Biology subjects

Baudis, M.

Publications and source records attributed to Baudis, M..

At least 19 recordsLinked to original sources

bioRxiv2026

CNVSIM: an interpretable copy-number reference atlas for cancer subtyping

Background: Genome-wide copy-number variation (CNV) profiles provide a structural representation of cancer genomes, but interpreting new profiles within a reusable reference remains challenging. Results: We developed CNVSIM, which organizes CNV profiles into cancer root-specific reference spaces and defines reference subtypes annotated with chromosome-scale and gene-level signatures. Applied to the Progenetix dataset, CNVSIM established 112 CNV-defined subtypes across 19 cancer roots, supported by 155,881 analyses from neoplastic samples. Within each cancer root, samples sharing a broad diagnosis separated into distinct CNV subtypes, whereas individual subtypes spanned multiple diagnostic labels. All subtypes included analyses from multiple assay platforms and data sources, and core subtypes remained reproducible after systematic reduction of reference analyses. Calibrated projection assigned 85.7% of held-out CNV profiles while maintaining high accuracy for supported assignments. Projection of 4,605 cell-line CNV profiles identified cell lines representative for 90/112 cancer subtypes while highlighting gaps in cell line representation. Conclusions: CNVSIM provides a reusable and interpretable CNV reference that complements diagnostic classification for profile comparison, projection, and cell line selection.

genomics↗

bioRxiv2026

High-resolution population structure inference using genome-wide short tandem repeat variations

Short tandem repeats (STRs) are a major source of genetic variation, yet their potential for genome-wide population structure inference remains underexplored. Here we present a multi-modal framework for STR-based population inference, integrating unsupervised clustering, supervised population assignment, and a novel admixture inference model, Directional Non-negative Matrix Factorization (dNMF). Applying this framework to thousands of genomes from multiple global cohorts, we first demonstrate that genome-wide STR variations provide substantially finer resolution of human population structure than single-nucleotide polymorphisms (SNPs), particularly at regional levels. The dNMF model estimates ancestry coefficients under the hypothesis that true ancestral populations are consistently encoded in the bidirectional mutation dynamics of STRs. Population structures inferred by dNMF are fine-grained, reproducible across datasets, and robust to technical artifacts. Motif-specific analyses further reveal directionbiased mutational tendencies and show that distinct STR motif classes encode complementary layers of population structure at different evolutionary scales. These results establish STRs as powerful and biologically interpretable markers for population structure inference, offering a mutation-aware perspective that complements traditional SNP-based frameworks and refines understanding of human demographic history.

bioinformatics↗

bioRxiv2025

CNAttention: an attention-based deep multiple-instance method for uncovering CNA signatures across cancers

Somatic copy number aberrations (CNAs) represent a distinct class of genomic mutations associated with oncogenetic effects. Over the past three decades, significant volumes of CNA data have been generated through molecular-cytogenetic and genome sequencing-based techniques. This data has been pivotal in identifying cancer-related genes and advancing research on the relationship between CNAs and histopathologically defined cancer types. However, comprehensive studies of CNA landscapes and disease parameters are challenging due to the vast diagnostic and genomic heterogeneity encountered in "pan-cancer" approaches. In this study, we introduce CNAttention, an attention-based deep multiple instance learning method designed to comprehensively analyze CNAs across different cancers and uncover specific CNA patterns within integrated gene-level CNA profiles of 30 cancer types. CNAttention effectively learns CNA features unique to each cancer type and generates CNA signatures for 30 cancer types using attention mechanisms, highlighting the distinctiveness of their CNA landscapes. CNAttention demonstrates high accuracy and exhibits stable performance even with the incorporation of external datasets or parameter adjustments, underscoring its effectiveness in tumor identification. Expanding these signatures to cancer classification trees reveals common patterns not only among physiologically related cancer types but also among clinico-pathologically distant types, such as different cancers originating from neural crest derived cells. Additionally, detected signatures also uncover genomic heterogeneity in individual cancer types, for instance in brain lower-grade glioma. Additional experiments with classification models underscore the efficacy of these signatures in representing various cancer types and their potential utility in clinical diagnosis.

