Tumor cell expression of HLA-DM associates with a Th1 profile and predicts improved survival in breast carcinoma patients
Bibliographic record
Abstract
Studies aimed at elucidating the immunological and prognostic significance of HLA-DR expression on breast carcinoma cells have yielded contradictory results. To expand on previous studies, we have investigated the associations of tumor cell expression of HLA-DR and its related co-chaperones, invariant chain (Ii) and HLA-DM, with infiltrating inflammatory cells, in situ cytokine mRNA levels and prognosis and outcome in 112 breast carcinoma patients with a median follow-up of 59 months. While the majority of HLA-DR+ tumors co-express Ii, only a minority express HLA-DM. Tumor cell expression of HLA-DR and co-chaperones positively associated with both infiltrating CD4+ and CD8+ T-cell subsets (P < 0.01). Expression of HLA-DR and Ii associated with decreased estrogen receptor alpha levels and younger age at diagnosis, suggesting a role for hormones in the control of HLA class II expression in breast carcinoma. Patients with DR+Ii+DM- tumors had markedly decreased recurrence-free and disease-specific survival as compared with patients with DR+Ii+DM+ tumors (P < 0.05) and HLA-DR/co-chaperone expression was an independent predictor of survival by multivariate Cox regression analysis, controlling for standard prognostic indicators. Tumors that co-express HLA-DR, Ii and HLA-DM have increased levels of IFN-gamma, IL-2 and IL-12 mRNA, suggesting improved survival of patients with DR+Ii+DM+ tumors may be attributable to Th1-dominated immunity. We conclude that expression of determinants of the immune response by tumor cells may influence breast tumor progression and patient outcome.
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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".