Abstract 5644: Identification of alexidine as a novel small molecule inhibitor of TAZ-TEAD interaction in breast cancer cell migration
Bibliographic record
Abstract
Abstract Breast cancer (BC) is the most commonly diagnosed malignancy in women, with a risk of distant metastases after a primary tumor has developed. Despite concerns about the aggressive pathology of metastatic BC (MBC), current medical interventions inadequately address the clinical needs of MBC patients, resulting in worse outcomes compared to other BC cases. This highlights the critical requirement for more effective therapies for MBC patients. The Hippo pathway is often dysregulated during tumorigenesis, and one of the Hippo signaling transducers, Transcriptional co-activator with PDZ-binding motif (TAZ), is involved in multiple stages of BC development. Since TAZ mostly interacts with TEAD to facilitates its function, targeting TAZ-TEAD interaction may be a treatment approach for MBC patients. To identify small molecules that inhibit TAZ-TEAD interaction, we established a highly sensitive NanoLuc and TR-FRET applicable biosensor for quantifying protein-protein interaction of TAZ and TEAD. Using this biosensor, we performed an ultra-high throughput screen (uHTS) and identified alexidine as a novel TAZ-TEAD binding inhibitor capable of suppressing TAZ-induced migration and invasion in BC cells. In conclusion, we describe a robust method for screening inhibitors of TAZ-TEAD interaction, contributing to the development of effective cancer treatments. Citation Format: Anni Ge, Yawei Hao, Kody Klupt, Zongchao Jia, Yuhong Du, Haian Fu, Xiaolong Yang. Identification of alexidine as a novel small molecule inhibitor of TAZ-TEAD interaction in breast cancer cell migration [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5644.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".