Safety and tolerability of AMG 330 in adults with relapsed/refractory AML: a phase 1a dose-escalation study.

AMG 330, a bispecific T-cell engager (BiTE®) that binds CD33 and CD3 on T cells facilitates T-cell-mediated cytotoxicity against CD33+ cells. This first-in-human, open-label, dose-escalation study evaluated the safety, pharmacokinetics, pharmacodynamics, and preliminary efficacy of AMG 330 in adults with relapsed/refractory acute myeloid leukemia (R/R AML). Amongst 77 patients treated with AMG 330 (0.5 µg/day-1.6 mg/day) on 14-day or 28-day cycles, maximum tolerated dose was not reached; median duration of treatment was 29 days. The most frequent treatment-related adverse events were cytokine release syndrome (CRS; 78%) and rash (30%); 10% of patients experienced grade 3/4 CRS. CRS was mitigated with stepwise dosing of AMG 330, prophylactic dexamethasone, and early treatment with tocilizumab. Among 60 evaluable patients, eight achieved complete remission or morphologic leukemia-free state; of the 52 non-responders, 37% had ≥50% reduction in AML bone marrow blasts. AMG 330 is a promising CD33-targeted therapeutic strategy for R/R AML.

Molecular subgroups of T-cell acute lymphoblastic leukemia in adults treated according to pediatric-based GMALL protocols.

In contrast to B-cell precursor acute lymphoblastic leukemia (ALL), molecular subgroups are less well defined in T-lineage ALL. Comprehensive studies on molecular T-ALL subgroups have been predominantly performed in pediatric ALL patients. Currently, molecular characteristics are rarely considered for risk stratification. Herein, we present a homogenously treated cohort of 230 adult T-ALL patients characterized on transcriptome, and partly on DNA methylation and gene mutation level in correlation with clinical outcome. We identified nine molecular subgroups based on aberrant oncogene expression correlating to four distinct DNA methylation patterns. The subgroup distribution differed from reported pediatric T-ALL cohorts with higher frequencies of prognostic unfavorable subgroups like HOXA or LYL1/LMO2. A small subset (3%) of HOXA adult T-ALL patients revealed restricted expression of posterior HOX genes with aberrant activation of lncRNA HOTTIP. With respect to outcome, TLX1 (n = 44) and NKX2-1 (n = 4) had an exceptionally favorable 3-year overall survival (3y-OS) of 94%. Within thymic T-ALL, the non TLX1 patients had an inferior but still good prognosis. To our knowledge this is the largest cohort of adult T-ALL patients characterized by transcriptome sequencing with meaningful clinical follow-up. Risk classification based on molecular subgroups might emerge and contribute to improvements in outcome.

Mutational and transcriptional landscape of pediatric B-cell precursor lymphoblastic lymphoma.

Pediatric B-cell precursor (BCP) lymphoblastic malignancies are neoplasms with manifestation either in bone marrow/blood (BCP acute lymphoblastic leukemia, BCP-ALL) or less common in extramedullary tissue (BCP lymphoblastic lymphoma, BCP-LBL). Although both presentations are similar in morphology and immunophenotype, molecular studies are virtually restricted to BCP-ALL so far. The lack of molecular studies on BCP-LBL is due to its rarity and the restriction to small, mostly formalin-fixed paraffin embedded (FFPE) tissues. Here we present the first comprehensive mutational and transcriptional analysis of what we consider the largest BCP-LBL cohort described to date (n=97). Whole exome sequencing indicates a mutational spectrum of BCP-LBL strikingly similar to that found in BCP-ALL. However, epigenetic modifiers were more frequently mutated in BCP-LBL, whereas BCP-ALL was more frequently affected by mutation in genes involved in B-cell development. Integrating copy number alterations, somatic mutations and gene expression by RNA-sequencing revealed that virtually all molecular subtypes originally defined in BCP-ALL are present in BCP-LBL too, with only 7% of lymphomas that were not assigned to a subtype. Similar to BCP-ALL, the most frequent subtypes of BCP-LBL were high hyperdiploidy and ETV6::RUNX1. Tyrosine kinase/cytokine-receptor rearrangements were detected in 7% of BCP-LBL. These results indicate that genetic subtypes can be identified in BCP-LBL using next-generation sequencing, even on FFPE tissue, and may be relevant to guide treatment.

Gemtuzumab ozogamicin plus midostaurin in combination with standard '7 + 3' induction therapy in newly diagnosed AML: Results from the SAL-MODULE phase I study.

