<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 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".