bioinformatics↗

bioRxiv2025

pgxRpi: an R/Bioconductor package for user-friendly access to the Beacon v2 API

MotivationThe Beacon v2 specification, established by the Global Alliance for Genomics and Health (GA4GH), consists of a standardized framework and data models for genomic and phenotypic data discovery. By enabling secure, federated data sharing, it fosters interoperability across genomic resources. Progenetix, a reference implementation of Beacon v2, exemplifies its potential for large-scale genomic data integration, offering open access to genomic mutation data across diverse cancer types. ResultsWe present pgxRpi, an open-source R/Bioconductor package that provides a streamlined interface to the Progenetix Beacon v2 REST API, facilitating efficient and flexible genomic data retrieval. Beyond data access, pgxRpi offers integrated visualization and analysis functions, enabling users to explore, interpret, and process queried data effectively. Leveraging the flexibility of the Beacon v2 standard, pgxRpi extends beyond Progenetix, supporting interoperable data access across multiple Beacon-enabled resources, thereby enhancing data-driven discovery in genomics. Availability and ImplementationpgxRpi is freely available under the Artistic-2.0 license from Bioconductor (https://doi.org/doi:10.18129/B9.bioc.pgxRpi), with actively maintained source code on GitHub (https://github.com/progenetix/pgxRpi). Comprehensive usage instructions and example workflows are provided in the package vignettes, available at https://github.com/progenetix/pgxRpi/tree/devel/vignettes.

bioinformatics↗

bioRxiv2025

Multicancer analyses of short tandem repeat variations reveal shared gene regulatory mechanisms

BackgroundShort tandem repeats (STRs) have been reported to influence gene expression across various human tissues. While STR variations are enriched in colorectal (CRC), stomach (STAD) and endometrial (UCEC) cancers, particularly in microsatellite instable (MSI) tumors, their functional effects and regulatory mechanisms on gene expression remain poorly understood across these cancer types. ResultsHere, we leverage whole-exome sequencing and gene expression data to identify STRs for which repeat lengths are associated with the expression of nearby genes (eSTRs) in CRC, STAD and UCEC tumors. Our analyses reveal that tumor STR profiles effectively capture both MSI phenotype and population structure. While most eSTRs are cancer-specific, shared eSTRs across multiple cancers exhibit consistent effects on gene expression. Notably, coding-region eSTRs identified in all three cancer types show positive correlations with nearby gene expression. We further validate the functional effects of eSTRs by demonstrating associations between somatic eSTR mutations and gene expression changes during the transition from normal to tumor tissues, suggesting their potential roles in tumorigenesis. Combined with DNA methylation data, we perform the first quantitative analysis of the interplay between STR variations and DNA methylation in tumors. We identify eSTRs where repeat lengths are associated with methylation levels of nearby CpG sites (meSTRs) and show that over 70% of eSTRs are significantly linked to local DNA methylation. Importantly, the effects of meSTRs on DNA methylation remain consistent across cancer types. ConclusionsOverall, our findings enhance the understanding of how functional STR variations influence gene expression and DNA methylation. Our study highlights shared regulatory mechanisms of STRs across multiple cancers, offering a foundation for future research into their broader implications in tumor biology.

bioinformatics↗

bioRxiv2024

In Search of the Perfect Model: How Cancer Cell Lines Relate to Native Cancers

Cancer cell lines are frequently used in biological and translational research to study cellular mechanisms and explore treatment options. However, cancer cell lines may display mutational profiles divergent from native cancers or may be misidentified or contaminated. We explored how similar cancer cell lines are to native cancers to find the most suitable representations for the corresponding diseases by utilising large collections of copy number variation (CNV) profiles and applied machine learning (ML) algorithms to predict cell line classifications. Our results confirm that cancer cell lines indeed accumulate more mutations compared to native cancers but retain similar CNV profiles. We demonstrate that many relevant oncogenes and tumor suppressor genes are altered by CNV events in both cancers and their corresponding cell lines. Based on the similarities between the two groups and the predictions of the ML model, we provide some recommendations about cell lines with good potential to represent selected cancer types in in vitro studies.

