CRISPR/Cas9-Mediated Foxp1 Silencing Restores Immune Surveillance in an Immunocompetent A20 Lymphoma Model
Bibliographic record
Abstract
The interaction of lymphoma cells with their microenvironment has an important role in disease pathogenesis and is being actively pursued therapeutically using immunomodulatory drugs, including immune checkpoint inhibitors. Diffuse large B-cell lymphoma (DLBCL) is an aggressive high-grade disease that remains incurable in ~40% of patients treated with R-CHOP immunochemotherapy. The FOXP1 transcription factor is abundantly expressed in such high-risk DLBCL and we recently identified its regulation of immune response signatures, in particular, its suppression of the cell surface expression of major histocompatibility class II (MHC-II), which has a critical role in antigen presentation to T cells. Using CRISPR/Cas9 genome editing we have depleted Foxp1 expression in the aggressive murine A20 lymphoma cell line. When grown in BALB/c mice, this cell line provides a high-fidelity immunocompetent disseminated lymphoma model that displays many characteristics of human DLBCL. Transient Foxp1-depletion using siRNA, and stable depletion using CRISPR (generated by independently targeting Foxp1 exon six or seven) upregulated cell surface I-Ab (MHC-II) expression without impairing cell viability in vitro. RNA sequencing of Foxp1-depleted A20 clones identified commonly deregulated genes, such as the B-cell marker Cd19, and hallmark DLBCL signatures such as MYC-targets and oxidative phosphorylation. Immunocompetent animals bearing Foxp1-depleted A20 lymphomas showed significantly-improved survival, and 20% did not develop tumors; consistent with modulating immune surveillance, this was not observed in immunodeficient NOD SCIDγ mice. The A20 Foxp1 CRISPR model will help to further characterize the contribution of Foxp1 to lymphoma immune evasion and the potential for Foxp1 targeting to synergize with other immunotherapies.
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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 | 0.000 | 0.001 |
| 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) | 0.000 | 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 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".