Biology subjects

Bendig, S.

Publications and source records attributed to Bendig, S..

7 recordsLinked to original sources

bioRxiv2026

A Novel Natural Killer Cell Expansion Technology for the Development of Cellular Immunotherapies

Adoptive cell therapy based on Natural Killer (NK) cells holds great promise for the treatment of cancer. For all approaches aiming at utilizing NK cells in immunotherapy, efficient ex vivo expansion technologies for the generation on of cytotoxic NK cells are a prerequisite for clinical translation. In this study, a novel multifunctional fusion protein consisting of a CD20-directed Fab-fragment, an agonistic anti-4-1BB single-chain Fragment variable (scFv), the Sushi domain of the interleukin (IL)-15 receptor and human IL-15 was generated. This molecule triggered strong NK cell expansion when bound to co-cultivated autologous B cells, due to trans-presentation of IL-15 and binding to 4-1BB/CD137. Expansion rates of up to 7,500-fold were achieved and the NK cells showed high cytotoxic capacity against a panel of tumor cell lines representing various tumor entities. Importantly, the activated NK cells did not show cytolytic activity against non-malignant B cells indicating that NK cells amplified by our novel approach were still physiologically regulated. The cytotoxic activity of the expanded NK cells was further enhanced by combination with therapeutic antibodies. Our molecule was additionally able to trigger efficient proliferation of NK cells from cord blood as well as multiple myeloma (MM) and acute myeloid leukemia (AML) patients. In conclusion, our novel platform technology provides ex vivo expansion of NK cells by using a single multifunctional fusion protein and may be well-suited for the development of NK cell-based immunotherapies. Key pointsA novel fusion protein that enables NK cell expansion from different sources including peripheral blood, bone marrow and cord blood

immunology↗

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

Blinatumomab-driven T-cell activation in αβ and γδ T-cell subsets: Insights from in vitro assays

Blinatumomab (BLN) is a bispecific T-cell engager that has revolutionized the treatment of B-cell precursor acute lymphoblastic leukemia (BCP-ALL), significantly improving outcomes in both adults and children. By simultaneously binding to CD19 on B cells and CD3 on T cells, BLN triggers target cell-dependent T-cell activation, resulting in the cytolysis of CD19+ BCP-ALL cells. Despite the remarkable clinical advancements achieved with BLN, the immunological mechanisms underlying treatment response or failure remain poorly characterized. {gamma}{delta} T cells are attractive candidates for adoptive T-cell therapy due to potent cytotoxicity, capacity to present antigens, broad lysis of different tumor entities, and low alloreactivity. Because {gamma}{delta} T cells can also be redirected by BLN, we systematically studied BLN-driven effector functions of conventional {beta} and unconventional {gamma}{delta} T cells. We evaluated cytotoxicity and cytokine/effector release in freshly isolated and in vitro-expanded {beta} and {gamma}{delta} T cells from healthy adults against CD19 BCP-ALL lines (NALM-6, HAL-01), and profiled dynamic phenotypic alterations by multiparametric flow cytometry. CD19 targets were consistently reduced in the presence of BLN. Freshly isolated {beta}, especially CD8, displayed superior BLN-mediated cytotoxicity as compared to {gamma}{delta} T cells, with donor-dependent variability in {gamma}{delta} killing. Notably, zoledronate-expanded V{gamma}9V{delta}2 {gamma}{delta} T-cell lines achieved cytotoxicity comparable to PHA-expanded {beta} cells. However, {gamma}{delta} T-cell-killing benefited from higher BLN concentration when challenged with high tumor load. BLN induced CD3 down-modulation in {beta} T cells but not in {gamma}{delta} T cells, alongside higher soluble Fas ligand in {beta} cultures, consistent with stronger early activation, preceding activation-induced cell death. {gamma}{delta} T cells showed no such changes, suggesting reduced susceptibility to activation-induced cell death. Single-cell RNA and flow analyses corroborated these findings, showing robust activation/exhaustion programs in {beta} T cells and a stable effector-memory state with low checkpoint expression in {gamma}{delta} T cells. Together, these data reveal subset-specific BLN responses and support expanded V{gamma}9V{delta}2 {gamma}{delta} T cells as a rational adoptive partner to BLN -- particularly in settings of favorable antigen density/low tumor burden -- providing complementary cytotoxicity with potentially reduced inflammatory liability. These findings provide a framework for combining {gamma}{delta} T-cell-based therapies in BLN-treated patients for improving BLN efficacy in BCP-ALL patients.

immunology↗

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

Myeloid cell-mediated killing of B-ALL by CD38 and CD20IgA antibody variants is enhanced by CD47/SIRPα interference

Enhancing myeloid effector cell recruitment may improve immunotherapy by monoclonal antibodies - including that against acute lymphoblastic leukemia (ALL). To assess expression of target antigens in B-ALL, we compared mRNA profiling of 559 patient leukemia samples across 18 molecular subtypes with that of representative cell lines. The latter served as target cells to compare human IgG1 or IgA2 variants against CD19, CD20 or CD38 in antibody-dependent cellular phagocytosis (ADCP) by macrophages and antibody-dependent cell-mediated cytotoxicity (ADCC) by polymorphonuclear leukocytes (PMN). Interestingly, antibodies against broadly expressed CD19 were negligibly effective in mediating ADCP or ADCC. Antibodies against CD20 or CD38, the former variably expressed across subtypes, triggered ADCP by macrophages both as IgG1 and IgA2. However, PMN mediated ADCC against CD20 or CD38 was only observed with IgA2 variants, but not with respective IgG1 antibodies. Blocking the myeloid checkpoint molecule CD47 with a CD47 antibody or a soluble SIRP-Fc fusion protein enhanced ADCP and ADCC by IgA2 antibodies. The binding site for SIRP on CD47 contains an N-terminal pyroglutamate (pGlu), whose formation is catalyzed by glutaminyl-peptide cyclotransferase like (QPCTL). The direct involvement of pGlu in CD47/SIRP interactions was shown by using engineered CD47 variants. Both CD47 and QPCTL were broadly expressed across BCP-ALL subtypes, indicating QPCTL inhibitors as additional therapeutic option. Importantly, the combination of anti-CD38 IgA2 and CD47 blockade was effective against xenografted B-ALL cells in human FcRI (CD89) transgenic (tg) NXG mice. Together, these studies support the combination of anti-CD38 IgA2 with CD47 interference to improve myeloid effector cell recruitment for immunotherapy of B-ALL. Data sharing statementRNA-Seq data from BCP-ALL patients and B cells from healthy donors are available in the European Genome-Phenome Archive (EGA) accession numbers EGAS00001006107 and EGAS00001007305, respectively. Mass spectrometry data of analyzed proteins will be made available after manuscript acceptance on PRIDE - PRoteomics IDEntifications Database. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=174 HEIGHT=200 SRC="FIGDIR/small/669665v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@15220daorg.highwire.dtl.DTLVardef@eace04org.highwire.dtl.DTLVardef@17b87daorg.highwire.dtl.DTLVardef@1142822_HPS_FORMAT_FIGEXP M_FIG C_FIG Key pointsO_LIIgG1 and IgA2 antibodies against CD38 or CD20 were effective in recruiting macrophages for ADCP, but only IgA2 triggered ADCC by PMN C_LIO_LIMyeloid effector cell activation was enhanced by interfering with the CD47/SIRP axis, especially when IgA2 antibodies were applied C_LI

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↗

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↗