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NPM1 mutations (NPM1+) are found in about 30% of adult AML. While NPM1+ are generally associated with favorable risk, prior studies have shown that NPM1+ older patients, defined as ≥ 65 years, have significantly worse outcomes. The updated European Leukemia Net 2022 (ELN22) now classifies NPM1+ AML patients with FLT3-ITD (ITD+) and MDS-associated mutations (MDSm+) as adverse-risk, while NPM1+/ITD-/MDSm+ patients remain favorable-risk unless they have an adverse-risk karyotype. A recent report (Chan et al., Blood Adv 2024) found MDSm+ conferred worse OS only in patients ≥ 60 years; however another report (Othman et al., Blood 2024) did not find MDSm+ were significantly associated with adverse outcomes in a larger cohort of NPM1+ patients. To further assess this question, we examined the mutational landscape in a large cohort of pre-treatment, diagnostic specimens from 456 patients enrolled in SWOG clinical trials (SWOG-9031, SWOG-9333, S0106, and S0112) and 126 patients from the UW/FHCC Hematopoietic Repository (Total N=582). Patients were treated with curative intent as per standard practice or clinical trials. Out of the 582 patients, 191 were NPM1+ (33%), of which 148 patients had cytogenetic and long-term outcome data. Kaplan-Meier and cumulative incidence estimates were estimated. C-statistics and AUC for Cox, cause-specific and logistic regression models for overall survival (OS), event-free survival (EFS), time to relapse (TTR, calculated in n=106 who achieved complete remission [CR], death in remission analyzed as a competing risk) and CR were calculated for age, ELN17, and ELN22, as well as ELN17 and ELN22 adjusting for age. ELN17 favorable-risk patients reclassified as adverse-risk (N=5 of 108) demonstrated significantly shorter EFS (HR=2.50, p=0.05) and CR rate (OR=0.20, p=0.024), although with similar OS (HR 1.30, p=0.66) and TTR (HR=1.20, p=0.86). ELN17 favorable-risk patients reclassified as ELN22 intermediate-risk (N=23 of 108) did not show a significant difference in OS (HR 1.47, p=0.20), EFS (HR 1.35, p=0.12), CR rate (OR 0.75, p=0.46), or TTR (HR=1.64, p=0.11) compared to ELN22 favorable-risk. For ELN17 intermediate-risk (N=40), 5 patients were reclassified as ELN22 adverse-risk (12.5%) and had significantly shorter EFS (HR 2.89, p=0.037) compared to ELN22 intermediate risk, although there was not a significant difference in CR rate (OR 0.31, p=0.23) or OS (HR 0.81, p=0.67). We next compared the prognostic significance of age relative to ELN risk classification. Age was associated with higher C-statistics (OS=0.65, TTR=0.57 and EFS=0.60) and AUC (CR=0.64) compared to both ELN17 (OS=0.56, TTR=0.54, EFS=0.54, CR=0.54) or ELN22 (OS=0.57, TTR=0.57, EFS=0.57, CR=0.58). The combination of age and ELN22 resulted in the highest C-statistics for OS, TTR, and EFS (OS=0.68, TTR=0.60, EFS=0.64). To further assess the impact of MDSm on risk-stratification of NPM1+ AML, we performed multivariable analyses adjusting for FLT3-ITD, MDSm, and age in patients without adverse-risk disease. These analyses showed MDSm did not have a significant association with OS when controlling for age or FLT3-ITD. We also compared ELN22 favorable-risk patients with and without MDSm (MDSm+ N=18; MDSm- N=61). Compared with the MDSm-, there was a trend toward an inferior OS (HR 1.85, p=0.054) for MDSm+ patients but not EFS (HR 1.38, p=0.3) or TTR (HR 1.2, p=0.64). When stratified by age (<65, N=53 vs. ≥65, N=26), MDSm+ in younger patients did not show a difference for OS (HR 0.96, p=0.96), EFS (HR 0.63, p=0.53) or TTR (HR 0.4, p=0.37). Older ELN22 favorable-risk patients (≥65) had similarly poor OS (MDSm- HR 3.33 and MDSm+ 3.27, respectively, p=0.001 for both) and EFS (MDSm- HR 3.11 and MDSm+ 2.39, p=0.001 and 0.01) compared to younger MDSm- patients. In our study, the ELN22 guidelines incrementally improve risk stratification for EFS in NPM1+ patients. However, age remains the most prognostic factor, and patients ≥ 65 years have similar adverse clinical outcomes, whether classified as favorable risk by ELN17, ELN22, or incorporating MDSm. Moreover, MDSm do not confer a worse prognosis in NPM1+ patients when accounting for age or FLT3-ITD status. These findings differ from a recent report by Chan et al. and are in keeping with the report by Othman et al. Therefore, further understanding of the underlying biology of age and its effect on NPM1+ AML is needed to improve outcomes for older patients.

