Role of Cyclin D1 and BCOR Immunohistochemistry in Differentiating Clear Cell Sarcoma of Kidney From its Mimics
Bibliographic record
Abstract
BACKGROUND AND AIM: Clear cell sarcoma of kidney (CCSK) is the second most common pediatric renal malignancy, constituting ∼3% of renal tumors. Due to its morphologic diversity, the diagnosis of CCSK is often challenging. Recent studies have identified internal tandem duplication of BCL6 corepressor (BCOR) gene in CCSKs which coupled with cyclin D1 immunoreactivity, is helpful in differentiating it from its mimics, particularly blastema-rich Wilms tumor (WT), malignant rhabdoid tumor (MRT), and congenital mesoblastic nephroma (CMN). We aimed to evaluate the utility of cyclin D1 and BCOR immunohistochemistry in differentiating CCSK from its morphologic mimics. MATERIALS AND METHODS: Our cohort comprised of 38 pediatric renal tumors which included CCSK (n=18), WT (n=10), MRT (n=5), and CMN (n=5) cases. A detailed clinicopathologic analysis was performed, and tissue microarray were constructed for CCSK and WT, while MRT and CMN tumors were individually stained. RESULTS: The age ranged from 2 months to 16 years with male:female ratio of 3:1. Strong, diffuse nuclear immunoreactivity for cyclin D1 and BCOR was noted in 61% (n=11/18) and 83% (n=15/18) of CCSK, respectively, while it was significantly less in WT (n=3/10 for cyclin D1) (n=2/10 for BCOR). None of the MRT and CMN examples demonstrated any immunoreactivity. Interestingly, only the blastemal component of WTs showed distinct, rare nuclear immunoreactivity for cyclin D1 or BCOR and the combination of these was never positive in a given case. CONCLUSION: Our results provide evidence that concurrent immunopositivity with cyclin D1 and BCOR is helpful in distinguishing CCSK from its morphologic mimics.
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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".