cancer biology↗

bioRxiv2023

cancercelllines.org - a Novel Resource for Genomic Variants in Cancer Cell Lines

Cancer cell lines are an important component in biological and medical research, enabling studies of cellular mechanisms as well as the development and testing of pharmaceuticals. Genomic alterations in cancer cell lines are widely studied as models for oncogenetic events and are represented in a wide range of primary resources. We have created a comprehensive, curated knowledge resource - cancercelllines.org - with the aim to enable easy access to genomic profiling data in cancer cell lines, curated from a variety of resources and integrating both copy number and single nucleotide variants (SNVs) data. We have gathered over 5,600 copy number profiles as well as SNV annotations for 16,000 cell lines and provide this data with mappings to the GRCh38 reference genome. Both genomic variations and associated curated metadata can be queried through the GA4GH Beacon v2 API and a graphical user interface with extensive data retrieval enabled using GA4GH data schemas under a permissive licensing scheme.

bioinformatics↗

bioRxiv2023

Short tandem repeat mutations regulate gene expression in colorectal cancer

Short tandem repeat (STR) mutations are prevalent in colorectal cancer (CRC), especially in tumours with the microsatellite instability (MSI) phenotype. While STR length variations are known to regulate gene expression under physiological conditions, the functional impact of STR mutations in CRC remains unclear. Here, we integrate STR mutation data with clinical information and gene expression levels to study the gene regulatory effects of STR mutations in CRC. We confirm that STR mutability in CRC highly depends on the MSI status, repeat unit size, and repeat length. Furthermore, we present a set of 1244 putative expression STRs (eSTRs) for which the STR length is associated with gene expression levels in CRC tumours. The length of 73 eSTRs is associated with expression levels of cancer-related genes, nine of which are CRC-specific genes. We show that linear models describing eSTR-gene expression relationships allow for predictions of gene expression changes in response to eSTR mutations. Moreover, we found an increased mutability of eSTRs in MSI tumours. Our evidence of gene regulatory roles for eSTRs in CRC highlights a mostly overlooked way through which tumours may modulate their phenotypes. The increased mutability of eSTRs in MSI tumours may be an early indication that eSTR mutations can confer a selective advantage to tumours. Future extensions of our findings into larger cohorts could uncover new STR-based targets in the treatment of cancer.

cancer biology↗

bioRxiv2023

LabelSeg: segment annotation for tumor copy number alteration profiles

1Somatic copy number alterations (SCNA) are a predominant type of oncogenomic alterations that affect a large proportion of the genome in the majority of cancer samples. Current technologies allow high-throughput measurement of such copy number aberrations, generating results consisting of frequently large sets of SCNA segments. However, the automated annotation and integration of such data are particularly challenging because the measured signals reflect biased, relative copy number ratios. In this study, we introduce labelSeg, an algorithm designed for rapid and accurate annotation of CNA segments, with the aim of enhancing the interpretation of tumor SCNA profiles. Leveraging density-based clustering and exploiting the length-amplitude relationships of SCNA, our algorithm proficiently identifies distinct relative copy number states from individual segment profiles. Its compatibility with most CNA measurement platforms makes it suitable for large-scale integrative data analysis. We confirmed its performance on both simulated and sample-derived data from The Cancer Genome Atlas (TCGA) reference dataset, and we demonstrated its utility in integrating heterogeneous segment profiles from different data sources and measurement platforms. Our comparative and integrative analysis revealed common SCNA patterns in cancer and protein-coding genes with a strong correlation between SCNA and mRNA expression, promoting the investigation into the role of SCNA in cancer development.