We conducted a phase I trial in newly diagnosed acute myeloid leukaemia (AML) to investigate the combination of two novel targeted agents, gemtuzumab ozogamicin (GO) and midostaurin, with intensive chemotherapy in FLT3-mutated AML and CBF leukaemia. Three dose levels of midostaurin and one to three sequential doses of 3 mg/m2 GO in combination with '7 + 3' induction were evaluated. Based on safety findings in 12 patients, our results show that 3 mg/m2 GO on Days 1 + 4 and 100 mg midostaurin on Days 8-21 can be safely combined with IC in newly diagnosed AML.

Time from diagnosis to treatment has no impact on survival in newly diagnosed acute myeloid leukemia treated with venetoclax-based regimens.

In newly diagnosed acute myeloid leukemia, immediate initiation of treatment is standard of care. However, deferral of antileukemic therapy may be indicated to assess comorbidities or pre-therapeutic risk factors. We explored the impact of time from diagnosis to treatment on outcomes in newly diagnosed acute myeloid leukemia undergoing venetoclax-based therapy in two distinct cohorts. By querying the Study Alliance Leukemia database and the global health network TriNetX, we identified 138 and 717 patients respectively with an average age of 76 and 72 years who received venetoclax-based firstline therapy. When comparing patients who started treatment earlier or later than 10 days after initial diagnosis, no significant difference in median overall survival was observed - neither in the SAL cohort (7.7 vs. 9.6 months, p=.42) nor in the TriNetX cohort (7.5 vs. 7.2 months, p=.41). Similarly, severe infections, bleeding, and thromboembolic events were equally observed between early and later treatments, both in the overall patient groups and specific subgroups (age ≥75 years or leukocytes ≥20x109/L). This retrospective analysis indicates that delaying the start of venetoclax-based therapy in newly diagnosed acute myeloid leukemia might be a safe option for selected patients, provided that close clinical monitoring is performed.

Mimicking clinical trials with synthetic acute myeloid leukemia patients using generative artificial intelligence.

Clinical research relies on high-quality patient data, however, obtaining big data sets is costly and access to existing data is often hindered by privacy and regulatory concerns. Synthetic data generation holds the promise of effectively bypassing these boundaries allowing for simplified data accessibility and the prospect of synthetic control cohorts. We employed two different methodologies of generative artificial intelligence - CTAB-GAN+ and normalizing flows (NFlow) - to synthesize patient data derived from 1606 patients with acute myeloid leukemia, a heterogeneous hematological malignancy, that were treated within four multicenter clinical trials. Both generative models accurately captured distributions of demographic, laboratory, molecular and cytogenetic variables, as well as patient outcomes yielding high performance scores regarding fidelity and usability of both synthetic cohorts (n = 1606 each). Survival analysis demonstrated close resemblance of survival curves between original and synthetic cohorts. Inter-variable relationships were preserved in univariable outcome analysis enabling explorative analysis in our synthetic data. Additionally, training sample privacy is safeguarded mitigating possible patient re-identification, which we quantified using Hamming distances. We provide not only a proof-of-concept for synthetic data generation in multimodal clinical data for rare diseases, but also full public access to synthetic data sets to foster further research.

Approaches for bridging therapy prior to chimeric antigen receptor T cells for relapsed/refractory acute lymphoblastic B-lineage leukaemia in children and young adults.

The ongoing development of immunotherapies, including chimeric antigen receptor (CAR) T cells, has revolutionized cancer treatment. In paediatric relapsed/refractory B-lineage acute leukaemia antiCD19-CARs induced impressive initial response rates, with event-free survival plateauing at 30-50% in long-term follow-up data. During the interval between diagnosis of relapse or refractoriness and CAR T cell infusion, patients require a bridging therapy. To date, this therapy has consisted of highly variable approaches based on local experience. Here, in an European collaborative effort of paediatric and adult haematologists, we summarise current knowledge with the aim of establishing a guidance for bridging therapy. This includes treatment strategies for different patient subgroups, the advantages and disadvantages of low- and highintensity regimens, and the potential impact of bridging therapy on outcome after CAR T cell infusion. This guidance is a step towards a cross-institutional harmonization of bridging therapy, including personalized approaches. This will allow better comparability of clinical data and increase the level of evidence for the treatment of children and young adults with relapsed/refractory B-lineage ALL until CAR T cell infusion.

Prognostic impact of CEBPA mutational subgroups in adult AML.

