Characterisation of immune checkpoints in Richter syndrome identifies LAG3 as a potential therapeutic target
Bibliographic record
Abstract
Richter syndrome (RS), an aggressive lymphoma occurring in the context of chronic lymphocytic leukaemia/small lymphocytic lymphoma, is associated with poor prognosis when treated with conventional immunochemotherapy, therefore, improved treatments are required. Immune checkpoint blockade has shown efficacy in some B-cell malignancies and modest responses in early clinical trials for RS. We investigated the immune checkpoint profile of RS as a basis to inform rational therapeutic investigations in RS. Formalin-fixed, paraffin-embedded biopsies of RS (n = 19), de novo diffuse large B-cell lymphoma (DLBCL; n = 58), transformed indolent lymphomas (follicular [tFL], n = 16; marginal zone [tMZL], n = 24) and non-transformed small lymphocytic lymphoma (SLL; n = 15) underwent gene expression profiling using the NanoString Human Immunology panel. Copy number assessment was performed using next-generation sequencing. Immunohistochemistry (IHC) for LAG3 and PD-1 was performed. LAG3 gene expression was higher in RS compared to DLBCL (P = 0·0002, log2FC 1·96), tFL (P < 0·0001, log2FC 2·61), tMZL (P = 0·0004, log2FC 1·79) and SLL (P = 0·0057, log2FC 1·45). LAG3 gene expression correlated with the gene expression of human leukocyte antigen Class I and II, and related immune genes and immune checkpoints. IHC revealed LAG3 protein expression on both malignant RS cells and tumour-infiltrating lymphocytes. Our findings support the investigation of LAG3 inhibition to enhance anti-tumour responses in RS.
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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 | 0.000 | 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) | 0.001 | 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 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".