Background: Flow cytometry (FC) is widely utilized for the identification of minimal residual disease (MRD) in B acute lymphoblastic leukemia (BALL), providing an essential laboratory indicator for prognostic assessment and therapeutic management. Unfortunately, the high level of expertise required, long manual analysis time, and complexity of FC data files limit the offering of this testing to selected reference laboratories. Methods: We developed an artificial intelligence (AI) pipeline to automatically enhance unaltered FC files, minimizing the expertise and manual analysis time needed to detect BALL-MRD on a single-tube 10-color panel (CD10, CD19, CD20, CD22, CD24, CD34, CD38, CD45, CD58, CD66c). Raw FC files corresponding to peripheral blood or bone marrow specimens from 171 BALL MRD positive cases (MRD < 5%, median = 0.11%, 107 patients), and 89 MRD-negative cases (63 patients) were processed through our CCADDAS (Clustering and Classification of All events, Dimensionality reduction, Downsampling and Aberrancy Scaling) pipeline on a cloud environment (Google Vertex AI). Automated processing steps included elimination of acquisition errors (FlowCut), state-of-the-art clustering (PARC), dimensionality reduction (UMAP), cluster-based anomaly detection compared to negative controls (24 bone marrow aspirates and 7 peripheral bloods), and cluster-informed downsampling with preservation of low-event subsets. In addition, a deep neural network (DNN) trained on expert-defined subpopulations from negative controls was included for automatic gating of normal subsets (Tensorflow v2). AI-enhanced data files were analyzed using a general purpose flow cytometry analysis software (Kaluza v 3.5, Beckman Coulter), and %MRD estimates compared to reported results based on expert analysis of original FC files. Results: Cluster-informed downsampling reduced the number of cells per case to be manually analyzed from 1.2 million to 155,884 cells on average (87% cellularity reduction); resulting in a smaller FC data file (from 64.5 MB to 13.7 MB; 79% data reduction). Importantly, low-level MRD events were adequately preserved after downsampling (median 100% retention for both MRD<0.01% and MRD 0.01-1%). True number of events were accurately estimated on gated subsets using an “upsampling factor” parameter [observed # events x mean (upsampling factor)]. Gating of normal subsets was completely automated using a DNN classifier parameter. Identification of MRD was aided by an AI-generated “aberrancy scale” parameter that discriminated BALL (median: 991) from both benign mononuclear cells (median: 486, p < 0.0001) and background B-lymphoid cells (median: 379, p < 0.0001), with a performance (AUC: 0.98 and 0.94 for mononuclear and B-lymphoid cells, respectively) superior to CD20 (0.54 and 0.69), CD38 (0.60 and 0.81), CD45 (0.75 and 0.74), CD58 (0.52 and 0.89), CD66c (0.56 and 0.72) and all other antigens studied. The use of AI-enhanced files reduced manual analysis time from 14.16 minutes (SD = 3.7) to 1.05 minutes (SD = 0.6) per case, on average (p < 0.0001) (93% reduction). BALL MRD was detected in all positive cases above clinically relevant threshold (≥0.01%, as recommended by NCCN guidelines), and in 28 of 32 (88%) cases below this threshold, with excellent quantitative correlation with conventional analysis on original FC files (linear regression slope = 1.18, intercept = 0.04%, R squared = 0.92, P < 0.0001). Conclusion: We introduce a largely unsupervised AI pipeline that transforms raw BALL-MRD FC data into a markedly smaller, AI-annotated and software-agnostic FC file, including comparison to normal controls. This CCADDAS pipeline simplifies and accelerates detection of BALL-MRD in clinical diagnostics, reducing number of cells analyzed by 87% and manual analysis time by 93%, without impacting test performance. Moreover, CCADDAS can be utilized for any labroatory-developed BALL-MRD assay, and its small-sized export is compatible with any clinical FC software, computing platform and analysis strategy. Adoption of CCADDAS is likely to facilitate the implementation of BALL-MRD FC analysis by more clinical laboratories.

