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Introduction Frontline therapy for pts with follicular lymphoma (FL) is not based on the molecular profile of the tumor. The PI3K pathway is central to FL biology, but the molecular profile of tumors most sensitive to PI3K inhibition (PI3Ki) is unknown. Copanlisib inhibits both PI3Kα and δ isoforms, and we hypothesized that most pts with treatment naïve FL will have PI3Ki-sensitive tumors and durable complete responses (CRs) may be achievable with short durations of targeted therapy. A gene expression profiling (GEP) predictor utilizes a combination of the expression of 45 selected informative genes and an additional 19 housekeeping genes to identify FL pts at high-risk for early progression within 24 months (Wright et al. ASH Annual Meeting2022). We also hypothesized that copanlisib would induce durable CRs in high-risk pts by the GEP assay. Here, we report a preliminary analysis of an ongoing “window of opportunity” study of copanlisib followed by response-adapted copanlisib and rituximab in treatment-naïve pts with FL (NCT03789240). Methods Pts with untreated grade 1-2, 3A FL, ≥stage 2 are eligible if systemic therapy is indicated: symptoms, progressive lymphadenopathy, or organ compromise. Prior radiation is permitted. Eligibility includes age ≥18 and adequate organ function unless due to lymphoma. HIV, CMV, Hep B or C, and autoimmune conditions are excluded. Pts first receive copanlisib 60mg on days 1, 8, and 15 of a 28-day “window of opportunity” to explore the activity of copanlisib. Following the window, pts receive 6 cycles of copanlisib 60mg on days 1, 8, and 15 of a 28-day cycle along with rituximab 375mg weekly x 4 then on day 1 of each cycle and response is assessed with FDG-PET and CT scans. Pts without response after C6 are taken off study and those with a partial response (PR) can continue for an additional 6 cycles and then stop therapy. Pts who achieve a CR after 6 cycles stop therapy. No maintenance is given. Supportive care includesPCP prophylaxis. The primary endpoint is the CR rate with secondary endpoints of safety, duration of CR, and PFS. Exploratory objectives include identification of a predictive signature of PI3Ki response and the response rate in high-risk pts identified by the GEP assay. Results Twenty-eight pts have enrolled. Median age was 56y (range, 24-80). Seventeen (61%) and 12 (43%) pts had high-risk FLIPI and FLIPI-2 scores ≥3, respectively; 6 (21%) pts had Grade 3A FL, and 17 (61%) pts met two or more GELF criteria. After copanlisib monotherapy in the window, 27 (96%) pts had tumor reductions by CT with a median change of -38% (-62% to +16%) (Figure 1). Among 22 pts who completed 6 cycles, 21 (96%) responded, including 9 (41%) pts with PR and 12 (55%) pts with CR. Of 9 pts with PR who received an additional 6 cycles of therapy, only 1 (11%) converted to a CR bringing the overall CR rate to 59% (13/22 pts). After a median follow-up of 25.8 months, the 2-year PFS was 43.2% (95% CI, 20.7-64.0%). The median duration of CR was 29.7 months (95% CI, 6.3 months - not estimable) with 8 (62%) CRs ongoing (Figure 2). Toxicity was evaluated in 28 pts across 220 cycles. Most common were rash (64%), mucositis (50%), and nausea/vomiting (50%), all G1-2 except 1 (4%) pt with G3 mucositis. The most common ≥G3 toxicities were neutropenia and infections in 5 (18%) pts each. ≥G3 neutropenia occurred in 10 (5%) cycles, responded to therapy interruption and/or growth factors, and did not require dose reduction. Two (8%) pts had copanlisib dose reduced, 1 (4%) each due to rash and mucositis. Therapy was prematurely discontinued in 5 pts: 3 due to COVID-19, 1 for hepatotoxicity that resolved, and 1 due to infusion reaction to rituximab. Nine of 24 (38%) pts were poor risk by the GEP assay. Complete responses were observed in 71%, 67%, and 57% of pts categorized as high-risk by FLIPI, FLIPI-2, and GEP assay, respectively. The CR rate was not affected by bulky disease (any LN >7cm; p=1.00), high-risk FLIPI (p=0.19), high-risk FLIPI-2 (p=0.67), poor-risk GEP (p=1.00), or metabolic tumor volume (p=0.33). Conclusion Nearly all pts with treatment naïve FL have PI3Ki-responsive tumors, and the combination of copanlisib with rituximab achieved a CR rate of 59% at the end of induction therapy. The median duration of CR is nearly 30 months, including ongoing remissions without maintenance. CRs were observed in pts deemed high-risk by the GEP assay and FLIPI score. The safety profile is excellent, with mostly G1-2 toxicities manageable with supportive care.

