Interaction between tumour-infiltrating B cells and T cells controls the progression of hepatocellular carcinoma
Bibliographic record
Abstract
OBJECTIVE: The nature of the tumour-infiltrating leucocytes (TILs) is known to impact clinical outcome in carcinomas, including hepatocellular carcinoma (HCC). However, the role of tumour-infiltrating B cells (TIBs) remains controversial. Here, we investigate the impact of TIBs and their interaction with T cells on HCC patient prognosis. DESIGN: Tissue samples were obtained from 112 patients with HCC from Singapore, Hong Kong and Zurich and analysed using immunohistochemistry and immunofluorescence. RNA expression of CD19, CD8A, IFNG was analysed using quantitative PCR. The phenotype of freshly isolated TILs was analysed using flow cytometry. A mouse model depleted of mature B cells was used for functional study. RESULTS: Tumour-infiltrating T cells and B cells were observed in close contact with each other and their densities are correlated with superior survival in patients with HCC. Furthermore, the density of TIBs was correlated with an enhanced expression of granzyme B and IFN-γ, as well as with reduced tumour viability defined by low expression of Ki-67, and an enhanced expression of activated caspase-3 on tumour cells. CD27 and CD40 costimulatory molecules and TILs expressing activation marker CD38 in the tumour were also correlated with patient survival. Mice depleted of mature B cells and transplanted with murine hepatoma cells showed reduced tumour control and decreased local T cell activation, further indicating the important role of B cells. CONCLUSIONS: The close proximity of tumour-infiltrating T cells and B cells indicates a functional interaction between them that is linked to an enhanced local immune activation and contributes to better prognosis for patients with HCC.
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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 | 0.000 |
| 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 | 0.001 |
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".