Application of the LymphGen classification tool to 928 clinically and genetically-characterised cases of diffuse large B cell lymphoma (DLBCL).

We recently published results of targeted sequencing applied to 928 unselected cases of DLBCL registered in the Haematological Malignancy Research Network (HMRN) registry (1). Clustering allowed us to resolve five genomic subtypes. These subtypes shared considerable overlap with those proposed in two independent genomic studies(2, 3), suggesting the potential to use genetics to stratify patients by both risk and biology. In the original studies, clustering techniques were applied to sample cohorts to reveal molecular substructure, but left open the challenge of how to classify an individual patient. This was addressed by the LymphGen classification tool (4). LymphGen assigns an individual case to one of six molecular subtypes. The tool accommodates data from exome or targeted sequencing, either with or without copy number variant (CNV) data. Separate gene expression data allows classification of a seventh, MYC-driven subtype defined by a double hit (DHL) or molecular high-grade (MHG) gene expression signature(5-7).HR was funded by a studentship from the Medical Research Council. DH was supported by a Clinician Scientist Fellowship from the Medical Research Council (MR/M008584/1). The Hodson laboratory receives core funding from Wellcome and MRC to the Wellcome-MRC Cambridge Stem Cell Institute and core funding from the CRUK Cambridge Cancer Centre. HMRN is supported by BCUK 15037 and CRUK 18362

γ-2 and GSG1L bind with comparable affinities to the tetrameric GluA1 core.

BACKGROUND: The AMPA-type ionotropic glutamate receptor mediates fast excitatory neurotransmission in the brain. A variety of auxiliary subunits regulate its gating properties, assembly, and trafficking, but it is unknown if the binding of these auxiliary subunits to the receptor core is dynamically regulated. Here we investigate the interplay of the two auxiliary subunits γ-2 and GSG1L when binding to the AMPA receptor composed of four GluA1 subunits. METHODS: We use a three-color single-molecule imaging approach in living cells, which allows the direct observation of the receptors and both auxiliary subunits. Colocalization of different colors can be interpreted as interaction of the respective receptor subunits. RESULTS: Depending on the relative expression levels of γ-2 and GSG1L, the occupancy of binding sites shifts from one auxiliary subunit to the other, supporting the idea that they compete for binding to the receptor. Based on a model where each of the four binding sites at the receptor core can be either occupied by γ-2 or GSG1L, our experiments yield apparent dissociation constants for γ-2 and GSG1L in the range of 2.0-2.5/µm2. CONCLUSIONS: The result that both binding affinities are in the same range is a prerequisite for dynamic changes of receptor composition under native conditions

Patterns of Oncogene Coexpression at Single-Cell Resolution Influence Survival in Lymphoma.

UNLABELLED: Cancers often overexpress multiple clinically relevant oncogenes, but it is not known if combinations of oncogenes in cellular subpopulations within a cancer influence clinical outcomes. Using quantitative multispectral imaging of the prognostically relevant oncogenes MYC, BCL2, and BCL6 in diffuse large B-cell lymphoma (DLBCL), we show that the percentage of cells with a unique combination MYC+BCL2+BCL6- (M+2+6-) consistently predicts survival across four independent cohorts (n = 449), an effect not observed with other combinations including M+2+6+. We show that the M+2+6- percentage can be mathematically derived from quantitative measurements of the individual oncogenes and correlates with survival in IHC (n = 316) and gene expression (n = 2,521) datasets. Comparative bulk/single-cell transcriptomic analyses of DLBCL samples and MYC/BCL2/BCL6-transformed primary B cells identify molecular features, including cyclin D2 and PI3K/AKT as candidate regulators of M+2+6- unfavorable biology. Similar analyses evaluating oncogenic combinations at single-cell resolution in other cancers may facilitate an understanding of cancer evolution and therapy resistance. SIGNIFICANCE: Using single-cell-resolved multiplexed imaging, we show that selected subpopulations of cells expressing specific combinations of oncogenes influence clinical outcomes in lymphoma. We describe a probabilistic metric for the estimation of cellular oncogenic coexpression from IHC or bulk transcriptomes, with possible implications for prognostication and therapeutic target discovery in cancer. This article is highlighted in the In This Issue feature, p. 1027

Patterns of Oncogene Coexpression at Single-Cell Resolution Influence Survival in Lymphoma

10.1158/2159-8290.CD-22-0998CANCER DISCOVERY1351144-116