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Introduction: Waldenström macroglobulinemia (WM) is a rare B-cell lymphoma that may be preceded by an asymptomatic IgM-monoclonal gammopathy of undetermined significance (MGUS). The mechanisms underlying the progression from IgM-MGUS to WM remain to be fully elucidated; however, the stepwise accumulation of genomic abnormalities has been suggested as a potential driver. Currently, the role of epigenetic regulation of DNA remains underexplored in this disease spectrum. Aberrant DNA methylation is a hallmark of oncogenesis that plays an important role in hematologic malignancies. The aim of this study was to assess differential DNA methylation in WM compared to IgM-MGUS and identify hyper- and hypomethylated pathways that may alter key cellular functions. Methods: This study included 15 patients with WM and 6 with IgM-MGUS. Bone marrow samples were prospectively collected and CD19+ and/or CD138+ B cells were positively selected. Subsequently, genome-wide DNA methylation analysis was performed. CpG methylation ratios were segmented into 200-bp regions and differentially methylated regions (DMRs) comparing WM to IgM-MGUS were identified. DMRs with a Q-value <0.05 and absolute methylation difference of ≥20% were considered significant and utilized for analysis. DMRs were annotated according to genomic regions of interest (promotors, 3'UTR, 5'UTR, introns, exons, CpG islands, CpG shores). Next, genes with significant promotor hyper- or hypomethylation were assessed using the Ingenuity Pathway Analysis. Pathways with a corrected p-value<0.05 were considered statistically significant and a Z-value was calculated based on the degree of differential methylation. Results: Genome-wide DNA methylation analysis revealed a significant number of DMRs (n= 15691, hyper: 8248; hypo: 7443) in WM compared to IgM-MGUS. Assessing genomic regions, promotors (hyper: 1379; hypo: 2403), introns (hyper: 1548; hypo: 2292), CpG shores (hyper: 587; hypo: 849) and 3'UTR (hyper: 66; hypo: 193) regions were observed to have a significantly greater number of hypomethylated DMRs (all comparisons, p<0.001) in WM compared to IgM-MGUS (Figure A). While exons (hyper: 1686; hypo: 1378), CpG islands (hyper: 2128; hypo: 82), and 5'UTR (hyper: 854; hypo: 246) regions had a significantly greater number of hypermethylated DMRs (all comparisons, p<0.001) (Figure A). Next, promotor methylation-based pathway analysis revealed differential methylation of multiple cellular pathways important in WM pathogenesis (Figure B). These included pathways involved in intracellular signaling and the regulation of cellular proliferation, such as the STAT3 and CXCR4 pathways which were hypomethylated, and the PTEN signaling pathway which was hypermethylated. Of interest, we have previously demonstrated the transcription factor, STAT3, to have an increased expression in WM and to be implicated in IgM secretion (Elsawa, 2011). Next, multiple cytokine signaling pathways were observed to be hypomethylated in WM compared to IgM-MGUS including members of the interleukin (IL) family (IL-1, IL-8, IL-15, IL-23), VEGF, and TGF-β. Of relevance, previous studies have demonstrated the elevation of IL-8, IL-15, and increased expression of angiogenic cytokines, including VEGF, in patients with WM (Lewicki, 2010; Anagnostopoulos, 2007). Furthermore, our group has also shown miRNA-based epigenetic regulation of IL-8 and IL-15 in WM compared to IgM-MGUS (Chohan, 2023). Lastly, several pathways critical in the regulation of the cell structure and the extracellular matrix (ECM) were found to be hypomethylated including the FGF, actin cytoskeleton, PAK, and FAK signaling pathways. Of note, the FGF/FGFR axis has been demonstrated to be dysregulated in WM, and its targeting has been shown to halt WM cell growth (Sacco, 2021). Conclusions: This study demonstrates differential DNA methylation of key genomic regions in WM compared to IgM-MGUS. Promotor methylation-based analysis reveals hypomethylation of several important cellular pathways involved in cytokine signaling, cell structure/ECM, and intracellular signaling. These results underscore the influence of epigenetics in the IgM-gammopathy disease spectrum and suggest the potential role of aberrant DNA methylation underlying the malignant progression from IgM-MGUS to WM.

