Comparison of MHG and DZsig reveals shared biology and a core overlap group with inferior prognosis in DLBCL
Bibliographic record
Abstract
Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease identified by morphology, immunophenotype, and a typically aggressive clinical course.1 DLBCL has long been stratified based on gene expression profiling (GEP) into activated B-cell–like (ABC) and germinal center B-cell–like (GCB) cell-of-origin (COO) subtypes.2 Recently, several studies stratified DLBCL into genetic subgroups based on the co-occurrence of mutational features with strong associations with COO.3-6 Previously, our 2 groups independently reported gene expression signatures associated with dark-zone–like biology in DLBCL. The molecular high-grade signature (MHG) identifies DLBCLs expressing a Burkitt lymphoma (BL)-like GEP signature,7 whereas the double-hit signature (since renamed dark-zone signature [DZsig]8) identifies DLBCLs with a GEP signature like high-grade B-cell lymphoma with MYC and BCL2 rearrangement (HGBCL-DH-BCL2) (whether the tumors harbor MYC and BCL2 rearrangements or not).9,10 Remarkably, despite the small overlap in the genes that comprise each signature, both classifiers identified a subset of DLBCL tumors enriched for certain genetic aberrations, including concomitant MYC and BCL2 rearrangements.7,9 Here, we present analyses that directly compare MHG and DZsig classifications applied to the same data sets, demonstrating that most tumors positive for 1 signature are positive for both. We evaluated the agreement between the 2 scores in several cohorts and investigated the association of the group of tumors positive for both signatures, “DZSig&MHG,” with outcome. Finally, we compared the mutation frequencies in tumors positive for 1 or both signatures and their association with 2 DLBCL genetic subgroup classifications. Our results demonstrate a clear biological similarity between the DZsig and MHG classifications and strong associations with the EZB genetic subgroup. All data used in this study were generated as part of studies reviewed and approved by the institutional review boards at each site, in accordance with the Declaration of Helsinki. GEP matrices were obtained for 3 cohorts: REMoDL-B (N = 928; Illumina DASL),7,11 Hematologic Malignancy Diagnostic Service (HMDS) (N = 1024; Illumina DASL),12 and DLC (N = 304; polyAselected RNAseq) (supplemental Tables 1-2).9 MHG classifications were generated for each data set using the BDC classifier.13 DZsig classification was performed using the PRPS-ST classifier.14 We
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".