Biology subjects

Fröhling, S.

Publications and source records attributed to Fröhling, S..

7 recordsLinked to original sources

bioRxiv2026

scIRES-seq reveals multimodal molecular heterogeneity in CTCs from small cell cervical cancer

Circulating tumor cells (CTCs) can provide a window into cellular and molecular mechanisms of cancer dissemination. However, the rarity and heterogeneity of CTCs complicate reliable identification and molecular characterization. Here, we introduce scIRES-seq, a framework linking immunophenotype, genetic variation, epigenomic profiles and gene expression within individual cells. In this proof-of-concept study, we coupled scIRES-seq to real-time imaging spectral flow cytometry and applied it to CTCs from a patient with metastatic cervical small cell neuroendocrine carcinoma. Tumor-matched copy-number alterations and targeted somatic variants established CTC identity, supported by DNA methylation profiles concordant with metastatic tissue and transcriptional features consistent with neuroendocrine differentiation. Genetic and DNA methylation analyses identified two largely concordant CTC subpopulations that were not clearly distinguished by their transcriptomes, highlighting the complementary information gained through single-cell multi-omics profiling. These findings demonstrate the feasibility of integrated CTC identification and characterization and provide a framework for investigating relationships between genetic diversity and molecular states in rare, heterogeneous cell populations.

cancer biology↗

bioRxiv2026

TOPAS: phosphoproteome data analysis and decision support platform for molecular tumor boards

The molecular tumor board (MTB) is central to precision oncology, providing personalized treatment recommendations based on molecular profiles of patient tumors. Genomics is instrumental for MTBs but often fails to identify clinically actionable targets, a gap that phosphoproteomics can fill. We present the tumor proteome activity status (TOPAS) platform, an end-to-end analysis pipeline that converts terabytes of phosphoproteomic data into patient-specific reports for MTB discussions, focusing on clinically relevant signaling linked to oncogenic mechanisms and therapeutic targets. Designed to scale with growing cohorts, the platform integrates data from 1,998 tumor samples to support patient- and cohort-level hypothesis generation. A web portal handles quality control, calculates TOPAS scores, identifies tumor antigens and immune checkpoints, and offers interactive analyses of differential protein abundance and outlier detection. The TOPAS platform is open source, addresses a critical unmet need and facilitates broader adoption of phosphoproteomics in precision oncology in the future.

bioinformatics↗

bioRxiv2026

Hierarchical classification of hematologic malignancies using epigenetic and genetic information

Molecular testing in hematology requires different assays for disease subgroup identification, risk stratification and selection of appropriate treatment regimens. Yet, molecular tests are not necessarily standardized between diagnostic laboratories, resulting in varying turnaround times and potentially divergent results. To resolve this issue and enable single-assay molecular testing, we have developed a hierarchical classification framework that combines epigenetic and genetic data from whole genome nanopore sequencing (WGNS) with machine learning to determine disease entities, epigenetic subgroups (epitypes) and genetic aberrations in hematopoietic neoplasms. We curated DNA methylation data from 5,420 samples and trained a classifier allowing entity-level diagnostics featuring 21 conditions, including healthy controls, acute and chronic myeloid and lymphoid neoplasms. This classifier was subsequently combined with entity-specific epitype classifiers predicting 44 therapeutically or prognostically relevant states, followed by integration of genetic data. Benchmarking of the combined (epi-)genetic testing strategy using WGNS confirmed high accuracy in the detection of diagnostic groups and risk stratification, and identified diagnosis-defining molecular alterations that were not reported by standard-of-care work-up.

cancer biology↗

bioRxiv2022

Dynamic DNA methylation reveals novel cis-regulatory elements in murine hematopoiesis

BackgroundThe differentiation of hematopoietic stem and progenitor cells (HSPCs) to terminally differentiated immune cells is accompanied by large-scale remodeling of the DNA methylation landscape. While significant insights into the molecular mechanisms of hematopoietic tissue regeneration were derived from mouse models, profiling of DNA methylation changes has been hampered by high cost or low resolution using the methods available. This problem has been overcome by the recent development of the Infinium Mouse Methylation BeadChip (MMBC) array, facilitating methylation profiling of the mouse genome at single CpG resolution at affordable cost. ResultsWe extended the RnBeads package to provide a computational framework for the analysis of MMBC data. This framework was applied to a newly generated MMBC reference map of mouse hematopoiesis encompassing nine different cell types. The analysis of dynamically regulated CpG sites showed progressive and unidirectional DNA methylation changes from HSPCs to differentiated hematopoietic cells and allowed the identification of lineage- and cell type-specific DNA methylation programs. Comparison to previously published catalogues of cis-regulatory elements (CREs) revealed 12,856 novel putative CREs which were dynamically regulated by DNA methylation (mdCREs). These mdCREs were predominantly associated with patterns of cell type-specific DNA hypomethylation and could be identified as epigenetic control regions regulating the expression of key hematopoietic genes during differentiation. ConclusionsWe established a publicly available analysis pipeline for MMBC datasets and provide a DNA methylation atlas of mouse hematopoiesis. This resource allowed us to identify novel putative CREs involved in hematopoiesis and will serve as a platform to study epigenetic regulation of normal and malignant hematopoiesis.