Background: Anaplastic large cell lymphomas represent a group of T-cell lymphomas that have long been broadly classified by the presence or absence of ALK fusion proteins (ALK+ or ALK−, respectively). However, recent discovery of extensive additional molecular heterogeneity has both promoted refinement of disease classification and uncovered new opportunities for biomarker-guided targeted therapy. In addition to recognition of a new genetic subtype, ALK− ALCL with DUSP22 rearrangement (R), a new molecular classification has been proposed based on the recent discovery of 2 overarching molecular types of ALCL, defined by the presence (Type I) or absence (Type II) of a gene expression signature highly enriched for JAK-STAT3 activation. BCL2 is a mitochondrial membrane protein that functions to inhibit the actions of pro-apoptotic proteins such as BAX and BAK and can be targeted clinically by BCL2 small molecule inhibitors. BCL2 has been reported to be expressed more frequently in ALK− ALCL than in ALK+ ALCL, but its expression in the context of current molecular findings is unknown. Methods: ALCLs were centrally reviewed and classified by current WHO/ICC criteria. BCL2 was performed using IHC and scored as positive if ≥30% of tumor cells showed staining. DUSP22-R was assessed by fluorescence in situ hybridization (FISH) using a breakapart probe set and a dual-color, dual-fusion (D) probe set for the most common DUSP22 translocation, t(6;7)(p25.3;q32.3). pSTAT3Y705 IHC, which is >90% accurate for Type I/II assignment based on gene expression, was performed and scored based on a previously validated cut-off of ≥30%. Results: We evaluated 217 ALCLs (148M/69F; age: mean, 57 y; range, 5-93 y), including 55 systemic ALK+ ALCLs, 127 systemic ALK− ALCLs, and 35 primary cutaneous (pc) ALCLs. Overall, 86/217 cases were positive for BCL2 (40%). BCL2 was positive in 67/127 ALK− ALCLs (53%) and 15/35 pcALCLs (43%; P=NS), but in only 4/51 ALK+ (7%; P<0.0001). DUSP22-R was present in 90 cases (63 ALK− ALCL and 27 pcALCL). BCL2 was positive in 57/90 cases with DUSP22-R (63%) compared to 29/127 without DUSP22-R (23%; P<0.0001). Among 85 cases with DUSP22-R and informative D-FISH testing, BCL2 was positive in 24/36 cases with t(6;7)(p25.3;q32.3) (67%) and 31/49 cases without t(6;7)(p25.3;q32.3) (63%; P=NS). Among 213 cases with informative pSTAT3Y705 staining, BCL2 was positive in 13/88 pSTAT3+ cases (Type I, 15%) and 70/125 pSTAT3− cases (Type II, 56%; P<0.0001). Conclusions: We identified a complex pattern of BCL2 expression across ALCLs that strongly correlates with a recently introduced molecular classification based on gene expression profiling. While our findings are in keeping with the previously reported low expression in ALK+ ALCL, we found that the higher expression in both systemic ALK− ALCL and pcALCL is particularly associated with DUSP22-R and the absence of pSTAT3Y705 expression (Type II ALCLs). The discovery of 2 overarching types of ALCL associated with the presence or absence JAK-STAT3 pathway activation represents a new conceptual framework for classifying ALCL and understanding its therapeutic vulnerabilities. Of note, previous data demonstrated that Type II ALCLs are enriched for epigenetic alterations and particularly overexpress the histone methyltransferase EZH2. Correlation between BCL2 expression and EZH2 expression also is observed in other lymphomas such as diffuse large B-cell lymphoma, where dual targeting with EZH2 and BCL2 inhibitors has been proposed. Thus, our findings provide further rationale for stratifying Type I and Type II ALCLs and identify BCL2 as a differentially expressed oncoprotein that might represent a target for precision therapy approaches.

