The IL-7R antagonist lusvertikimab reduces leukemic burden in xenograft ALL via antibody-dependent cellular phagocytosis.

ABSTRACT: Acute lymphoblastic leukemia (ALL) arises from the uncontrolled proliferation of B-cell precursors (BCP-ALL) or T cells (T-ALL). Current treatment protocols obtain high cure rates in children but are based on toxic polychemotherapy. Novel therapies are urgently needed, especially in relapsed/refractory (R/R) disease, high-risk (HR) leukemias and T-ALL, in which immunotherapy approaches remain scarce. Although the interleukin-7 receptor (IL-7R) plays a pivotal role in ALL development, no IL-7R-targeting immunotherapy has yet reached clinical application in ALL. The IL-7Rα chain (CD127)-targeting IgG4 antibody lusvertikimab (LUSV; formerly OSE-127) is a full antagonist of the IL-7R pathway, showing a good safety profile in healthy volunteers. Here, we show that ∼85% of ALL cases express surface CD127. We demonstrate significant in vivo efficacy of LUSV immunotherapy in a heterogeneous cohort of BCP- and T-ALL patient-derived xenografts (PDX) in minimal residual disease (MRD) and overt leukemia models, including R/R and HR leukemias. Importantly, LUSV was particularly effective when combined with polychemotherapy in a phase 2-like PDX study with CD127high samples leading to MRD-negativity in >50% of mice treated with combination therapy. Mechanistically, LUSV targeted ALL cells via a dual mode of action comprising direct IL-7R antagonistic activity and induction of macrophage-mediated antibody-dependent cellular phagocytosis (ADCP). LUSV-mediated in vitro ADCP levels significantly correlated with CD127 expression levels and the reduction of leukemia burden upon treatment of PDX animals in vivo. Altogether, through its dual mode of action and good safety profile, LUSV may represent a novel immunotherapy option for any CD127+ ALL, particularly in combination with standard-of-care polychemotherapy.

The landscape of cytogenetic and molecular genetic methods in diagnostics for hematologic neoplasia.

Improvements made during the last decades in the management of patients with hematologic neoplasia have resulted in increase of overall survival. These advancements have become possible through progress in our understanding of genetic basis of different hematologic malignancies and their role in the current risk-adapted treatment protocols. In this review, we provide an overview of current cytogenetic and molecular genetic methods, commonly used in the genetic characterization of hematologic malignancies, describe the current developments in the cytogenetic and molecular diagnostics, and give an outlook into their future development. Furthermore, we give a brief overview of the most important public databases and guidelines for sequence variant interpretation.

An artificial intelligence-assisted clinical framework to facilitate diagnostics and translational discovery in hematologic neoplasia.

BACKGROUND: The increasing volume and intricacy of sequencing data, along with other clinical and diagnostic data, like drug responses and measurable residual disease, creates challenges for efficient clinical comprehension and interpretation. Using paediatric B-cell precursor acute lymphoblastic leukaemia (BCP-ALL) as a use case, we present an artificial intelligence (AI)-assisted clinical framework clinALL that integrates genomic and clinical data into a user-friendly interface to support routine diagnostics and reveal translational insights for hematologic neoplasia. METHODS: We performed targeted RNA sequencing in 1365 cases with haematological neoplasms, primarily paediatric B-cell precursor acute lymphoblastic leukaemia (BCP-ALL) from the AIEOP-BFM ALL study. We carried out fluorescence in situ hybridization (FISH), karyotyping and arrayCGH as part of the routine diagnostics. The analysis results of these assays as well as additional clinical information were integrated into an interactive web interface using Bokeh, where the main graph is based on Uniform Manifold Approximation and Projection (UMAP) analysis of the gene expression data. At the backend of the clinALL, we built both shallow machine learning models and a deep neural network using Scikit-learn and PyTorch respectively. FINDINGS: By applying clinALL, 78% of undetermined patients under the current diagnostic protocol were stratified, and ambiguous cases were investigated. Translational insights were discovered, including IKZF1plus status dependent subpopulations of BCR::ABL1 positive patients, and a subpopulation within ETV6::RUNX1 positive patients that has a high relapse frequency. Our best machine learning models, LDA and PASNET-like neural network models, achieve F1 scores above 97% in predicting patients' subgroups. INTERPRETATION: An AI-assisted clinical framework that integrates both genomic and clinical data can take full advantage of the available data, improve point-of-care decision-making and reveal clinically relevant insights promptly. Such a lightweight and easily transferable framework works for both whole transcriptome data as well as the cost-effective targeted RNA-seq, enabling efficient and equitable delivery of personalized medicine in small clinics in developing countries. FUNDING: German Ministry of Education and Research (BMBF), German Research Foundation (DFG) and Foundation for Polish Science.