Acyl-protein thioesterase 1 ( <i>LYPLA1</i> ) activity promotes the growth of MDA-MB-468 triple-negative breast cancer cells
Bibliographic record
Abstract
ABSTRACT Protein S- acylation is a lipid-based, often reversible post-translational modification that can regulate many aspects of protein behavior, including subcellular localization, protein-interactions, and activity. Emerging evidence has identified roles for individual protein acyltransferases encoded by the ZDHHC in cancers, yet the roles of de- S- acylation enzymes are less clear. Recent evidence suggests that acyl-protein thioesterase (APT1)/ LYPLA1 can impact epithelial-mesenchymal transition and metastasis. This study integrates patient datasets, CRISPR dependency data, and in vitro assays to find APT1 as a context-dependent vulnerability in triple-negative breast cancer (TNBC). Despite the highest protein abundance in luminal MCF7 cells, basal-like MDA-MB-468 cells exhibited the most prominent specific APT1 activity, reflecting subtype-specific regulation. Inhibition of APT1 with ML348 increased S -acylation of nuclear and mitochondrial proteins without altering global acylation. Functionally, APT1 inhibition reduced cell proliferation while inducing minimal apoptosis, consistent with cytostatic growth arrest. Cell-cycle analysis revealed G1 accumulation and reduced S/G2 transition, linking proteomic changes to impaired replication. These findings establish APT1 as a regulator of TNBC proliferation through dynamic de- S- acylation of cell-cycle and mitochondrial proteins, highlighting it as a potential therapeutic vulnerability in aggressive breast cancers.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".