Background: Anaplastic large cell lymphomas (ALCLs) represent a heterogeneous group of T-cell lymphomas that currently are classified by the presence or absence of ALK tyrosine kinase (TK) fusion genes (ALK+ or ALK−) and clinical presentation (systemic, cutaneous, or breast implant-associated). Two overarching molecular types of ALCL recently were discovered, defined by the presence (Type I) or absence (Type II) of a gene expression signature highly enriched for JAK-STAT3 activation. Fusions involving non-ALK TK genes occur in some ALK− cases, but the fusion landscape of ALCL remains incompletely characterized. Methods: Expert consensus pathology review was conducted in the Lymphoma/Leukemia Molecular Profiling Project (LLMPP). RNAseq was performed and fusions were identified using FusionCatcher. Here, we focused on recurrent in-frame coding fusions. Previously unreported fusions were validated by RT-PCR. Type I/II was assigned using a previously validated gene expression-based model. Genes with adjusted (adj) P<0.05 were considered differentially expressed. Overall survival (OS) was assessed for systemic ALCL, when available. Results: We evaluated 379 ALCLs (229M/150F; mean age, 56 y). Of 199 candidate fusions (excluding reciprocal events), 28 were recurrent and passed quality metrics. At least 1 of these 28 fusions was present in 150 cases (40%). ALK fusions were present in 106 ALK+ ALCLs (all Type I; P<0.0001). Of these, NPM1::ALK was seen in 68/106 (64%). Alternate partners (X::ALK) included ATIC (N=24) and CTLC, COL1A2, MSN, MYH9, RNF213, SATB1, TFH, TPM3, and TRAF1 (1-3 cases each). X::ALK was associated with older age (mean, 52 y) than NPM1::ALK (32 y; P<0.0001) and showed relative overexpression of 49 genes, including multiple activators of small GTPases such as CGNL1 (FC, 5.6; Padj=2.4×10-6), SRGAP1, ALS2, DOCK1, and NCKAP1. ALK expression was similar in cases with NPM1::ALK and X::ALK and there was no significant difference in OS. Non-ALK TK fusions were seen in 17/273 ALK− cases (6%), including TYK2 (N=9); JAK2 (N=6); and ROS1 (N=2). All were Type I ALCLs (P<0.0001). ALK− cases with non-ALK TK fusions overexpressed 38 genes compared to ALK− cases without TK fusions, including cytokines involved in the IL17 signaling pathway such as CXCL6 (FC, 12.5; Padj=4.2×10-3), CXCL1, and CSF3. We then assessed the relationship between ALK− cases with non-ALK TK fusions and ALK+ cases by separately comparing each subset to ALK− cases without TK fusions. FC values for ALK− cases with non-ALK TK fusions were significantly correlated with FC values for ALK+ ALCLs over the set of expressed genes (R=0.54; P<0.0001). At a median follow-up of 32 months, 0/4 systemic ALCL patients with non-ALK TK fusions had died, while 30% of those with ALK− ALCL without TK fusions had died, but this was not statistically significant (P=0.26). The other 13 ALK− cases with non-ALK TK fusions were either localized ALCLs or did not have outcome data available. Of 20 TP63 fusions, 17 (85%) were TBL1XR1::TP63 and 3 were X::TP63. As previously reported, TP63 fusions were associated with poor OS (median OS, 10 months; not reached for other ALK− ALCLs; P=0.007). Four ALCLs had a novel NCL::UBTF fusion involving genes encoding the nucleolar proteins nucleolin and nucleolar (upstream binding) transcription factor-1. Four cases each had PARG::BMS1 and TNK1::GPS2 (the latter seen only in Type I ALCLs). Conclusions: This large consortium-based analysis of coding fusions further elucidates the molecular distinction between Type I and Type II ALCLs. Both ALK and non-ALK TK fusions were seen exclusively in Type I ALCLs, which we have shown previously are enriched for JAK-STAT3 pathway genes and correlate strongly with pSTAT3Y705 positivity by immunohistochemistry. These fusions were not identified in Type II ALCLs, which previous data suggest are associated with epigenetic alterations. ALK+ ALCLs with NPM1::ALK and X::ALK showed differences in gene expression, suggesting biological differences. Furthermore, ALCLs with non-ALK TK fusions shared gene expression features with ALK+ ALCL. Clinical fusion detection could enhance molecular classification of Type I and Type II ALCLs and may guide precision therapy.

