Project Details
Projekt
Processing, management, analyses, and integrated modeling of multiscale OMICS data
Subject Area
Hematology, Oncology
Term
since 2025
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 444949889
The Bioinformatics and Modeling (INF) platform is an interdisciplinary, multi-PI component of the clinical research initiative CATCH ALL ("Towards a Cure for Adults and Children with Acute Lymphoblastic Leukemia"). INF has successfully processed, managed, and integrated OMICS data, facilitating data-driven research and enhancing clinical decision-making. It has broadened bioinformatics and analytical capabilities across research projects while advancing machine learning and data analysis pipelines, adhering to FAIR principles (Findable, Accessible, Interoperable, and Reusable). The long-term goals include (1) clinical data management: to continue supporting research with efficient data handling, (2) AI & machine learning: to develop molecular and cellular leukemia classification models aimed at improving the understanding of biological and clinical disease, and (3) computational modeling: to enhance our existing tools and create new computational resources to study disease evolution and optimize treatment strategies. Our key activities are: Data Processing & Management: Handling and classifying OMICS and imaging data, custom & Standardized Analysis Pipelines: Adapting workflows for various investigational and translational needs within the clinical research unit, datadriven Modeling: Applying AI to study molecular composition and leukemia progression. INF's data-driven research includes molecular characterization of ALL, differential gene expression analysis, immune deconvolution of the tumor microenvironment, and computational modeling of leukemia growth and evolution. To this end, our objectives are to: 1. Enhance Data Management & Analysis Services across clinical research projects. 2. Maintain & Develop Pipelines for multi-level data integration. 3. Investigate digital Models for leukemia evolution, prediction, and prognosis. This proposal is organized into three work packages. Our first work package aims to deliver the data management and analysis infrastructure within CATCH ALL, supporting all other projects. Second, we will focus on maintaining and further developing computational pipelines to facilitate the analysis of multi-level data sets. We will develop and implement Bayesian neural networks for disease classification, investigate the relationship between molecular heterogeneity (at both bulk and single-cell levels) and clinical outcomes, integrate image analysis, create predictive models for molecular subtype diagnostics, and examine the significance of sex-biased gene expression patterns. Third, we will utilize these tools to develop new strategies to explore ALL evolution and the potential to implement digital twin models of the disease. Finally, INF promotes community engagement and public outreach, expanding the research unit’s reach in academia and the public in local and regional settings.
DFG Programme
Clinical Research Units