Introduction: During allogeneic hematopoietic cell transplantation (allo-HCT), conditioning regimens disrupt the epithelial barrier of the gastrointestinal (GI) tract and broad-spectrum antibiotics cause microbiome disruption characterized by low microbial diversity and high abundance of pathogens that can dominate gut communities. Microbiome disruption is associated with increased translocation of pathogenic bacteria leading to risk of bloodstream infections (BSIs) and acute graft-vs-host disease (aGvHD). Nonclinical studies have demonstrated that domination with pathogens leads to immune activation and subsequent GvHD following exposure to bacterial antigens in the gut-associated lymphoid tissue [ Jenq 2015 Biol Blood Marrow Transplant, Stein-Thoeringer 2019 Science, Peled 2020 NEJM]. SER-155, an oral investigational cultivated microbiome therapeutic comprised of 16 bacterial Firmicutes strains, was designed to engraft in the GI tract, improve GI barrier integrity, and restructure the GI microbiome by introducing bacteria with immunomodulatory and anti-inflammatory properties and driving a reduction in abundance of proinflammatory bacteria [ Halvorsen 2021 OFID, 2022 Bone Marrow Transplant]. Herein, we present preliminary clinical data through Day 100 from an open-label cohort of a Phase 1b study in patients undergoing allo-HCT (NCT04995653) and compare the incidence of GI pathogen domination in this cohort to a historical cohort. Methods: Study patients received two treatment courses of SER-155 (before conditioning and after neutrophil engraftment), each comprised of microbiome conditioning with 4 days oral vancomycin (to facilitate engraftment of SER-155 strains into the GI microbiome) followed by 10 days of oral SER-155. The primary endpoint was safety. Stool samples collected pre- and post-HCT were used to evaluate secondary endpoints, including engraftment of SER-155 strains (following each course of SER-155) and GI pathogen domination, defined as a relative abundance of a specific taxa at ≥30% ( Peled, 2020). Engraftment of SER-155 strains was assessed via strain-specific molecular probes (NanoString). Relative abundance of Enterobacteriaceae, Enterococcaceae, Streptococcaceae and Staphylococcaceae was assessed with whole metagenomic shotgun sequencing (WMS). A post-hoc comparison of GI domination was made with a historical cohort of 459 HCT patients with similar patient characteristics from one of the study sites (MSKCC) using stool samples profiled by 16S-amplicon sequencing. Results: Fifteen adults (median age 67 years; 54% male) were enrolled and 13 received study drug. SER-155 was generally well-tolerated. Following each course of SER-155, the majority of the 16 strains in SER-155 were detected in evaluable patients (median = 11 strains in 8 patients before conditioning; median = 12 strains in 7 patients after neutrophil engraftment).Between the day of transplant (Day 0) and Day 30, GI domination with potentially pathogenic bacteria occurred in only 1 of 9 (11%) evaluable patients (Figure 1). In contrast, in the historical comparator cohort, 64% of patients (294 of 459) had pathogen domination during the same timeframe, with domination by Enterococcus being most common. Conclusions: The observation of SER-155 strain engraftment before transplant and low incidence of GI pathogen domination after transplant through Day 30 are encouraging, although these preliminary data are limited by the small size of this open-label cohort of HCT patients. These observations are consistent with the design and preclinical evaluation of SER-155, supporting further development of this investigational cultivated microbiome therapeutic. Enrollment in the second double-blind, randomized, placebo-controlled cohort (approximately 60 participants) has initiated following a preplanned review by the Data Safety Monitoring Committee.

