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Hübschmann, D.

Publications and source records attributed to Hübschmann, D..

3 recordsLinked to original sources

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↗

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↗

Deep molecular characterization linked to drug response profiling of pancreatic ductal adenocarcinoma using patient-derived organoids.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by high drug resistance and poor prognosis. Novel therapeutic and stratification strategies are urgently needed. Here, we present an integration of in-depth genomic and transcriptomic characterization with drug screening and clinical outcome based on a catalogue of 51 patient-derived tumor organoids (PDOs) from resected PDAC. Known PDAC molecular subtypes and their prognostic value are conserved in organoids. Integration of transcriptomic and drug response profiles suggest a metabolism-mediated modulations of drug resistance. Copy number alterations on chromosome 13q and wild-type status of TP53 emerged as potential novel genomic biomarkers for sensitivity to 5-FU and oxaliplatin treatment, respectively. Functional testing of targeted drugs in PDOs revealed its additional value for genome-driven personalized oncology. Co-deletion of TP53/POLR2A increased vulnerability to RNA polymerase II inhibition, pointing to a promising target for personalized treatment in PDAC. SignificancePatient-derived PDAC organoids hold great promise as surrogate tumor models for personalized oncology. By integrating highly granular molecular, drug sensitivity and clinical data, we demonstrate that PDOs are valid models for molecular characterization and response prediction that also enable identification of novel drug sensitivity biomarkers and resistance mechanisms in PDAC.

cancer biology↗