LySeqST: a targeted sequencing assay for robust genomic classification of diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a biologically heterogeneous disease. Two genomic classification systems, LymphGen and DLBclass, are capable of reproducibly classifying single tumors into subtypes with prognostic relevance in the context of standard-of-care R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) chemoimmunotherapy. Preliminary data from subgroup analyses of clinical trials suggest that the distinct biological features are targetable with drugs that exploit subtype-specific vulnerabilities, and large clinical trials are being designed to test this hypothesis. Although whole-exome sequencing (WES) remains the gold standard for each of these classification systems, smaller targeted sequencing panels reduce costs associated with sequencing, data storage, and processing. Here, we describe the design and validation of a targeted sequencing panel (LySeqST) that captures the genomic features required specifically for LymphGen classification. We perform in silico theoretical and real-world validation vs WES data and demonstrate that LySeqST can be used to accurately classify DLBCL tumors using both LymphGen and DLBclass. In an unselected population-based cohort, we determine the real-world proportions and validate the prognostic relevance of LymphGen subtypes, confirming that only tumors expressing the dark zone gene expression signature are consistently associated with inferior outcomes. Our results support the use of LySeqST for accurate genomic classification of DLBCL.