[Characteristics of PD-L1 expression in tumor cells and tumor microenvironment of DLBCL with MYD88 L265P mutation]

Gene set-based identification of two immune subtypes of diffuse large B cell lymphoma for guiding immune checkpoint blocking therapy

Background: Diffuse large B cell lymphoma (DLBCL) is the most common lymphoma in adults. Tumour microenvironment is closely related to tumour prognosis and immune checkpoint blocking therapy (ICBT). This study aimed to investigate the immunological and prognostic characteristics of the tumour microenvironment (TME), as well as the regulatory mechanisms.

Methods: Gene expression profiles and clinical data of patients with DLBCL were obtained from GEO database. ESTIMATE, CIBERSORT, and ssGSEA analyses were used to explore microenvironment characteristics and regulatory mechanism of the immune subtypes, which were identified by consistent clustering. The differences in enriched pathways were showed by GSEA. Hub genes associated with CD8⁺ T cells, which were identified by WCGNA, were exhibited biological functions through GO and KEGG. Immune-related gene scores (IRGSs) based on hub genes were used to evaluate the prediction of immune subtypes and ICBT, and retrospective analysis was used for validation. Finally, prognostic genes were screened to construct risk models.

Results: Consensus clustering divided patients with DLBCL into two subtypes with significant heterogeneities in prognosis and immune microenvironment. Low immune infiltration was associated with poor prognosis. Subtype C1 with high immune infiltration was enriched in multiple immune pathways. We observed that two common mutated genes (B2M and EZH2) in DLBCL were closely related to MHC-I and microenvironment. Our further analysis manifested that MYD88^L265P may be the main cause of TLR signalling pathway activation in subtype C1. Hub genes (SH2D1A, CD8A, GBP2, ITK, CD3D, RORA, IL1R2, CD28, CD247, CD3G, PRKCQ, CXCR6, and CD3E) in relation with CD8⁺ T cells were used to establish IRGS, which was proved an accurate predictor of immune subtypes, and patients in high-IRGS group were more likely to benefit from ICBT. Retrospective analysis showed that absolute lymphocyte count (ALC) was higher in the group that responded to the PD-1 inhibitor. Finally, the risk model was constructed based on two genes (CD3G and CD3D), and the low-risk group showed better prognosis.

Conclusion: DLBCL immune classifications with highly heterogeneity are a powerful predictor of prognosis and ICBT. The IRGS is proved to be a reliable tool to distinguish immune subtypes as a substitute for gene expression profile.