Reprogramming of cellular metabolism is a hallmark of cancer leading to improved cellular fitness and providing a selective advantage to malignant cells. Recently, we found that follicular lymphoma (FL) patients with poor prognosis display increased transcriptional and protein expression of IRF4 (IRF4 high), altered immune signaling and an immunosuppressive tumor microenvironment (TME). Furthermore, high expression of IRF4 rewired B cell metabolism towards glycolysis ( Mondello P et al, Blood 2022;140 Suppl 1:168-169). However, it's unknown whether this metabolic reprogramming may be responsible in part for the impairment of immune cells in the TME, which also rely on glucose for their proliferation and function. Using RNA-seq on 88 FL patients, we found a negative correlation between IRF4-regulated genes associated with glycolysis and tumor infiltration by major immune cell subsets in IRF4 high compared to IRF4 low tumors ( Fig 1A), suggesting that IRF4-driven tumor glycolysis may reinforce an immune suppressed phenotype. Especially, we observed that ALDOA and GPI genes were inversely correlated with expression of CD4 + T cells (r=0.34, p=0.003; and r=0.38, p=0.015, respectively) and T reg cells (r=0.3, p=0.05; and r=0.35, p=0.027, respectively) in IRF4 high but not in IRF4 low tumors. As radioactive glucose is the cornerstone of PET assessment, we investigated the correlation between baseline SUV values and IRF4 expression. Remarkably, we found significantly higher levels of FDG uptake in FL IRF4 high compared to IRF4 low cases (p= 0.003), supporting the glycolytic features of Iymphoma expressing IRF4. To define the metabolic activity and function of lymphoma cells with different IRF4 status, we performed seahorse assay in TMD8 and SUDHL2 cells (lymphoma cells with IRF4 high expression) transfected with siIRF4 or siCtr for 72 hours. In line with the IRF4-dependence on glycolysis, TMD8 transfected with siCtr had a low baseline glycolytic ECAR, while the same value doubled after silencing IRF4. Notably, after adding oligomycin, which stimulates glycolysis, TMD8 control cells showed a lower glycolytic capability compared with those deprived of IRF4, probably due to the already maximal addiction to glycolysis ( Fig 1B left). We then investigated the glycolytic demand of germinal center (GC) B cells differently expressing IRF4. Using irf4 fl/fl mice ( Klein et al, Nat Immunol 2006) crossed with Cγ 1cre animals ( Cγ 1cre; irf4 fl/fl) to induce conditional deletion of irf4 in GC B cells, we tested ex-vivo sorted GC B cells from Cγ 1cre; irf4 fl/fl, Cγ 1cre; irf4 fl/+ and WT mice immunized for 10 days. This time point is critical for studying IRF4 as it corresponds to the GC reaction peak when GC B cells express elevated IRF4 levels promoting terminal differentiation to plasma cells. As expected, GC B cells minimally relied on glycolysis and showed a low ability to respond to oligomycin in WT mice ( Weiser et al, Nat Immunol 2020). However, GC B cells with partial or complete loss of irf4 demonstrated a superior ability to response to increased glycolytic demand ( Fig 1B right), further supporting the conclusion that IRF4 influences B cell dependency on glycolysis. As the malignant cell metabolism can alter nutrient availability in the TME, we explored if lymphoma cells differently expressing IRF4 may change glucose levels in the media and in turn impact T cells activity. We found low glucose and high lactate levels in the media of cell lines with IRF4 high expression, while the opposite was observed for those that were IRF4 low. We then cocultured primary human CD4 + T cells with TMD8 cells transfected with either siIRF4 or siCtr for 3 days at three different concentrations of D-glucose: high (22.2 mM, Gluc hi), normal (5.5 mM, Gluc nor) or absent (0 mM, Gluc nil). In line with our prior findings, coculture of B cells in which IRF4 was knocked out showed a significant decrease in abundance of T reg cells and an increase in T FH cells compared to control in presence of Gluc hi or Gluc nor. In contrast, there was no difference in the overall amount of either T cell subtype irrespective of IRF4 status with Gluc nil, supporting the prerequisite of microenvironmental glucose as primary fuel for T cell survival and function. In conclusion, IRF4 rewires the metabolism in B cells with profound functional implications on the TME. Our data suggest that glucose metabolism represents a functional vulnerability that might be exploited therapeutically.

