Citations

Introduction: Patients requiring postcardiotomy veno-arterial extracorporeal membrane oxygenation (V-A-ECMO) have a high risk of early mortality. In this analysis, we evaluated whether any interinstitutional difference exists in the results of postcardiotomy V-A-ECMO. Methods: Studies on postcardiotomy V-A-ECMO were identified through a systematic review for individual patient data (IPD) meta-analysis. Analysis of interinstitutional results was performed using direct standardization, estimation of observed/expected in-hospital mortality ratio and propensity score matching. Results: Systematic review of the literature yielded 31 studies. Data from 10 studies on 1269 patients treated at 25 hospitals were available for the present analysis. In-hospital mortality was 66.7%. The relative risk of in-hospital mortality was significantly higher in six hospitals. Observed versus expected in-hospital mortality ratio showed that four hospitals were outliers with significantly increased mortality rates, and one hospital had significantly lower in-hospital mortality rate. Participating hospitals were classified as underperforming and overperforming hospitals if their observed/expected in-hospital mortality was higher or lower than 1.0, respectively. Among 395 propensity score matched pairs, the overperforming hospitals had significantly lower in-hospital mortality (60.3% vs 71.4%, p = 0.001) than underperforming hospitals. Low annual volume of postcardiotomy V-A-ECMO tended to be predictive of poor outcome only when adjusted for patients’ risk profile. Conclusions: In-hospital mortality after postcardiotomy V-A-ECMO differed significantly between participating hospitals. These findings suggest that in many centers there is room for improvement of the results of postcardiotomy V-A-ECMO.

Despite notable advancements in treating chronic lymphocytic leukemia (CLL) with targeted therapies, a significant clinical challenge arises as a subset of CLL patients progress and develop Richter's transformation (RT), an aggressive form of lymphoma. While immune cell dysfunction and T cell defects are well-known characteristics of CLL, the alterations in immune cell subsets during CLL progression to RT remain largely unexplored. While CLL patients typically fail to respond to anti-PD1 pembrolizumab, a positive response has been observed in 20-40% of RT patients. Previously, our study involving three RT patients (1 responder and 2 progressors) revealed enriched regulatory T cells (Tregs) in the responders compared to progressors after pembrolizumab treatment (Iyer et al, ASH2020). Hence, we propose that dynamic changes in T cell subsets at baseline may be associated with response to pembrolizumab treatment. To this end, we performed single-cell RNA sequencing on sorted T cells (CD3 +) from PBMCs derived from 20 patients (responders (RES), n=5; stable disease (SD), n=5; progressive disease/progressors (PD), n=10). A total of 408,627 cells were captured, and an average of 1,393 genes were detected per cell per patient. Using a graph-based clustering approach (Seurat V4.3), we identified 19 clusters based on marker genes and transcriptional signatures and identified enriched pathways using Enrichr analysis. Our results showed that RES had an increased Th17 subset at baseline compared to PD (15% vs. 5%, p=0.01) and reduced Th2 T cells compared to SD (p=0.004) and PD (p=0.05). The Th17/Th2 ratio was higher in RES at baseline (Th17/Th2 R: 1.24, PD: 0.21, SD: 0.25, p0.05) and post-pembrolizumab (Th17/Th2 RES: 1.31, PD: 0.34, SD: 0.30, p0.05). Furthermore, RES had an increased fraction of proliferating CD4 T cells at baseline (4% RES vs. 0.4% SD and 0.4% PD, p=0.02) and post-treatment (1.9% RES vs. 0.8% PD and 0.34% SD, p=0.02). Although we observed an increasing trend in the fraction of Tregs in the RES at baseline [(7.6% RES vs.6.1% PD and 6.5% SD, p=0.4; ns) and post-treatment (10.3% RES vs. 8.3% PD and 5.8% SD, p= 0.2; ns] compared to PD and SD, the difference was not significant. Similarly, among CD8+ T cells, we observed an increasing trend in CD8+ effector T cell fraction in the responders at baseline (29.8% RES vs. 21% PD and 15.6% SD, p=0.9, ns) and post-treatment (30.1% RES vs. 21.2% PD and 19.9% SD, p=0.44, ns). However, the difference was not statistically significant. These findings highlight the dynamic changes in T cell subsets associated with pembrolizumab response. In terms of transcriptional changes in Tregs following pembrolizumab treatment among RES, PD, and SD, we observed dynamic changes on a few Treg marker genes, including TIGIT and TOX. TIGIT and TOX expression drastically decreased after treatment in RES (p < 0.0001), while expression did not change in PD and SD. There were no significant changes in the expression of FOXP3 or CD27 in RES, PD, and SD before and after the treatment. In CD8 effector T cells, GZMK, GZMA (granzymes), and PRF1 (perforin) are marker genes. We observed higher expression of GZMK and GZMA in RES (p <0.0001) at the baseline compared to SD and PD. In the proliferating CD4 T cells, we observed increased expression of IDH2 (Isocitrate dehydrogenase 2-key enzyme of TCA cycle) in RES at the baseline, as compared to PD and SD, indicating increased TCA cycle activity in the proliferating T cells. Pathway analysis of T cell clusters using the Enrichr database showed TNFA-IFNG-MYC targets are enriched in CD8 + cells, IL2-STAT5 signaling is upregulated in Th17, TREGs, and E2F cell cycle associated pathways enriched in CD4 + proliferation cluster. In conclusion, our study unveils significant baseline differences in CD4 subsets between RES and PD/SD subjects and dynamic changes in T cell subsets and transcriptional signatures. These findings offer potential predictors of pembrolizumab response in RT, such as increased Th17/Th2 ratio, enriched proliferating CD4 + T cells, and elevated TIGIT, TOX gene expression in Tregs, GZMK, and GZMA in CD8 effector cells. This deeper understanding of the immunological landscape in RT patients sheds light on the interplay between T cell subsets and treatment response in RT, paving the way for personalized therapeutic strategies. Further validation and exploration can lead to improved outcomes and tailored interventions for RT.

Background ABBV-383 is an off-the-shelf, fully human B-cell maturation antigen (BMCA) X CD3 bispecific antibody with bivalent BCMA domain and low CD3 affinity that targets both BCMA-expressing multiple myeloma (MM) cells and CD3-expressing T cells. ABBV-383 has shown promising activity in an ongoing phase 1 study in patients (pts) with relapsed/refractory (RR) MM ( Voorhees et al. ASH 2022; Weisel et al. EHA 2023). Here, we report the safety and efficacy results from the ABBV-383 IV Q3W at 20 mg, 40 mg and 60 mg dose levels. Methods The phase 1 dose escalation (ESC) and expansion (EXP) study (NCT03933735) of ABBV-383 enrolled pts with RRMM with ≥3 prior lines of therapy (LOT), including a PI, IMiD, and an anti-CD38 mAb with no prior BCMA exposure. After exploring 14 dose levels in dose ESC, 3 dose levels were further explored in dose EXP including ABBV-383 Q3W IV 20 mg, 40 mg, and 60 mg, until disease progression/unacceptable toxicity. The primary objectives were to determine safety/tolerability, PK/PD, and RP2D. The secondary