The Beneficial Role of Sunitinib in Tumor Immune Surveillance by Regulating Tumor PD‐L1
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it