Biology subjects

Walter, W.

Publications and source records attributed to Walter, W..

2 recordsLinked to original sources

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↗