Kinome-Wide Screening Identifies FAK as a Novel Post-Translational Regulator of PD-L1 Stability and Immune Evasion in Triple-Negative Breast Cancer
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by limited treatment options and poor prognosis. Although immune checkpoint inhibitors targeting the PD-1/PD-L1 axis have shown clinical promise, many TNBC patients exhibit resistance or limited response, underscoring the need to understand regulatory mechanisms of PD-L1 expression. Here, we performed a kinome-wide inhibitor screen using a HEK293A cell line stably expressing a NanoLuc-tagged PD-L1 construct lacking its endogenous promoter, to identify post-translational regulators of PD-L1 stability. We identified focal adhesion kinase (FAK) as a novel modulator of PD-L1. FAK inhibition significantly decreased PD-L1 levels in HEK293A cells but paradoxically increased PD-L1 expression in TNBC cell lines. Mechanistically, FAK directly interacts with PD-L1 to modulate its stability independently of its kinase activity. Functionally, FAK inhibition enhanced membrane PD-L1 expression and reduced T-cell-mediated cancer cell killing, suggesting increased immune evasion. These findings reveal a novel role for FAK in immune modulation and suggest that combining FAK inhibitors with PD-L1 blockade may offer a promising strategy for TNBC treatment.
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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) | 0.001 | 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".