Biology subjects

Walter, W.

Publications and source records attributed to Walter, W..

8 recordsLinked to original sources

bioRxiv2026

Unbiased, systematic surfaceome profiling of Acute Leukemia to identify novel immunotherapy targets

Cell surface proteomics provides a direct topological assessment of the outer membrane of cells and enables the capture of low abundance proteins that may be missed by whole cell proteomics. Here we present an unbiased atlas of the whole cell and surface proteomes of 25 commonly used leukemic cell lines, encompassing both lymphoid and myeloid lineages, and a variety of driver mutations. Paired-wise analysis highlights recurrent surface proteins that are not detected by whole cell proteomics. Coupling this dataset to RNA-sequencing, we also discovered genes where protein and RNA abundances are discordant. In KMT2A-rearranged AML, CD70 expression was increased across cell lines and validated in primary patient samples, supporting CD70 as a candidate therapeutic target in this disease. Several proteins are enriched in the surface proteomes but lack surface annotation, adding to the growing list of potential non-canonical cell surface proteins. These findings reveal a substantial pool of proteins absent from conventional surface annotations, including RNA-binding proteins, an emerging class of candidate immunotherapeutic targets.

molecular biology↗

bioRxiv2026

Localized heme sensing through a ternary molecular glue

Molecular glues are an emerging class of therapeutics that stabilize binary interactions and there-by rewire disease-relevant protein networks. Whether glues can integrate additional information to orchestrate signaling beyond initial complex formation is unknown. Here, we report that cells use an endogenous glue strategy to sense heme, an essential metabolite with deleterious pro-oxidant properties. Distinct from other glues, heme bridges three polypeptides to trigger degradation of the transcriptional repressor BACH1 through cytoplasmic, but not mitochondrial, CUL2FEM1B. This mechanism allows cells to eliminate toxic heme in the cytoplasm by inducing expression of the heme-degrading oxygenase HMOX1, yet ignore mitochondrial heme destined for function in the electron transport chain. While protective in healthy cells, ternary glue signaling creates a therapeutic vulnerability for Acute Myeloid Leukemias dependent on high rates of ETC assembly. Molecular glues can therefore drive assembly of higher-order complexes to establish localized signaling, which offers unexplored opportunities for induced proximity therapeutics.

biochemistry↗

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

IntegrateALL: an end-to-end RNA-seq analysis pipeline for multilevel data extraction and interpretable subtype classification in B-precursor ALL

Transcriptome sequencing (RNA-seq) is emerging as a diagnostic standard for B-cell precursor acute lymphoblastic leukemia (B-ALL). Expression-based classifiers reach [~]95% accuracy, but reproducible end-to-end solutions that also integrate transcript-derived genomic drivers and quantitative virtual karyotyping are lacking. We developed IntegrateALL, a Snakemake pipeline that standardizes RNA-seq analysis from FASTQ to rule-based subtype assignment across 26 WHO-HAEM5/ICC entities by integrating expression-based subtype prediction, gene fusion- / hotspot SNV calling and virtual karyotyping. We introduce KaryALL, a machine-learning classifier that uses normalized expression and minor-allele-frequency features (RNASeqCNV) to distinguish near haploid, hypodiploid and high hyperdiploid B-ALL and chromosome-21 gains/iAMP21 (accuracy: 0.98 / F1-score: 0.96 on 615 independent test samples). SNP-array concordance supported RNA-based karyotyping. Applied to 774 unselected B-ALL cases, IntegrateALL yielded unambiguous subtype assignments in 81.5%, based on concordance of gene expression class with a defining driver (75.3% of all cases) or, in selected cases, high-confidence expression-based classification alone (6.2%); the remainder (18.5%) were flagged for manual curation. Independent validation (3 cohorts; n=436, including pediatric cases) reproduced these distributions. Across all patients (n=1,210), 2.6% harbored two subtype defining drivers, including hyperdiploidy in fusion-driven subtypes where it was not expected or subtype-defining SNVs (e.g., PAX5 P80R / IKZF1 N159Y) co-occurring with BCR::ABL1-positive/-like, KMT2A- or DUX4-fusions. In most dual-driver cases, one subtype gene expression signature predominated, indicating a hierarchy of oncogenic control and the value of systematic driver screening alongside expression-based calls. IntegrateALL provides an adaptable fully reproducible workflow for molecular B-ALL characterization by systematically integrating genomic drivers and downstream gene regulation.

cancer biology↗

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↗

bioRxiv2024

Pyjacker identifies enhancer hijacking events in acute myeloid leukemia including MNX1 activation via deletion 7q