bioinformatics↗

bioRxiv2022

Candidate targets of copy number deletion events across 17 cancer types

Genome variation is the direct cause of cancer and driver of its clonal evolution. While the impact of many point mutations can be evaluated through their modification of individual genomic elements, even a single copy number aberration (CNA) may encompass hundreds of genes and therefore pose challenges to untangle potentially complex functional effects. However, consistent, recurring and disease-specific patterns in the genome-wide CNA landscape imply that particular CNA may promote cancer-type-specific characteristics. Discerning essential cancer-promoting alterations from the inherent co-dependency in CNA would improve the understanding of mechanisms of CNA and provide new insights into cancer biology and potential therapeutic targets. Here we implement a model using segmental breakpoints to discover non-random gene coverage by copy number deletion (CND). With a diverse set of cancer types from multiple resources, this model identified common and cancer-type-specific oncogenes and tumor suppressor genes as well as cancer-promoting functional pathways. Confirmed by differential expression analysis of data from corresponding cancer types, the results show that for most cancer types, despite dissimilarity of their CND landscapes, similar canonical pathways are affected. In 25 analyses of 17 cancer types, we have identified 19 to 169 significant genes by copy deletion, including RB1, PTEN and CDKN2A as the most significantly deleted genes among all cancer types. We have also shown a shared dependence on core pathways for cancer progression in different cancers as well as cancer type separation by genome-wide significance scores. While this work provides a reference for gene specific significance in many cancers, it chiefly contributes a general framework to derive genomewide significance and molecular insights in CND profiles with a potential for the analysis of rare cancer types as well as non-coding regions.

cancer biology↗

bioRxiv2021

Copy number variant heterogeneity among cancer types reflects inconsistent concordance with diagnostic classifications

Due to frequent genome instability and accumulation of mutations during the neoplastic process, malignant tumors present with patterns of somatic genome variants on diverse levels of heterogeneity. The delineation of pathophysiological consequences of these patterns remains one of the main challenges in cancer prognosis and treatment. Although continuous efforts aim for better characterization of cancer entities through inclusion of molecular characteristics, current ontology systems still heavily rely on clinico-pathological features. Traditionally, malignant diseases have been classified using domain-specific or generalized classification systems, based on histopathological features and clinical gestalt. Aside from the general purpose "International Classification for Diseases in Oncology" (ICD-O; WHO), hierarchical terminologies such as NCIt promote data interoperability and ontology-driven computational analysis. To evaluate two prominent, general cancer classification systems (NCIt and ICD-O) towards their concordance with genomic mutation patterns we have performed a data-driven meta-analysis of 83505 curated cancer samples with genome-wide CNA (copy number aberration) profiles from our Progenetix database. The analysis provides a basis to assess the correspondence level of existing classification systems with respect to homogeneous molecular groups, and how individual codes represent an adequately detailed classification.

cancer biology↗

bioRxiv2021

The Progenetix oncogenomic resource in 2021

In cancer, copy number aberrations (CNA) represent a type of nearly ubiquitous and frequently extensive structural genome variations. To disentangle the molecular mechanisms underlying tumorigenesis as well as identify and characterize molecular subtypes, the comparative and meta-analysis of large genomic variant collections can be of immense importance. Over the last decades, cancer genomic profiling projects have resulted in a large amount of somatic genome variation profiles, however segregated in a multitude of individual studies and datasets. The Progenetix project, initiated in 2001, curates individual cancer CNA profiles and associated metadata from published oncogenomic studies and data repositories with the aim to empower integrative analyses spanning all different cancer biologies. During the last few years, the fields of genomics and cancer research have seen significant advancement in terms of molecular genetics technology, disease concepts, data standard harmonization as well as data availability, in an increasingly structured and systematic manner. For the Progenetix resource, continuous data integration, curation and maintenance have resulted in the most comprehensive representation of cancer genome CNA profiling data with 138663 (including 115357 tumor) CNV profiles. In this article, we report a 4.5-fold increase in sample number since 2013, improvements in data quality, ontology representation with a CNV landscape summary over 51 distinctive NCIt cancer terms as well as updates in database schemas, and data access including new web front-end and programmatic data access. Database URL: progenetix.org

cancer biology↗

bioRxiv2021

The GA4GH Variation Representation Specification (VRS): a Computational Framework for the Precise Representation and Federated Identification of Molecular Variation

