Biology subjects

Schrappe, M.

Publications and source records attributed to Schrappe, M..

6 recordsLinked to original sources

bioRxiv2025

A novel Fc-optimized antibody drug conjugate targeting CD7 for the therapy of T-cell acute lymphoblastic leukemia

While treatment for patients with T-cell acute lymphoblastic leukemia (T-ALL) has improved in the last decades, therapeutic options for patients refractory to standard therapy or with relapsing disease are limited. In particular, no immunotherapy option has been approved in T-ALL yet. Here, a novel dual antibody engineering approach for targeting CD7 was evaluated. The chimeric CD7 antibody chimTH69 was modified by Fc engineering to improve antibody-dependent cell-mediated cytotoxicity (ADCC) and antibody-dependent cellular phagocytosis (ADCP). In addition, it was conjugated to monomethyl auristatin E (MMAE), a microtubule-disrupting agent. The resulting Fc-optimized antibody-drug conjugate (ADC), designated chimTH69-DE-vcMMAE, showed a unique set of effector functions in vitro. It triggered ADCC by mononuclear cells at picomolar concentrations, mediated ADCP by macrophages and directly inhibited the growth of a panel of T-ALL cell lines by delivering the cytotoxic compound to induce G2 cell cycle arrest and apoptosis. In addition, due to its specific linker design, chimTH69-DE-vcMMAE demonstrated bystander killing activity against CD7-negative leukemia cells. In mice, CD7-directed therapy with chimTH69-DE-vcMMAE inhibited the growth of subcutaneous CCRF-CEM T-ALL xenografts. Moreover, chimTH69-DE-vcMMAE exerted strong antileukemic effects in a phase II-like patient-derived xenograft preclinical trial in pediatric and adult patients when applied in an experimental overt leukemia setting. ChimTH69-DE-vcMMAE induced minimal residual disease-negativity in one PDX model. These findings indicate that targeting CD7 with the novel Fc-optimized ADC is a potent strategy to trigger anti-leukemia responses and may open a novel therapeutic avenue for T-ALL treatment. Key PointA novel antibody drug conjugate targeting CD7 showed efficient anti-leukemia activity in preclinical models of T-ALL.

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

Role of Stem-Like Cells in Chemotherapy Resistance and Relapse in pediatric T Cell Acute Lymphoblastic Leukemia

T cell acute lymphoblastic leukemia (T-ALL) is an aggressive leukemia predominantly affecting adolescents and young adults. T-ALL relapses are characterized by chemotherapy resistance, cellular heterogeneity and dismal outcome. To gain a deeper understanding of the cellular heterogeneity and mechanisms driving relapse, we conducted single-cell full-length RNA sequencing of 13 matched pediatric T-ALL patient-derived xenografts (PDX) samples obtained at initial diagnosis and relapse, generating the to date most comprehensive longitudinal single cell study in paired T-ALL samples, along with 5 non-relapsing PDX samples collected at initial diagnosis. This dataset identifies considerable transcriptomic diversity among individual T-ALL cell populations. Notably however, 11 of the 18 patients exhibit a small T-ALL cell subpopulation with a shared set of gene regulatory networks characterized by a common set of active regulons, expression patterns and splice isoforms. This profile involves the upregulation of a stem-like cell signature with enrichment of cell adhesion, and inhibition of the cell cycle and metabolic activity. Comprehensive investigations of these networks identify transcripts enforcing drug resistance through NF-{kappa}B expression and TGF-{beta} signaling and of anti-apoptotic T cell signaling. Longitudinal monitoring of these stem-like cells demonstrates this subpopulation to account for only a small proportion of leukemia cells initially with a substantial expansion at relapse suggesting resistance to first line therapy. Chemotherapy resistance is functionally corroborated through in vitro and in vivo drug testing. We thus report the discovery of inherently treatment-resistant stem-like T-ALL cell populations underscoring the potential for devising future therapeutic strategies aimed at targeting stemness pathways in pediatric T-ALL. Key PointsO_LISingle-cell full-length RNA sequencing reveals dormant, treatment-resistant cells expanding upon T cell acute lymphoblastic leukemia relapse. C_LIO_LIStem-like T-ALL cells exhibit a shared gene regulatory network conferring chemotherapy resistance and relapse. 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↗

bioRxiv2019

Single cell tri-channel-processing reveals structural variation landscapes and complex rearrangement processes

