CD19 expression in acute leukemia is not restricted to the cytogenetically aberrant populations

Finding potential targets in cell-based immunotherapy for handling the challenges of acute myeloid leukemia

Acute myeloid leukemia (AML) is a hostile hematological malignancy under great danger of relapse and poor long-term survival rates, despite recent therapeutic advancements. To deal with this unfulfilled clinical necessity, innovative cell-based immunotherapies have surfaced as promising approaches to improve anti-tumor immunity and enhance patient outcomes. In this comprehensive review, we provide a detailed examination of the latest developments in cell-based immunotherapies for AML, including chimeric antigen receptor (CAR) T-cell therapy, T-cell receptor (TCR)-engineered T-cell therapy, and natural killer (NK) cell-based therapies. We critically evaluate the unique mechanisms of action, current challenges, and evolving strategies to improve the efficacy and safety of these modalities. The review emphasizes how promising these cutting-edge immune-based strategies are in overcoming the inherent complexities and heterogeneity of AML. We discuss the identification of optimal target antigens, the importance of mitigating on-target/off-tumor toxicity, and the need to enhance the persistence and functionality of engineered immune effector cells. All things considered, this review offers a thorough overview of the rapidly evolving field of cell-based immunotherapy for AML, underscoring the significant progress made and the ongoing efforts to translate these innovative approaches into more effective and durable treatments for this devastating disease.

Automated Deep Learning-Based Diagnosis and Molecular Characterization of Acute Myeloid Leukemia Using Flow Cytometry

The current flow cytometric analysis of blood and bone marrow samples for diagnosis of acute myeloid leukemia (AML) relies heavily on manual intervention in the processing and analysis steps, introducing significant subjectivity into resulting diagnoses and necessitating highly trained personnel. Furthermore, concurrent molecular characterization via cytogenetics and targeted sequencing can take multiple days, delaying patient diagnosis and treatment. Attention-based multi-instance learning models (ABMILMs) are deep learning models that make accurate predictions and generate interpretable insights regarding the classification of a sample from individual events/cells; nonetheless, these models have yet to be applied to flow cytometry data. In this study, we developed a computational pipeline using ABMILMs for the automated diagnosis of AML cases based exclusively on flow cytometric data. Analysis of 1820 flow cytometry samples shows that this pipeline provides accurate diagnoses of acute leukemia (area under the receiver operating characteristic curve [AUROC] 0.961) and accurately differentiates AML vs B- and T-lymphoblastic leukemia (AUROC 0.965). Models for prediction of 9 cytogenetic aberrancies and 32 pathogenic variants in AML provide accurate predictions, particularly for t(15;17)(PML::RARA) [AUROC 0.929], t(8;21)(RUNX1::RUNX1T1) (AUROC 0.814), and NPM1 variants (AUROC 0.807). Finally, we demonstrate how these models generate interpretable insights into which individual flow cytometric events and markers deliver optimal diagnostic utility, providing hematopathologists with a data visualization tool for improved data interpretation, as well as novel biological associations between flow cytometric marker expression and cytogenetic/molecular variants in AML. Our study is the first to illustrate the feasibility of using deep learning-based analysis of flow cytometric data for automated AML diagnosis and molecular characterization.

Secondary acute myeloid leukemia and de novo acute myeloid leukemia with myelodysplasia-related changes - close or complete strangers?

AIM

To compare the main features of patients with secondary acute myeloid leukemias (AMLs) after post-myelodysplastic syndrome (AML-post-MDS) or post-myeloproliferative neoplasms (AML-post-MPN) and myeloid blast crisis of chronic myeloid leukemia (CML-BC) vs. de novoAMLs with myelodysplastic characteristics (dn-AML-MDS).

Immunotherapy in Acute Myeloid Leukemia: A Literature Review of Emerging Strategies

In the last twenty years, we have witnessed a paradigm shift in the treatment and prognosis of acute myeloid leukemia (AML), thanks to the introduction of new efficient drugs or approaches to refine old therapies, such as Gemtuzumab Ozogamicin, CPX 3-5-1, hypomethylating agents, and Venetoclax, the optimization of conditioning regimens in allogeneic hematopoietic stem cell transplantation and the improvement of supportive care. However, the long-term survival of non-M3 and non-core binding factor-AML is still dismal. For this reason, the expectations for the recently developed immunotherapies, such as antibody-based therapy, checkpoint inhibitors, and chimeric antigen receptor strategies, successfully tested in other hematologic malignancies, were very high. The inherent characteristics of AML blasts hampered the development of these treatments, and the path of immunotherapy in AML has been bumpy. Herein, we provide a detailed review of potential antigenic targets, available data from pre-clinical and clinical trials, and future directions of immunotherapies in AML.

