Genomic studies of pediatric acute lymphoblastic leukemia (ALL) have shown remarkable heterogeneity in initial diagnosis, with multiple (sub)clones harboring lesions in relapse-associated genes. However, the clinical relevance of these subclonal alterations remains unclear. We assessed the clinical relevance and prognostic value of subclonal alterations in the relapse-associated genes IKZF1, CREBBP, KRAS, NRAS, PTPN11, TP53, NT5C2, and WHSC1 in 503 ALL cases. Using molecular inversion probe sequencing and breakpoint-spanning polymerase chain reaction analysis we reliably detected alterations with an allele frequency below 1%. We identified 660 genomic alterations in 285 diagnostic samples of which 495 (75%) were subclonal. RAS pathway mutations were common, particularly in minor subclones, and comparisons between RAS hotspot mutations revealed differences in their capacity to drive clonal expansion in ALL. We did not find an association of subclonal alterations with unfavorable outcome. Particularly for IKZF1, an established prognostic marker in ALL, all clonal but none of the subclonal alterations were preserved at relapse. We conclude that, for the genes tested, there is no basis to consider subclonal alterations detected at diagnosis for risk group stratification of ALL treatment.

Pediatric acute lymphoblastic leukemia (ALL) is marked by low mutational load at initial diagnosis, which increases at relapse. To determine which processes are active in (relapsed) ALL and how they behave during disease progression before and after therapy, we performed whole genome sequencing on 97 tumor samples of 29 multiply relapsed ALL patients. Mutational load increased upon relapse in 28 patients and upon every subsequent relapse in 22 patients. In addition to two clock-like mutational processes, we identified UV-like damage, APOBEC activity, reactive oxygen species, thiopurine-associated damage and an unknown therapy component as drivers of mutagenesis. Mutational processes often affected patients over longer time periods, but could also occur in isolated events, suggesting the requirement of additional triggers. Thiopurine exposure was the most prominent source of new mutations in relapse, affecting over half of the studied patients in first and/or later relapse and causing potential relapse-driving mutations in multiple patients. Our data demonstrate that multiple mutational processes frequently act in parallel as prominent secondary drivers with dynamic activity during ALL development and progression.

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 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. 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. By applying clinALL, 78% of undetermined patients under the current diagnostic protocol were stratified, and ambiguous cases were investigated. Translational insights were discovered, including IKZF1^plus 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. 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. German Ministry of Education and Research (BMBF), German Research Foundation (DFG) and Foundation for Polish Science.

B-cell precursor acute lymphoblastic leukemia (BCP-ALL) is the most common cancer in children, and significant progress has been made in diagnostics and the treatment of this disease based on the subtypes of BCP-ALL. However, in a large proportion of cases (B-other), recurrent BCP-ALL-associated genomic alterations remain unidentifiable by current diagnostic procedures. In this study, we performed RNA sequencing and analyzed gene fusions, expression profiles, and mutations in diagnostic samples of 185 children with BCP-ALL. Gene expression clustering showed that a subset of B-other samples partially clusters with some of the known subgroups, particularly DUX4-positive. Mutation analysis coupled with gene expression profiling revealed the presence of distinctive BCP-ALL subgroups, characterized by the presence of mutations in known ALL driver genes, e.g., PAX5 and IKZF1. Moreover, we identified novel fusion partners of lymphoid lineage transcriptional factors ETV6, IKZF1 and PAX5. In addition, we report on low blast count detection thresholds and show that the use of EDTA tubes for sample collection does not have adverse effects on sequencing and downstream analysis. Taken together, our findings demonstrate the applicability of whole-transcriptome sequencing for personalized diagnostics in pediatric ALL, including tentative classification of the B-other cases that are difficult to diagnose using conventional methods.