Introduction: Previously we demonstrated that HIV associated diffuse large B-cell lymphomas (DLBCL) of Germinal Center B-cell subtype (GCB) are more proliferative, with enhanced genomic stability and increased expression of DNA repair genes, particularly Fanconi anemia (FA) and homologous recombination (HR) genes compared to their HIV negative counterparts (Maguire et al, 2019). It is unknown if this signature is unique to the GCB subtype or if it is also observed in Activated B-cell (ABC) and Unclassified (UNC) DLBCL. Herein we validate our HIV associated GCB DLBCL findings in a larger independent cohort and report for the first time the aberrational profile of molecularly profiled HIV associated ABC DLBCL compared to their HIV negative counterparts respectively. Methods: An independent cohort of 353 DLBCL FFPE cases was assembled, including 125 HIV-DLBCL samples. All samples underwent hematopathological review to confirm diagnosis and determine tumor content. Samples with <60% tumor content were macro-dissected before they were RNA/DNA extracted. Cell of origin (COO) profiles were assessed using the Lymph3Cx molecular assay which can distinguish primary mediastinal B-cell lymphoma (PMBL) like from GCB, ABC and UNC DLBCL (Mottok et al 2018). Copy number (CN) analyses were performed on samples with sufficient DNA (41 HIV neg, 32 HIV pos) using the OncoScan FFPE assay kit and the resulting data analyzed using Chromosome Analysis Suite (ChAS) v4.1 and the multisample viewer module of Reproductive Health Research Analysis Software (RHAS) v1.1. Additional digital gene expression profiling was performed using the Pan-Cancer panel (NanoString, Seattle, WA) customized to include 9 BCL2 family genes for a total of 739 genes and analyzed using the NanoString nSolver Advanced Analysis module. Results: GCB, ABC, UNC and PMBL-like proportions were 50%, 30%, 14% and 6% in HIV unassociated (HIV neg) and 50%, 23%, 25% and 2% in HIV pos tumors respectively, confirming both cohorts were GCB-enriched by molecular profiling. PMBL-like cases were excluded from further analyses. OncoScan CN analyses showed that HIV pos DLBCL were significantly less aberrant than HIV neg DLBCL (median CN per sample 0.56 vs 1.01, p<0.001). HIV pos GCB tumors were significantly less aberrant than both HIV neg GCB tumors (median CN per sample 0.50 vs 1.11, p<0.001), and HIV pos ABC tumors (median CN per sample 0.50 vs 0.81, p=0.009). HIV pos ABC tumors were also less aberrant than HIV neg ABC tumors, however, the difference was not significant overall (median CN per sample 0.81 vs 1.03, p=0.162) or for gain/amplifications (median CN per sample 0.38 vs 0.42, p=0.814), but was borderline significant for loss/deletions (median CN per sample 0.31 vs 0.47, p=0.06). GEP analyses identified Cell Cycle and DNA repair as the top two upregulated pathways in HIV pos GCB tumors compared to HIV neg GCB tumors. In contrast, Cell Cycle and DNA repair were downregulated in HIV pos ABC tumors compared to HIV neg ABC tumors. However, one gene ( RBX1) was identified as significantly upregulated in both pathways in HIV pos GCB (adjusted p-value 0.0215) and HIV pos ABC (adjusted p-value 0.0046) tumors compared to their HIV neg counterparts. A second gene ( SKP1) was also significantly upregulated in HIV pos GCB (adjusted p-value 3.94E-5) and HIV pos ABC (adjusted p-value 0.0031) tumors. Together RBX1 and SKP1 encode the two principal functional subunits of the SKP1, CUL1, F-box protein (SCF) complex which is part of the ubiquitin proteasome system (UPS) and has been reported as essential for maintaining genome and chromosome stability (Thompson et al 2021). Conclusions: These results demonstrate that HIV pos DLBCL tumors are less aberrant than their HIV neg counterparts irrespective of COO subtype. However, the degree of genomic stability differs by COO, with HIV pos GCB tumors being more genomically stable than HIV pos ABC tumors. Although both have enhanced expression of UPS related SCF genes ( RBX1, SKP1), only HIV pos GCB tumors have enhanced expression of key DNA repair genes as well including Fanconi Anemia ( BRCA2/FANCD1, FANCL) and mismatch repair ( MSH2, MSH6) genes (adjusted p-values of ≤ 0.0263). Given the reported role of SCF in genomic stability maintenance, the HIV pos COO CN differences could be due to the contributions of both DNA repair and SCF mechanisms in HIV pos GCB and mainly SCF mechanisms in HIV pos ABC tumors.

