Double-hit Signature with <i>TP53</i> Abnormalities Predicts Poor Survival in Patients with Germinal Center Type Diffuse Large B-cell Lymphoma Treated with R-CHOP
Bibliographic record
Abstract
Abstract Purpose: We performed detailed genomic analysis on 87 cases of de novo diffuse large B-cell lymphoma of germinal center type (GCB DLBCL) to identify characteristics that are associated with survival in those treated with R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone). Experimental Design: The cases were extensively characterized by combining the results of IHC, cell-of-origin gene expression profiling (GEP; NanoString), double-hit GEP (DLBCL90), FISH cytogenetic analysis for double/triple-hit lymphoma, copy-number analysis, and targeted deep sequencing using a custom mutation panel of 334 genes. Results: We identified four distinct biologic subgroups with different survivals, and with similarities to the genomic classifications from two large retrospective studies of DLBCL. Patients with the double-hit signature, but no abnormalities of TP53, and those lacking EZH2 mutation and/or BCL2 translocation, had an excellent prognosis. However, patients with an EZB-like profile had an intermediate prognosis, whereas those with TP53 inactivation combined with the double-hit signature had an extremely poor prognosis. This latter finding was validated using two independent cohorts. Conclusions: We propose a practical schema to use genomic variables to risk-stratify patients with GCB DLBCL. This schema provides a promising new approach to identify high-risk patients for new and innovative therapies.
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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 | 0.001 | 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 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".