Structural variation (SV), where rearrangements delete, duplicate, invert or translocate DNA segments, is a major source of somatic cell variation. It can arise in rapid bursts, mediate genetic heterogenity, and dysregulate cancer-related pathways. The challenge to systematically discover SVs in single cells remains unsolved, with copy-neutral and complex variants typically escaping detection. We developed single cell tri-channel-processing (scTRIP), a computational framework that jointly integrates read depth, template strand and haplotype phase to comprehensively discover SVs in single cells. We surveyed SV landscapes of 565 single cell genomes, including transformed epithelial cells and patient-derived leukemic samples, and discovered abundant SV classes including inversions, translocations and large-scale genomic rearrangements mediating oncogenic dysregulation. We dissected the molecular karyotype of the leukemic samples and examined their clonal structure. Different from prior methods, scTRIP also enabled direct detection and discrimination of SV mutational processes in individual cells, including breakage-fusion-bridge cycles. scTRIP will facilitate studies of clonal evolution, genetic mosaicism and somatic SV formation, and could improve disease classification for precision medicine.

genomics↗

bioRxiv2018

Long non-coding RNAs defining major subtypes of B cell precursor acute lymphoblastic leukemia

Recent studies implicated that long non-coding RNAs (lncRNAs) may play a role in the progression and development of acute lymphoblastic leukemia, however, this role is not yet clear. In order to unravel the role of lncRNAs associated with B-cell precursor Acute Lymphoblastic Leukemia (BCP-ALL) subtypes, we performed transcriptome sequencing and DNA methylation array across 82 BCP-ALL samples from three molecular subtypes (DUX4, Ph-like, and Near Haploid or High Hyperdiploidy). Unsupervised clustering of BCP-ALL samples on the basis of their lncRNAs on transcriptome and DNA methylation profiles revealed robust clusters separating three molecular subtypes. Using extensive computational analysis, we developed a comprehensive catalog of 1235 aberrantly dysregulated BCP-ALL subtype-specific lncRNAs with altered expression and methylation patterns from three subtypes of BCP-ALL. By analyzing the co-expression of subtype-specific lncRNAs and protein-coding genes, we inferred key molecular processes in BCP-ALL subtypes. A strong correlation was identified between the DUX4 specific lncRNAs and activation of TGF-{beta} and Hippo signaling pathways. Similarly, Ph-like specific lncRNAs were correlated with genes involved in activation of PI3K-AKT, mTOR, and JAK-STAT signaling pathways. Interestingly, the relapse-specific differentially expressed lncRNAs correlated with the activation of metabolic and signaling pathways. Finally, we showed a set of epigenetically altered lncRNAs facilitating the expression of tumor genes located at their cis location. Overall, our study provides a comprehensive set of novel subtype and relapse-specific lncRNAs in BCP-ALL. Our findings suggest a wide range of molecular pathways are associated with lncRNAs in BCP-ALL subtypes and provide a foundation for functional investigations that could lead to new therapeutic approaches.\n\nAuthor SummaryAcute lymphoblastic leukemia is a heterogeneous blood cancer, with multiple molecular subtypes, and with high relapse rate. We are far from the complete understanding of the rationale behind these subtypes and high relapse rate. Long non-coding (lncRNAs) has emerged as a novel class of RNA due to its diverse mechanism in cancer development and progression. LncRNAs does not code for proteins and represent around 70% of human transcripts. Recently, there are a number of studies used lncRNAs expression profile in the classification of various cancers subtypes and displayed their correlation with genomic, epigenetic, pathological and clinical features in diverse cancers. Therefore, lncRNAs can account for heterogeneity and has independent prognostic value in various cancer subtypes. However, lncRNAs defining the molecular subtypes of BCP-ALL are not portrayed yet. Here, we describe a set of relapse and subtype-specific lncRNAs from three major BCP-ALL subtypes and define their potential functions and epigenetic regulation. Our data uncover the diverse mechanism of action of lncRNAs in BCP-ALL subtypes defining how lncRNAs are involved in the pathogenesis of disease and the relevance in the stratification of BCP-ALL subtypes.

cancer biology↗