Biology subjects

Barz, M. J.

Publications and source records attributed to Barz, M. J..

3 recordsLinked to original sources

bioRxiv2026

Quantitative and sensitive neuroblastoma minimal residual disease detection using extrachromosomal DNA (ecDNA) breakpoints

Sensitive detection of minimal residual disease (MRD) remains a major unmet need in high-risk neuroblastoma. MYCN amplification, a hallmark of high-risk disease, typically occurs on extrachromosomal DNA (ecDNA), but the potential of ecDNA-associated genomic rearrangements for individualized MRD monitoring has not been fully exploited. Here, we applied neuroblastoma-specific hybrid capture-based panel sequencing to identify patient-unique breakpoints within MYCN amplicons, and used Circle-seq and Nanopore sequencing to resolve the extrachromosomal amplicon structure in representative samples. Analysis of 8 neuroblastoma cell lines and 22 primary tumors identified 69 tumor-specific breakpoints. Those selected for assay development were validated by breakpoint-specific PCR and Sanger sequencing. Breakpoints detected in primary tumors remained detectable at relapse, supporting their stability as MRD markers. Breakpoint-specific real-time quantitative PCR and droplet digital PCR detected these junctions in bone marrow aspirates with high specificity and reached sensitivities down to a tumor DNA fraction of 10^-6. We applied this approach to 53 serial bone marrow aspirates from 14 patients with high-risk neuroblastoma to monitor MRD dynamics, resolving treatment response and molecular persistence. In six samples, breakpoint-positive DNA was detected in bone marrow that was negative by conventional cytology and immunocytology, highlighting the added value of molecular monitoring. Together, these findings establish ecDNA breakpoint-based detection as a strategy for MRD assessment in neuroblastoma, that is, in principle, applicable to any ecDNA-amplified oncogene.

cancer biology↗

bioRxiv2026

ALLCatchR, a machine-learning classifier identifies now 20 T-ALL subtypes across cohorts and age groups

T-cell acute lymphoblastic leukemia (T-ALL) comprises molecularly diverse subtypes, but robust cross-cohort validations and operational gene-expression definitions are lacking. To establish a gene-expression-anchored framework for T-ALL subtyping, we aggregated 2,314 transcriptomes (15 cohorts, age: 0.8 to 90.8 years). An extended unsupervised approach defined 17 main clusters and 3 subclusters in samples with high blast fractions. Supervised analyses added an overarching immature T-ALL (ETP-like) definition and resolved the LMO2{gamma}{delta} -like subtype. All clusters contained samples from at least two cohorts. Characteristic genomic driver enrichments were consistent across cohorts, while gene expression clusters did not correspond exclusively to single driver events but also reflected developmental origins. A machine learning classifier based on ALLCatchR, our B-ALL classifier, identified these 20 transcriptomic subtypes and the immature T-ALL (ETP-like) signature with 0.995-1.0 accuracy in a validation set (n=203). Testing the classifier on a second hold-out data set (n=265 samples) showed that 92.7% of predictions matched with corresponding driver alterations. Across all samples, 83.2% of cases received high-confidence predictions, 7.3% candidate predictions, and 9.5% remained unclassified, largely because of low blast fractions. We identified a novel gene expression cluster markedly enriched (P<0.001) for clonal hematopoiesis mutations (IDH2 R140Q, DNMT3A) and a stem-/progenitor cell-like gene expression. This novel clonal hematopoiesis-related T-ALL subtype was observed in six cohorts representing 8.9% of adults and 39.5% of patients aged >50 years. We advanced ALLCatchR, as a free R package that now enables B-/T-lineage separation, gene-expression subtyping, blast estimation, and developmental annotation to harmonize T-ALL classification across studies and clinical contexts. Key PointsO_LIEstablished using 2,314 T-ALL transcriptomes, ALLCatchR2 assigns 20 RNA-Seq subtypes and their developmental underpinnings across ages. C_LIO_LIClonal hematopoiesis-related T-ALL (DNMT3A, IDH2 R140Q) defines an immature gene-expression cluster in [~]40% of patients aged >50 years. C_LI

bioinformatics↗

bioRxiv2025

Intra-subtype heterogeneity shapes treatment response in KMT2A-rearranged ALL across all age groups

BackgroundKMT2A-rearranged B-cell acute lymphoblastic leukemia (KMT2Ar B-ALL) exhibits significant heterogeneity in age of onset, developmental origins, and clinical outcomes. The interplay of individual factors influencing early treatment response within this high-risk molecular subtype remains poorly elucidated. We aimed to comprehensively assess how leukemic developmental state, fusion partner, and patient age jointly influence early treatment response and drug sensitivity. MethodsTo identify determinants of early treatment response to induction chemotherapy, we analysed 465 KMT2Ar B-ALL cases spanning a wide age range (1 month to 89 years) by integrating transcriptomic and genomic profiling with functional drug response and measurable residual disease (MRD) kinetics. Transcriptomic profiling was used to derive a developmental maturity score based on proximity to physiological B-cell differentiation from a normal B-lymphopoiesis reference. Using an ordinal regression model we identify gene expression programs associated with early MRD response. ResultsWe observed a strong inverse correlation between MRD clearance with advancing age (p=2.1E-04), proximity to early B-cell-precursor developmental state (low maturity score, p=1.3E-03) and AFF1 as fusion partner (p=7.0E-04). A combined model confirmed the predominate impact of both maturity and KMT2A fusion partner on MRD response, supporting the concept that the cells developmental state defines therapy response. Gene expression analysis identified cellular traits that relate to MRD response (e.g. chromatin organization, immune modulation and proliferation). This gene expression classifier grouped cases by MRD response but also by ex-vivo induction drug sensitivity. Notably, good responders to ex-vivo induction drugs were characterized by a higher maturity score (p=1.8E-03), whereas for less mature KMT2Ar B-ALL cases response profiles suggested higher Venetoclax sensitivity. ConclusionsOur study provides an integrative framework linking developmental phenotype, fusion partner, and MRD kinetics across the full age spectrum of KMT2Ar B-ALL. The maturity score, derived from bulk transcriptome data, offers a biologically relevant predictor of early treatment response and drug sensitivity. These insights may support future risk-adapted strategies and therapeutic targeting, particularly in immature KMT2Ar B-ALL.

cancer biology↗