Introduction CD19-directed CAR T-cell therapies are an important option for the management of relapsed/refractory (R/R) DLBCL. Studies suggest that ~50% of pts receiving CAR T-cell therapies will relapse within 6 months, with an estimated overall survival (OS) of only 5 months. Additionally, 25-35% of pts do not receive subsequent treatment, indicating a high unmet need. CD19 loss is a potential mechanism of resistance to CAR T-cell therapies; thus, treatments that target alternate B-cell antigens are an attractive option. Here, we report for the first time the primary analysis of the ELM-1 expansion cohort investigating the efficacy and safety of odronextamab, a CD20×CD3 bispecific antibody, in pts with R/R DLBCL progressing after CAR T-cell therapy (NCT02290951). Methods Pts aged ≥18 with DLBCL and disease progression after CAR T-cell therapy received intravenous odronextamab weekly in 21-day (D) cycles during Cycles (C) 1-4, with steroid prophylaxis and step-up dosing in C1 (described previously), followed by 160 mg on D 1, 8, and 15 of C2-4. After C4, odronextamab maintenance treatment continued at 320 mg Q2W until disease progression or unacceptable toxicity. Pts with complete response (CR) for ≥9 months transitioned to Q4W dosing. All pts received infection prophylaxis. The primary analysis was conducted when all pts had completed ≥9 months of follow-up. Primary endpoint was objective response rate (ORR), as assessed by independent central review (ICR) according to the Lugano classification. Key secondary endpoints included duration of response (DOR), progression-free survival (PFS), and OS. Exploratory endpoints included immune biomarker assessment. Results As of January 22, 2024, 60 pts were treated and evaluable for efficacy and safety, with median follow-up of 16.2 months. Prior CAR T-cell therapies were axi-cel (50%), tisa-cel (12%), liso-cel (10%), and not specified (28%). Median time since prior CAR T-cell therapy was 6.5 months (range 1.2-46.2). Median age was 63 years (range 27-82; aged ≥75 years: 10%), and 65% were male. Pts had received a median of 3 prior lines of therapies (range 2-9); 77% of pts were refractory to their last therapy, 77% were double refractory, and 72% were refractory to prior CAR T-cell therapy. 48% of pts relapsed within 90 D of CAR T-cell therapy, and 67% relapsed within 180 D. Of the 20 pts with available tumor biopsies at baseline, 10 were CD19(-) and only 2 were CD20(-). 92% of pts completed C1 and 52% completed ≥4 C of treatment. The median duration of treatment was 12 weeks (range 0.7-154.1). The ORR and CR rate by ICR were 48% and 32%, respectively, and were consistent across pts with high-risk features and across prior CAR T products. ORR in pts relapsing in ≤90 D was 21% (6/29) and in pts relapsing in ≤180 D was 33% (13/40). ORR was observed even in pts with low CD20. Median DOR was 14.8 months (95% CI 2.8-not estimable [NE]) and median duration of CR was not reached (NR) (95% CI 3.3-NE); the probability of maintaining CR for 24 months was 68%. Median PFS was 4.8 months (95% CI 2.6-5.4) and median OS was 10.2 months (95% CI 4.6-15.8). Median PFS and OS in complete responders was NR. T-cell counts increased at Week 14 and demonstrated a functional phenotype (CD69 expression and TEMRA count), which was confirmed by ex vivo stimulation. T-cell expansion was observed regardless of CAR T product or time from CAR T administration. The most common TEAE was CRS (any grade [G]: 48%; G1: 25%; G2: 23%; G≥3: 0%). G≥3 TEAEs occurred in 77% of pts (most common: neutropenia 17%, anemia 13%, and neutrophil and white blood cell count decrease 10% each). Five pts (8%) permanently discontinued odronextamab due to AEs (COVID-19, pneumonia, encephalopathy, epilepsy, and gait disturbance [n=1 each]). There was one treatment-related death (COVID-19 pneumonia). No cases of ICANS were reported. G≥3 infections occurred in 12 pts (20%); 2 were due to COVID-19. No cases of tumor flare or TLS were reported. Conclusions ELM-1 is the only study to prospectively evaluate efficacy and safety of a CD20×CD3 bispecific antibody in pts with R/R DLBCL progressing after CAR T-cell therapy. Odronextamab monotherapy was associated with encouraging efficacy and generally manageable safety in a setting with limited treatment options and historically dismal outcomes. Efficacy was observed even in pts relapsing in ≤90 D, supporting the potential of odronextamab as an accessible, off-the-shelf option in the post-CAR T setting.