Maximizing the personal, public, research, and clinical value of genomic information will require that clinicians, researchers, and testing laboratories exchange genetic variation data reliably. Developed by a partnership among national information resource providers, public initiatives, and diagnostic testing laboratories under the auspices of the Global Alliance for Genomics and Health (GA4GH), the Variation Representation Specification (VRS, pronounced "verse") is an extensible framework for the semantically precise and computable representation of variation that complements contemporary human-readable and flat file standards for variation representation. VRS objects are designed to be semantically precise representations of variation, and leverage this design to enable unique, federated identification of molecular variation. We describe the components of this framework, including the terminology and information model, schema, data sharing conventions, and a reference implementation, each of which is intended to be broadly useful and freely available for community use. The specification, documentation, examples, and community links are available at https://vrs.ga4gh.org/.

bioinformatics↗

bioRxiv2020

Genomic Copy Number Signatures Based Classifiers for Subtype Identification in Cancer

Copy number aberrations (CNA) are one of the most important classes of genomic mutations related to oncogenetic effects. In the past three decades, a vast amount of CNA data has been generated by molecular-cytogenetic and genome sequencing based methods. While this data has been instrumental in the identification of cancer-related genes and promoted research into the relation between CNA and histo-pathologically defined cancer types, the heterogeneity of source data and derived CNV profiles pose great challenges for data integration and comparative analysis. Furthermore, a majority of existing studies have been focused on the association of CNA to pre-selected "driver" genes with limited application to rare drivers and other genomic elements. In this study, we developed a bioinformatics pipeline to integrate a collection of 44,988 high-quality CNA profiles of high diversity. Using a hybrid model of neural networks and attention algorithm, we generated the CNA signatures of 31 cancer subtypes, depicting the uniqueness of their respective CNA landscapes. Finally, we constructed a multi-label classifier to identify the cancer type and the organ of origin from copy number profiling data. The investigation of the signatures suggested common patterns, not only of physiologically related cancer types but also of clinico-pathologically distant cancer types such as different cancers originating from the neural crest. Further experiments of classification models confirmed the effectiveness of the signatures in distinguishing different cancer types and demonstrated their potential in tumor classification.

bioinformatics↗

bioRxiv2019

Geographic assessment of cancer genome profiling studies

Cancers arise from the accumulation of somatic genome mutations, which can be influenced by inherited genomic variants and external factors such as environmental or lifestyle-related exposure. Due to the heterogeneity of cancers, precise information about the genomic composition of germline and malignant tissues has to be correlated with morphological, clinical and extrinsic features to advance medical knowledge and treatment options. With global differences in cancer frequencies and disease types, geographic data is of importance to understand the interplay between genetic ancestry and environmental influence in cancer incidence, progression and treatment outcome. In this study, we analysed the current landscape of oncogenomic screening publications for geographic information content and quality, to address underrepresented study populations and thereby to fill prominent gaps in our understanding of interactions between somatic variations, population genetics and environmental factors in oncogenesis. We conclude that while the use of proxy derived geographic annotations can be useful for coarse-grained associations, the study of geo-correlated factors in cancer causation and progression will benefit from standardized geographic provenance annotations. Additionally, publication derived geographic provenance data allowed us to highlight stark inequality in the geographies of cancer genome profiling, with a near lack of sizeable studies from Africa and other large regions.

