Molecular Determinants of Clinical Outcomes In a Real-World Diffuse Large B-cell Lymphoma Population
Bibliographic record
Abstract
Molecular heterogeneity of diffuse large B-cell lymphoma (DLBCL) underlies the variable outcomes achieved with immunochemotherapy. However, outcomes of gene expression profiling (GEP)-defined molecular subgroups in a real-world DLBCL population remain unknown. Here we examined the prevalence and outcomes of molecular subgroups in an unselected population of 1149 patients with de novo DLBCL in British Columbia, Canada. Evaluable biopsies were profiled by fluorescence in situ hybridization (FISH), immunohistochemistry, and digital GEP to assign cell-of-origin and the so-called "double-hit signature" (DHITsig)-a signature originally described as being characteristic for high-grade B-cell lymphoma with MYC and BCL2 rearrangements (HGBCL-DH-BCL2). DHITsig was expressed in 21% of 431 germinal center B-cell-like (GCB)-DLBCL and all 55 Burkitt lymphomas examined. Reflecting this latter finding, DHITsig has been renamed the "dark zone signature" (DZsig). DZsigpos-DLBCL, non-DZsigpos GCB-DLBCL and activated B-cell-like (ABC)-DLBCL were associated with a 2 year overall survival of 57%, 89%, and 71%, respectively. 62% of DZsigpos tumors were negative for HGBCL-DH-BCL2 by FISH, but were associated with outcomes similar to HGBCL-DH-BCL2. A small group of HGBCL-DH-BCL2 that lacked DZsig expression had different molecular features compared with DZsig-expressing HGBCL-DH-BCL2 and were associated with favorable outcomes comparable to DLBCL, not otherwise specified. DZsigpos and ABC-DLBCL had a shorter diagnosis-to-treatment interval (DTI) than GCB-DLBCL, with this metric being associated with outcome. In conclusion, DZsig expression extends beyond HGBCL-DH-BCL2 and captures a poor-prognosis DLBCL subgroup with short DTI, including patients unidentifiable by routine FISH testing, that should be considered for treatment intensification or novel therapies in prospective trials.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".