Introduction: Non-Follicular Low-Grade B-Cell Lymphomas (LGBCL) are a subset of lymphomas that share overlapping clinical and histologic features. While most cases of LGBCL have an indolent course, some experience aggressive disease with no definitive biomarkers to identify aggressive cases at diagnosis. Significant advances have been made in the molecular classification of other lymphomas, yet molecular profiling and identification of mechanisms driving aggressive disease in LGBCL remain an unmet need. We previously used a multi-omic approach to characterize the molecular and immune landscapes of LGBCL tumors and identified a transcriptomic signature associated with cell proliferation and inferior overall survival (OS) in two independent cohorts (HR 7.82; 95% CI 2.4-25.4; p < 0.001 & HR 10.07; 95% CI 2.00-50.61; p = 0.005) and cases that transformed to DLBCL (Hopper et al., 2023). Here, we identify the oncoprotein DEK as a novel transcriptional regulator of this signature and use a functional genomic approach to further interrogate the role of DEK in B-cell lymphoma. Methods: Tumors from 64 newly-diagnosed/untreated LGBCL cases enrolled in the University of Iowa/Mayo Clinic Molecular Epidemiology Resource were included in this study (SMZL = 48, NMZL = 6, LPL = 5, B-NOS = 3, EMZL = 2). Tumor RNA-seq and paired tumor-normal WES were used to assess DEK expression and identify copy number alterations (CNAs), respectively. DEK deficient (KO) cell line models were generated using CRIPSR/Cas9. DEK expression was assessed by western blot (WB) and immunofluorescence (IF) using standard techniques. Proliferation was assessed by MTT assay. Results: To identify transcriptional regulators of our previously identified signature, we used the tool MAGIC to mine publicly available ChIP-seq data and identified DEK, an oncoprotein associated with tumorigenesis and poor prognosis in several cancers, as a key regulator of genes in our signature (p = 0.008). RNA-seq indicated that high levels of DEK expression associated with inferior OS in LGBCL (HR 2.99; 95% CI 1.21-7.36; p = 0.02). Additionally, we identified DEK as a target of copy number gains/amps in LGBCL (7% of cases), which corresponded to significantly higher DEK expression (p = 0.04). The association of DEK with aggressive lymphoma was confirmed when we assessed its expression in public cell line RNA-seq data and observed the highest expression in lines derived from aggressive lymphomas, which we further validated at the protein level in a panel of B-cell lymphoma cell lines by WB and IF confocal microscopy. Additionally, we detected nuclear localization of DEK, consistent with its role as a transcriptional regulator. As a model of DEK overexpression for functional characterization, we used the SUDHL6 lymphoma cell line, which has an amplification of DEK at 6p22.3 and expresses high levels of DEK protein. Using CRISPR technology, we generated DEK deficient SUDHL6 and tested the impact of loss of DEK expression. DEK KO cells proliferated significantly slower than WT SUDHL6 cells in vitro (p < 0.0001)(Panel A), indicating DEK plays a role in regulating proliferative capacity in lymphoma. To identify mechanisms responsible for reduced proliferation in DEK KOs, we assessed baseline protein expression levels of apoptosis proteins in WT versus KO cells. We found near complete abrogation of anti-apoptotic protein Bcl-2 and reduction of Bcl-xL expression in DEK KO cells (Panel B). These results indicate DEK may contribute to tumor proliferative capacity by regulating susceptibility to apoptosis. Conclusion: DEK has been implicated in regulation of gene expression, but its precise role has remained elusive, with little known about its role in lymphoma. Using an integrative molecular and functional genomics approach, we identified DEK as a novel oncoprotein in LGBCL that is upregulated in cases with inferior clinical outcome and is regulated by copy number gains/amps. DEK plays a role in regulating tumor proliferative capacity by altering susceptibility to apoptosis through regulation of Bcl-2 and Bcl-xL. Collectively, this data suggests high-risk LGBCL tumors may enrich for cells with DEK overexpression or CNAs as a mechanism driving oncogene addiction, highlighting DEK, and its downstream targets, as potential therapeutic targets in a subset of high-risk LGBCL.

