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Uhrig, S.

Publications and source records attributed to Uhrig, S..

3 recordsLinked to original sources

Multidimensional Characterization of Soft-Tissue Sarcomas with FUS-TFCP2 or EWSR1-TFCP2 Fusions

Linking clinical multi-omics analyses with mechanistic studies provides opportunities to explore the pathogenesis of rare cancers. We leveraged two precision oncology programs to investigate rhabdomyosarcoma with FUS/EWSR1-TFCP2 fusions, an orphan malignancy without effective systemic therapies. All tumors exhibited outlier expression of the ALK receptor tyrosine kinase, which was partly accompanied by intragenic deletions and aberrant splicing, resulting in truncated ALK variants that were oncogenic and sensitive to ALK inhibitors. Additional recurrent alterations included CKDN2A/MTAP co-deletions, providing a rationale for therapies targeting CDK4/6 and PRMT5. Functional studies showed that FUS-TFCP2 blocks myogenic differentiation and induces transcription of ALK and a truncated form of TERT through binding outside their regular promoters. Furthermore, FUS-TFCP2 inhibited DNA double-strand break repair. Consistent with this, and unlike other fusion-driven sarcomas, TFCP2-rearranged tumors exhibited marked genomic instability and signs of defective homologous recombination. DNA methylation profiling indicated a close relationship with undifferentiated sarcomas rather than rhabdomyosarcoma. Finally, we identified patients in whom overt disease was preceded by benign lesions carrying TFCP2 fusions, providing insight into stepwise sarcomagenesis and suggesting new approaches to early detection and interception. SIGNIFICANCEMost rare cancers are poorly understood, and pathogenesis-directed therapies are often lacking, resulting in poor patient outcomes. This study illustrates the potential of linking precision oncology programs with preclinical research to gain insight into the classification, pathogenesis, and therapeutic vulnerabilities of rare cancers that could improve the clinical management of such diseases.

cancer biology↗

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↗

Proteogenomic analysis reveals RNA as an important source for tumor-agnostic neoantigen identification correlating with T-cell infiltration

Systemic pan-tumor analyses may reveal the significance of common features implicated in cancer immunogenicity and patient survival. Here, we provide a comprehensive multi-omics data set for 32 patients across 25 tumor types by combining proteogenomics with phenotypic and functional analyses. By using an optimized computational approach, we discovered a large number of novel tumor-specific and tumor-associated antigens including shared common target candidates. To create a pipeline for the identification of neoantigens in our cohort, we combined deep DNA and RNA sequencing with MS- based immunopeptidomics of tumor specimens, followed by the assessment of their immunogenicity. In fact, we could detect a broad variety of non-wild type HLA-binding peptides in the majority of patients and confirmed the immunogenicity of 24 neoantigens. Most interestingly, the majority of total and immunogenic neoantigens originated from variants identified in the RNA dataset, illustrating the importance of RNA as a still understudied source of cancer antigens. Moreover, the amount of these mainly RNA-based immunogenic neoantigens correlated positively with overall CD8+ tumor-infiltrating T cells. This study therefore underlines the importance of RNA-centered variant detection for the identification of shared biomarkers and potentially relevant neoantigen candidates. Statement of significanceThe significance of this study lies not only in the potential of our optimized proteogenomic workflow for the discovery of neoantigens (in particular RNA-derived neoantigens) for clinical application, but sheds light on the entity-agnostic prevalence of HLA class I peptide presentation of RNA processing events to be used for tumor targeting.

cancer biology↗