Biology subjects

Huellein, J.

Publications and source records attributed to Huellein, J..

5 recordsLinked to original sources

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

Prospective pan-cancer phosphoproteomics at clinical scale extends therapeutic options in precision oncology

Genomics-guided precision oncology has improved survival in cancer entities with actionable mutations but cannot capture oncogenic signaling that manifests at the protein level. Here, we report a prospective, real-world pan-cancer study profiling proteomes and phosphoproteomes of 1,998 tumor samples from adults and children with rare or advanced cancers enrolled in the German precision oncology programs DKFZ/NCT/DKTK MASTER, CATCH and INFORM and their molecular tumor boards (MTBs). We developed tumor proteome activity status (TOPAS) scores for 46 clinically relevant kinases, an immune activity score capturing antigen presentation and T-cell activation and identified therapeutically targetable cell-surface proteins for 94% of patients. These readouts enhance MTB recommendations by exposing actionable non-genomic kinase activity, refining interpretation of oncogenic genome alterations, and highlighting cell-surface treatment options. Three proof-of-concept analyses indicate clinical utility including kinase activity-stratified pazopanib response in sarcoma, immune activity score-tracked checkpoint-inhibitor outcomes pan-cancer, and a phosphoproteomic biomarker distinguishing EGFR-inhibitor response in chordoma.

cancer biology↗

bioRxiv2025

Cancer pathway connectivity resolved by drug perturbation and RNA sequencing

Pathway inhibitors are a backbone of cancer treatment. The configuration of pathway dependencies varies from tumour to tumour. Better treatment for individual patients could be designed if the pathway wiring were readily measurable. Here, we characterise the transcriptional responses of 116 lymphoma patient samples exposed to ten drug perturbations. We used factor analysis to decompose individual and shared drug effects, thereby generating a pathway connectivity map of chronic lymphocytic leukemia (CLL). The expression profiles of the major disease subgroups, defined by IGHV mutation status, became more similar to each other after BTK inhibition, consistent with B-cell receptor (BCR) signalling as a driver of their phenotypic difference. An even stronger convergence was observed with combined IRAK4 and BTK inhibition, indicating cooperation of BCR and toll-like receptor (TLR) signalling in CLL. We identified genetic aberrations (BRAF, TP53, deletion 17p, deletion 15q, trisomy 12) that modulated drug effects in CLL and constituted specific interaction patterns. IRAK4 inhibition effects depended on the presence of trisomy 12, a finding that suggests that the trisomy 12 driver event in CLL acts by gene dosage-dependent IRAK4 upregulation and amplification of the BCR/TLR cooperation. Our results highlight the potential of systematic drug perturbation assays with transcriptome readout to map pathway interconnectivity and functionally annotate tumour drivers.

systems biology↗

bioRxiv2022

aRgus: multilevel visualization of non-synonymous single nucleotide variants & advanced pathogenicity score modeling for genetic vulnerability assessment

The widespread use of high-throughput sequencing techniques is leading to a rapidly increasing number of disease-associated variants of unknown significance and candidate genes. Integration of knowledge concerning their genetic, protein as well as functional and conservational aspects is necessary for an exhaustive assessment of their relevance and for prioritization of further clinical and functional studies investigating their role in human disease. In order to collect the necessary information, a multitude of different databases has to be accessed and data extraction from the original sources commonly is not user-friendly and requires advanced bioinformatics skills. This leads to a decreased data accessibility for a relevant number of potential users such as clinicians, geneticist, and clinical researchers. Here, we present aRgus (https://argus.urz.uni-heidelberg.de/), a standalone webtool for simple extraction and intuitive visualization of multi-layered gene, protein, variant, and variant effect prediction data. aRgus provides interactive exploitation of these data within seconds for any known gene of the human genome. In contrast to existing online platforms for compilation of variant data, aRgus complements visualization of chromosomal exon-intron structure and protein domain annotation with ClinVar and gnomAD variant distributions as well as position-specific variant effect prediction score modeling. aRgus thereby enables timely assessment of protein regions vulnerable to variation with single amino acid resolution and provides numerous applications in variant and protein domain interpretation as well as in the design of in vitro experiments.

bioinformatics↗

bioRxiv2018

MDM4 is an essential disease driver targeted by 1q gain in Burkitt lymphoma

Oncogenic MYC activation promotes cellular proliferation in Burkitt lymphoma (BL), but also induces cell cycle arrest and apoptosis mediated by TP53, a tumor suppressor gene that is mutated in 40% of BL cases. To identify therapeutic targets in BL, we investigated molecular dependencies in BL cell lines using RNAi-based, loss-of-function screening. By integrating genotypic and RNAi data, we identified a number of genotype-specific dependencies including the dependence of TCF3/ID3 mutant cell lines on TCF3 and of MYD88 mutant cell lines on TLR signaling. TP53 wild-type (TP53wt) BL were dependent on MDM4, a negative regulator of TP53. In BL cell lines, MDM4 knockdown induced cell cycle arrest and decreased tumor growth in a xenograft model in a p53-dependent manner, while small molecule inhibition of the MDM4-p53 interaction restored p53 activity resulting in cell cycle arrest. Consistent with the pathogenic effect of MDM4 upregulation in BL, we found that TP53wt BL samples were enriched for gain of chromosome 1q which includes the MDM4 locus. 1q gain was also enriched across non-BL cancer cell lines (n=789) without TP53 mutation (23% in TP53wt and 12% in TP53mut, p<0.001). In a set of 216 cell lines representing 19 cancer entities from the Achilles project, MDM4 was the strongest genetic dependency in TP53wt cell lines (p<0.001).\n\nOur findings show that in TP53wt BL, MDM4-mediated inhibition of p53 is a mechanism to evade cell cycle arrest. The data highlight the critical role of p53 as a tumor suppressor in BL, and identifies MDM4 as a key functional target of 1q gain in a wide range of cancers, which is therapeutically targetable.

cancer biology↗