Density of T-cell Subsets in Colorectal Cancer in Relation to Disease-Specific Survival
Bibliographic record
Abstract
BACKGROUND: Prior studies have demonstrated that the overall density of T cells in colorectal tumors is favorably associated with colorectal cancer survival; however, few studies have considered the potentially distinct roles of heterogeneous T-cell subsets in different tissue regions in relation to colorectal cancer outcomes. METHODS: Including 1,113 colorectal cancer tumors from three observational studies, we conducted in situ T-cell profiling using a customized nine-plex [CD3, CD4, CD8, CD45RA, CD45RO, FOXP3, KRT (keratin), MKI67 (Ki-67), and DAPI] multispectral immunofluorescence assay. Multivariable-adjusted Cox proportional hazards models were used to estimate HRs and 95% confidence intervals for the associations of T-cell subset densities in both epithelial and stromal tissue areas in colorectal cancer with disease-specific survival. RESULTS: Higher CD3+CD4+ and CD3+CD8+ naïve, memory, and regulatory T-cell densities were significantly associated with better colorectal cancer-specific survival in both epithelial and stromal tissue areas (HR highest quantile vs. lowest quantile ranging 0.41-0.68). These associations persisted in models further adjusted for stage at diagnosis and were largely consistent when stratified by microsatellite instability status. However, the further stratification into CD4+ or CD8+ T-cell subsets beyond CD3+ subsets did not significantly improve the performance of our model in explaining colorectal cancer prognosis. CONCLUSIONS: The density of T cells in colorectal cancer tissue, both overall and for several T-cell subset populations, is significantly associated with colorectal cancer-specific survival independent of microsatellite instability status and stage at diagnosis. IMPACT: Higher levels of T-cell densities in different locations with different functions are associated with better colorectal cancer-specific survival.
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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 | 0.004 |
| 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 | 0.000 | 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".