Lennart Kester

Malignant rhabdoid tumors (MRTs) are aggressive pediatric malignancies driven by loss of a single SWI/SNF subunit in an otherwise near-silent genome, yet they display extensive transcriptional heterogeneity. Whether this heterogeneity reflects the outgrowth of transcriptionally distinct clones, or states adopted independently of lineage, is unknown. Here we combined lentiviral lineage barcoding with single-cell RNA sequencing in two patient-derived renal MRT organoid models, followed through long-term in vitro culture and in a novel orthotopic xenograft setting. In both modalities, tumors and cultures became progressively restricted to a small number of lineages, and the clones that expanded were transcriptionally distinct from one another. Decomposing into gene expression programs showed that some transcriptional states were used broadly across lineages while others were confined to a few, and that lineages expanding independently, in separate cultures and separate animals, converged on the same restricted states. Individual lineages generally maintained their transcriptional identity across passaging and across anatomical sites, including in metastases, which arose from clones already expanded in the matched primary tumor. Which lineages came to dominate, however, differed between animals given the same starting population. Clonal outcome is therefore variable while the transcriptional states available to a tumor are drawn from a constrained and recurring repertoire.

DNA methylation-based classification is increasingly a cornerstone of CNS tumor diagnostics. Recently,nanopore sequencing enables very rapid methylation profiling to support surgical decision making. Time-constrained sequencing yields sparse methylation profiles, necessitating specialized classifiers for accurate predictions. All publicly available sparse-profile classifiers rely on a single reference set of only 2,801 samples published in 2018 and its accompanying classification system. Here we present Sturgeon V2, trained on nearly 10,000 methylation profiles assembled from a range of public repositories and collaborations. To harmonize differences in tumor class labels, we developed an iterative semi-supervised relabeling strategy with a two-tiered concordance filter. We demonstrate that this filtering is essential for optimal performance. Compared to Sturgeon V1, we further redesigned the classifier architecture to provide more stable, and consistent confidence estimates while accumulating sequencing data and we introduce an updated class system including new classes, such as meningioma subtypes, PATZ-fusion neuroepithelial tumors and PLAG1/2-amplified embryonal tumors. In a retrospective intraoperative cohort of 94 pediatric cases, Sturgeon V2 was confidently correct in 85 cases (Sturgeon V1: 78) with one confident misclassification, and in simulated sequencing runs (n=1880) reduced the error rate among confident calls from 7.1% to 2.8%, outperforming publicly available alternatives. Sturgeon V2 is the first openly available, locally executable classifier for sparse methylation profiles trained beyond the 2018 reference era.

Although immunotherapy has revolutionized cancer treatment, its benefits in pediatric acute myeloid leukemia (AML) remain limited, partly due to an incomplete understanding of the AML microenvironment. While most studies focus on T cells, innate immune populations such as macrophages are less well explored. In pediatric AML patients, we demonstrate that immunosuppressive macrophages dominate the AML microenvironment and support growth of RUNX1::RUNX1T1 and KMT2A -rearranged leukemic blasts. In co-culture experiments, AML cells reprogrammed naïve and pro-inflammatory macrophages toward an anti-inflammatory state, characterized by suppression of NF-κB and other inflammatory pathways. This phenotypic switch requires direct cell contact and involves tunneling nanotube–mediated transfer of AML mitochondria to macrophages associated with metabolic rewiring toward oxidative phosphorylation. Functionally, these reprogrammed macrophages exhibited impaired phagocytosis, convey chemoresistance and reduced T cell– and bispecific antibody–mediated killing, facilitating immune escape. Our findings reveal a novel mechanism of immune suppression and suggest that targeting AML-macrophage interactions could enhance the efficacy of T cell–based immunotherapies.

