HLA‐DRB alleles are differentially expressed by tumor cells in breast carcinoma
Bibliographic record
Abstract
The biologic and prognostic significance of HLA-DR expression and T-cell infiltration in breast carcinoma are presently controversial. To test the hypothesis that these factors are influenced by particular HLA-DRB alleles, 52 breast tumor samples, composed of 26 DRB1*04 and 26 non-DRB1*04 tumors, were assessed using immunohistochemistry for expression of DR and its associated invariant chain (Ii) and for infiltrating CD3+ T cells. While DR expression by tumor cells was significantly associated with T-cell infiltration, DRB1*04 tumors were more frequently DR+ Ii+ and contained smaller CD3+ infiltrates than non-DRB1*04 tumors. This difference was largely attributable to DRB1*07 tumors, which were typically DR- Ii-, although they contained similar numbers of T cells to DR+ Ii+ tumors. Further analysis of DR+ tumors using allotype discriminating antibodies revealed that DRB1*04 alleles were always expressed, while non-DRB1*04 alleles were inconsistently expressed. The results of this study provide the first reported evidence that DRB alleles influence DR expression and T-cell infiltration in breast carcinoma and suggest that multiple factors contribute to DR expression. Ongoing studies aimed at elucidating the molecular and immunologic mechanisms controlling differential DR expression and implications for prognosis and outcome should further our understanding of the antitumor immune response and evasion strategies employed by tumor cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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".