Automated Deep Learning-Based Diagnosis and Molecular Characterization of Acute Myeloid Leukemia using Flow Cytometry

Current flow cytometric analysis of blood and bone marrow samples for diagnosis of acute myeloid leukemia (AML) relies heavily on manual intervention in both the processing and analysis steps, introducing significant subjectivity into resulting diagnoses and necessitating highly trained personnel. Furthermore, concurrent molecular characterization via cytogenetics and targeted sequencing can take multiple days, delaying patient diagnosis and treatment. Attention-based multi-instance learning models (ABMILMs) are deep learning models which make accurate predictions and generate interpretable insights regarding the classification of a sample from individual events/cells; nonetheless, these models have yet to be applied to flow cytometry data. In this study, we developed a computational pipeline using ABMILMs for the automated diagnosis of AML cases based exclusively on flow cytometric data. Analysis of 1,820 flow cytometry samples shows that this pipeline provides accurate diagnoses of acute leukemia [AUROC 0.961] and accurately differentiates AML versus B- and T-lymphoblastic leukemia [AUROC 0.965]. Models for prediction of 9 cytogenetic aberrancies and 32 pathogenic variants in AML provide accurate predictions, particularly for t(15;17)(PML::RARA) [AUROC 0.929], t(8;21)(RUNX1::RUNX1T1) [AUROC 0.814], and NPM1 variants [AUROC 0.807]. Finally, we demonstrate how these models generate interpretable insights into which individual flow cytometric events and markers deliver optimal diagnostic utility, providing hematopathologists with a data visualization tool for improved data interpretation, as well as novel biological associations between flow cytometric marker expression and cytogenetic/molecular variants in AML. Our study is the first to illustrate the feasibility of using deep learning-based analysis of flow cytometric data for automated AML diagnosis and molecular characterization.

Targets for chimeric antigen receptor T-cell therapy of acute myeloid leukemia

Acute Myeloid Leukemia (AML) is an aggressive myeloid malignancy associated with high mortality rates (less than 30% 5-year survival). Despite advances in our understanding of the molecular mechanisms underpinning leukemogenesis, standard-of-care therapeutic approaches have not changed over the last couple of decades. Chimeric Antigen Receptor (CAR) T-cell therapy targeting CD19 has shown remarkable clinical outcomes for patients with acute lymphoblastic leukemia (ALL) and is now an FDA-approved therapy. Targeting of myeloid malignancies that are CD19-negative with this promising technology remains challenging largely due to lack of alternate target antigens, complex clonal heterogeneity, and the increased recognition of an immunosuppressive bone marrow. We carefully reviewed a comprehensive list of AML targets currently being used in both proof-of-concept pre-clinical and experimental clinical settings. We analyzed the expression profile of these molecules in leukemic as well normal tissues using reliable protein databases and data reported in the literature and we provide an updated overview of the current clinical trials with CAR T-cells in AML. Our study represents a state-of-art review of the field and serves as a potential guide for selecting known AML-associated targets for adoptive cellular therapies.

A Novel Feature Selection Method Based on MRMR and Enhanced Flower Pollination Algorithm for High Dimensional Biomedical Data

Background:

The massive amount of biomedical data accumulated in the past decades can be utilized for diagnosing disease.

Objective:

However, the high dimensionality, small sample sizes, and irrelevant features of data often have a negative influence on the accuracy and speed of disease prediction. Some existing machine learning models cannot capture the patterns on these datasets accurately without utilizing feature selection.

Methods:

Filter and wrapper are two prevailing feature selection methods. The filter method is fast but has low prediction accuracy, while the latter can obtain high accuracy but has a formidable computation cost. Given the drawbacks of using filter or wrapper individually, a novel feature selection method, called MRMR-EFPATS, is proposed, which hybridizes filter method Minimum Redundancy Maximum Relevance (MRMR) and wrapper method based on an improved Flower Pollination Algorithm (FPA). First, MRMR is employed to rank and screen out some important features quickly. These features are further chosen for individual populations following the wrapper method for faster convergence and less computational time. Then, due to its efficiency and flexibility, FPA is adopted to further discover an optimal feature subset.

Result:

FPA still has some drawbacks, such as slow convergence rate, inadequacy in terms of searching new solutions, and tends to be trapped in local optima. In our work, an elite strategy is adopted to improve the convergence speed of the FPA. Tabu search and Adaptive Gaussian Mutation are employed to improve the search capability of FPA and escape from local optima. Here, the KNN classifier with the 5-fold-CV is utilized to evaluate the classification accuracy.