Mesenchymal neoplasms driven by protein kinases represent an increasing group with a broad clinicopathologic and genetic spectrum. Recently, tumors harboring a GAB1::ABL1 fusion have been reported, resulting in constitutive activation of the non-receptor tyrosine kinase ABL1. We describe six cases (one previously reported) investigated by fusion gene analysis and DNA-methylation profiling. Tumors were from three males and three females aged 7–71 years and arose in the neck, axilla, lower back, thigh, and digits. Lesions were excised. Available follow-up was uneventful. Neoplasms were circumscribed but unencapsulated, with infiltration of the surrounding tissue, and characterized by haphazardly arranged monomorphous spindle cells. There was a fibromyxoid stroma and staghorn-like vessels were present. Immunohistochemically, expression of CD34 (3/6), S100 (1/6), EMA (2/6), and GLUT1 (3/3) was observed. All cases harbored a GAB1::ABL1 fusion. RNA expression profile (n = 2) showed high-confidence similarity to dermatofibrosarcoma protuberans (DFSP). DNA-methylation profiling demonstrated that all cases clustered together in close proximity to DFSP. Application of the latest Heidelberg methylation sarcoma classifier did not confidentially classify these neoplasms. However, four showed low-confidence matches to NTRK-rearranged spindle cell neoplasms. GAB1::ABL1 spindle cell neoplasms represent a distinct member of the family of kinase-driven mesenchymal tumors with an indolent clinical course, while the activating ABL1 fusion raises the possibility of targeted therapy in selected cases.

Background: Central nervous system (CNS) tumors represent the leading cause of pediatric cancer-related deaths. Primary treatment often involves neurosurgical tumor resection. Accurate diagnosis is crucial to perform the best-suited extent of resection. Recently, we published Sturgeon, an AI-based validated intraoperative nanopore sequencing tool, delivering accurate methylation-based diagnoses during surgery. Although several other nanopore sequencing strategies have been described, clear evidence of clinical impact remains critically absent. Here, we report a care evaluation analyzing the impact of Sturgeon on real-time pediatric neurosurgical decision-making. Methods: Since May 2023, a dual diagnostic neuro-oncological workflow incorporating intraoperative frozen section analyses and ultra-fast nanopore sequencing using Sturgeon has been fully implemented as standard of care at the Princess Máxima Center for Pediatric Oncology in the Netherlands. This intraoperative diagnostic workflow has been applied to all pediatric patients (<19 years of age) with primary CNS tumors and the multidisciplinary indication for tumor resection surgery. Results: Sturgeon delivered correct diagnoses in 82 out of 94 consecutive patients (87.2%, <90 minutes), no diagnosis in 11.7% and one incorrect diagnosis (1.1%). The diagnosis obtained by Sturgeon supported the intended surgical strategy (85.7%) or changed the strategy (14.3%) toward a more aggressive or limited resection. This contributed to a low complication rate and less second-look surgeries. Earlier communication of the histomolecular diagnosis to patients and their families was observed. Conclusions: Intraoperative use of Sturgeon provides essential guidance toward the most optimal neurosurgical strategy for pediatric CNS tumors and thereby has great potential to contribute to better clinical outcome.

Hematologic malignancies are diagnosed through a fragmented, sequential workup of morphology, immunophenotyping, cytogenetics, and molecular testing that can take days to weeks and is unavailable at many centers. DNA methylation profiling has transformed central nervous system tumor diagnosis, yet hematologic classifiers have remained confined to narrow acute leukemia panels. Here we present Lamprey, a deep-learning methylation classifier spanning 86 hematologic malignancy entities, trained on a reference cohort of 8,544 patients and deployed directly from nanopore sequencing. A depth-aware training framework allows confident classification from the first minutes of a run. Against blinded integrated reference diagnoses across retrospective, external, and prospective cohorts, Lamprey exceeded 98% accuracy among classified cases. Lamprey reaches a confident call within minutes, and cost as little as $82 per sample. Lamprey consolidates a sequential diagnostic workup into a single, rapid, same-day molecular readout.