Despite recent refinements in the diagnostic and prognostic assessment of CEBPA mutations in AML, several questions remain open, i.e. implications of different types of basic region leucin zipper (bZIP) mutations, the role of co-mutations and the allelic state. Using pooled primary data analysis on 1010 CEBPA-mutant adult AML patients, a comparison was performed taking into account the type of mutation (bZIP: either typical in-frame insertion/deletion (InDel) mutations (bZIPInDel), frameshift InDel or nonsense mutations inducing translational stop (bZIPSTOP) or single base-pair missense alterations (bZIPms), and transcription activation domain (TAD) mutations) and the allelic state (single (smCEBPA) vs. double mutant (dmCEBPA)). Only bZIPInDel patients had significantly higher rates of complete remission and longer relapse free and overall survival (OS) compared with all other CEBPA-mutant subgroups. Moreover, co-mutations in bZIPInDel patients (e.g. GATA2, FLT3, WT1 as well as ELN2022 adverse risk aberrations) had no independent impact on OS, whereas in non-bZIPInDel patients, grouping according to ELN2022 recommendations added significant prognostic information. In conclusion, these results demonstrate bZIPInDel mutations to be the major independent determinant of outcome in CEBPA-mutant AML, thereby refining current classifications according to WHO (including all dmCEBPA and smCEBPA bZIP) as well as ELN2022 and ICC recommendations (including CEBPA bZIPms).

Developmental trajectories and cooperating genomic events define molecular subtypes of BCR::ABL1-positive ALL.

ABSTRACT: Distinct diagnostic entities within BCR::ABL1-positive acute lymphoblastic leukemia (ALL) are currently defined by the International Consensus Classification of myeloid neoplasms and acute leukemias (ICC): "lymphoid only", with BCR::ABL1 observed exclusively in lymphatic precursors, vs "multilineage", where BCR::ABL1 is also present in other hematopoietic lineages. Here, we analyzed transcriptomes of 327 BCR::ABL1-positive patients with ALL (age, 2-84 years; median, 46 years) and identified 2 main gene expression clusters reproducible across 4 independent patient cohorts. Fluorescence in situ hybridization analysis of fluorescence-activated cell-sorted hematopoietic compartments showed distinct BCR::ABL1 involvement in myeloid cells for these clusters (n = 18/18 vs n = 3/16 patients; P < .001), indicating that a multilineage or lymphoid BCR::ABL1 subtype can be inferred from gene expression. Further subclusters grouped samples according to cooperating genomic events (multilineage: HBS1L deletion or monosomy 7; lymphoid: IKZF1-/- or CDKN2A/PAX5 deletions/hyperdiploidy). A novel HSB1L transcript was highly specific for BCR::ABL1 multilineage cases independent of HBS1L genomic aberrations. Treatment on current German Multicenter Study Group for Adult ALL (GMALL) protocols resulted in comparable disease-free survival (DFS) for multilineage vs lymphoid cluster patients (3-year DFS: 70% vs 61%; P = .530; n = 91). However, the IKZF1-/- enriched lymphoid subcluster was associated with inferior DFS, whereas hyperdiploid cases showed a superior outcome. Thus, gene expression clusters define underlying developmental trajectories and distinct patterns of cooperating events in BCR::ABL1-positive ALL with prognostic relevance.

Ex vivo drug response profiling for response and outcome prediction in hematologic malignancies: the prospective non-interventional SMARTrial.

Ex vivo drug response profiling is a powerful tool to study genotype-drug response associations and is being explored as a tool set for precision medicine in cancer. Here we conducted a prospective non-interventional trial to investigate feasibility of ex vivo drug response profiling for treatment guidance in hematologic malignancies (SMARTrial, NCT03488641 ). The primary endpoint to provide drug response profiling reports within 7 d was met in 91% of all study participants (N = 80). Secondary endpoint analysis revealed that ex vivo resistance to chemotherapeutic drugs predicted chemotherapy treatment failure in vivo. We confirmed the predictive value of ex vivo response to chemotherapy in a validation cohort of 95 individuals with acute myeloid leukemia treated with daunorubicin and cytarabine. Ex vivo drug response profiles improved ELN-22 risk stratification in individuals with adverse risk. We conclude that ex vivo drug response profiling is clinically feasible and has the potential to predict chemotherapy response in individuals with hematologic malignancies beyond clinically established genetic markers.

Mutated IKZF1 is an independent marker of adverse risk in acute myeloid leukemia.

