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Record W4416519959 · doi:10.1016/j.isci.2025.114116

Alexidine is a TAZ-specific small-molecule inhibitor that suppresses breast cancer invasion and metastasis

2025· article· en· W4416519959 on OpenAlexafffund
Anni Ge, Lishui Niu, Rachel Rubino, Kimberly Seaman, Xin Song, Yawei Hao, Kody Klupt, Natasha Iaboni, Zongchao Jia, Lidan You, Christopher J.B. Nicol, Haian Fu, Yuhong Du, Xiaolong Yang

Bibliographic record

VenueiScience · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHippo pathway signaling and YAP/TAZ
Canadian institutionsUniversity of TorontoQueen's University
FundersNational Cancer InstituteCanadian Institutes of Health Research
KeywordsMetastasisBreast cancerBreast cancer metastasisMalignancyCancer metastasisMetastatic breast cancer

Abstract

fetched live from OpenAlex

Breast cancer (BC) is the most diagnosed malignancy in women and often progresses to distant metastasis. Unfortunately, current treatments inadequately address the clinical needs of metastatic BC (MBC) patients. This highlights the importance of developing effective therapies for MBC patients. One of the Hippo signaling transducers, transcriptional co-activator with PDZ-binding motif (TAZ), plays a major role in BC progression. Since TAZ mostly interacts with TEAD to facilitate its function, targeting TAZ-TEAD interaction may become a treatment approach for MBC patients. To identify inhibitors of TAZ-TEAD binding, we established a sensitive TR-FRET biosensor and performed an ultra-high throughput screen. Alexidine was identified as a TAZ-TEAD binding inhibitor capable of suppressing TAZ-induced migration and invasion in BC cells as well as metastasis in bone-on-a-chip and mouse models. In conclusion, we describe a robust method for screening inhibitors of TAZ-TEAD interaction, contributing to the development of effective cancer treatments.

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 score0.025
Threshold uncertainty score0.527

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 head0.024
GPT teacher head0.263
Teacher spread0.239 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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