of cells that are initiators of tumor growth, recurrence, and are implicated in tumor metastasis formation. Because of their clonogenic potential, these cells are referred to as tumor initiating cells (TICs) or cancer stem cells. TICs express ABC transporters and can reside in the host in a quiescent state within their preferred niche, which contributes to their resistance to therapies that are effective against bulk tumor cells. Patients having significant objective responses often develop recurrent resistant disease that is thought to be due to TIC outgrowth, and this is thought to contribute to a lack of clinically durable responses. Therefore agents targeting TICs should be included in current therapeutic strategies to assure more effective disease control in patients. Telomerase activation is a common phenotype for the majority of cancers and is essential for maintaining their immortal phenotype. Most tumors having elevated telomerase activity also have shorter telomere lengths than their normal tissue counterparts, and these characteristics make telomerase a promising therapeutic target for cancer. Tumor initiating cells, while distinct from the bulk tumor cell population, share the common traits of increased telomerase activity and relatively short telomeres, suggesting that inhibiting telomerase would be an effective modality for targeting these cells across multiple tumor types. Imetelstat is a potent and specific competitive inhibitor of telomerase currently in phase I clinical trials in solid tumor and hematological malignancies. We have carried out in vitro and in vivo studies demonstrating that imetelstat inhibits telomerase and is effective in targeting TICs from myeloma, melanoma, breast, pancreatic, pediatric glioma, neuroblastoma, prostatic, lung and glioblastoma multiforme tumor types. Our data show that while treatment of TICs with imetelstat is effective, the mechanisms of TIC inhibition may vary depending on the tumor type, and can include antiproliferative effects, induction of apoptotosis or senescence, terminal differentiation, and inhibition of clonogenicity in vitro, as well as inhibition of tumor engraftment and spontaneous metastasis formation in vivo. In summary, imetelstat, a first in class telomerase inhibitor currently in clinical trials, is a promising agent for targeting TICs with broad activity against multiple cancer stem cell types, but the mechanism of inhibition of TICs may vary depending on the specific tumor type. 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 4291.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".