Genetic lesions of IKZF1 are frequent events and well-established markers of adverse risk in acute lymphoblastic leukemia. However, their function in the pathophysiology and impact on patient outcome in acute myeloid leukemia (AML) remains elusive. In a multicenter cohort of 1606 newly diagnosed and intensively treated adult AML patients, we found IKZF1 alterations in 45 cases with a mutational hotspot at N159S. AML with mutated IKZF1 was associated with alterations in RUNX1, GATA2, KRAS, KIT, SF3B1, and ETV6, while alterations of NPM1, TET2, FLT3-ITD, and normal karyotypes were less frequent. The clinical phenotype of IKZF1-mutated AML was dominated by anemia and thrombocytopenia. In both univariable and multivariable analyses adjusting for age, de novo and secondary AML, and ELN2022 risk categories, we found mutated IKZF1 to be an independent marker of adverse risk regarding complete remission rate, event-free, relapse-free, and overall survival. The deleterious effects of mutated IKZF1 also prevailed in patients who underwent allogeneic hematopoietic stem cell transplantation (n = 519) in both univariable and multivariable models. These dismal outcomes are only partially explained by the hotspot mutation N159S. Our findings suggest a role for IKZF1 mutation status in AML risk modeling.

Long-Term Survivors after Failure of Chimeric Antigen Receptor T Cell Therapy for Large B Cell Lymphoma: A Role for Allogeneic Hematopoietic Cell Transplantation? A German Lymphoma Alliance and German Registry for Stem Cell Transplantation Analysis.

The outcome of patients with large B cell lymphoma (LBCL) who relapse or progress after CD19-directed chimeric antigen receptor T cell therapy (CAR-T) administered as salvage therapy beyond the second treatment line is poor. However, a minority of patients become long-term survivors despite CAR-T failure. The German Lymphoma Alliance (GLA) has proposed a hierarchical management algorithm for CAR-T failure in LBCL, aimed at allogeneic hematopoietic cell transplantation (alloHCT) as definite therapy in eligible patients. The purpose of this study was to investigate characteristics, relapse patterns, and management strategies in long-term survivors after CAR-T failure, with a particular focus on the feasibility and outcome of alloHCT. This was a retrospective analysis of all evaluable patients with a relapse/progression event (REL) observed in a previously reported GLA sample between November 2018 and May 2021. REL occurred in 214 of 356 patients (60%) who underwent CAR-T for LBCL in the previous GLA study. An evaluable dataset was available for 143 of these 214 patients (67%). Twenty-six of 143 patients (18%) survived 12 months or longer from REL, 109 (76%) died within the first year after REL, and 8 (6%) were alive but had not reached the 12-month landmark. Long-term survivors had more favorable pre-CAR-T features, had a longer interval between CAR-T and REL, and had more often received a tumor biopsy after CAR-T failure, whereas the choice of the first salvage regimen had no impact. AlloHCT was feasible in 40 of 53 patients (75%) intended and resulted in a 12-month post-transplantation overall survival of 36% in those patients who underwent transplantation with sensitive or untreated REL. AlloHCT after CAR-T failure in LBCL is feasible and may be an important contributor to long-term survival, although selection bias must be taken into account. Thus, alloHCT should be considered as a reasonable treatment option for eligible patients in this setting. However, because the overall outlook after CAR-T failure remains poor, novel effective therapeutic approaches are needed, either to allow long-term disease control per se or to improve the preconditions for successful alloHCT.

Impact of BMI on patient outcome in acute myeloid leukaemia patients receiving intensive induction therapy: a real-world registry experience.

BACKGROUND: Acute myeloid leukaemia (AML) is treated with intensive induction chemotherapy (IT) in medically fit patients. In general, obesity was identified as a risk factor for all-cause mortality, and there is an ongoing debate on its impact on outcome and optimal dosing strategy in obese AML patients. METHODS: We conducted a registry study screening 7632 patients and assessed the impact of obesity in 1677 equally IT treated, newly diagnosed AML patients on the outcome (OS, EFS, CR1), comorbidities, toxicities and used dosing strategies. RESULTS: Obese patients (BMI ≥ 30) displayed a significant inferior median OS (29.44 vs. 47.94 months, P = 0.015) and CR1 rate (78.7% vs. 84.3%, P = 0.015) without differences in median EFS (7.8 vs. 9.89 months, P = 0.3) compared to non-obese patients (BMI < 30). The effect was predominantly observed in older (≥60 years) patients. Obesity was identified as an independent risk factor for death, and obese patients demonstrated higher rates of cardiovascular or metabolic comorbidities. No differences for OS, EFS, CR1 or treatment-related toxicities were observed by stratification according to used dosing strategy or dose reduction. CONCLUSIONS: In conclusion, this study identifies obesity as an independent risk factor for worse OS in older AML patients undergoing curative IT most likely due to obesity-related comorbidities and not to dosing strategy.