Background Diffuse large B cell lymphoma (DLBCL) is the most common aggressive lymphoid malignancy in adults. Though standard immunochemotherapy regimens can result in clinical remission and cure in a majority of patients, approximately 30% of patients are primary treatment resistant or eventually relapse (rrDLBCL), and their prognosis is very poor. Recent molecular classification by large scale genomic and transcriptomic profiling of newly diagnosed DLBCL tumors has resulted in a deeper understanding of disease drivers, however, the molecular heterogeneity of rrDLBCL is yet to be fully explored. Therefore, in this study, we used an unsupervised approach to better define transcriptional and genetic programs in rrDLBCL samples hypothesizing that molecular stratification within this high-risk group may lead to a better understanding of this disease, which remains an unmet need, and a more personalized therapeutic approach. Methods Data used in this study included RNA (n=143) and whole exome (n=126) sequencing data from available FFPE tumor samples at the time of a relapse (any line of treatment, r1-r10 relapse timepoints included in analysis, one per patient), consented to the Molecular Epidemiology Resource (n=61), banked in the Mayo Lymphoma Biobank (n=50), or consented to the CC-122-ST-001 clinical trial (n=32, NCT01421524). Unsupervised clustering was performed on protein coding gene expression values using the non-negative matrix factorization (NMF) approach. NMF was carried out by performing 200 runs on cluster sizes from 2 to 6. Cell of origin (COO) was determined using the method described by Reddy et al. The tumor microenvironment was analyzed using CIBERSORTx and Lymphoma Microenvironment Classification (LME). Genetic classification was done using LymphGen. Results In order to identify unique transcriptional programs in rrDLBCL, we implemented NMF and consensus clustering to define an ideal quantity of stable patient clusters. We identified 4 rrDLBCL patient clusters (rrC1-rrC4) as the most stable solution, based on consensus clustering selection criteria of iterative NMF runs. While relapse timepoint was not associated with individual clusters, we did observe significant differences in tumor characteristics between the clusters. COO classification found that rrC1 and rrC4 were enriched for GCB-DLBCL while rrC3 was enriched for ABC-DLBCL classification (P < .001). To explore the biologic programs in each cluster, we used pathFindR, which leverages protein-protein interaction networks to identify disease related pathways, rrC1 and rrC4 were defined by pathways involved in cell cycle and mismatch repair; oxidative phosphorylation in rrC2; and MAPK and NF-kB in rrC3. Next, LME classification was done to define patterns in the tumor microenvironment and we found a significant difference between groups (P < .001), with rrC2 and rrC4 tumors enriched for a depleted TME, while rrC1 and rrC3 had a more immune rich or inflammatory TME. This was supported by CIBERSORTx analysis identifying significant differences (P > .001) in abundance of CD8 T cells, memory CD4 T cells, and M1 macrophages between clusters, with rrC4 having the lowest abundance. We then explored genetic patterns in each cluster. Gene level mutation enrichment analysis showed BCL7A mutation exclusively in C4 (6/6, P = .03). The clusters were not associated with LymphGen classification. Lastly, our recently published high risk signature classification that is associated with early clinical failure and is characterized by metabolic dysregulation, a depleted TME, T53 alterations, and poor outcome, was significantly different between the groups (P = .03) with enrichment for high-risk cases in rrC4. In summary, we show for the first time that rrDLBCL patients can be classified into four gene expression clusters that are associated with distinct pathway, TME, and genetic programs. These clusters should now be tested to learn if they can help select patients for newer therapies for rrDLBCL such as CAR-T and bispecific antibodies.

