Decoil: Reconstructing Extrachromosomal DNA Structural Heterogeneity from Long-Read Sequencing Data - Giurgiu, Mădălina; Wittstruck, Nadine; Rodriguez-Fos, Elias; González, Rocío Chamorro; Brückner, Lotte; Krienelke-Szymansky, Annabell; Helmsauer, Konstantin; Hartebrodt, Anne; Euskirchen, Philipp; Koche, Richard P.; Haase, Kerstin; Reinert, Knut; et al. (Research in Computational Molecular Biology. 28th Annual International Conference, RECOMB 2024, Cambridge, MA, USA, April 29–May 2, 2024, Proceedings, 2024)
Circular extrachromosomal DNA (ecDNA) is a form of oncogene amplification found across cancer types and associated with poor outcome in patients. EcDNA can be structurally complex and contain rearranged DNA sequences derived from multiple chromosome locations. As the structure of ecDNA can impact oncogene regulation and may indicate mechanisms of its formation, disentangling it at high resolution from sequencing data is essential. Even though methods have been developed to identify and reconstruct ecDNA in cancer genome sequencing, it remains challenging to resolve complex ecDNA structures, in particular amplicons with shared genomic footprints. We here introduce Decoil, a computational method which combines a breakpoint-graph approach with LASSO regression to reconstruct complex ecDNA and deconvolve co-occurring ecDNA elements with overlapping genomic footprints from long-read nanopore sequencing. Decoil outperforms de-novo assembly and alignment-based methods in simulated long-read sequencing data for both simple and complex ecDNAs. Applying Decoil on whole genome sequencing data uncovered different ecDNA topologies and explored ecDNA structure heterogeneity in neuroblastoma tumors and cell lines, indicating that this method may improve ecDNA structural analyzes in cancer.
CRIS-Team Scopus Importer:2024-08-09
Keywords: ecDNA; heterogeneity; long-read; nanopore; reconstruction

Federated horizontally partitioned principal component analysis for biomedical applications - Hartebrodt, Anne; Röttger, Richard (Bioinformatics Advances, 2022)
Motivation: Federated learning enables privacy-preserving machine learning in the medical domain because the sensitive patient data remain with the owner and only parameters are exchanged between the data holders. The federated scenario introduces specific challenges related to the decentralized nature of the data, such as batch effects and differences in study population between the sites. Here, we investigate the challenges of moving classical analysis methods to the federated domain, specifically principal component analysis (PCA), a versatile and widely used tool, often serving as an initial step in machine learning and visualization workflows. We provide implementations of different federated PCA algorithms and evaluate them regarding their accuracy for high-dimensional biological data using realistic sample distributions over multiple data sites, and their ability to preserve downstream analyses. Results: Federated subspace iteration converges to the centralized solution even for unfavorable data distributions, while approximate methods introduce error. Larger sample sizes at the study sites lead to better accuracy of the approximate methods. Approximate methods may be sufficient for coarse data visualization, but are vulnerable to outliers and batch effects. Before the analysis, the PCA algorithm, as well as the number of eigenvectors should be considered carefully to avoid unnecessary communication overhead.
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Federated Principal Component Analysis for Genome-Wide Association Studies - Hartebrodt, Anne; Nasirigerdeh, Reza; Blumenthal, David B.; Röttger, Richard (21st IEEE International Conference on Data Mining (ICDM), 2021)
Federated learning (FL) has emerged as a privacy-aware alternative to centralized data analysis, especially for biomedical analyses such as genome-wide association studies (GWAS). The data remains with the owner, which enables studies previously impossible due to privacy protection regulations. Principal component analysis (PCA) is a frequent preprocessing step in GWAS, where the eigenvectors of the sample-by-sample covariance matrix are used as covariates in the statistical tests. Therefore, a federated version of PCA suitable for vertical data partitioning is required for federated GWAS. Existing federated PCA algorithms exchange the complete sample eigenvectors, a potential privacy breach. In this paper, we present a federated PCA algorithm for vertically partitioned data which does not exchange the sample eigenvectors and is hence suitable for federated GWAS. We show that it outperforms existing federated solutions in terms of convergence behavior and scalability. Additionally, we provide a user-friendly privacy-aware web tool to promote acceptance of federated PCA among GWAS researchers.<br