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.

Background: cHL is one of the most curable forms of childhood cancer, but patients (pts) who have a SER to initial chemo are at high risk of relapse. The approaches currently used to treat pts with SER, such as dose intensification and radiotherapy (RT), can cause long-term toxicity.An unmet need remains to optimize treatment in pts with cHL and SER while minimizing long-term toxicity. The open-label, phase 2 KEYNOTE-667 study (NCT03407144) is being conducted to evaluate the efficacy and safety of pembro plus chemo in pediatric pts with cHL and SER to front-line chemo. Early analysis of pts in KEYNOTE-667 with high-risk cHL showed that pembro plus cyclophosphamide, vincristine, prednisone/prednisolone, and dacarbazine (COPDAC-28) consolidation had manageable safety and promising antitumor activity, with 66% of pts being spared RT. We present updated results for pts with high-risk cHL and SER. Methods: Pts eligible for the high-risk group were 3-25 y old and had newly diagnosed stage IIEB, IIIEA, IIIEB, IIIB, IVA, or IVB cHL, measurable disease per investigator assessment, and a Lansky Play-Performance Scale ≥50 (<16 y old) or a Karnofsky score ≥50 (≥16 y old). All pts received 2 cycles of induction therapy with vincristine, etoposide, prednisone/prednisolone, and doxorubicin (OEPA). After induction, response was assessed by PET/MRI/CT (early response assessment). Pts with rapid early response received nonstudy therapy. Pts with SER (Deauville score, 4 or 5) received consolidation with pembro 2 mg/kg up to 200 mg IV Q3W (3-17 y) or 200 mg IV Q3W (18-25 y) plus 4 cycles of COPDAC-28. Response was then assessed by PET/CT/MRI (late response assessment [LRA]). Pts with PET positivity at LRA (Deauville score, 4 or 5) received involved-site RT (28.8 Gy) to late PET-positive residua plus maintenance pembro Q3W for up to 17 cycles; pts with PET negativity received maintenance pembro Q3W for up to 17 cycles without RT. The primary end point was ORR by BICR per Cheson 2007 IWG criteria in pts with SER, determined at any time after starting pembro and before start of new anticancer therapy. Secondary end points included PET negativity after consolidation, EFS, OS, and safety. Results: This analysis included 62 pts with high-risk cHL and SER to frontline OEPA. The median time from enrollment to data cutoff (March 1, 2023) was 16.8 mo (range, 2.3-36.4). At the data cutoff, 34 pts (55%) had completed consolidation and maintenance therapy, 23 (37%) were ongoing on treatment, and 5 (8%) had discontinued because of adverse events (AEs; n = 1), progressive disease (n = 2), or withdrawal by pt (n = 2). Median age was 14 y (range, 5-22), 30 pts (48%) were male, 29 (47%) had bulky disease, and 41 (66%) had Ann Arbor stage IV disease. Pts had received a median of 17 administrations of pembro (range, 1-17); the median time on pembro was 11.0 mo (range, <0.1-11.8). Of 62 pts who received consolidation with pembro plus COPDAC-28, 53 (85%) had a LRA (9 are still in consolidation and have not reached the LRA time point), of whom 35 (66%) were PET negative by BICR (37 [70%] PET negative by investigator review). AEs regardless of causality that occurred during consolidation and maintenance were reported in 54 pts (87%), most commonly (≥15%) headache (n = 11; 18%), nausea (n = 11; 18%), cough (n = 10; 16%), COVID-19 (n = 10; 16%), and pyrexia (n = 10; 16%). Grade 3/4 AEs occurred in 21 pts (34%). 