Abstract

TCF3 is a lymphopoietic transcription factor that acquires somatic driver mutations in diffuse large B-cell lymphoma (DLBCL). Hypothesizing that expression patterns of TCF3-regulated genes can inform clinical management, we found that unsupervised clustering analysis with 15 TCF3-regulated genes and eight additional ones resolved local DLBCL cases into two main clusters, denoted Groups A and B, of which Group A manifested inferior overall survival (OS, p = 0.0005). We trained a machine learning model to classify samples into the Groups based on expression of the 23 transcripts in an independent validation cohort of 569 R-CHOP-treated DLBCL cases. Group A overlapped with the ABC cell-of-origin subgroup but its prognostic power was superior. GSEA analysis demonstrated asymmetric expression of 30 gene sets between the Groups, pointing to biological differences. We present, validate and make available a novel method to assign DLBCL cases into biologically-distinct groups with divergent OS following R-CHOP therapy.

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