Stefan Fröhling

This study introduces scIRES-seq to analyze CTCs from cervical cancer, revealing multimodal molecular heterogeneity through integrated genetic, epigenomic, and transcriptomic profiling.

TOPAS converts phosphoproteomic data into patient-specific reports for molecular tumor boards, focusing on clinically relevant signaling and therapeutic targets.

The paper develops a hierarchical classification framework combining epigenetic and genetic data with machine learning to improve diagnosis and risk stratification in hematologic malignancies.

The paper introduces Knowledge Connector, a decision support system that integrates multiomics data with clinical information to aid in precision oncology decision-making.

This study identifies oncofetal reprogramming in juvenile myelomonocytic leukemia, linking RAS-pathway activation to fetal gene expression and revealing CD52 as a therapeutic target.

This paper introduces a method to infer signaling pathway activities from H&E slides in HNSCC, revealing two prognostic tumor states.

This study uses T-DARPins to inhibit TBXT in chordoma cells, revealing its regulome and identifying therapeutic vulnerabilities, including sensitivity to JAK2 inhibitors.

This study identifies rare tumor-reactive T cells and conserved antigen repertoires in multiple myeloma and acute myeloid leukemia, developing a TCR fitness model to predict immunotherapy response.

This study identifies biallelic CD38 loss as a mechanism of resistance to anti-CD38 antibodies in multiple myeloma, with specific mutations affecting antibody efficacy.

This study compares deep learning and machine learning for biomarker prediction in cancer genomes, finding DL performs better in cross-assay and data-sparse settings.

This study identifies WEE1 kinase activity as a therapeutic target in FUS::DDIT3-driven myxoid liposarcoma by showing its role in replication stress tolerance and cell survival.

ZygosityPredictor evaluates the number of gene copies affected by mutations in sequencing data, providing confidence measures for precision oncology applications.

The paper develops a computational framework for analyzing DNA methylation data in mouse hematopoiesis, identifying novel cis-regulatory elements associated with gene expression during differentiation.