Acute myeloid leukemia with complex karyotype (ckAML) is characterized by high genomic complexity, including frequent TP53 mutations and chromothripsis. We hypothesized that the numerous genomic rearrangements could reposition active enhancers near proto-oncogenes, leading to their aberrant expression. We developed pyjacker, a computational tool for the detection of enhancer hijacking events, and applied it to a cohort of 39 ckAML samples. Pyjacker identified motor neuron and pancreas homeobox 1 (MNX1), a gene aberrantly expressed in 1.4% of AML patients, often as a result of del(7)(q22q36) associated with hijacking of a CDK6 enhancer. MNX1-activated cases show significant co-occurrence with BCOR mutations and a gene signature shared with t(7;12)(q36;p13) pediatric AML. We demonstrated that MNX1 is a dependency gene, as its knockdown in a xenograft model reduces leukemia cell fitness. In conclusion, enhancer hijacking is a frequent mechanism for oncogene activation in AML. Statement of significanceThis study examines the consequences of structural alterations and demonstrates that proto-oncogene activation by enhancer hijacking is an overlooked pathomechanism in AML. MNX1 overexpression demonstrates that deletions on chromosome 7q can not only lead to haploinsufficiency, but also to activation of oncogenes by enhancer hijacking, providing a novel leukemogenic mechanism.

cancer biology↗

bioRxiv2023

A lineage-specific STAT5BN642H mouse model to study NK-cell leukemia

Patients with T- and NK-cell neoplasms frequently have somatic STAT5B gain-of-function mutations. The most frequent STAT5B mutation is STAT5BN642H, which is known to drive murine T-cell leukemia although its role in NK-cell malignancies is unclear. Introduction of the STAT5BN642H mutation into human NK-cell lines enhances their potential to induce leukemia in mice. We have generated a mouse model that enables tissue-specific expression of STAT5BN642H and have selectively expressed the mutated STAT5B in hematopoietic cells (N642Hvav/+) or exclusively in NK cells (N642HNK/NK). All N642Hvav/+ mice rapidly develop an aggressive T-/NK T-cell leukemia, whereas N642HNK/NK mice display an indolent chronic lymphoproliferative disorder of NK cells (CLPD-NK) that progresses to an aggressive leukemia with age. Samples from NK-cell leukemia patients have a distinctive transcriptional signature driven by mutant STAT5B, which overlaps with that of murine STAT5BN642H-expressing NK cells. We have generated the first reliable STAT5BN642H-driven pre-clinical mouse model that displays an indolent CLPD-NK progressing to aggressive NK-cell leukemia. This novel in vivo tool will enable us to explore the transition from an indolent to an aggressive disease and will thus permit the study of prevention and treatment options for NK-cell malignancies. Key pointsO_LIGeneration of a lineage-specific STAT5BN642H transgenic mouse model which develops NK-cell leukemia C_LIO_LILeukemic NK cells with a STAT5B gain of function mutation have a unique transcriptional profile in mice and human patients C_LI

cancer biology↗

bioRxiv2023

The gene expression classifier ALLCatchR identifies B-precursor ALL subtypes and underlying developmental trajectories across age

Current classifications (WHO-HAEM5 / ICC) define up to 26 molecular B-cell precursor acute lymphoblastic leukemia (BCP-ALL) disease subtypes, which are defined by genomic driver aberrations and corresponding gene expression signatures. Identification of driver aberrations by RNA-Seq is well established, while systematic approaches for gene expression analysis are less advanced. Therefore, we developed ALLCatchR, a machine learning based classifier using RNA-Seq expression data to allocate BCP-ALL samples to 21 defined molecular subtypes. Trained on n=1,869 transcriptome profiles with established subtype definitions (4 cohorts; 55% pediatric / 45% adult), ALLCatchR allowed subtype allocation in 3 independent hold-out cohorts (n=1,018; 75% pediatric / 25% adult) with 95.7% accuracy (averaged sensitivity across subtypes: 91.1% / specificity: 99.8%). High confidence predictions were achieved in 84.6% of samples with 99.7% accuracy. Only 1.2% of samples remained unclassified. ALLCatchR outperformed existing tools and identified novel candidates in previously unassigned samples. We established a novel RNA-Seq reference of human B-lymphopoiesis. Implementation in ALLCatchR enabled projection of BCP-ALL samples to this trajectory, which identified shared patterns of proximity of BCP-ALL subtypes to normal lymphopoiesis stages. ALLCatchR sustains RNA-Seq routine application in BCP-ALL diagnostics with systematic gene expression analysis for accurate subtype allocations and novel insights into underlying developmental trajectories.

cancer biology↗