Highlights d Diffuse large B cell lymphoma (DLBCL) consists of seven genetic subtypes d The LymphGen algorithm classifies a DLBCL biopsy into one or more genetic subtypes d The genetic subtypes have distinct clinical outcomes and pathway dependencies d The genetic subtypes will aid the development of rationally targeted therapy of DLBCL

Purpose High-grade B-cell lymphoma with MYC and BCL2 and/or BCL6 rearrangements (HGBL-DH/TH) has a poor outcome after standard chemoimmunotherapy. We sought to understand the biologic underpinnings of HGBL-DH/TH with BCL2 rearrangements (HGBL-DH/TH- BCL2) and diffuse large B-cell lymphoma (DLBCL) morphology through examination of gene expression. Patients and Methods We analyzed RNA sequencing data from 157 de novo germinal center B-cell-like (GCB)-DLBCLs, including 25 with HGBL-DH/TH- BCL2, to define a gene expression signature that distinguishes HGBL-DH/TH- BCL2 from other GCB-DLBCLs. To assess the genetic, molecular, and phenotypic features associated with this signature, we analyzed targeted resequencing, whole-exome sequencing, RNA sequencing, and immunohistochemistry data. Results We developed a 104-gene double-hit signature (DHITsig) that assigned 27% of GCB-DLBCLs to the DHITsig-positive group, with only one half harboring MYC and BCL2 rearrangements (HGBL-DH/TH- BCL2). DHITsig-positive patients had inferior outcomes after rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone immunochemotherapy compared with DHITsig-negative patients (5-year time to progression rate, 57% and 81%, respectively; P < .001), irrespective of HGBL-DH/TH- BCL2 status. The prognostic value of DHITsig was confirmed in an independent validation cohort. DHITsig-positive tumors are biologically characterized by a putative non–light zone germinal center cell of origin and a distinct mutational landscape that comprises genes associated with chromatin modification. A new NanoString assay (DLBCL90) recapitulated the prognostic significance and RNA sequencing assignments. Validating the association with HGBL-DH/TH- BCL2, 11 of 25 DHITsig-positive–transformed follicular lymphomas were classified as HGBL-DH/TH- BCL2 compared with zero of 50 in the DHITsig-negative group. Furthermore, the DHITsig was shared with the majority of B-cell lymphomas with high-grade morphology tested. Conclusion We have defined a clinically and biologically distinct subgroup of tumors within GCB-DLBCL characterized by a gene expression signature of HGBL-DH/TH- BCL2. This knowledge has been translated into an assay applicable to routinely available biopsy samples, which enables exploration of its utility to guide patient management.

High-grade B-cell lymphomas with MYC and BCL2 and/or BCL6 rearrangements (HGBL-DH/THs) include a group of diffuse large B-cell lymphomas (DLBCLs) with inferior outcomes after standard chemoimmunotherapy. We recently described a gene expression signature that identifies 27% of germinal center B-cell DLBCLs (GCB-DLBCLs) as having a double-hit–like expression pattern (DHITsig) and inferior outcomes; however, only half of these cases have both MYC and BCL2 translocations identifiable using standard breakapart fluorescence in situ hybridization (FISH). Here, 20 DHITsig+ GCB-DLBCLs apparently lacking MYC and/or BCL2 rearrangements underwent whole-genome sequencing. This revealed 6 tumors with MYC or BCL2 rearrangements that were cryptic to breakapart FISH. Copy-number analysis identified 3 tumors with MYC and 6 tumors with MIR17HG gains or amplifications, both of which may contribute to dysregulation of MYC and its downstream pathways. Focal deletions of the PVT1 promoter were observed exclusively among DHITsig+ tumors lacking MYC translocations; this may also contribute to MYC overexpression. These results highlight that FISH fails to identify all HGBL-DH/THs, while revealing a range of other genetic mechanisms potentially underlying MYC dysregulation in DHITsig+ DLBCL, suggesting that gene expression profiling is more sensitive for identifying the biology underlying poor outcomes in GCB-DLBCL.

Many inborn errors of metabolism (IEMs) are amenable to treatment; therefore, early diagnosis and treatment is imperative. Despite recent advances, the genetic basis of many metabolic phenotypes remains unknown. For discovery purposes, whole exome sequencing (WES) variant prioritization coupled with clinical and bioinformatics expertise is the primary method used to identify novel disease-causing variants; however, causation is often difficult to establish due to the number of plausible variants. Integrated analysis of untargeted metabolomics (UM) and WES or whole genome sequencing (WGS) data is a promising systematic approach for identifying disease-causing variants. In this review, we provide a literature-based overview of UM methods utilizing liquid chromatography mass spectrometry (LC-MS), and assess approaches to integrating WES/WGS and LC-MS UM data for the discovery and prioritization of variants causing IEMs. To embed this integrated -omics approach in the clinic, expansion of gene-metabolite annotations and metabolomic feature-to-metabolite mapping methods are needed.