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Abstract A020: Neural stem cells expressing PTEN-L suppress glioblastoma invasion <i>in vivo</i>

2024· article· en· W4392372688 on OpenAlexaff
Ian A.J. Lorimer, Margarita Lui, Sylvie J. Lavictoire, Josée Coulombe

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsPTENGlioblastomaIn vivoNeural stem cellCancer researchBiologyStem cellCancerCell biologyPI3K/AKT/mTOR pathwaySignal transductionGenetics

Abstract

fetched live from OpenAlex

Abstract Purpose: The purpose of this study is to evaluate the therapeutic potential of neural stem cells expressing PTEN-L (a variant of the tumor suppressor PTEN that can transfer between cells) in preclinical glioblastoma models. Experimental procedures: Human neural stem cells derived by direct reprogramming of peripheral blood erythroblasts were used. Using lentiviral transduction, these were engineered for doxycycline-inducible expression of a modified version of PTEN-L with optimized expression and enhanced cell-to-cell transfer properties. Expression of PTEN-L was assayed by Western blot analysis. These engineered neural stem cells were evaluated in an orthotopic mouse xenograft model in which human glioblastoma cells show extensive invasion into the uninjected hemisphere. Neural stem cells and glioblastoma cells were co-injected and mice were then randomized to chow with or without doxycycline. Mice were evaluated for overall survival and for invasion of glioblastoma cells into the uninjected hemisphere. Results: When injected intracerebrally into immunocompromised mice, peripheral blood-derived neural stem cells persisted for over four months and showed an ability to spontaneously migrate to sites distant from the injection site. When co-injected with human glioblastoma cells from patients, the neural stem cells co-migrated with glioblastoma cells along the corpus callosum and to distant sites. Peripheral blood-derived neural stem cells transduced with lentiviral vectors for doxycycline-inducible PTEN-L efficiently secreted PTEN-L. In the orthotopic mouse xenograft glioblastoma model, expression of PTEN-L by neural stem cells did not significantly improve survival in mice (log rank P = 0.5) but did significantly repress glioblastoma cell invasion into the uninjected hemisphere (two-tailed t-test P = 0.02). Conclusions. Peripheral blood-derived neural stem cells show good persistence and migrate with human glioblastoma cells in vivo. They can be engineered to efficiently secret functional PTEN-L. Peripheral blood-derived neural stem cells expressing PTEN-L do not increase survival in the one patient model tested but do repress invasion. This initial evidence for bioactivity supports further studies evaluating the ability of neural stem cells expressing PTEN-L to sensitize glioblastoma to therapeutics for which PTEN loss has been implicated in resistance. Citation Format: Ian A. Lorimer, Margarita Lui, Sylvie J. Lavictoire, Josee Coulombe. Neural stem cells expressing PTEN-L suppress glioblastoma invasion in vivo [abstract]. In: Proceedings of the AACR Special Conference on Brain Cancer; 2023 Oct 19-22; Minneapolis, Minnesota. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_1):Abstract nr A020.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.040
GPT teacher head0.355
Teacher spread0.315 · 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 designBench or experimental
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
Published2024
Admission routes1
Has abstractyes

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