Tumor mutational burden and immune infiltrates in renal cell carcinoma and matched brain metastases
Bibliographic record
Abstract
BACKGROUND: Tumor mutational burden (TMB) and density of tumor-infiltrating lymphocytes (TIL) have been postulated as predictive biomarkers for immunotherapy. Therefore, we investigated the concordance of TMB and TIL of primary/extracranial renal cell carcinoma (RCC) specimens and matched brain metastases (BM). PATIENTS AND METHODS: Twenty specimens from 10 patients were retrieved from the Vienna Brain Metastasis Registry (6/10 primary tumor, 4/10 lung metastasis, 10/10 matched BM). TMB was assessed using the TruSight Oncology 500 gene panel with libraries sequenced on a NextSeq instrument. TIL subsets (CD3+, CD8+, CD45RO+, FOXP3+, PD-L1+) were investigated using immunohistochemistry (Ventana Benchmark Ultra system) and automated tissue analysis (Definiens software). RESULTS: No significant difference in TMB, CD3+, CD8+, CD45RO+, FOXP3+ or PD-L1+ expression was observed between extracranial and matched intracranial specimens (P > 0.05). Higher CD8+ TIL (P = 0.053) and CD45RO+ TIL (P = 0.030) densities in the primary tumor compared with the intracranial samples were observed in specimens collected after exposure to systemic treatment. Neither extracranial sample origin (lung metastasis versus primary RCC) nor extracranial disease status at BM diagnosis (progressive versus stable disease) were significantly associated with TMB or TIL densities in extracranial and intracranial samples (P > 0.05). No significant correlation was found between the median differences of TMB or TIL densities from extracranial to intracranial samples and BM-free survival. CONCLUSION: The comparable immunological microenvironment of extra- and intracranial tumor samples in our study underscores the immunological activation also in BM from RCC, and therefore, supports the development of immune modulatory treatments also in patients with brain metastatic RCC.
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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 | 0.000 | 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".