Abstract 3221: Identification and characterization of FAK kinase as a novel regulator of PD-L1 stability and activity in cancer immune evasion
Bibliographic record
Abstract
Abstract The PD-1/PD-L1 axis is an essential immune checkpoint that modulates T cell-mediated immune responses. Cancer cells exploit this checkpoint to inhibit anti-tumour immune activity, enabling immune evasion. Thus, blocking PD-1/PD-L1 using antibodies has become a cornerstone of cancer immunotherapies. However, despite promising results, these therapies show limited response rates in many cancer patients and often lead to adverse effects, impacting its overall efficacy. Therefore, understanding of the molecular mechanism underlying immunotherapy resistance is critical for successful cancer immunotherapy. Emerging research indicates that phosphorylation of PD-L1 by kinases significantly influence PD-L1 stability and functionality, thereby contributing to cancer cell immune evasion and influencing immunotherapy outcomes. However, although 560 kinases have been identified in the human kinome, there is no systematic screening of kinases regulating PD-L1 stability and its function in immune evasion. By using a PD-L1 NanoLuc luciferase biosensor, in this study, we performed a systematic kinome-wide screening and identified focal adhesion kinase (FAK) as a novel regulator of PD-L1 stability in cancer. Further investigation of FAK-mediated PD-L1 signalling and its role in immune evasion may provide a new therapeutic strategy for cancer treatment. Citation Format: Asia-lily Boyd, Prem Khanal, Yawei Hao, Xiaolong Yang. Identification and characterization of FAK kinase as a novel regulator of PD-L1 stability and activity in cancer immune evasion [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 3221.
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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".