Introduction Lymphoid malignancy (LM) is a heterogenous neoplasm with respect to biology, aggressiveness, and treatment with over 100 subtypes, including Hodgkin lymphoma (HL), non-Hodgkin lymphoma (NHL), and multiple myeloma (MM). Genome-wide association studies have identified common variants associated with risk of specific LM subtypes, but less is known about the contribution of rare inherited pathogenic variants (PV) in the genetic architecture of LM risk. Rare PV have been shown to contribute to the genetic architecture of solid tumor cancer risk, many of which have been found in genes that are important to the DNA-damage repair (DDR) pathway (e.g., BRCA1, BRCA1, ATM). Here, we investigated the prevalence of PV in cancer predisposition genes regularly included on multi-gene panel testing based on NCCN guidelines and correlated with risk of LM overall and by common subtypes. Methods In this association study, cases were newly diagnosed lymphomas (excluding chronic lymphocytic leukemia) from Mayo Clinic who were enrolled between 2002 and 2015 in the Lymphoma SPORE Molecular Epidemiology Resource (MER) and MM from Mayo Clinic enrolled primarily between 1998 and 2022 in a clinical registry. Controls were from the Mayo Clinic Biobank who were enrolled between 2009 and 2016, from which we excluded individuals with a history of hematologic malignancy. All participants provided a blood sample for whole exome sequencing, performed by Regeneron on an Illumina NovaSeq panel (mean coverage of 48X). Variants were called using GATK v4 and variant annotation was performed using BioR. All loss-of-function variants (i.e., nonsense, frameshift, and consensus splice sites) and pathogenic missense in 19 cancer predisposition genes with a minor allele frequency < 0.5% were included in the analyses. Logistic regression was used to estimate odds ratio (OR) and 95% confidence intervals (CI) for the association between mutation status by gene (burden test) and risk of LM overall, and by subtype that had more than 5 mutation carriers. All analyses were adjusted for age (at diagnosis for cases and at enrollment for controls) and sex. Results A total of 6990 cases and 42432 controls were included. Median age was 63 years for both cases and controls (case range 18-94 years, control range 18-99 years). Males accounted for 58.5% (n=4090) of the cases and 41.7% (n=17762) of the controls. MM accounted for 31% (n=2138) of the cases, followed by diffuse large B-cell lymphoma (DLBCL, n=1146) and follicular lymphoma (FL, n=1132) each accounting for 16% of cases. CHEK2 (1.0%), ATM (0.4%), BRCA2 (0.4%), and BRCA1 (0.3%) had the most PV carriers in controls; these frequencies are in the range of published control populations. A total of 1816 (3.7%) individuals had a PV across the 19 genes, with 4.7% of cases and 3.5% of controls carrying any PV (OR=1.34, 95% CI: 1.18-1.51). ATM (OR=1.86, 95% CI: 1.36-2.49), CHEK2 (OR=1.74, 95% CI: 1.42-2.13) and TP53 (OR=9.07, 95% CI: 4.51-18.87) were all significantly associated with increased risk of LM overall (Figure 1). When investigating subtypes (Table 1), CHEK2 (OR=1.93, 95% CI: 1.23-2.89) and TP53(OR=10.97, 95% CI: 3.06-31.43) were significantly associated with DLBCL. ATM (OR=2.22, 95% CI: 1.13-3.90) was significantly associated with FL. ATM (OR=1.97, 95% CI: 1.16-3.12), CHEK2 (OR=1.75, 95% CI: 1.24-2.41) and TP53 (OR=7.67, 95% CI: 2.43-20.72) were significantly associated with MM and ATM (OR=8.62, 95% CI: 4.47-15.12), NBN (OR=6.94, 95% CI: 2.41-15.76) and TP53 (OR=38.30, 95% CI: 10.27-116.69) were significantly associated with mantle cell lymphoma (MCL). When subset to LM cases overall, there was no evidence of enrichment for young onset (age <60 years), male sex, or family history of hematologic malignancy between PV carriers and non-PV carriers. However, FL cases with an ATM PV tended to be less than 60 years (OR=3.21, P=0.09) compared to non- ATM PV carriers. Similarly, MM cases with a TP53 PV tended to be less than 60 years of age (OR=7.39, P=0.07) compared non-carriers. Discussion In this large case-control study, PV in established cancer predisposition genes were associated with increased risk of LM overall, demonstrating that rare inherited variants may play an important etiologic role. This study also suggests that PV in cancer predisposition genes may be subtype specific. These results will help inform LM risk for individuals who undergo genetic testing of cancer predisposition genes.

