Presence of lymphocytic infiltrate cytotoxic T lymphocyte CD3+, CD8+, and immunoscore as prognostic marker in patients after radical cystectomy
Bibliographic record
Abstract
Tumor-Infiltrating Lymphocytes (TILs) has been shown to be essential to predict disease outcome in several types of cancers. Moreover, the distribution of cytotoxic T lymphocytes (CD8+) and T cells in general (CD3+) have been used to establish an Immunoscore, as a new cancer prognosticator for survival in colon and lung cancer. In bladder cancer, immune activation has been shown to be associated with genomic subtypes of muscle invasive bladder cancer (MIBC). We sought to evaluate the prognostic impact of these immune cell types in MIBC patients treated with radical cystectomy. For this purpose, cystectomy sections (n = 67) with identifiable invasive margin were selected and stained for CD8+ and CD3+ tumour infiltrating lymphocytes (TILs); both tumor core (CT) and invasive margin (IM) were assessed. Immunoscore was calculated based on previously defined criteria and used to illustrate differences in survival. High density of CD8IM TILs was associated with better disease-free (DFS) (P = 0.01) and overall survival (OS) (P = 0.02) whereas CD3IM TILs were associated with better OS (P = 0.05). Immunoscore was associated with improved DFS (P = 0.02) and OS (P = 0.05). The expression of cytotoxic T cells (CD8+ T cells) in TCGA bladder cancer was also investigated from RNA-Seq profiles of 344 cases. T cell cytotoxicity associated genes (n = 113) were derived from MSig GSEA database. Luminal (n = 121) and basal (n = 68) samples were used to evaluate expression differences. Differential expression (P<0.05) of cytotoxic T cell genes was noted across different molecular subsets of bladder cancer within TCGA analysis. Our data suggests host immune system appears to play a valuable prognostic role in MIBC.
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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 | 0.000 | 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".