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Record W2319796016 · doi:10.1158/1538-7445.am10-4291

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

2010· article· en· W2319796016 on OpenAlexaff
Immanual Joseph, William Matsui, Uri Tabori, Harley I. Kornblum, Brittney‐Shea Herbert, Calvin B. Harley, Robert Tressler

Bibliographic record

VenueCancer Research · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsSickKids FoundationHospital for Sick Children
Fundersnot available
KeywordsTelomeraseCancer researchTelomereCancerCancer stem cellClonogenic assayMetastasisCancer cellImmunologyBiologyStem cellMedicineCellInternal medicineGenetics

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0080.002

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.

Opus teacher head0.104
GPT teacher head0.436
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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