An integrative bioinformatics approach to characterize molecular landscapes and clinical outcomes in acute leukemias
type: CreativeWork
description: 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.
abstract: 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.
author: Wolgast, Nadine
keywords: Bioinformatics, Cancer Research, Acute Leukemia
date published: 2026-06-19
date modified: 2026-07-24T01:35:26Z
provider: Christian-Albrechts Universität Kiel
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Christian-Albrechts-Universität zu Kiel