Data from Association of <i>Fusobacterium nucleatum</i> with Specific T-cell Subsets in the Colorectal Carcinoma Microenvironment
Bibliographic record
Abstract
AbstractPurpose: While evidence indicates that Fusobacterium nucleatum (F. nucleatum) may promote colorectal carcinogenesis through its suppressive effect on T-cell–mediated antitumor immunity, the specific T-cell subsets involved remain uncertain. Experimental Design: We measured F. nucleatum DNA within tumor tissue by quantitative PCR on 933 cases (including 128 F. nucleatum–positive cases) among 4,465 incident colorectal carcinoma cases in two prospective cohorts. Multiplex immunofluorescence combined with digital image analysis and machine learning algorithms for CD3, CD4, CD8, CD45RO (PTPRC isoform), and FOXP3 measured various T-cell subsets. We leveraged data on Bifidobacterium, microsatellite instability (MSI), tumor whole-exome sequencing, and M1/M2-type tumor-associated macrophages [TAM; by CD68, CD86, IRF5, MAF, and MRC1 (CD206) multimarker assay]. Using the 4,465 cancer cases and inverse probability weighting method to control for selection bias due to tissue availability, multivariable-adjusted logistic regression analysis assessed the association between F. nucleatum and T-cell subsets. Results: The amount of F. nucleatum was inversely associated with tumor stromal CD3+ lymphocytes [multivariable OR, 0.47; 95% confidence interval (CI), 0.28–0.79, for F. nucleatum–high vs. -negative category; Ptrend = 0.0004] and specifically stromal CD3+CD4+CD45RO+ cells (corresponding multivariable OR, 0.52; 95% CI, 0.32–0.85; Ptrend = 0.003). These relationships did not substantially differ by MSI status, neoantigen load, or exome-wide tumor mutational burden. F. nucleatum was not significantly associated with tumor intraepithelial T cells or with M1 or M2 TAMs. Conclusions: The amount of tissue F. nucleatum is associated with lower density of stromal memory helper T cells. Our findings provide evidence for the interactive pathogenic roles of microbiota and specific immune cells.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".