Introduction: Cilta-cel is approved in the US and EU for the treatment (tx) of patients (pts) with len-refractory MM after ≥1 line based on the randomized, phase 3 CARTITUDE-4 trial (NCT04181827). At the first interim analysis (15.9-month [mo] median follow-up), cilta-cel significantly improved progression-free survival (PFS) vs SoC (hazard ratio [HR], 0.26 [protocol-specified weighted analysis]; P<0.0001). At the second interim analysis, overall survival (OS) was significantly improved with cilta-cel vs SoC (HR, 0.55; P=0.0009). MRD negativity is a prognostic marker of prolonged survival outcomes for pts with MM. We report MRD negativity, including overall and sustained MRD negativity, overall MRD-negative complete response or better (≥CR), and MRD-negative ≥CR at mo 12, from the prespecified second interim analysis of CARTITUDE-4. Methods: Eligibility criteria were previously described. Pts were assigned 1:1 to cilta-cel or SoC (pomalidomide, bortezomib, and dexamethasone [PVd]/daratumumab, pomalidomide, and dexamethasone [DPd]). Pts in the cilta-cel arm underwent apheresis, received bridging therapy (PVd/DPd), and then a single cilta-cel infusion (target dose, 0.75×106 CAR+ viable T cells/kg) 5-7 days (d) after the start of lymphodepletion. PFS was the primary endpoint; ≥CR rate, overall response rate, overall MRD-negativity rate, and OS were key secondary endpoints. MRD was assessed centrally via next-generation sequencing (clonoSEQ v2.0; Adaptive Biotechnologies). MRD was evaluated at d 56 post infusion in the cilta-cel arm; and in both arms at suspected ≥CR and at 6, 12, 18, and 24 mo post infusion (cilta-cel arm) or cycle 1 d 1 (SoC arm), as well as yearly until progression/start of subsequent therapy in pts in ≥CR. Sustained MRD negativity (10−5) was defined as confirmed MRD negativity ≥12 mo apart and without MRD positivity in between. Pts were evaluable for sustained MRD negativity if they achieved MRD negativity and had ≥1 evaluable MRD sample ≥12 mo after the first negative result or progressed/died/started subsequent tx <12 mo after the first negative result. Results: 419 pts were randomized (intent-to-treat [ITT] set; cilta-cel, n=208; SoC, n=211); 176 pts in the cilta-cel arm received cilta-cel as study tx. As of May 1, 2024, median follow-up for the study was 33.6 mo. At the 10−5 threshold, 145 pts in the cilta-cel arm and 103 in the SoC arm were evaluable for MRD. MRD-negativity rates (10−5) in the ITT set and the MRD-evaluable subset were higher with cilta-cel vs SoC (ITT, 62% vs 18%; MRD evaluable, 89% vs 38%; both P<0.0001). Across subgroups, cilta-cel vs SoC consistently increased overall MRD-negativity rates (10−5). In the ITT set, 48% of the cilta-cel arm achieved MRD negativity (10−5) by d 56, with the MRD-negative rate rising to 60% by 6 mo post cilta-cel infusion. Overall MRD-negativity rates at the 10−6 threshold in the ITT set were higher with cilta-cel vs SoC (57% vs 9%; P<0.0001). In the cilta-cel arm, 119 (57%) pts vs 26 (12%) in the SoC arm achieved overall MRD-negative (10−5) ≥CR (P<0.0001). At the 12-mo MRD assessment, 92 (44%) pts in the cilta-cel arm vs 17 (8%) in the SoC arm (P<0.0001) had MRD-negative (10−5) ≥CR. In pts with MRD-negative (10−5) ≥CR at mo 12, median PFS was not reached (NR; 95% CI, not estimable [NE]-NE) with cilta-cel and 37.8 mo (95% CI, 25.0-NE) with SoC; median OS was NR (95% CI, NE-NE) and NR (95% CI, 37.8 mo-NE), respectively. Sustained MRD-negativity rates (10−5) in the ITT set were 40% in the cilta-cel arm vs 6% in the SoC arm (P<0.0001); among 110 and 26 evaluable pts for sustained MRD, sustained MRD-negativity rates were 75% vs 50% (P=0.0159). Among the 176 pts who received cilta-cel as study tx, overall MRD negativity at 10−5 was achieved by 129 (73%) pts (89% of 145 evaluable pts). Conclusions: At 33.6-mo median follow-up in CARTITUDE-4, cilta-cel vs SoC significantly increased overall MRD-negativity rates >3-fold in the ITT set, with pts achieving MRD negativity rapidly post cilta-cel. The prognostic value of MRD-negative ≥CR at mo 12 was shown, as median PFS was >3 years in pts with MRD-negative ≥CR at mo 12, regardless of tx. These data further underscore the benefit of cilta-cel, which led to significant >3-fold increases vs SoC in rates of MRD-negative ≥CR at any time and at mo 12, and sustained MRD negativity. Our MRD data demonstrate higher rates of deep and sustained MRD negativity achieved with cilta-cel vs SoC in tx of len-refractory MM as early as first relapse.

