Non-Hodgkin B-cell lymphoma (B-NHL) are aggressive lymphoid malignancies that develop in patients due to oncogenic activation, chemo-resistance, and immune evasion. Tumor biopsies show that B-NHL frequently uses several immune escape strategies, which has hindered the development of checkpoint blockade immunotherapies in these diseases. To gain a better understanding of B-NHL immune editing, we hypothesized that the transcriptional hallmarks of immune escape associated with these diseases could be identified from the meta-analysis of large series of microarrays from B-NHL biopsies. Thus, 1446 transcriptome microarrays from seven types of B-NHL were downloaded and assembled from 33 public Gene Expression Omnibus (GEO) datasets, and a method for scoring the transcriptional hallmarks in single samples was developed. This approach was validated by matching scores to phenotypic hallmarks of B-NHL such as proliferation, signaling, metabolic activity, and leucocyte infiltration. Through this method, we observed a significant enrichment of 33 immune escape genes in most diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL) samples, with fewer in mantle cell lymphoma (MCL) and marginal zone lymphoma (MZL) samples. Comparing these gene expression patterns with overall survival data evidenced four stages of cancer immune editing in B-NHL: non-immunogenic tumors (stage 1), immunogenic tumors without immune escape (stage 2), immunogenic tumors with immune escape (stage 3), and fully immuno-edited tumors (stage 4). This model complements the standard international prognostic indices for B-NHL and proposes that immune escape stages 3 and 4 (76% of the FL and DLBCL samples in this data set) identify patients relevant for checkpoint blockade immunotherapies.
Keywords: Data mining; immune escape; lymphoma.