Rituximab and Immune Molecule Modulation in Burkitt’s Lymphoma Cell Lines
Bibliographic record
Abstract
Objectives: Burkitt’s lymphoma is an aggressive B cell malignancy that is associated with EBV infection. The monoclonal antibody rituximab is used to treat many B cell malignancies, including Burkitt’s lymphoma. Studying immune molecule modulation in Burkitt’s lymphoma allows insight on developing new or refining existing immunotherapy agents for refractory or chemotherapy-resistant patients. The main purpose of this study was to explore rituximab’s impact on expression of immune molecules associated with immune activation or immune inhibition. Methods: Burkitt’s lymphoma cell lines Raji, Ramos, Bjab, and an EBV-transformed B cell line, COX, were cultured and treated with an optimally-determined concentration of rituximab or human IgG for 24 hours. Immune modulation was determined by flow cytometric analysis of HLA-I, HLA-DR, PD-L1, and CD40. Three experiments were conducted for Raji, Ramos, Bjab, while 2 experiments were conducted for COX. Results: Treatment of cells with rituximab, 10 µg/ml, completed downregulated CD20 expression and modulated expression of immune molecules. Compared to human IgG control, rituximab treatment decreased HLA-1, HLA-DR and CD40 expression on all cell lines, but significantly only for HLA-I on Bjab. Interestingly, the immune inhibitor PD-L1 was decreased on EBV-positive COX and Raji, but increased on EBV-negative Ramos and Bjab. Conclusion: Downregulation of HLA-I could contribute to an immune escape mechanism. As this was a small study, there is limited transferability of the results to the clinical setting and further experiments are needed.
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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 | 0.001 |
| 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".