Integrative genomic analysis of DLBCL identifies immune environments associated with bispecific antibody response
Bibliographic record
Abstract
ABSTRACT: Most patients with diffuse large B-cell lymphoma (DLBCL) treated with immunotherapies such as bispecific antibodies (BsAbs) or chimeric antigen receptor (CAR) T cells fail to achieve durable treatment responses, underscoring the need for a deeper understanding of mechanisms that regulate the immune environment and response to treatment. Here, an integrative multiomics approach was applied to multiple large independent data sets to characterize DLBCL immune environments and to define their association with tumor cell-intrinsic genomic alterations and outcomes to CD19-directed CAR T-cell and CD20 × CD3 BsAb therapies. This approach effectively segregated DLBCLs into 4 immune quadrants (IQs) defined by cell-of-origin and immune-related gene set expression scores. These quadrants consisted of activated B cell-like (ABC) hot, ABC cold, germinal center B cell-like (GCB) hot, and GCB cold DLBCLs. Recurrent genomic alterations were enriched in each IQ, suggesting that lymphoma cell-intrinsic alterations contribute significantly to orchestrating unique DLBCL immune environments. For instance, SOCS1 loss-of-function mutations were significantly enriched among GCB hot DLBCLs, identifying a putative subset of inflamed DLBCLs that may be inherently susceptible to immunotherapy. In patients with relapsed/refractory DLBCL, DLBCL-IQ assignment correlated significantly with clinical benefit with a CD20 × CD3 BsAb (N = 74), but not with CD19-directed CAR T cells (Stanford, N = 51; Memorial Sloan Kettering Cancer Center, N = 69). Thus, DLBCL-IQ provides a new framework to conceptualize the DLBCL immune landscape and suggests the endogenous immune environment has a more significant impact on outcomes to BsAb than CAR T-cell treatment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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 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".