The Beneficial Role of Sunitinib in Tumor Immune Surveillance by Regulating Tumor PD‐L1
Bibliographic record
Abstract
Immune checkpoints blockades have shown promising clinical effects in various malignancies, but the overall response rate is low. Here, the immune features are comprehensively characterized in >10 000 cancer patients from The Cancer Genome Atlas and significantly positive correlations are observed between targets of Sunitinib and inhibitory immune checkpoints and suppressive immune cells. It is further confirmed that Sunitinib treatment increases the antitumor immunity in a phase III trial. Mechanistically, it is discovered that Sunitinib regulates the stability of tumor PD-L1 via p62, that p62 can bind to PD-L1 and specifically promote its translocation into autophagic lysosome for degradation. Preclinically, Sunitinib shows a synergistic antitumor effect with cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) monoclonal antibody (mAb) in melanoma and nonsmall cell lung cancer (NSCLC) immune competent mice by promoting the tumor-infiltrating lymphocytes activity. Clinically, a higher PD-L1 level but a lower p62 level in the tumor region of responders as compared to those of nonresponders among anti-PD-1-treated NSCLC patients is observed. Taken together, by utilizing rigorous computational analysis, functional characterization in vitro and in vivo, and neoadjuvent clinical trial, a novel molecular mechanism is revealed regarding the regulation of PD-L1 via p62, thus providing a novel therapeutic strategy by the combination treatment of CTLA-4 with Sunitinib.
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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".