PD‐L1 Expression is Mediated by microRNA Processing, Wnt/β‐Catenin Signaling, and Chemotherapy in Wilms Tumor
Bibliographic record
Abstract
BACKGROUND: Inhibition of immune checkpoint proteins is effective in adult cancers but has shown limited efficacy in pediatric cancers. While factors regulating expression of immune checkpoint proteins such as PD-L1 are well documented in adult cancers, their regulation is poorly understood in pediatric cancers. METHODS: We analyzed Wilms tumor specimens with reverse-phase protein arrays. We validated correlations using published sequencing data, flow cytometry, and immunoblots. RESULTS: Using unsupervised clustering of protein arrays, we found that immune markers like PD-L1 are upregulated in distinct subsets of Wilms tumor, the most common pediatric kidney cancer. Specifically, chemotherapy-exposed Wilms tumor specimens exhibited higher levels of PD-L1 expression, and common chemotherapeutics upregulated PD-L1 in vitro. Furthermore, mutations in CTNNB1 and DROSHA, the two most commonly mutated genes in Wilms tumor, correlated with higher PD-L1. Activation of Wnt/β-catenin signaling and knockdown of DROSHA or DICER1 both increase PD-L1 in vitro. CONCLUSIONS: Together, our results identify clinical and biological properties regulating PD-L1 in Wilms tumor that may inform precision therapy approaches in pediatric immuno-oncology.
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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".