13 pts (21%) had a serious AE. No pts died because of AEs. Treatment-related AEs occurred in 38 pts (61%). Grade 3/4 treatment-related AEs occurred in 10 pts (16%). One pt (2%) discontinued treatment because of a treatment-related AE (grade 3 AST increased). Pembro-related AEs occurred in 23 (37%) pts; most commonly (≥5%) hypothyroidism (n = 4; 6%), nausea (n = 3; 5%), and ALT increased (n = 3; 5%). Three pts (5%) experienced grade 3/4 pembro-related AEs. One serious AE was related to pembro (abdominal pain). Six pts (10%) experienced immune-mediated AEs (hypothyroidism, n = 4 [2 grade 1, 2 grade 2]; colitis, n = 1 [grade 2]; pneumonitis, n = 1 [grade 2]). Conclusion: In this updated analysis, consolidation with pembro plus COPDAC-28 continued to have manageable safety and promising antitumor activity in pediatric pts with high-risk cHL and SER to front-line OEPA. Of pts with a LRA, 66% had a PET-negative response and were spared RT. These findings support the results of prior analyses and suggest that adding pembro to COPDAC-28 consolidation may augment responses in the high-risk cHL population.

Abstract Inactivating mutations of genes encoding the cohesin complex are common in a wide range of human cancers. STAG2 is the most commonly mutated cohesin complex subunit. Here we report the impact of stable correction of endogenous, naturally occurring STAG2 mutations on gene expression, 3D genome organization, chromatin loops, and Polycomb signaling in glioblastoma multiforme (GBM). In two GBM cell lines, correction of their STAG2 mutations significantly altered the expression of ~10% of all expressed genes. Virtually all the most highly regulated genes were negatively regulated by STAG2 (i.e., expressed higher in STAG2-mutant cells than in STAG2-corrected cells), and one of them – HEPH – was regulated by STAG2 in uncultured GBM tumors as well. While STAG2 correction had little effect on large scale features of 3D genome organization (A/B compartments, TADs), STAG2 correction did alter thousands of individual chromatin loops, some of which controlled the expression of adjacent genes. Loops specific to STAG2-mutant cells, which were regulated by STAG1-containing cohesin complexes, were very large, supporting prior findings that STAG1-containing cohesin complexes have greater loop extrusion processivity than STAG2-containing cohesin complexes and suggesting that long loops may be a general feature of STAG2-mutant cancers. Finally, STAG2-regulated chromatin loops were enriched for the H3K27me3 Polycomb epigenetic mark, revealing that inactivation of STAG2 can activate Polycomb signaling in GBM, identifying a potential target for therapeutic intervention in STAG2-mutant tumors. Together, these findings illuminate the landscape of STAG2-regulated genes, A/B compartments, chromatin loops, and pathways in GBM, providing important clues into the largely still unknown mechanism of STAG2 tumor suppression.

