The presence of tumour‐infiltrating neutrophils is an independent adverse prognostic feature in clear cell renal cell carcinoma
Bibliographic record
Abstract
Tumour-promoting inflammation is an emerging hallmark of cancer that is increasingly recognised as a therapeutic target. As a constituent measure of inflammation, tumour-infiltrating neutrophils (TINs) have been associated with inferior prognosis in several cancers. We analysed clinically annotated cohorts of clear cell renal cell carcinoma (ccRCC) to assess the presence of neutrophils within the tumour microenvironment as a function of outcome. We centrally reviewed ccRCC surgical resection and fine-needle aspiration (FNA) specimens, including primary and metastatic sites, from three centres. TINs were scored based on the presence of neutrophils in resection and FNA specimens by two pathologists. TIN count was correlated with tumour characteristics including stage, WHO/ISUP grade, and immunohistochemistry (IHC). In parallel, we performed CIBERSORT analysis of the tumour microenvironment in a cohort of 516 ccRCCs from The Cancer Genome Atlas (TCGA). We included 102 ccRCC cases comprising 65 resection specimens (37 primary and 28 metastatic resection specimens) and 37 FNAs from primary lesions. High TINs were significantly associated with worse overall survival (p = 0.009) independent of tumour grade and stage. In ccRCCs sampled via FNA, all cases with high TINs had distant metastasis, whereas they were seen in only 19% of cases with low TINs (p = 0.0003). IHC analysis showed loss of E-cadherin in viable tumour cells in areas with high TINs, and neutrophil activation was associated with elastase and citrullinated histone H3 expression (cit-H3). In the TCGA cohort, neutrophilic markers were also associated with worse survival (p < 0.0001). TINs are an independent predictor of worse prognosis in ccRCC, which have the potential to be assessed at the time of first biopsy or FNA. Neutrophils act directly on tumour tissue by releasing elastase, a factor that contributes to the breakdown of cell-cell adhesion and to facilitate tumour dissemination.
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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 | 0.000 | 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".