Identification of a Novel Function of B2M Involving T-Cell Exhaustion Via P-Selectin Ligand Upregulation in Diffuse Large B-Cell Lymphoma
Bibliographic record
Abstract
Background: Despite improvement of outcomes with current immuno-chemotherapies, approximately 40% of diffuse large B-cell lymphoma (DLBCL) patients suffer from recurrence or relapse, making it a refractory lymphoma. We previously reported that down-regulation of major histocompatibility complex (MHC) molecules contributes to a cold immune microenvironment and treatment resistance (Ennishi D & Takata K et al. Cancer Discov. 2019). However, the detailed mechanisms by which MHC reduction induces a cold immune microenvironment remain unclear. Here, we studied the functional consequences of loss of MHC class I and its major component beta-2-microglobulin (B2M) with a focus on the host immune microenvironment. Methods: We used the CRISPR-Cas9 gene editing system to knock out (KO) the B2M gene in two DLBCL-derived cell lines expressing MHC class I (Pfeiffer and SU-DHL-8). mRNA sequencing was performed and gene signatures that were commonly up- and down-regulated in both cell lines were identified. Concurrently, we conducted multicolor immunohistochemical (IHC) analysis using DLBCL patient samples (N=100) to examine the correlation between candidate molecules, MHC class I, and CD8+ T-cells. We also performed ligand stimulation assays and co-culture experiments in the generated isogeneic cell line systems, and generated an in vivo syngeneic mouse (A20 B2M KO) model to investigate antibody treatment. Results: Filtering the mRNA sequencing data (B2M-wt vs. heterozygous and homozygous KO) with P < 0.05 and a fold change > 1.5 revealed 383 up-regulated genes in Pfeiffer and 111 in SU-DHL-8, with four genes commonly up-regulated in both cell lines. Among these, the SELPLG (P selectin ligand) gene was the top upregulated gene. Additional B2M KO in other DLBCL cell lines (SU-DHL-4 and SU-DHL-10) confirmed the up-regulation of SELPLG. To explore mechanistic links between B2M and SELPLG, we performed gene set enrichment analysis (co-upregulated genes and pathways in B2M KO group), co-immunoprecipitation (co-IP), and ligand stimulation assays, and found that B2M binds to TNFRSF12A (TWEAKR) and up-regulates SELPLG via the NF-kB pathway. IHC of DLBCL patient samples showed an exclusive relationship between MHC class I and SELPLG expression in tumor cells (P < 0.001) without any links to DLBCL subtypes (GCB or non-GCB). Multi-color IHC revealed that SELPLG-positive samples had significantly more CD8+ PD1+ T-cells near tumor cells compared to SELPLG-negative samples, suggesting that SELPLG may cause exhaustion of host T-cells. Next, using B2M wt and KO DLBCL cells, we inhibited P-selectin and its ligand with a SELPLG antibody (Neihulizumab) and a P-selectin inhibitor (PSI-697) and conducted an in vitro co-culture with CD8+ T-cells. CD69-positive activated T-cell population was less abundant in B2M KO compared with B2M wt groups, which supports our previous findings (Cancer Discov. 2019). Inhibition of SELPLG/P-selectin significantly increased the CD69-positive activated T cell population in the KO group, suggesting that inhibition of SELPLG/P-selectin induces restoration of CD8+ T-cell activation. Of note, inhibition of SELPLG by Neihulizumab did not result in tumor growth suppression in DLBCL cell lines. Finally, we examined the therapeutic effects of the SELPLG antibody using a syngeneic mouse model. SELPLG was found to be upregulated in B2M KO A20 cells compared to B2M WT cells. Tumor growth reduction was observed in both A20 B2M wt and KO cell transplanted groups treated with the SELPLG antibody, with a stronger tumor reduction effect in the KO group. Single-cell expression analysis of the formed tumors revealed Th and CTL populations specifically expressing IL-7R, CXCL13, and ITGB in the treated KO group, suggesting their key role in tumor reduction. Conclusion: We discovered a novel function of the major component of MHC class I, B2M, which involves the exhaustion of CD8+ T-cells via SELPLG, contributing to the formation of a cold immune microenvironment. Targeting SELPLG could provide a new therapeutic avenue for refractory DLBCL that evades host immunity.
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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".