The Gene Expression Classifier ALLCatchR Identifies B-cell Precursor ALL Subtypes and Underlying Developmental Trajectories Across Age.

Current classifications (World Health Organization-HAEM5/ICC) define up to 26 molecular B-cell precursor acute lymphoblastic leukemia (BCP-ALL) disease subtypes by genomic driver aberrations and corresponding gene expression signatures. Identification of driver aberrations by transcriptome sequencing (RNA-Seq) is well established, while systematic approaches for gene expression analysis are less advanced. Therefore, we developed ALLCatchR, a machine learning-based classifier using RNA-Seq gene expression data to allocate BCP-ALL samples to all 21 gene expression-defined molecular subtypes. Trained on n = 1869 transcriptome profiles with established subtype definitions (4 cohorts; 55% pediatric / 45% adult), ALLCatchR allowed subtype allocation in 3 independent hold-out cohorts (n = 1018; 75% pediatric / 25% adult) with 95.7% accuracy (averaged sensitivity across subtypes: 91.1% / specificity: 99.8%). High-confidence predictions were achieved in 83.7% of samples with 98.9% accuracy. Only 1.2% of samples remained unclassified. ALLCatchR outperformed existing tools and identified novel driver candidates in previously unassigned samples. Additional modules provided predictions of samples blast counts, patient's sex, and immunophenotype, allowing the imputation in cases where these information are missing. We established a novel RNA-Seq reference of human B-lymphopoiesis using 7 FACS-sorted progenitor stages from healthy bone marrow donors. Implementation in ALLCatchR enabled projection of BCP-ALL samples to this trajectory. This identified shared proximity patterns of BCP-ALL subtypes to normal lymphopoiesis stages, extending immunophenotypic classifications with a novel framework for developmental comparisons of BCP-ALL. ALLCatchR enables RNA-Seq routine application for BCP-ALL diagnostics with systematic gene expression analysis for accurate subtype allocation and novel insights into underlying developmental trajectories.

Unsupervised meta-clustering identifies risk clusters in acute myeloid leukemia based on clinical and genetic profiles.

BACKGROUND: Increasingly large and complex biomedical data sets challenge conventional hypothesis-driven analytical approaches, however, data-driven unsupervised learning can detect inherent patterns in such data sets. METHODS: While unsupervised analysis in the medical literature commonly only utilizes a single clustering algorithm for a given data set, we developed a large-scale model with 605 different combinations of target dimensionalities as well as transformation and clustering algorithms and subsequent meta-clustering of individual results. With this model, we investigated a large cohort of 1383 patients from 59 centers in Germany with newly diagnosed acute myeloid leukemia for whom 212 clinical, laboratory, cytogenetic and molecular genetic parameters were available. RESULTS: Unsupervised learning identifies four distinct patient clusters, and statistical analysis shows significant differences in rate of complete remissions, event-free, relapse-free and overall survival between the four clusters. In comparison to the standard-of-care hypothesis-driven European Leukemia Net (ELN2017) risk stratification model, we find all three ELN2017 risk categories being represented in all four clusters in varying proportions indicating unappreciated complexity of AML biology in current established risk stratification models. Further, by using assigned clusters as labels we subsequently train a supervised model to validate cluster assignments on a large external multicenter cohort of 664 intensively treated AML patients. CONCLUSIONS: Dynamic data-driven models are likely more suitable for risk stratification in the context of increasingly complex medical data than rigid hypothesis-driven models to allow for a more personalized treatment allocation and gain novel insights into disease biology.

There are various ways in which clinicians can predict the risk of disease progression in patients with leukemia, helping them to treat the patients accordingly. However, these approaches are usually designed by human experts and might not fully capture the complexity of a patient's disease. Here, with a large cohort of patients with acute myeloid leukemia, we design an unsupervised machine learning model ­ a type of computer model that learns from patterns in data without human input­to separate these patients into subgroups according to risk. We identify four distinct groups which differ with regards to patient genetics, laboratory values, and clinical characteristics. These groups have differences in response to treatment and patient survival, and we validate our findings in another dataset. Our approach might help clinicians to better predict outcomes in patients with leukemia and make decisions on treatment.