Introduction Pulmonary Embolism (PE) is a major cause of morbidity and mortality worldwide. Diagnosis is essential as untreated PE has a 30% mortality rate. Currently, computer tomography pulmonary angiogram (CTPA) is the standard of care to diagnose PE. However, this modality is expensive, exposes patients to radiation and increases risk of contrast induced nephropathy. Furthermore, only 5% of CTPAs performed are positive for PE signifying that many unnecessary tests are being performed. To address this gap, Pre-Test Probability (PTP) tools have been developed to safely exclude patients with low risk of PE and limit iatrogenic harm from over testing. Despite the tools being developed over 20 years ago, they are not widely available in electronic health record (EHR) systems and even when they are built in, uptake remains low. Methods Three large integrated health systems using Epic EHR systems across five states (MA, WI. MN, AZ, FL) built electronic PTP assessments into their EHR systems to safely exclude patients with low risk of PE and limit over testing. Two sites made the PTP optional, and one site made it a forced function (providers were forced to complete PTP prior to ordering CTPA unless a D-dimer result was present in the prior 48 hours). To assess the usability of these EHR tools, the team conducted a survey with ED clinicians across the three test sites. The survey was created to identify which PE risk stratification methods were employed by ED clinicians (EHR embedded PTP tool, apps on their phone, web applications like MDcalc, calculations from memory, or if they relied on clinical gestalt), and to identify barriers to using the EHR embedded PTP. Results Site 1 had 47 respondents, Site 2 had 94 respondents, and Site 3 had 16 respondents. Two key findings emerged from the survey (Figure 1). First, clinicians used a variety of risk stratification approaches and few of them documented their risk assessments in structured fields in the medical record. Second, clinical gestalt was the most used PE risk stratification approach, and this was true across all three test sites. In addition, the health system with a PTP prompt before clinicians could proceed with CTPA orders (Site 3) did not have a higher percentage of clinicians who reported using the EHR embedded tool than sites that did not prompt clinicians to fill in the PTP. Clinicians cited a variety of different barriers to using the EHR embedded PTP tool (Table 1). Conclusion While many clinicians indicated intermittent use of PTP criteria to aid in the diagnosis of PE, risk factors and scores from phone apps, web tools, and calculations from memory are unlikely to be documented in the patient's medical record, which means that they are not accessible to all members of the ED care team and cannot be used for electronic clinical quality measure. The biggest hurdles to increasing the use of structured PTP assessments in the ED setting to safely exclude PE and limit iatrogenic harm from over testing are (1) obtaining buy-in from ED clinicians that the PTP tools provide value over clinical gestalt, at least for a subset of patients who could most benefit, and (2) identifying best practices for incorporating PTP tools into EHR systems that make the tools easier to access and use 3) developing and implementing effective education and awareness programs around the PTP tools. This survey adds to the body of literature on PTP assessments in the ED setting and can help inform further research that is needed to continue to improve the diagnosis of PE. Funding: This project is funded in part by the Gordon and Betty Moore Foundation through Grant GBMF10778 to the American Society of Hematology.

