Loss of MHC class II gene and protein expression in diffuse large B-cell lymphoma is related to decreased tumor immunosurveillance and poor patient survival regardless of other prognostic factors: a follow-up study from the Leukemia and Lymphoma Molecular Profiling Project
Bibliographic record
Abstract
The Leukemia and Lymphoma Molecular Profiling Project recently published results from DNA microarray analyses of 240 diffuse large B-cell lymphomas (DLBCLs). Four gene expression "signatures" were identified as correlated with patient outcome, including the major histocompatibility complex (MHC) class II genes (eg, HLA-DRA) which correlated with better survival. We further analyzed the effects of HLA-DRA on survival and correlated gene expression with protein status and tumor-infiltrating lymphocytes. The 5-year overall survival was 24% in the lowest 10% of HLA-DRA expression, 37% in the 10% to 25% group, 50% in the 25% to 50% group, and 55% for patients in the highest 50%. Further analysis demonstrated that the hazard ratio of death was a nonlinear function of HLA-DRA expression. Adjustment for the International Prognostic Index did not alter the impact of HLA-DRA on survival. Other MHC class II genes were found to predict survival similarly. Microarray HLA-DRA expression correlated with the presence or absence of human leukocyte antigen-DR (HLA-DR) protein in 20 of 22 cases assessed. Fewer tumor-infiltrating CD8(+) T cells were detected in MHC class II-negative cases compared with positive cases (2.8% versus 11.0%; P =.001), supporting the hypothesis that loss of tumor immunosurveillance has a devastating effect on patient outcome in DLBCL.
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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".