Improved Identification of von Hippel-Lindau Gene Alterations in Clear Cell Renal Tumors
Bibliographic record
Abstract
PURPOSE: To provide a comprehensive, thorough analysis of somatic mutation and promoter hypermethylation of the von Hippel-Lindau (VHL) gene in the cancer genome, unique to clear cell renal cancer (ccRCC). Identify relationships between the prevalence of VHL gene alterations and alteration subtypes with patient and tumor characteristics. EXPERIMENTAL DESIGN: As part of a large kidney cancer case-control study conducted in Central Europe, we analyzed VHL mutations and promoter methylation in 205 well-characterized, histologically confirmed patient tumor biopsies using a combination of sensitive, high-throughput methods (endonuclease scanning and Sanger sequencing) and analysis of 11 CpG sites in the VHL promoter. RESULTS: We identified mutations in 82.4% of cases, the highest VHL gene mutation prevalence reported to date. Analysis of 11 VHL promoter CpG sites revealed that 8.3% of tumors were hypermethylated and all were mutation negative. In total, 91% of ccRCCs exhibited alteration of the gene through genetic or epigenetic mechanisms. Analysis of patient and tumor characteristics revealed that certain mutation subtypes were significantly associated with Fuhrman nuclear grade, metastasis, node positivity, and self-reported family history of RCC. CONCLUSION: Detection of VHL gene alterations using these accurate, sensitive, and practical methods provides evidence that the vast majority of histologically confirmed ccRCC tumors possess genetic or epigenetic alteration of the VHL gene and support the hypothesis that VHL alteration is an early event in ccRCC carcinogenesis. These findings also indicate that VHL molecular subtypes can provide a sensitive marker of tumor heterogeneity among histologically similar ccRCC cases for etiologic, prognostic, and translational studies.
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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 | 0.001 |
| 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".