Conclusion:

Extensive experimental results on six public high dimensional biomedical datasets show that the proposed MRMR-EFPATS has achieved superior performance compared to other state-of-theart methods.

Elucidation of Novel Therapeutic Targets for Acute Myeloid Leukemias with RUNX1-RUNX1T1 Fusion

The RUNX1-RUNX1T1 fusion is a frequent chromosomal alteration in acute myeloid leukemias (AMLs). Although RUNX1-RUNX1T1 fusion protein has pivotal roles in the development of AMLs with the fusion, RUNX1-RUNX1T1, fusion protein is difficult to target, as it lacks kinase activities. Here, we used bioinformatic tools to elucidate targetable signaling pathways in AMLs with RUNX1-RUNX1T1 fusion. After analysis of 93 AML cases from The Cancer Genome Atlas (TCGA) database, we found expression of 293 genes that correlated to the expression of the RUNX1-RUNX1T1 fusion gene. Based on these 293 genes, the cyclooxygenase (COX), vascular endothelial growth factor receptor (VEGFR), platelet-derived growth factor receptor (PDGFR), and fibroblast growth factor receptor (FGFR) pathways were predicted to be specifically activated in AMLs with RUNX1-RUNX1T1 fusion. Moreover, the in vitro proliferation of AML cells with RUNX1-RUNX1T1 fusion decreased significantly more than that of AML cells without the fusion, when the pathways were inhibited pharmacologically. The results indicate that novel targetable signaling pathways could be identified by the analysis of the gene expression features of AMLs with non-targetable genetic alterations. The elucidation of specific molecular targets for AMLs that have a specific genetic alteration would promote personalized treatment of AMLs and improve clinical outcomes.

Chimeric Antigen Receptor-Engineered T Cell Therapy in Acute Myeloid Leukaemia

Acute myeloid leukaemia (AML) is a disease with a very poor outcome and remains an area of significant unmet need, necessitating novel therapeutic strategies. The progress made in the field of immunotherapy, in particular chimeric antigen receptor (CAR)-engineered T cells, has given rise to many hopes for pathologies such as B cell acute lymphoblastic leukaemia and B cell lymphoma, and many studies have attempted to translate these successes to AML. This review summarises the recent advances in, and defines an ideal target for, CAR T cell therapy in AML.

Novel immunotherapies for adult patients with B-lineage acute lymphoblastic leukemia

The past decade witnessed the rapid development of adult B-lineage acute lymphoblastic leukemia (ALL) treatment. Beyond the development of chemotherapy regimens, immunotherapy is starting a new era with unprecedented complete remission (CR) rate. Targeting B-lineage-specific surface markers such as CD19, CD20, CD22, or CD52, immunotherapy has been demonstrating promising clinical results. Among the immunotherapeutic methods, naked monoclonal antibodies (mAbs), antibody-drug conjugate (ADC), bispecific T cell engager (BiTE), and chimeric antigen receptor (CAR) T cells are the main types. In this review, we will examine the emerging preclinical and clinical development on (1) anti-CD20 naked mAbs rituximab, ofatumumab, and obinutuzumab; (2) anti-CD19 ADCs SAR3419 and SGN-CD19A and anti-CD19 BiTE blinatumomab; (3) anti-CD22 naked mAb epratuzumab and anti-CD22 ADC inotuzumab ozogamicin; (4) anti-CD52 naked mAb alemtuzumab; and (5) anti-CD19 CAR T cells. We will discuss their efficacy, adverse effects, as well as future development.

Dual CD19 and CD123 targeting prevents antigen-loss relapses after CD19-directed immunotherapies

Potent CD19-directed immunotherapies, such as chimeric antigen receptor T cells (CART) and blinatumomab, have drastically changed the outcome of patients with relapsed/refractory B cell acute lymphoblastic leukemia (B-ALL). However, CD19-negative relapses have emerged as a major problem that is observed in approximately 30% of treated patients. Developing approaches to preventing and treating antigen-loss escapes would therefore represent a vertical advance in the field. Here, we found that in primary patient samples, the IL-3 receptor α chain CD123 was highly expressed on leukemia-initiating cells and CD19-negative blasts in bulk B-ALL at baseline and at relapse after CART19 administration. Using intravital imaging in an antigen-loss CD19-negative relapse xenograft model, we determined that CART123, but not CART19, recognized leukemic blasts, established protracted synapses, and eradicated CD19-negative leukemia, leading to prolonged survival. Furthermore, combining CART19 and CART123 prevented antigen-loss relapses in xenograft models. Finally, we devised a dual CAR-expressing construct that combined CD19- and CD123-mediated T cell activation and demonstrated that it provides superior in vivo activity against B-ALL compared with single-expressing CART or pooled combination CART. In conclusion, these findings indicate that targeting CD19 and CD123 on leukemic blasts represents an effective strategy for treating and preventing antigen-loss relapses occurring after CD19-directed therapies.