Background: Venetoclax (Ven) in combination with hypomethylating agent (HMA) is the preferred treatment for elderly/unfit patients with newly-diagnosed acute myeloid leukemia (AML). Current prognostication for AML is based on European LeukemiaNet (ELN) genetic risk stratification which has limited applicability in patients treated with Ven-HMA ( Blood, 2022). Accordingly, our primary objective was to determine predictors of treatment response and survival outcomes in treatment-naïve AML patients receiving Ven-HMA. Methods: Our study population was recruited from Mayo Clinic (MN, FL, AZ), after institutional review board approval and based on documentation of newly-diagnosed AML treated with Ven-HMA outside of clinical trials between November 2018 and April 2023. Cytogenetic and molecular studies were performed by conventional karyotype, and next-generation sequencing, respectively. Response was assessed according to the 2022 ELN criteria Results: Patient characteristics A total of 301 newly-diagnosed AML patients (median age 73 years, 66% male, 62% de novo) received a median of 3 cycles (range 1-48) of azacitidine 75 mg/m2 days 1-7 ( n=100) or decitabine 20 mg/m2 days 1-5 ( n=201) with Ven. ELN 2022 cytogenetic risk included favorable (1%, n=4), intermediate (61%, n=184) or adverse (38%, n=113). Mutations involved TP53 in 77/301 (25%), ASXL1 in 54/279 (19%), RUNX1 in 54/296 (18%), NPM1 in 44/299 (15%), DNMT3A in 40/296 (13%), K/NRAS in 41/296 (14%), IDH1 in 20/301 (6%), IDH2 in 30/301 (10%), FLT3-ITD in 25/301 (8%) and DDX41 in 9/247 (4%) of informative cases. Predictors of response Complete remission, with (CR) or without (CRi), count recovery, was documented in 182 (60%) patients, including CR in 36% and measurable residual disease (MRD) negative by flow cytometry in 77% of 105 informative cases that achieved CR/CRi. Median time to CR/CRi was 1.4 months. In univariate analysis, CR/CRi was more likely to occur in the presence of NPM1 mutation, (86% vs. 56%; p < .01), IDH2 mutation, (80% vs. 58%; p = .02), DNMT3A mutation, (78% vs. 57%; p = .01), or DDX41 mutation, (100% vs. 58%; p <.01), and in the absence of adverse karyotype, (71% vs. 42%; p < .01), TP53 mutation, (67% vs. 40%; p <.01), FLT3-ITD mutation, (63% vs. 36%; p = .01), or RUNX1 mutation, (64% vs. 44%; p = .01); in multivariable analysis, adverse karyotype, ( p < .01), FLT3-ITD ( p < .01), RUNX1 ( p < .01), NPM1 ( p = .03), IDH2 ( p = .04), and DDX41, ( p = .01) mutations remained significant. Predictors of survival After a median follow-up of 8.5 months (range 0.5-56), 13.2 months for living patients, 174 (58%) deaths, 73 relapses (40% of those achieving CR/CRi), and 41 (14%; including 35 in CR/CRi) allogeneic hematopoietic stem cell tranplants (AHSCT) were documented. In univariate analysis of pre-treatment variables, thrombocytopenia < 100 x 10 9/l, (HR 1.5, 95% CI 1.03-2.2; p = .03), adverse karyotype, (HR 2.8, 95% CI 2.1-3.8; p < .01) TP53 mutation, (HR 2.5, 95% CI 1.8-3.5; p < .01), or absence of IDH2 mutation, (HR 3.5, 95% CI 1.7-7.6; p < .01) predicted inferior survival. Failure to achieve CR/CRi (HR 4.5, 95% CI 3.3-6.1; p < .01) was also associated with inferior survival and in multivariable analysis, failure to achieve CR/CRi, (HR 3.4, 95% CI 2.5-4.8; p< .01), presence of adverse karyotype, (HR 1.6, 95% CI 1.1-2.6; p = .02), TP53 mutation, (HR 1.6, 95% CI 1.002-2.4; p = .04), and absence of IDH2 mutation (HR 2.2, 95% CI 1.004-4.7; p = .04) were identified as risk factors for survival. Subsequent HR-weighted scoring resulted in three-tiered risk stratification: low (0-1 point; n= 130), intermediate (2-3 points; n= 105), and high (4-5 points; n= 66), with respective median survival (3-year rate) of 28.9 (48%), 9.6 (6%), and 3.1 (0%) months ( p < .01) (Figure). AHSCT had an independent favorable impact on survival (HR 0.3, 95% CI 0.1-0.6; p < .01), most apparent in low ( p =0.04), and intermediate ( p <.01), as opposed to high ( p = .06) risk. Conclusions: In the current single institutional series of Ven-HMA treated newly-diagnosed AML, response to Ven-HMA was the foremost predictor of survival. Additional risk factors for survival included adverse karyotype, presence of TP53 and absence of IDH2 mutations. These observations allowed for a three-tiered genetics-enhanced survival model with CR/CRi as a backbone, and also confirmed survival advantage from AHSCT, regardless of risk category.

