Nadine Wolgast

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.

Acute leukemias are biologically and clinically heterogeneous hematologic malignancies. This dissertation examines this heterogeneity at three levels: population-based disease patterns, RNA-seq-based molecular classification, and developmental interpretation. First, nationwide German cancer registry data were used to characterize acute leukemias. The analyses revealed age-dependent differences in incidence, treatment, and survival. Rare entities, including T-cell prolymphocytic leukemia, showed particularly poor outcomes. These findings show that molecular and clinical observations require an epidemiological context. Second, this work developed IntegrateALL, an RNA-seq pipeline for molecular characterization of B-cell precursor acute lymphoblastic leukemia. IntegrateALL combines gene expression profiling, fusion detection, variant analysis, and RNA-derived karyotype inference in a reproducible workflow. A rule-based algorithm integrates these evidence layers to generate transparent subtype assignments. The pipeline shows that complex B-ALL subtypes can be inferred from RNA-seq data within a scalable framework. Third, the dissertation investigated whether transcriptome-derived developmental states correspond to immunoglobulin and T-cell receptor rearrangement patterns. These rearrangements were integrated with transcriptional developmental staging inferred by ALLCatchR. Transcriptional maturation was more strongly associated with molecular subtype architecture than with immunoglobulin rearrangement progression. IG/TR rearrangements therefore provide complementary information on locus-specific recombination history, but are not direct indicators of the global transcriptional differentiation state. Overall, epidemiological analyses, RNA-seq-based molecular classification, and immunogenetic profiling provide complementary perspectives on leukemic heterogeneity. Together, they connect population context, molecular diagnostics, and developmental biology.

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.

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.

Background: 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. We aimed to comprehensively assess how leukemic developmental state, fusion partner, and patient age jointly influence early treatment response and drug sensitivity. Methods: To identify determinants of early treatment response to induction chemotherapy, we analysed 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. 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. Results: 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 combined model confirmed the predominate impact of both maturity and KMT2A fusion partner on MRD response, supporting the concept that the cell’s 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 KMT2A r B-ALL cases response profiles suggested higher Venetoclax sensitivity. Conclusions: Our study provides an integrative framework linking developmental phenotype, fusion partner, and MRD kinetics across the full age spectrum of KMT2A r 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 KMT2A r B-ALL.