Diffuse large B-cell lymphoma subgroups have distinct genetic profiles that influence tumor biology and improve gene-expression-based survival prediction
Bibliographic record
Abstract
Gene-expression profiling has identified 3 major subgroups of diffuse large B-cell lymphoma (DLBCL): germinal center B-cell-like (GCB), activated B-cell-like (ABC), and primary mediastinal DLBCL (PMBCL). Using comparative genomic hybridization (CGH), we investigated the genetic alterations of 224 cases of untreated DLBCL (87 GCB-DLBCL, 77 ABC-DLBCL, 19 PMBCL, and 41 unclassified DLBCL) previously characterized by gene-expression profiling. The DLBCL subgroups differed significantly in the frequency of particular chromosomal aberrations. ABC-DLBCL had frequent trisomy 3, gains of 3q and 18q21-q22, and losses of 6q21-q22, whereas GCB-DLBCL had frequent gains of 12q12, and PMBCL had gains of 9p21-pter and 2p14-p16. Parallel analysis of CGH alterations, locus-specific gene-expression profiles, and global gene-expression signatures revealed that DNA amplifications and gains had a substantial impact on the expression of genes in the involved chromosomal regions, and some genes were overexpressed in a DLBCL subgroup-specific fashion. Unexpectedly, specific chromosomal alterations were associated with significant changes in gene-expression signatures that reflect various aspects of lymphoma cell biology as well as the host response to the lymphoma. In addition, gains involving the chromosomal region 3p11-p12 provided prognostic information that was statistically independent of the previously defined gene-expression-based survival model, thereby improving its predictive power.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".