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Record W2033314544 · doi:10.1158/0008-5472.can-10-1295

Maternal Embryonic Leucine Zipper Kinase Is Upregulated and Required in Mammary Tumor-Initiating Cells <i>In vivo</i>

2010· article· en· W2033314544 on OpenAlexaff
Lionel Hebbard, Jochen Maurer, Amber Miller, Jacqueline Lesperance, John A. Hassell, Robert G. Oshima, Alexey V. Terskikh

Bibliographic record

VenueCancer Research · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFOXO transcription factor regulation
Canadian institutionsMcMaster University
FundersNational Cancer Institute
KeywordsCarcinogenesisBiologyCancer researchMammary tumorProgenitor cellMammary glandCancerTransplantationBreast cancerStem cellCell biologyInternal medicineMedicine

Abstract

fetched live from OpenAlex

Maternal embryonic leucine zipper kinase (MELK) is expressed in several developing tissues, in the adult germ line, and in adult neural progenitors. MELK expression is elevated in aggressive undifferentiated tumors, correlating with poor patient outcome in human breast cancer. To investigate the role of MELK in mammary tumorigenesis in vivo, we used a MELK-green fluorescent protein (GFP) reporter mouse, which allows prospective isolation of MELK-expressing cells based on GFP fluorescence. We found that in the normal mammary gland, cells expressing high levels of MELK were enriched in proliferating cells that express markers of mammary progenitors. The isolation of cells with high levels of MELK in mammary tumors from MMTV-Wnt1/MELK-GFP bitransgenic mice resulted in a significant enrichment of tumorsphere formation in culture and tumor initiation after transplantation into mammary fat pads of syngeneic mice. Furthermore, using lentiviral delivery of MELK-specific shRNA and limiting dilution cell transplantations, we showed that MELK function is required for mammary tumorigenesis in vivo. Our findings identify MELK as a potential target in breast tumor-initiating cells.

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 score0.029
Threshold uncertainty score0.496

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 head0.027
GPT teacher head0.326
Teacher spread0.299 · 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

Citations76
Published2010
Admission routes1
Has abstractyes

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