genomics↗

bioRxiv2022

Shallow nanopore RNA sequencing enables transcriptome profiling for precision cancer medicine

Transcriptome profiling is a mainstay of translational cancer research and is increasingly finding its way into precision oncology. While bulk RNA sequencing (RNA-seq) is widely available, high costs and long data return time are limiting factors for clinical applications. We investigated a portable nanopore long-read sequencing device (MinION, Oxford Nanopore Technologies) for transcriptome profiling of tumors. In particular, we investigated the impact of lower coverage than that of larger sequencing devices by comparing shallow nanopore RNA-seq data with short-read RNA-seq data generated using reversible dye terminator technology (Illumina) for ten samples representing four cancer types. Coupled with ShaNTi (Shallow Nanopore Sequencing for Transcriptomics), a newly developed data processing pipeline, a turnaround time of five days was achieved. The correlation of normalized gene-level counts between nanopore and Illumina RNA-seq was high for MinION but not for very low-throughput Flongle flow cells (r = 0.89 and r = 0.24, respectively). A cost-saving approach based on multiplexing of four samples per MinION flow cell maintained a high correlation with Illumina data (r = 0.56 - 0.86). In addition, we compared the utility of nanopore and Illumina RNA-seq data for analysis tools commonly applied in translational oncology: (i) Shallow nanopore and Illumina RNA-seq were equally useful for inferring signaling pathway activities with PROGENy. (ii) Highly expressed genes encoding kinases targeted by clinically approved small-molecule inhibitors were reliably identified by shallow nanopore RNA-seq. (iii) In tumor microenvironment composition analysis, quanTIseq performed better than CIBERSORT, likely due to higher average expression of the gene set used for deconvolution. (iv) Shallow nanopore RNA-seq was successfully applied to validate known gene fusions by breakpoint analysis. These findings suggest that shallow nanopore RNA-seq enables rapid, cost-effective, and biologically meaningful transcriptome profiling of tumors and warrants further exploration in precision cancer medicine studies.

cancer biology↗

bioRxiv2019

Dissecting intratumor heterogeneity of nodal B cell lymphomas on the transcriptional, genetic, and drug response level

Tumor heterogeneity encompasses both the malignant cells and their microenvironment. While heterogeneity between individual patients is well-known to affect the efficacy of anti-cancer drugs, most personalized treatment approaches do not account for intratumor heterogeneity. We addressed this issue by studying the heterogeneity of lymph node-derived B cell non-Hodgkin lymphoma (B-NHL) by single cell RNA-sequencing (scRNA-seq) and transcriptome-informed flow cytometry. We identified transcriptionally distinct malignant subclones and compared their drug response and genomic profiles. Malignant subclones of the same patient responded strikingly different to anti-cancer drugs ex vivo, which recapitulated subclone-specific drug sensitivity during in vivo treatment. Tumor infiltrating T cells represented the majority of non-malignant cells, whose gene expression signatures were similar across all donors, whereas the frequencies of T cell subsets varied significantly between the donors. Our data provide new insights into the heterogeneity of B-NHL and highlight the relevance of intratumor heterogeneity for personalized cancer therapies.

cancer biology↗

bioRxiv2019

Distinct immune evasion in APOBEC-enriched, HPV-negative HNSCC

Immune checkpoint inhibition leads to response in some patients with head and neck squamous cell carcinoma (HNSCC). Robust biomarkers are lacking to date.\n\nWe analyzed viral status, gene expression signatures, mutational load and mutational signatures in whole exome and RNA-sequencing data of the HNSCC TCGA dataset (N = 496) and a validation set (DKTK MASTER cohort, N = 10). Public single-cell gene expression data from 17 HPV-negative HNSCC were separately re-analyzed.\n\nAmong HPV-negative HNSCC, APOBEC3-associated TCW motif mutations but not total single nucleotide variant burden were significantly associated with inflammation. APOBEC3-enriched HPV-negative HNSCC showed higher T-cell inflammation and immune checkpoint expression. Mutations in immune-evasion pathways were enriched in these tumors. APOBEC3B and 3C expression was identified in tumor cells and correlated with tumor inflammation.\n\nWe identified an APOBEC-enriched subgroup of HPV-negative HNSCC with a distinct immunogenic phenotype, potentially mediating response to immunotherapy.

cancer biology↗