Introduction Atrial fibrillation/flutter (Afib) contributes independently to the hematopoietic cell transplant comorbidity index (HCTCI) and is a known complication of allogeneic hematopoietic transplantation (AlloHCT) which has been linked to inferior outcomes, particularly in older patients. An electrocardiogram (ECG)-based artificial intelligence (AI) algorithm to predict Afib and cardiac contractile dysfunction risk has been validated (Lancet 394:861, 2019). We hypothesized that assessment of pre-transplant Afib risk using the AI algorithm may predict future development of Afib and help identify patients at higher risk for this complication. Methods 191 consecutive MDS patients undergoing AlloHCT with curative intent using myeloablative (28.3%) or reduced intensity conditioning (62.3%) in the Mayo Clinic Comprehensive Cancer Center (Rochester, Jacksonville, Phoenix) between November 2015 and January 2022 were included in this retrospective analysis. Primary outcome was Afib within 100 days and 365 days of transplantation, and we also assessed the outcomes of Death and Relapse. Revised IPSS (IPSS-R) and HCTCI scores were included for all patients. Elevated Afib risk score using the AI algorithm was calculated from pre-transplant ECG, performed during vital organ testing at AlloHCT clearance. In addition to left ventricular (LV) ejection fraction (EF) we evaluated detailed echocardiographic (Echo) parameters including LV volume and mass indices, left atrial volume index (LAVI), maximum trans mitral flow velocities during LV filling (E velocity) and during the diastolic phase due to atrial contraction (A velocity), the velocity of the annulus away from the apex during the protodiastolic phase (e' velocity), E/A and E/e' ratios, the presence of valvular disease, and right ventricular systolic pressure (RVSP). We performed an analysis using a Cox proportional hazards regression models (adjusting for age and sex) to evaluate associations with Afib risk, Relapse, and Overall Survival. Results The median age was 64 years (18-75), 58.6% were male, 6.9% non-white race, and 3.7% Hispanic/Latino ethnicity. Most patients had an HCTCI score of 0-3 (76.3%). GVHD prophylaxis was predominantly with calcineurin inhibitor-based regimes plus/minus methotrexate or mycophenolate mofetil (83.8%). 5.2% of patients had a history of Afib before transplant. Non-relapse mortality was 7.9% at 100 days, and 18.3% at 365 days. N=25 patients (13.1%) developed Afib within 100 days, and n=28 (14.7%) within 1-year after AlloHCT. On multivariable analysis, Age [Hazard Ratio (HR) 2.83, 95% Confidence Interval (CI) 1.36-5.89, p=0.0055] and Echo parameters [including LAVI (HR 2.10, CI 1.23-3.58, p=0.0067), E velocity (HR 3.28, CI 1.29-8.80, p=0.013), E/A ratio (HR 3.74, CI 1.75-7.97, p=0.0006), E/e ' ratio (HR 1.81, CI 1.07-3.07, p=0.026) and RVSP (HR 1.68, CI 1.04-2.70, p=0.032)] were significantly associated with Afib risk; elevated AI risk score was significant in the univariate model (HR 2.74, CI 1.15-6.53, p=0.023) but was borderline in the multivariate model (HR 2.34, CI 0.98-5.60, p=0.056) ( Table 1). Based on previous observations we evaluated the association of AI and Echo parameters with Relapse and Death after AlloHCT ( Table 1). Age (HR 1.32, CI 1.01-1.71, p=0.041) and IPSS-R (HR 1.20, CI 1.08-1.33, p=0.0009) were significantly associated with Death after AlloHCT, while HCTCI (HR 1.10, CI 1.00-1.22, p=0.052) and higher LV EF (HR 0.63, CI 0.38-1.06, p=0.084) were borderline. For Relapse, only IPSS-R (HR 1.52, CI 1.31-1.75, p=1.22E-08) was significant, while increase Echo E velocity was of borderline significance (HR 0.45, CI 0.20-1.02, p=0.057). Conclusions Afib is a common post-AlloHCT complication and is more frequent with increasing Age. Echo parameters (specifically E/A ratio) predict significantly higher Afib risk, while the baseline ECG AI algorithm was of borderline significance. The selection of patients with lower HCTCI scores, and the low background prevalence of Afib in this cohort (i.e., selection of patients with lower baseline cardiac morbidity) are possible explanations for non-significance of the AI algorithm. As anticipated Age and IPSS-R are the greatest predictors of Death after AlloHCT, although the suggestion of an association with baseline Echo parameters highlights the importance of identifying those who may benefit from preemptive Cardiology intervention.