Multicenter study identified molecular blood‐born protein signatures for Wilms Tumor
Bibliographic record
Abstract
Wilms Tumor (WT) is the most common renal childhood tumor. Recently, we reported a cDNA microarray expression pattern that varied between WTs with different risk histology. Since the Societé Internationale d'Oncologie Pédiatrique (SIOP) in Europe initiates treatment without a histological confirmation, it is important to identify blood-born markers that indicate WT development. In a multicenter study, we established an autoantibody signature by using an array with 1,827 recombinant E. coli clones. This array was screened with sera of patients with WT recruited by SIOP or the Children's Oncology Group (COG). We report an extended number of antigens that are reactive with autoantibodies present in sera from patients with WT. We established an autoantibody signature that separates untreated patients with WT recruited in SIOP from non-WT controls with a specificity of 0.83 and a sensitivity of 0.82 at standard deviations of 0.02 and 0.04, respectively. Likewise, patients recruited in the COG in the United States were separated from the controls with an accuracy of 0.83 at a standard deviation of 0.02. Proteins that were most significant include zinc finger proteins (e.g., ZFP 346), ribosomal proteins and the protein fascin that has been associated with various types of cancer including renal cell carcinoma. Our study provides first evidence for autoantibody signatures for WTs and suggests that these may be most informative before chemotherapy. We present the first multicenter study of autoantibody signatures in patients with WT. We established an autoantibody signature that separates patients with WT from controls.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".