Federated singular value decomposition for high-dimensional data - Hartebrodt, Anne; Röttger, Richard; Blumenthal, David B. (Data Mining and Knowledge Discovery, 2024)
Federated learning (FL) is emerging as a privacy-aware alternative to classical cloud-based machine learning. In FL, the sensitive data remains in data silos and only aggregated parameters are exchanged. Hospitals and research institutions which are not willing to share their data can join a federated study without breaching confidentiality. In addition to the extreme sensitivity of biomedical data, the high dimensionality poses a challenge in the context of federated genome-wide association studies (GWAS). In this article, we present a federated singular value decomposition algorithm, suitable for the privacy-related and computational requirements of GWAS. Notably, the algorithm has a transmission cost independent of the number of samples and is only weakly dependent on the number of features, because the singular vectors corresponding to the samples are never exchanged and the vectors associated with the features are only transmitted to an aggregator for a fixed number of iterations. Although motivated by GWAS, the algorithm is generically applicable for both horizontally and vertically partitioned data.
CRIS-Team Scopus Importer:2023-11-03
Keywords: Federated learning; Genome-wide association studies; Principal component analysis; Singular value decomposition

Privacy of Federated QR Decomposition Using Additive Secure Multiparty Computation - Hartebrodt, Anne; Röttger, Richard (IEEE Transactions on Information Forensics and Security, 2023)
Federated learning (FL) is a privacy-aware data mining strategy keeping the private data on the owners' machine and thereby confidential. The clients compute local models and send them to an aggregator which computes a global model. In hybrid FL, the local parameters are additionally masked using secure aggregation, such that only the global aggregated statistics become available in clear text, not the client specific updates. In this context, we investigate the data leakage of three popular algorithms for QR decomposition, Gram-Schmidt orthonormalization, the Householder algorithm and Givens rotation. We show that, even when using additive SMPC, Givens rotation and the Householder matrix leak raw data and are therefore not suited for this computation paradigm. Gram-Schmidt orthonormalization relies on inner vector products and does not leak raw data points.
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Keywords: Federated learning; federated matrix orthonormalization; linear regression; privacy analysis; QR factorization

Reconstructing extrachromosomal DNA structural heterogeneity from long-read sequencing data using Decoil - Giurgiu, Mădălina; Wittstruck, Nadine; Rodriguez-Fos, Elias; González, Rocío Chamorro; Brückner, Lotte; Krienelke-Szymansky, Annabell; Helmsauer, Konstantin; Hartebrodt, Anne; Euskirchen, Philipp; Koche, Richard P.; Haase, Kerstin; Reinert, Knut; et al. (Genome Research, 2024)
Circular extrachromosomal DNA (ecDNA) is a form of oncogene amplification found across cancer types and associated with poor outcome in patients. ecDNA can be structurally complex and can contain rearranged DNA sequences derived from multiple chromosome locations. As the structure of ecDNA can impact oncogene regulation and may indicate mechanisms of its formation, disentangling it at high resolution from sequencing data is essential. Even though methods have been developed to identify and reconstruct ecDNA in cancer genome sequencing, it remains challenging to resolve complex ecDNA structures, in particular amplicons with shared genomic footprints. We here introduce Decoil, a computational method that combines a breakpoint-graph approach with LASSO regression to reconstruct complex ecDNA and deconvolve co-occurring ecDNA elements with overlapping genomic footprints from long-read nanopore sequencing. Decoil outperforms de novo assembly and alignment-based methods in simulated long-read sequencing data for both simple and complex ecDNAs. Applying Decoil on whole-genome sequencing data uncovered different ecDNA topologies and explored ecDNA structure heterogeneity in neuroblastoma tumors and cell lines, indicating that this method may improve ecDNA structural analyses in cancer.
CRIS-Team Scopus Importer:2024-11-01