Background Human germ cell tumors (GCTs) occur in infants, children, and adults, and present as germinomatous and/or non-germinomatous (embryonal carcinoma (EC), teratoma, yolk sac tumor (YST), and choriocarcinoma) histologies at gonadal or extragonadal locations. Accurate subtyping is crucial for prognosis and treatment, but current clinical biomarkers lack sensitivity and specificity (serum proteins), or require a tissue biopsy (for histological and immunohistochemical characterization). Hence, less-invasive and improved subtype-specific biomarkers have significant potential for clinical utility. Methods We conducted an integrated DNA methylation analysis (450K/EPIC array) from 16 (three original and 13 published) datasets, including 719 GCTs, 109 healthy testis, and 221 healthy peripheral blood samples. Unsupervised hierarchical clustering, UMAP, and Heidelberg Central Nervous System tumor methylation classification were implemented for GCT global methylation profiling, followed by differential methylation analysis for in silico GCT subtype-specific biomarker identification, and methylation-sensitive restriction enzyme-based qPCR for in vitro and in vivo biomarker validation. Results We demonstrate that GCTs contain unique methylation profiles based on their histology, regardless of tumor location and the patient’s age or sex. Per GCT histology, we identify numerous differentially methylated regions as potential biomarkers. As proof of concept, two YST-specific (APC and DPP7) and two EC-specific (GATA4 and FBRS) biomarkers are validated in tumor DNA, of which DPP7 is also detectable in GCT serum-derived cell-free DNA. Conclusions We present a method for in silico identification and subsequent in vitro and in vivo validation of GCT subtype-specific methylation-derived liquid biopsy-based biomarkers with potential to complement the limitations of current biomarkers. Our bioinformatic pipeline is easily transferrable encouraging additional applications in pan(pediatric)-cancer studies beyond GCTs.

The high heterogeneity of pediatric cancers presents significant diagnostic challenges, underscoring the need for accurate classification. Although molecular profiling supports first-line diagnostics and guides treatment, it can delay final diagnosis. While Nanopore-based methylation analysis has enabled rapid CNS tumor diagnosis, its application to pediatric solid tumors and lymphomas has remained largely unexplored. We developed Tucan, a deep-learning classifier trained on 3,818 methylation array profiles representing 84 subtypes, designed to classify tumors from sparse Nanopore methylation data. In retrospective validation (n=514), Tucan generated confident predictions (CFT ≥ 0.7) within 30 minutes of sequencing in 385 cases, achieving 372 correct diagnoses (F1-score: 0.98). In prospective testing (n=74; 63 classifiable), 52 samples reached the confidence threshold with 96% accuracy, confirming the original diagnosis in 47 cases and correctly refining or revising it in three. Together, Tucan enables rapid, high-confidence molecular classification of pediatric solid tumors and lymphomas.

Background/Objectives: Some patients initially diagnosed with non-high-risk neuroblastoma follow a high-risk clinical course and have poor survival compared to those initially diagnosed with high-risk neuroblastoma. We aimed to identify molecular aberrations present at diagnosis that may explain the high-risk clinical course in this patient group. Methods: Data were collected from non-high-risk neuroblastoma patients diagnosed at our center between 2014 and 2021. Segmental chromosomal aberrations (SCAs), gene amplifications and mutations at diagnosis were detected by a single-nucleotide polymorphism array and next-generation sequencing. Telomere maintenance mechanisms (TMMs) were investigated using fluorescent in situ hybridization, whole genome sequencing (WGS) and RNA sequencing. SCA counts were imputed by using multiple imputation. Results: The total cohort included 89 patients. Thirteen patients developed a high-risk clinical course (group A) due to progression (n = 4), local relapse (n = 4), refractory disease (n = 3) or metastases (n = 2). Seventy-six patients followed a non-high-risk clinical course (group B). An SCA profile (≥1 SCA) was present in 76% of patients in group A and only 15% in group B (p = 0.004). 1p deletion was associated with a high-risk clinical course (p = 0.034). Gains of 1q, 2p and 17q, and deletions of 4p and 11q were more common in group A. After imputation, SCA count was associated with a high-risk clinical course (pooled OR 1.256 with 95% CI 1.006–1.568, p = 0.044). Two patients, both group A, exhibited MDM2/CDK4 amplification. Alternative lengthening of telomeres (ALT) was activated in 57% of group A. Conclusions: SCA profile and 1p deletion are associated with a high-risk clinical course. ALT activation, MDM2/CDK4 co-amplification, SCA count, gains of 1q, 2p, and 17q, and deletions of 4p and 11q may also be relevant molecular markers. Larger studies are needed for confirmation of these findings.