Clinical significance of recurrently mutated genes in renal cell carcinoma: a closer look at VHL wild-type tumors
Bibliographic record
Abstract
Clear cell renal cell carcinoma (ccRCC), the most common subtype of kidney cancer, is known for its genomic heterogeneity and high risk of recurrence after nephrectomy. While most ccRCC tumors undergo early inactivation of the VHL tumor suppressor gene, approximately 10% are identified as VHL-wild-type (VHLwt) and may follow distinct evolutionary trajectories. Notably, the VHLwt group remains significantly underrepresented in genomic studies and is often overlooked in prognostic stratification methods. While VHL mutation alone is not associated with clinical outcomes, somatic genomic variations in other ccRCC-associated genes have been linked to prognosis. However, the clinical implications of these mutations, in the context of VHL mutation status, remain poorly understood, despite recent genomic studies that have proposed different biological underpinnings for VHLwt and VHLmut tumors.Using genomic and clinical data of 914 patients from the Cancer Genomics of the Kidney (CAGEKID) cohort, this thesis compares clinical features, mutation landscape, and survival outcomes between VHL-mutant (VHLmut, n = 720) and VHLwt (n = 194) tumors. The differences between groups were also studied, particularly through investigating the prognostic impact of co-occurring mutations, reflecting on genomic evolution in ccRCC-associated genes.The results demonstrate that the prognostic significance of mutation co-occurrence in ccRCC-associated genes is context-dependent. For example, in VHLmut tumors, particularly in stage III disease, co-occurring mutations in SETD2 and PBRM1 were significantly associated with poorer clinical outcomes. In contrast, the same mutation pattern did not show significant prognostic influence in the VHLwt group. Furthermore, co-occurring mutations in TP53 and SETD2 show an association to poor survival in patients with VHLwt only. These observations reflect on the unique biology of VHLwt ccRCC, which highlights the need to consider VHLwt separately from VHLmut tumors in predictive models.In summary, this study emphasizes the importance of stratifying patients by VHL status while investigating the prognostic potential of co-occurring mutations in ccRCC. These findings support the integration of VHL status and mutation co-occurrence patterns into future risk models and therapeutic strategies
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".