Multiomic analysis identifies a high-risk signature that predicts early clinical failure in DLBCL.

Recent genetic and molecular classification of DLBCL has advanced our knowledge of disease biology, yet were not designed to predict early events and guide anticipatory selection of novel therapies. To address this unmet need, we used an integrative multiomic approach to identify a signature at diagnosis that will identify DLBCL at high risk of early clinical failure. Tumor biopsies from 444 newly diagnosed DLBCL were analyzed by WES and RNAseq. A combination of weighted gene correlation network analysis and differential gene expression analysis was used to identify a signature associated with high risk of early clinical failure independent of IPI and COO. Further analysis revealed the signature was associated with metabolic reprogramming and identified cases with a depleted immune microenvironment. Finally, WES data was integrated into the signature and we found that inclusion of ARID1A mutations resulted in identification of 45% of cases with an early clinical failure which was validated in external DLBCL cohorts. This novel and integrative approach is the first to identify a signature at diagnosis, in a real-world cohort of DLBCL, that identifies patients at high risk for early clinical failure and may have significant implications for design of therapeutic options.

Discrete class I molecules on brain endothelium differentially regulate neuropathology in experimental cerebral malaria.

Cerebral malaria is the deadliest complication that can arise from Plasmodium infection. CD8 T-cell engagement of brain vasculature is a putative mechanism of neuropathology in cerebral malaria. To define contributions of brain endothelial cell major histocompatibility complex (MHC) class I antigen-presentation to CD8 T cells in establishing cerebral malaria pathology, we developed novel H-2Kb LoxP and H-2Db LoxP mice crossed with Cdh5-Cre mice to achieve targeted deletion of discrete class I molecules, specifically from brain endothelium. This strategy allowed us to avoid off-target effects on iron homeostasis and class I-like molecules, which are known to perturb Plasmodium infection. This is the first endothelial-specific ablation of individual class-I molecules enabling us to interrogate these molecular interactions. In these studies, we interrogated human and mouse transcriptomics data to compare antigen presentation capacity during cerebral malaria. Using the Plasmodium berghei ANKA model of experimental cerebral malaria (ECM), we observed that H-2Kb and H-2Db class I molecules regulate distinct patterns of disease onset, CD8 T-cell infiltration, targeted cell death and regional blood-brain barrier disruption. Strikingly, ablation of either molecule from brain endothelial cells resulted in reduced CD8 T-cell activation, attenuated T-cell interaction with brain vasculature, lessened targeted cell death, preserved blood-brain barrier integrity and prevention of ECM and the death of the animal. We were able to show that these events were brain-specific through the use of parabiosis and created the novel technique of dual small animal MRI to simultaneously scan conjoined parabionts during infection. These data demonstrate that interactions of CD8 T cells with discrete MHC class I molecules on brain endothelium differentially regulate development of ECM neuropathology. Therefore, targeting MHC class I interactions therapeutically may hold potential for treatment of cases of severe malaria.

Multiomic Analysis Identifies a High-Risk Metabolic and TME Depleted Signature that Predicts Early Clinical Failure in DLBCL.

PURPOSE: 60-70% of newly diagnosed diffuse large B-cell lymphoma (DLBCL) patients avoid events within 24 months of diagnosis (EFS24) and the remainder have poor outcomes. Recent genetic and molecular classification of DLBCL has advanced our knowledge of disease biology, yet were not designed to predict early events and guide anticipatory selection of novel therapies. To address this unmet need, we used an integrative multiomic approach to identify a signature at diagnosis that will identify DLBCL at high risk of early clinical failure. PATIENTS AND METHODS: Tumor biopsies from 444 newly diagnosed DLBCL were analyzed by WES and RNAseq. A combination of weighted gene correlation network analysis and differential gene expression analysis followed by integration with clinical and genomic data was used to identify a multiomic signature associated with high risk of early clinical failure. RESULTS: Current DLBCL classifiers are unable to discriminate cases who fail EFS24. We identified a high risk RNA signature that had a hazard ratio (HR, 18.46 [95% CI 6.51-52.31] P < .001) in a univariate model, which did not attenuate after adjustment for age, IPI and COO (HR, 20.8 [95% CI, 7.14-61.09] P < .001). Further analysis revealed the signature was associated with metabolic reprogramming and a depleted immune microenvironment. Finally, WES data was integrated into the signature and we found that inclusion of ARID1A mutations resulted in identification of 45% of cases with an early clinical failure which was validated in external DLBCL cohorts. CONCLUSION: This novel and integrative approach is the first to identify a signature at diagnosis that will identify DLBCL at high risk for early clinical failure and may have significant implications for design of therapeutic options.

Molecular classification and identification of an aggressive signature in low-grade B-cell lymphomas.

Non-follicular low-grade B-cell lymphomas (LGBCL) are biologically diverse entities that share clinical and histologic features that make definitive pathologic categorization challenging. While most patients with LGBCL have an indolent course, some experience aggressive disease, highlighting additional heterogeneity across these subtypes. To investigate the potential for shared biology across subtypes, we performed RNA sequencing and applied machine learning approaches that identified five clusters of patients that grouped independently of subtype. One cluster was characterized by inferior outcome, upregulation of cell cycle genes, and increased tumor immune cell content. Integration of whole exome sequencing identified novel LGBCL mutations and enrichment of TNFAIP3 and BCL2 alterations in the poor survival cluster. Building on this, we further refined a transcriptomic signature associated with early clinical failure in two independent cohorts. Taken together, this study identifies unique clusters of LGBCL defined by novel gene expression signatures and immune profiles associated with outcome across diagnostic subtypes.