Background: Tumor mutational burden (TMB), defined as the number of nonsynonymous mutations per megabase (Mb) in the coding regions of a tumor genome, has been associated with improved response to immune checkpoint blockade treatments and more broadly with overall survival (OS) in many cancers. Limited prior studies have found that TMB is higher in diffuse large B-cell lymphoma (DLBCL) compared to many other cancers. However, only two studies have evaluated TMB and prognosis in newly diagnosed DLBCL patients. The prognostic role of TMB in diffuse large B-cell lymphoma (DLBCL) remains unclear as prior studies have been limited to panel sequencing of cancer genes and/or lacked matched germline-tumor samples for filtering. Methods: We characterized TMB from whole exome sequencing (WES) in newly diagnosed DLBCL patients with matched diagnostic tumor-normal samples prior to treatment with standard immunochemotherapy (IC). Tumor DNA was extracted from formalin-fixed, paraffin embedded tissue sections and germline DNA from peripheral blood. We evaluated the association between TMB and event-free (EFS) and OS using Cox regression adjusted for the international prognostic index (IPI) and other factors including cell of origin (COO), MYC double hit (DH) with BLC2 or BCL6 by FISH, and TP53 mutation status. EFS and OS were defined as time from diagnosis to earliest of treatment failure (EFS only), death, or last follow-up; EFS at 24 months (EFS24) was also modeled as a marker of early aggressive DLBCL. An optimal high/low TMB cutpoint was determined based on EFS using maximally selected rank statistics; sensitivity analyses used an 20/80 percentile to define the threshold. We also evaluated the association of driver tumor mutational load (driverTML) with EFS and OS, defined as the total number of mutations in 292 DLBCL driver genes. Results: In the analysis cohort of 394 DLBCL patients, the median age at diagnosis was 65 years (interquartile range: 56-73), 43% were female, 62.2% had IPI 0-2, and 57.4% germinal center B-cell COO. MYC was DH in 7.5% and TP53 was mutated in 16.2% of patients. Through a median follow-up of 7.5 years, there were 141 events and 122 deaths. EFS24 was achieved in 75.1% of the cohort; 5-year EFS was 69% (95% CI 64-74%) and 5-year OS was 75% (95% CI 71-80%). Median TMB in our cohort of DLBCL patients was 3.6 mutations/Mb, which was the fifth highest compared with 32 cancers in The Cancer Genome Atlas. As a continuous variable, increasing TMB (mutation/Mb) was associated with superior EFS (HR=0.89, 95% CI 0.81-0.98) and OS (HR=0.89, 95% CI 0.81-0.99) after adjustment for IPI. Spline analyses confirmed linear associations of TMB with EFS and OS. The optimal modeled cutpoint based on EFS was 4.5 mutations/Mb, with 31% classified as high TMB (>4.5). No significant ( P >.05) differences were observed between high and low TMB patients with clinical (i.e., age, sex, IPI category) or molecular (cell of origin, MYC DH, TP53 mutated) factors. In Cox regression, high-TMB was significantly protective for EFS and for OS compared to low-TMB after adjustment for each clinical factor ( Figure 1), with simultaneous adjustment for all these factors associated with superior EFS (HR=0.52, 95% CI 0.33-0.84) and OS (HR=0.59, 95% CI 0.36-1.00). TMB (high/low) combined with IPI (0-2, 3-5) was significantly ( P =.001) associated with EFS in Kaplan-Meier analysis ( Figure 2). Among patients with IPI 3-5, high TMB was significantly associated with superior EFS (HR=0.47, 95% CI 0.27-0.81) and OS (HR=0.57, 95% CI 0.33-0.99). Among patients with IPI 0-2, we also observed longer EFS (HR=0.62, 95% CI 0.34-1.11) and OS (HR=0.62, 95% CI 0.32-1.20) in high TMB patients, although the associations were not statistically significant ( P >.05). In sensitivity analyses, a 20/80 percentile TMB threshold found similar results for EFS (HR=0.64, 95% CI 0.41-1.03) and OS (HR=0.69, 95% CI 0.42-1.12) as in our main analyses. TMB and driverTML were significantly correlated ( R=0.65, P <10 -16) and we found similar effect sizes for EFS (HR=0.45, 95% CI 0.22-0.91) and OS (HR=0.55, 95% 0.27-1.13) as in our main analyses. Conclusions: In a large and well-defined cohort of IC treated DLBCL patients, we found DLBCL patients with higher TMB had superior EFS and OS after adjustment for other clinical and molecular prognostic factors, and the prognostic association for TMB was robust to threshold and gene set.