Background: Flow cytometry (FC) is widely utilized for the identification of minimal residual disease (MRD) in chronic lymphocytic leukemia (CLL), providing an essential laboratory indicator for prognostic assessment and therapeutic management. Unfortunately, the high level of expertise required, long manual analysis time, and complexity of FC data files limit the offering of this testing to selected reference laboratories. Methods: We developed an artificial intelligence (AI) pipeline to automatically enhance unaltered FC files, minimizing the expertise and manual analysis time needed to detect CLL MRD on a single-tube 10-color panel (CD5, CD19, CD20, CD22, CD38, CD43, CD45, CD200, kappa and lambda). Raw FC files corresponding to peripheral blood or bone marrow specimens from 166 CLL MRD positive cases (MRD < 5%, median = 0.17%, 122 patients), and 61 MRD-negative cases (45 patients) were processed through our CCADDAS (Clustering and Classification of All events, Dimensionality reduction, Downsampling and Aberrancy Scaling) pipeline on a cloud environment (Google Vertex AI). Automated processing steps included elimination of acquisition errors (FlowCut), state-of-the-art clustering (PARC), dimensionality reduction (UMAP), cluster-based anomaly detection compared to negative controls (15 bone marrow aspirates and 14 peripheral bloods), and cluster-informed downsampling with preservation of low-event subsets. In addition, a deep neural network (DNN) trained on expert-defined subpopulations from negative controls was included for automatic gating of normal subsets (Tensorflow v2). AI-enhanced data files were analyzed using a general purpose flow cytometry analysis software (Kaluza v 3.5, Beckman Coulter), and %MRD estimates compared to reported results based on expert analysis of original FC files. Results: Cluster-informed downsampling reduced the number of cells per case to be manually analyzed from 1.1 million to 165,010 cells on average (85% cellularity reduction); resulting in a smaller FC data file (from 62.1 MB to 15.2 MB; 75% data reduction). Importantly, low-level MRD events were adequately preserved after downsampling (median 100% retention for both MRD<0.01% and MRD 0.01-1%). True number of events were accurately estimated on gated subsets using an “upsampling factor” parameter [observed # events x mean (upsampling factor)]. Gating of normal subsets was completely automated using a DNN classifier parameter. Rapid identification of suspected MRD was aided by an AI-generated “aberrancy scale” parameter that discriminated CLL MRD (mean ± 2SD: 969 ± 144) from background B-lymphoid cells (mean ± 2SD: 395 ± 401) (p<0.0001) (AUC = 0.98), with a performance superior to CD5 (AUC = 0.93), CD20 (AUC = 0.83), CD43 (AUC = 0.8) and CD200 (AUC = 0.7). Immunoglobulin light chain-positive CLL MRD was visually identified on a pre-calculated UMAP plot as a single-colored cluster, after dual coloring for kappa and lambda. The use of AI-enhanced files reduced manual analysis time from 11.6 minutes (SD = 4.5) to 0.8 minutes (SD = 0.4) per case, on average (p < 0.01) (93% reduction). CLL MRD was detected in all positive cases above clinically relevant threshold (≥0.01%, as recommended by NCCN guidelines), and in 17 of 21 (81%) cases below this threshold, with excellent quantitative correlation with conventional analysis on original FC files (linear regression slope = 1.02, intercept = 0.001%, R squared = 0.98, P < 0.0001). Conclusion: We introduce a largely unsupervised AI pipeline that transforms raw CLL-MRD FC data into a markedly smaller, AI-annotated and software-agnostic FC file, including comparison to normal controls. This CCADDAS pipeline simplifies and accelerates detection of CLL-MRD in clinical diagnostics, reducing number of cells analyzed by 85% and manual analysis time by 93%, without impacting test performance. Moreover, CCADDAS can be utilized for any laboratory-developed CLL-MRD assay, and its small-sized export is compatible with any clinical FC software, computing platform and analysis strategy. Adoption of CCADDAS is likely to facilitate the implementation of CLL MRD FC analysis by more clinical laboratories.