objective was to evaluate clinical activity. Responses were investigator assessed per IMWG 2016 criteria and adverse events (AEs) by CTCAE v5.0. Results As of 17May2023, 220 pts were treated with ABBV-383, 73 pts in the dose ESC and 147 in the dose EXP with 32 pts at 20 mg, 55 pts at 40 mg, and 61 pts at 60 mg Q3W. Median duration of follow up was 7.1 (0.8-36.9), 12.2 (1.3-34.4) and 24.2 (0.6-33.4) months (mo) in the 20 mg, 40 mg and 60 mg dose levels with 25%, 36% and 21% pts continuing treatment (tx), respectively. Median age for all pts was 68 yrs (range, 35-92 yrs), Median prior LOT was 5 (range, 3-23), and 80% were triple-class refractory. Baseline characteristics are summarized in Table 1 by dose level. Treatment-emergent (TE) AEs were reported in 97% (78% ≥G3) of pts at 20 mg, 100% (84% ≥G3) of pts at 40 mg, and 100% (82% ≥G3) of pts at 60 mg. CRS, the most common TEAE, occurred in 50% (34% G1/13% G2/3% G3) of pts at 20 mg, 71%(45% G1/25% G2/0% ≥G3) of pts at 40 mg, and 70%(51% G1/18% G2/2% G3) of pts at 60mg. At the 3 dose levels, CRS occurring after the first dose had median time to onset of 1 day (range, 1-2) and median duration was 2 days (range, 1-10). ICANS occurred in 3% (3% G1/0% ≥G2) of pts at 20 mg, 5% (2 % G1/4% G2/0% ≥G3) of pts at 40 mg, and 5% (3% G2/2% G3) of pts at 60 mg. No G4/5 CRS or ICANS occurred. Infections occurred in 56% (25% ≥G3) of pts at 20 mg, 71% (26% ≥G3) of pts at 40 mg, and 57% (34% ≥G3) of pts at 60 mg; most frequently (≥10%) reported were upper respiratory tract infections (15%, G3/4 1%), pneumonia (14%, G3/4 12%), urinary tract infections (12%, G3/4 3%), and COVID-19 (11%, G3/4 1%). At 20 mg, 40 mg, and 60 mg, the incidence of neutropenia was 41% (25% ≥G3), 42% (31% ≥G3), and 43% (34% ≥G3); anemia 50% (25% ≥G3), 55% (31% ≥G3), and 38% (13% ≥G3); thrombocytopenia 44% (28% ≥G3), 36% (16% ≥G3) and 26% (13% ≥G3), respectively. The tx discontinuation due to AEs was 6%, 11%, 10% in 20 mg, 40 mg and 60 mg, respectively, with no single PT reported in >2 pts. Clinical responses are shown in Figure 1. The median time to ≥VGPR was 0.7mo (0.7-5.8) at 20 mg, 1.0mo (0.7-10.4) at 40 mg, and 1.4 mo (0.7-7.8) at 60 mg. The median DOR was 9.7 mo (3.5-not reached (NR)) at 20 mg, and NR at 40 mg and 60 mg. The Kaplan-Meier (KM) estimates for DOR at 12 mo was 42% at 20 mg, 70% at 40 mg, 66% at 60 mg. The mPFS was 3.8 mo (2.4-7.9) at 20mg, 13.7mo (5.0-NR) at 40 mg, and 11.2 mo (4.8-NR) at 60 mg. The KM estimates for PFS at 12 mo was 19% at 20 mg, 51% at 40 mg, 49% at 60 mg. The median OS was 21.6 mo (7.9-NR) at 20 mg, and NR at 40 mg and 60 mg. ABBV-383 at 20 mg, 40 mg, and 60 mg led to rapid and transient production of key proinflammatory cytokines (IL-6, IL8, IFN-γ, and TNF-α), and reduction of soluble BCMA levels over time that were associated with disease response. ABBV-383 also promoted T-cell activation and proliferation with upregulation of CD69 and KI67 markers on peripheral CD4 and CD8 T cells in evaluable pts. Conclusions ABBV-383 showed a manageable safety profile at dosages of 20 mg, 40 mg, and 60 mg IV Q3W, with low incidence of TEAEs leading to discontinuation. Also, extended Q3W dosing interval with no step-up dosing schedule of ABBV-383 may provide enhanced convenience. However, 20 mg yielded numerically lower responses when compared to 40 mg and 60 mg. At 40 mg and 60 mg, ABBV-383 monotherapy yielded deep and durable responses with mPFS of 13.7 mo and 11.2 mo, and 12-month DOR of 70% and 66% (mDOR NR in pts with ≥CR), respectively. These results in heavily pretreated RRMM pts support further clinical evaluation.

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.