bioinformatics↗

bioRxiv2019

Minimum Error Calibration and Normalization for Genomic Copy Number Analysis

Copy number variations (CNV) are regional deviations from the normal autosomal bi-allelic DNA content. While germline CNVs are a major contributor to genomic syndromes and inherited diseases, the majority of cancers accumulate extensive \"somatic\" CNV (sCNV or CNA) during the process of oncogenetic transformation and progression. While specific sCNV have closely been associated with tumorigenesis, intriguingly many neoplasias exhibit recurrent sCNV patterns beyond the involvement of a few cancer driver genes.\n\nCurrently, CNV profiles of tumor samples are generated using genomic micro-arrays or high-throughput DNA sequencing. Regardless of the underlying technology, genomic copy number data is derived from the relative assessment and integration of multiple signals, with the data generation process being prone to contamination from several sources. Estimated copy number values have no absolute and linear correlation to their corresponding DNA levels, and the extent of deviation differs between sample profiles which poses a great challenge for data integration and comparison in large scale genome analysis.\n\nIn this study, we present a novel method named Minimum Error Calibration and Normalization of Copy Numbers Analysis (Mecan4CNA). For each sCNV profile, Mecan4CNA reduces the noise level, calculates values representing the normal DNA copies (baseline) and the change of one copy (level distance), and finally normalizes all values. Experiments of Mecan4CNA on simulated data showed an overall accuracy of 93% and 91% in determining the baseline and level distance, respectively. Comparison of baseline and level distance estimation with existing methods and karyotyping data on the NCI-60 tumor cell line produced coherent results. To estimate the methods impact on downstream analyses we performed GISTIC analyses on original and Mecan4CNA data from the Cancer Genome Atlas (TCGA) where the normalized data showed prominent improvements of both sensitivity and specificity in detecting focal regions.\n\nIn general, Mecan4CNA provides an advanced method for CNA data normalization especially in research involving data of high volume and heterogeneous quality. but with its informative output and visualization can also facilitate analysis of individual CNA profiles. Mecan4CNA is freely available as a Python package and through Github.

bioinformatics↗

bioRxiv2018

DNA copy number imbalances in primary cutaneous lymphomas (PCL)

Cutaneous lymphomas (CL) represent a clinically defined group of extranodal non-Hodgkin lymphomas harboring heterogeneous and incompletely delineated molecular aberrations. Over the past decades, molecular studies have identified several chromosomal aberrations, but the interpretation of individual genomic studies can be challenging.\n\nWe conducted a meta-analysis to delineate genomic alterations for different types of PCL. Searches of PubMed and ISI Web of Knowledge for the years 1996 to 2016 identified 32 publications reporting the investigation of PCL for genome-wide copy number alterations, by means of comparative genomic hybridization techniques and whole genome and exome sequencing. For 449 samples from 22 publications, copy number variation data was accessible for sample based meta-analysis. Summary profiles for genomic imbalances, generated from case-specific data, identified complex genomic imbalances, which could discriminate between different subtypes of CL and promise a more accurate classification. The collected data presented in this study are publicly available through the \"Progenetix\" online repository.

cancer biology↗

bioRxiv2018

Population assignment from cancer genome profiling data

For a variety of human malignancies, incidence, treatment efficacy and overall prognosis show considerable variation between different populations and ethnic groups. Disentangling the effects related to particular population backgrounds can help in both understanding cancer biology and in tailoring therapeutic interventions. Because self-reported or inferred patient data can be incomplete or misleading due to migration and genomic admixture, a data-driven ancestry estimation should be preferred. While algorithms to analyze ancestry structure from healthy individuals have been developed, an easy-to-use tool to assign population groups based on genotyping data from SNP profiles is still missing and benchmarking for the validity of population assignment strategy for aberrant cancer genomes was not tested.\n\nWe benchmarked the consistency and accuracy of cross-platform population assignment. We also demonstrated its high accuracy to process unaltered as well as cancer genomes. Despite widespread and extensive somatic mutations of cancer profiling data, population assignment consistency between germline and highly mutated samples from cancer patients reached of 97% and 92% for assignment into 5 and 26 populations re-spectively. Comparison of our benchmarked results with self-reported meta-data estimated a matching rate between 88% to 92%. Despite a relatively high matching rate, the ethnicity labels indicated in meta-data are vague compared to the standardized output from our tool.\n\nWe have developed a bioinformatics tool to assign the populations from genome profiling data and validated its performance in healthy as well as aberrant cancer genomes. It is ready-to-use for genotyping data from nine commercial SNP array platforms or sequencing data. This tool is effective to scrutinize the population structure in cancer genomes and provides better measure to integrate genotyping data from various platforms instead of self-reported information. It will facilitate research on interplay between ethnicity related genetic background and molecular patterns in cancer entities and disentangling possible hereditary contributions.\n\nThe docker image of the tool is provided in DockerHub as \"baudisgroup/snp2pop\".

bioinformatics↗