Abstract The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.

Importance Bleeding is the most common cause of preventable death after trauma. Objective To determine the effectiveness of resuscitative endovascular balloon occlusion of the aorta (REBOA) when used in the emergency department along with standard care vs standard care alone on mortality in trauma patients with exsanguinating hemorrhage. Design, Setting, and Participants Pragmatic, bayesian, randomized clinical trial conducted at 16 major trauma centers in the UK. Patients aged 16 years or older with exsanguinating hemorrhage were enrolled between October 2017 and March 2022 and followed up for 90 days. Intervention Patients were randomly assigned (1:1 allocation) to a strategy that included REBOA and standard care (n = 46) or standard care alone (n = 44). Main Outcomes and Measures The primary outcome was all-cause mortality at 90 days. Ten secondary outcomes included mortality at 6 months, while in the hospital, and within 24 hours, 6 hours, or 3 hours; the need for definitive hemorrhage control procedures; time to commencement of definitive hemorrhage control procedures; complications; length of stay; blood product use; and cause of death. Results Of the 90 patients (median age, 41 years [IQR, 31-59 years]; 62 [69%] were male; and the median Injury Severity Score was 41 [IQR, 29-50]) randomized, 89 were included in the primary outcome analysis because 1 patient in the standard care alone group declined to provide consent for continued participation and data collection 4 days after enrollment. At 90 days, 25 of 46 patients (54%) had experienced all-cause mortality in the REBOA and standard care group vs 18 of 43 patients (42%) in the standard care alone group (odds ratio [OR], 1.58 [95% credible interval, 0.72-3.52]; posterior probability of an OR >1 [indicating increased odds of death with REBOA], 86.9%). Among the 10 secondary outcomes, the ORs for mortality and the posterior probabilities of an OR greater than 1 for 6-month, in-hospital, and 24-, 6-, or 3-hour mortality were all increased in the REBOA and standard care group, and the ORs were increased with earlier mortality end points. There were more deaths due to bleeding in the REBOA and standard care group (8 of 25 patients [32%]) than in standard care alone group (3 of 18 patients [17%]), and most occurred within 24 hours. Conclusions and Relevance In trauma patients with exsanguinating hemorrhage, a strategy of REBOA and standard care in the emergency department does not reduce, and may increase, mortality compared with standard care alone. Trial Registration isrctn.org Identifier: ISRCTN16184981

Abstract Lithium is the gold standard treatment for bipolar disorder (BD). However, its mechanism of action is incompletely understood, and prediction of treatment outcomes is limited. In our previous multi-omics study of the Pharmacogenomics of Bipolar Disorder (PGBD) sample combining transcriptomic and genomic data, we found that focal adhesion, the extracellular matrix (ECM), and PI3K-Akt signaling networks were associated with response to lithium. In this study, we replicated the results of our previous study using network propagation methods in a genome-wide association study of an independent sample of 2,039 patients from the International Consortium on Lithium Genetics (ConLiGen) study. We identified functional enrichment in focal adhesion and PI3K-Akt pathways, but we did not find an association with the ECM pathway. Our results suggest that deficits in the neuronal growth cone and PI3K-Akt signaling, but not in ECM proteins, may influence response to lithium in BD.