Papillary renal cell carcinoma with high‐ <scp>ABCC2</scp> shows an immune‐evasive profile associated with favorable response to immunotherapy
Bibliographic record
Abstract
The use of immune checkpoint inhibitors is a promising therapeutic strategy for metastatic papillary renal cell carcinoma (PRCC); however, predictive biomarkers remain limited. PRCCs with high ABCC2 expression represent an aggressive subset frequently associated with metastasis. The tumor microenvironment (TME) profile of these tumors remains poorly defined. This study aims to characterize the TME of PRCC in relation to its ABCC2 status. A discovery cohort of 157 ABCC2-high PRCCs, 156 ABCC2-low PRCCs, and 72 normal kidneys was evaluated. Using RNA sequencing data, immune cell composition, immune checkpoint markers, and immune signature scores were assessed. Validation was performed in an independent cohort (31 ABCC2-high, 36 ABCC2-low, and 15 normal kidneys) using RNA in situ hybridization (RNA-ISH) and immunohistochemistry (IHC). ABCC2-high PRCCs demonstrated increased infiltration of cytotoxic T cells (p < 0.001), M2 macrophages (p = 0.021), and regulatory T cells (p < 0.001) compared to ABCC2-low tumors. ABCC2-high PRCCs also had higher expression of immune checkpoint biomarkers including programmed cell death ligand 1 (PD-L1) (p < 0.001). The validation cohort showed this similar TME profile. Additionally, ABCC2-high PRCCs had higher PD-L1 IHC positivity (combined positive score ≥ 1, p = 0.035; tumor proportion score ≥ 1%, p = 0.006) and immune predictive signature score (p = 0.029). NRF2-Antioxidant Response Element signaling pathway was enriched in ABCC2-high PRCCs as evidenced by overrepresentation in pathway analysis, higher gene signature score (p < 0.001), and elevated transcript signals (NFE2L2, p < 0.001; NQO1, p < 0.001), compared to ABCC2-low PRCCs. In conclusion, ABCC2-high PRCCs are immune-infiltrated tumors with a suppressive phenotype potentially responsive to immune checkpoint inhibitors. ABCC2 IHC may serve as a predictive biomarker to help identify patients likely to benefit from such therapy. © 2025 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".