Alina Maria Hartmann

Acute myeloid leukemia (AML) is characterized by complex molecular alterations including mutations in epigenetic regulators such as IDH1 and DNMT3A , which are associated with globally altered DNA methylation affecting gene transcription, treatment choices, and outcomes. Additionally, IDH1 mutations are linked to changes in DNA-loop formation leading to oncogene upregulation. Here, we assessed 3D-DNA conformational changes in IDH1- and DNMT3A -mutated (mut) AML on a genome-wide scale as a global driver of leukemic signaling to identify novel therapeutic vulnerabilities. Using a cellular model with either IDH1 p.R132H or DNMT3A p.R882H mutation and primary AML samples, we analyzed mutation-specific 3D-DNA architecture by chromatin-conformation-capture (HiC) and transcriptional alterations by RNA sequencing. We identified both shared and distinct changes in compartmentalization and underlying DNA-loop formation in both DNMT3A- and IDH1 -mut AML, which were linked to differential gene expression, supporting that 3D-genome architecture broadly influences transcription. Thereby, loop-mediated upregulation of IGF1R was identified in IDH1 -mut AML, corresponding to specific sensitivity to IGF1R inhibitor BMS-754807 as mono- and combination therapy with ivosidenib, while DNMT3A -mut AML exhibited sensitivity to p38/MAPK inhibitor ralimetinib. Our data present a comprehensive map of global 3D-DNA alterations associated with IDH1 - and DNMT3A mutations as a basis for further assessment of novel therapeutic strategies for both AML entities.

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 2314 transcriptomes (15 cohorts, age: 0.8–90.8 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 (early T cell precursor [ETP]‐like) definition and resolved the LMO2 γδ‐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‐cell acute lymphoblastic leukemia (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 and accounted for 8.9% of adults and 39.5% of patients aged >50 years. We extended ALLCatchR into ALLCatchR2, 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.

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 (three cohorts; n = 436, including pediatric cases) reproduced these distributions. Across all patients ( n = 1210), 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, consistent with oncogenic hierarchies, but also with the possibility of technical artifacts, which should prompt individual orthogonal validations. IntegrateALL provides an adaptable fully reproducible workflow for molecular B‐ALL characterization by systematically integrating genomic drivers and downstream gene regulation.

Large scale sequencing efforts have defined up to 27 diagnostic entities in B-ALL, leaving few samples without subtype assignment. Extended genomic and transcriptomic profiling in routine diagnostics broadens the sample collection and holds the potential to identify novel B-ALL subtypes. By analyzing an aggregated set of 4,857 B-ALL patients from three cohorts, we identified a novel group of twenty cases (age 18-66 years, median: 34 years) characterized by a previously undescribed IGH::FENDRR rearrangement exclusive to this subtype (n=17/20), KRAS p.A146T/V/P mutations (n=17/20 vs. n=86/4,857; p<0.001) and distinct DNA-methylation/gene expression profiles, including overexpression of the lncRNA FENDRR and the transcription factor FOXF1 ('FOXF1/FENDRR') as well as JAK/STAT and RAS signaling signatures. A gene expression machine learning classifier identified FOXF1/FENDRR cases in two independent cohorts with high accuracy. Patients treated according to GMALL/GRAALL protocols showed very poor chemotherapy response with n=8/13 having induction failure or MRD ≥10-3 and n=8/12 remaining MRD positive after 1st consolidation / salvage. MRD-stratified intensification including blinatumomab (n=10) and/or allogenic stem cell transplantation (n=12) resulted in ongoing molecular remission in 13/16 cases. FOXF1/FENDRR patients represent a novel B-ALL subtype which might benefit from early immunotherapeutic treatment or targeted interventions.

KMT2A ‐rearranged B‐cell acute lymphoblastic leukemia ( KMT2A r 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. To identify determinants of early treatment response to induction chemotherapy, we analyzed 465 KMT2A r 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. We 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 multivariable analysis confirmed the strong impact of maturity (P = 0.02) and KMT2A fusion partner (P = 0.03) on MRD clearance, supporting the concept that the cell's developmental state defines therapy response. Gene expression analysis identified cellular traits that relate to MRD clearance (e.g., chromatin organization, immune modulation, and proliferation). This gene expression classifier grouped cases not only by MRD clearance but also by ex vivo sensitivity to induction therapy drugs. Notably, good responders to ex vivo induction drugs were characterized by a higher maturity score (P = 1.8E−03), whereas for less mature KMT2A r B‐ALL cases, response profiles suggested higher Venetoclax sensitivity. Our study provides an integrative framework linking developmental phenotype, fusion partner, and MRD kinetics across the full age spectrum of KMT2A r B‐ALL. These insights may support future risk‐adapted strategies and therapeutic targeting, particularly in immature KMT2A r B‐ALL.

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 γδ-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 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 Points: Established using 2,314 T-ALL transcriptomes, ALLCatchR2 assigns 20 RNA-Seq subtypes and their developmental underpinnings across ages. Clonal hematopoiesis-related T-ALL ( DNMT3A , IDH2 R140Q ) defines an immature gene-expression cluster in ∼40% of patients aged >50 years.

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.

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 SIRPα-Fc fusion protein enhanced ADCP and ADCC by IgA2 antibodies. The binding site for SIRPα 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/SIRPα 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 FcαRI (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 statement: RNA-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 Key points: IgG1 and IgA2 antibodies against CD38 or CD20 were effective in recruiting macrophages for ADCP, but only IgA2 triggered ADCC by PMN Myeloid effector cell activation was enhanced by interfering with the CD47/SIRPα axis, especially when IgA2 antibodies were applied