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Record W2337562041 · doi:10.1093/neuonc/nov219.08

MTR-08MODELING TMZ RESISTANCE IN PATIENT-DERIVED BRAIN TUMOR- INITIATING CELLS

2015· article· en· W2337562041 on OpenAlexaff
Xueqing Lun, Jennifer C. King, Ngoc Ha Dang, Donna L. Senger, Stephen M. Robbins

Bibliographic record

VenueNeuro-Oncology · 2015
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineBrain tumorTemozolomideInternal medicineGlioblastomaCancer researchPathology

Abstract

fetched live from OpenAlex

Intrinsic and required resistance hampers the inability for Temozolomide to give prolonged therapeutic response in glioblastoma. To this end we developed an in vivo model to investigate the mechanism behind TMZ resistance. Using TMZ-sensitive patient-derived brain tumor-initiating cells (BTIC; BT73 and BT206), intracranial tumors were established in immunocompromised mice. Once visible, animals were treated with TMZ [50 mg/kg/day (one cycle) followed by 5 cycles of 10 mg/kg/day (cycle = 5d on, 2d off)] and followed for recurrence. Tumor cells were isolated and re-implanted into animals for a second round of selection [TMZ 50 mg/kg/day (one cycle); 30 mg/kg/day (two cycles)]. BTICs established from these animals were termed BT73R and BT206R and confirmed for their acquired resistance to TMZ both in vitro and in vivo. Using cytokine profiling arrays and global gene expression arrays, we identified candidate genes/factors related to the development of TMZ resistance. Assessment of tumor interstitial fluid from BT73R and BT206R uncovered changes in several cytokines/chemokines including an increase in CX3CL1, IL16 and IL23 and a decrease in MDC, IFNa2, IP-10 and CCL2. In addition, we observed a greater than two-fold change in 131 genes found in common between the TMZ-resistant xenografts including 30 genes related to tumor adhesive, invasive and drug resistance that were selected for further validation. To date, of the common genes, 11 were confirmed including 7 up-regulated genes (IGFBP5, EFEMP1, TCEAL7, SLFN11, FAM129A, PCDH10 and DCX) and 4 down-regulated genes (DACH1, ICAM1, MLKL and NFTAC2). In addition, MGMT protein was detected in BT73R but not BT206R. The data presented here suggests that acquired resistance to TMZ in the BTICs may not be mediated through a single mechanism, but rather via multiple genes/pathways warranting further evaluation of these models in the hope of identifying potential therapeutic markers or targets for acquired TMZ resistance.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.042
GPT teacher head0.304
Teacher spread0.262 · 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 designSimulation or modeling
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
Published2015
Admission routes1
Has abstractyes

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