5 | LARGE B‐CELL LYMPHOMA MICROENVIRONMENT ARCHETYPE PROFILES (LYMPHOMAPS) IDENTIFY SUBGROUPS WITH GREATEST BENEFIT FROM CD19 CAR T‐CELL THERAPY
Bibliographic record
Abstract
Introduction: Immunotherapies such as chimeric antigen receptor (CAR) T-cells are approved for patients with relapsed/ refractory large B-cell lymphoma (LBCL) and are being assessed in earlier lines of therapy.Efficacy of these therapies is likely influenced by the lymphoma microenvironment (LME), but comprehensive LME characterization in LBCL is lacking.Methods: We performed single-nucleus multiome (RNA-þATAC), bulk RNA sequencing, and whole exome sequencing on 232 biopsies (217 from patients with LBCL [114 newlydiagnosed; 103 relapsed/refractory] and 15 benign controls) to assess hematopoietic and non-hematopoietic cell (NHC) types.After stringent quality control, 1,886,312 cells were analyzed.Non-B-cell lineages were classified into 71 transcriptionallydistinct cell subsets by unsupervised clustering (21 T/NK, 25 myeloid, and 25 NHC subsets), including subpopulations not previously characterized in lymphoma.Results: We defined LME archetypes by non-negative matrix factorization of non-B cell types, yielding five cell modules condensing into three dominant archetypes (LymphoMAPs): lymph-node (LN; 33% of tumors) characterized by lymph-node structural cells, antigen presenting cells, and naïve and memory
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".