Single-Cell Multi-Omics Map of Cell Type-Specific Mechanistic Drivers of Multiple Sclerosis Lesions - Elkjaer, Maria L.; Hartebrodt, Anne; Oubounyt, Mhaned; Weber, Anna; Vitved, Lars; Reynolds, Richard; Thomassen, Mads; Rottger, Richard; Baumbach, Jan; Illes, Zsolt (Neurology, Neuroimmunology and Neuroinflammation, 2024)
BACKGROUND AND OBJECTIVES: In progressive multiple sclerosis (MS), compartmentalized inflammation plays a pivotal role in the complex pathology of tissue damage. The interplay between epigenetic regulation, transcriptional modifications, and location-specific alterations within white matter (WM) lesions at the single-cell level remains underexplored. METHODS: We examined intracellular and intercellular pathways in the MS brain WM using a novel dataset obtained by integrated single-cell multi-omics techniques from 3 active lesions, 3 chronic active lesions, 3 remyelinating lesions, and 3 control WM of 6 patients with progressive MS and 3 non-neurologic controls. Single-nucleus RNA-seq and ATAC-seq were combined and additionally enriched with newly conducted spatial transcriptomics from 1 chronic active lesion. Functional gene modules were then validated in our previously published bulk tissue transcriptome data obtained from 73 WM lesions of patients with progressive MS and 25 WM of non-neurologic disease controls. RESULTS: Our analysis uncovered an MS-specific oligodendrocyte genetic signature influenced by the KLF/SP gene family. This modulation has potential associations with the autocrine iron uptake signaling observed in transcripts of transferrin and its receptor LRP2. In addition, an inflammatory profile emerged within these oligodendrocytes. We observed unique cellular endophenotypes both at the periphery and within the chronic active lesion. These include a distinct metabolic astrocyte phenotype, the importance of FGF signaling among astrocytes and neurons, and a notable enrichment of mitochondrial genes at the lesion edge populated predominantly by astrocytes. Our study also identified B-cell coexpression networks indicating different functional B-cell subsets with differential location and specific tendencies toward certain lesion types. DISCUSSION: The use of single-cell multi-omics has offered a detailed perspective into the cellular dynamics and interactions in MS. These nuanced findings might pave the way for deeper insights into lesion pathogenesis in progressive MS.
CRIS-Team Scopus Importer:2024-04-19

Spatial cell graph analysis reveals skin tissue organization characteristic for cutaneous T cell lymphoma - Sarkar, Suryadipto; Möller, Anna; Hartebrodt, Anne; Erdmann, Michael; Ostalecki, Christian; Baur, Andreas; Blumenthal, David B. (npj Systems Biology and Applications, 2024)

ZEB1-mediated fibroblast polarization controls inflammation and sensitivity to immunotherapy in colorectal cancer - Menche, Constantin; Schuhwerk, Harald; Armstark, Isabell; Gupta, Pooja; Fuchs, Kathrin; van Roey, Ruthger; Mosa, Mohammed H.; Hartebrodt, Anne; Hajjaj, Yussuf; Clavel Ezquerra, Ana; Selvaraju, Manoj K.; Geppert, Carol-Immanuel; et al. (EMBO Reports, 2024)
(Figure presented.) Fibroblast-specific Zeb1 knock-out in colorectal cancer models leads to a shift in myCAF and iCAF populations, promoting immune infiltration. This increases colitis-associated tumorigenesis, reduces progression and metastasis and renders tumors susceptible to immune checkpoint blockade. Zeb1 loss in cancer-associated fibroblasts (CAFs) accelerates inflammation-driven colorectal tumorigenesis. In sporadic tumors CAF-specific Zeb1-loss results in decreased progression and metastasis. Impaired myofibroblast and increased inflammation result in increased immune cell infiltration and checkpoint activation. Zeb1 deficiency in CAFs sensitizes colorectal cancers to immune checkpoint inhibition.
CRIS-Team Scopus Importer:2024-07-19
Keywords: Cancer-Associated Fibroblast Plasticity; Colorectal Cancer; Immune Checkpoint Blockade; Tumor Microenvironment