Background/Objectives: Respiratory syncytial virus (RSV) causes symptoms similar to a mild cold for adults, but in case of infants, it causes bronchitis and/or pneumonia, and in some cases, mortality. Mucosal immunity within the respiratory tract includes tissue-resident memory T (TRM) cells and tissue-resident memory B (BRM) cells, which provides rapid and efficient protection against RSV re-infection. Therefore, vaccine strategies should aim to generate mucosal immune responses. However, the interactions between RSV vaccines and mucosal immune responses within the respiratory tract are poorly understood. We evaluated a mucosal immune system following immunization by RSV vaccine with poly-sorbitol transporter (RSV-PST), a nanoparticle adjuvant. Methods: We intranasally immunized the RSV-PST and identified the systemic and mucosal immune responses. Furthermore, we challenged with RSV A2 strain after immunization and investigated the protective effects. Results: Consequently, antigen-specific CD8+ TRM cells were markedly elevated in the lung parenchyma, yet exhibited impaired cytokine expression. In contrast, humoral immunity, with systemic antibody production from serum, but not in the respiratory tract, was significantly increased by RSV-PST immunization. Interestingly, the production of respiratory mucosal antigen-specific IgG after RSV A2 challenge dramatically increased in the bronchoalveolar lavage fluid (BALF) of the RSV-PST immunized group in the presence of FTY720, and the lung-infected RSV titer was significantly lower in this group. Furthermore, after RSV A2 challenge, CD69+ IgG+ BRM cells were significantly increased in lung tissues in the RSV-PST group. Conclusions: The RSV-PST vaccine has protective effects against RSV infection by promoting both systemic and local humoral immunity rather than cellular immunity.

Abstract Aims Excessive alcohol use is associated with an increased risk of cognitive impairment. Since increased amyloid plaque burden exacerbates cognitive decline, we sought to assess the potential impact of alcohol use disorder (AUD) on cognition, memory, and amyloid burden corresponding with age. Methods We conducted the retrospective analysis with 6036 subjects, including 269 AUD+ subjects. A four-item CAGE (C—Cutting Down, A—Annoyance by Criticism, G—Guilty Feeling, E—Eye-openers) alcohol questionnaire was given during the recruitment to determine AUD in each participant. We assessed cognitive function, focusing on memory using neuropsychological testing. For 1038 participants, including 57 AUD+ subjects, we measured amyloid burden using the 11C Pittsburgh Compound B tracer-based positron emission tomography imaging. Results AUD+ was significantly associated with lower scores of cognition and memory function relative to AUD− individuals. No significant association was found with AUD and elevated brain amyloid under the age of 65. However, further analysis showed that those aged ≥65 showed greater odds for abnormal amyloid in AUD+ compared to AUD− participants. Conclusions Our results underscore AUD as a risk factor for cognitive decline and diminished memory, particularly in aging populations. The role of AUD in brain amyloid accumulation requires further study.

Abstract The phosphorylation of histone H2A.X into γH2A.X is a crucial early event in the DNA damage response, marking DNA damage sites and initiating repair processes. While ATM kinase is traditionally recognized as the primary mediator of H2A.X phosphorylation, our study identifies serine/threonine kinase 39 (STK39) as a novel enhancer of this critical signaling pathway. We demonstrate that after DNA damage, STK39 undergoes phosphorylation by the ATM kinase, facilitating its interaction with the Mre11-Rad50-Nbs1 complex and subsequent recruitment to chromatin. This recruitment enables STK39 to further phosphorylate H2A.X, thus amplifying γH2A.X production and promoting homologous recombination repair. Notably, we observe a significant upregulation of STK39 in pancreatic adenocarcinoma (PAAD) tissues, correlating with heightened resistance to PARPi therapy. Furthermore, we demonstrate the synergistic efficacy of combining STK39 inhibition with PARP inhibitors in suppressing and reversing PAAD growth. This study not only provides new insights into the molecular dynamics of H2A.X phosphorylation but also highlights the therapeutic potential of targeting STK39 to enhance PARPi sensitivity in PAAD (created with BioRender).