Clinical updates in adult acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is a clonal disease characterized by B or T lineage. Here we cover the clinical manifestations, pathophysiology and therapy for ALL. Additionally, we will discuss the evidence for minimal residual disease assessment, novel molecular targets and newly developed targeted therapies. The separation of ALL into Philadelphia chromosome positive and recently into Philadelphia-like disease represents the most exciting developments in this disease. Finally, the advent of new immunotherapeutic approaches led us to predict that in few years, ALL therapy might be based heavily on non-chemotherapeutic approaches.

Blinatumomab for the Treatment of Philadelphia Chromosome-Negative, Precursor B-cell Acute Lymphoblastic Leukemia

Blinatumomab is a CD19/CD3 bispescific antibody designed to redirect T cells toward malignant B cells and induce their lysis. It recently gained accelerated approval by the FDA for the treatment of relapsed or refractory Philadelphia chromosome-negative B-cell acute lymphoblastic leukemia (RR-ALL). In the phase II trial that served as the basis for approval, blinatumomab demonstrated significant single-agent activity and induced remission [complete remission (CR) and CR with incomplete recovery of peripheral blood counts (CRh)] in 43% of 189 adult patients with RR-ALL; the majority of responders (82%) also attained negative minimal residual disease (MRD(-)) status that did not generally translate into long-term remissions in most cases. Additional studies show that blinatumomab can induce high response rates associated with lasting remissions in patients in first remission treated for MRD positivity, suggesting a role for blinatumomab in the upfront, MRD-positive setting. Blinatumomab infusion follows a predictable immunopharmacologic profile, including early cytokine release that can be associated with a clinical syndrome, T-cell expansion, and B-cell depletion. Neurologic toxicities represent a unique toxicity that shares similarities with adverse effects of other T-cell engaging therapies. Further studies are needed to clarify the optimal disease setting and timing for blinatumomab therapy. Additional insights into the pathogenesis, risk factors, and prevention of neurologic toxicities as well as a better understanding of the clinical consequences and biologic pathways that are associated with drug resistance are needed.

Blinatumomab: enlisting serial killer T-cells in the war against hematologic malignancies

INTRODUCTION

The approval of blinatumomab signals the long awaited arrival of immunotherapy for acute lymphoblastic leukemia (ALL). Previous options for relapsed or refractory disease were restricted to cytotoxic chemotherapy with limited efficacy and significant toxicity. Through an innovative mechanism of action, blinatumomab stimulates a polyclonal antitumor T-cell response, yielding unprecedented single agent efficacy in the relapsed/refractory setting. Success comes at the cost of immunological toxicities rarely encountered with previous therapies and challenging administration logistics requiring clinical expertise.

AREAS COVERED

All published clinical and preclinical studies using blinatumomab were reviewed in addition to all registered ongoing clinical trials and data published in abstract form. The search was limited to the English language. The pharmacology, clinical efficacy, toxicity profile, and logistical considerations for drug administration are discussed.

EXPERT OPINION

Blinatumomab is an exciting addition to the treatment armamentarium for relapsed/refractory ALL, yet several questions remain regarding optimal implementation into the current treatment paradigm. A unique toxicity profile should be weighed against promising benefits in a poor prognosis population. Other emerging therapies, such as chimeric antigen receptor-modified T-cells and inotuzumab ozogamicin, with different side effect profiles and administration schedules, may prove to be more beneficial for specific patient populations.

Core-binding factor acute myeloid leukemia: Heterogeneity, monitoring, and therapy

Core binding factor acute myelogenous leukemia (CBF AML) constitutes 15% of adult AML and carries an overall good prognosis. CBF AML encodes two recurrent cytogentic abnormalities referred to as t(8;21) and inv (16). The two CBF AML entities are usually grouped together but there is a considerable clinical, pathologic and molecular heterogeneity within this group of diseases. Recent and ongoing studies are addressing the molecular heterogeneity, minimal residual disease and targeted therapies to improve the outcome of CBF AML. In this article, we present a comprehensive review about CBF AML with emphasis on molecular heterogeneity and new therapeutic options.