Molecular profiling in diffuse large B‐cell lymphoma: why so many types of subtypes?
Bibliographic record
Abstract
The term diffuse large B-cell lymphoma (DLBCL) includes a heterogeneous collection of biologically distinct tumours. This heterogeneity currently presents a barrier to the successful deployment of novel, biologically targeted therapies. Molecular profiling studies have recently proposed new molecular classification systems. These have the potential to resolve the biological heterogeneity of DLBCL into manageable subgroups of tumours that rely on shared oncogenic programmes. In many cases these biological programmes straddle the boundaries of our existing systems for classifying B-cell lymphomas. Here we review the findings from these major molecular profiling studies with a specific focus on those that propose new genetic subgroups of DLBCL. We highlight the areas of consensus and discordance between these studies and discuss the implications for current clinical practice and for clinical trials. Finally, we address the outstanding challenges and solutions to the introduction of genomic subtyping and precision medicine 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".