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Record W2567730055 · doi:10.1016/j.celrep.2016.12.024

Biphasic Dependence of Glioma Survival and Cell Migration on CD44 Expression Level

2017· article· en· W2567730055 on OpenAlexaff
Rebecca Klank, Stacy A. Decker Grunke, Benjamin L. Bangasser, Colleen L. Forster, Matt A. Price, Thomas J. Odde, Karen S. SantaCruz, Steven S. Rosenfeld, Peter Canoll, Eva A. Turley, James B. McCarthy, John R. Ohlfest, David J. Odde

Bibliographic record

VenueCell Reports · 2017
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsWestern University
FundersNational Institute of Neurological Disorders and StrokeNational Institute of General Medical SciencesNational Institute on Drug AbuseUniversity of MinnesotaNational Cancer InstituteMinnesota Supercomputing Institute, University of MinnesotaNational Science FoundationChildren's Cancer Research FundNational Institutes of HealthAmerican Cancer Society
KeywordsCD44GliomaCancer researchBiologyCellMesenchymal stem cellCell migrationIn vivoTumor progressionCancerInternal medicineCell biologyMedicineGenetics

Abstract

fetched live from OpenAlex

While several studies link the cell-surface marker CD44 to cancer progression, conflicting results show both positive and negative correlations with increased CD44 levels. Here, we demonstrate that the survival outcomes of genetically induced glioma-bearing mice and of high-grade human glioma patients are biphasically correlated with CD44 level, with the poorest outcomes occurring at intermediate levels. Furthermore, the high-CD44-expressing mesenchymal subtype exhibited a positive trend of survival with increased CD44 level. Mouse cell migration rates in ex vivo brain slice cultures were also biphasically associated with CD44 level, with maximal migration corresponding to minimal survival. Cell simulations suggest that cell-substrate adhesiveness is sufficient to explain this biphasic migration. More generally, these results highlight the potential importance of non-monotonic relationships between survival and biomarkers associated with cancer progression.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.020
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

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

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 head0.049
GPT teacher head0.299
Teacher spread0.250 · 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 teacher head, 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

Citations100
Published2017
Admission routes1
Has abstractyes

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