<scp>ABCC2</scp> brush‐border expression predicts outcome in papillary renal cell carcinoma: a multi‐institutional study of 254 cases
Bibliographic record
Abstract
AIMS: Papillary renal cell carcinoma (PRCC) histologic subtyping is no longer recommended in the 2022 WHO classification. Currently, WHO/ISUP nucleolar grade is the only accepted prognostic histologic parameter for PRCC. ABCC2, a renal drug transporter, has been shown to significantly predict outcomes in PRCC. In this study we evaluated the prognostic significance of ABCC2 IHC staining patterns in a large, multi-institutional PRCC cohort and assessed the association of these patterns with ABCC2 mRNA expression. METHODS AND RESULTS: We assessed 254 PRCCs for ABCC2 IHC reactivity patterns that were stratified into negative, cytoplasmic, brush-border <50%, and brush-border ≥50%. RNA in situ hybridization (ISH) was used to determine the transcript level of each group. Survival analysis was performed with SPSS and GraphPad software. RNA-ISH showed that the ABCC2 group with any brush-border staining was associated with a significant increase in the transcript level, when compared to the negative/cytoplasmic group (P = 0.034). Both ABCC2 groups with brush-border <50% (P = 0.024) and brush-border ≥50% (P < 0.001) were also associated with worse disease-free survival (DFS) in univariate analysis. Multivariate analysis showed that only ABCC2 IHC brush-border (<50% and ≥50%) reactivity groups (P = 0.037 and P = 0.003, respectively), and high-stage disease (P < 0.001) had a DFS of prognostic significance. In addition, ABCC2 brush-border showed significantly worse DFS in pT1a (P = 0.014), pT1 (P = 0.013), ≤4 cm tumour (P = 0.041) and high stage (P = 0.014) groups, while a similar analysis with high WHO/ISUP grade in these groups was not significant. CONCLUSION: ABCC2 IHC brush-border expression in PRCC correlates with significantly higher gene expression and also independently predicts survival outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".