Super-enhancer-driven CACNA2D2 is an EWSR1::WT1 signature gene encoding a diagnostic marker for desmoplastic small round cell tumor (DSRCT)
Geyer, F. H.; Ritter, A.; Kinn-Gurzo, S.; Faehling, T.; Li, J.; Jarosch, A.; Ngo, C.; Vinca, E.; Aljakouch, K.; Orynbek, A.; Ohmura, S.; Kirchner, T.; Imle, R.; Romero-Perez, L.; Bertram, S.; de Alava, E.; Postel-Vilnay, S.; Banito, A.; Sill, M.; Versleijen-Jonkers, Y. M. H.; Mayer, B. F. B.; Ebinger, M.; Sparber-Sauer, M.; Stegmaier, S.; Baumhoer, D.; Hartmann, W.; Krijgsveld, J.; Horst, D.; Delattre, O.; Grohar, P. J.; Gruenewald, T. G. P.; Cidre-Aranaz, F.
Show abstract
Desmoplastic small round cell tumor (DSRCT) is a highly aggressive cancer predominantly occurring in male adolescents and young adults. The lack of a comprehensive understanding on the biology of the disease is paralleled by its dismal survival rates (5-20%). To overcome this challenge, we first identified and prioritized urgently needed resources for clinicians and researchers. Thus, we established genome-wide single-cell RNA-sequencing and bulk proteomic data of in vitro and in vivo-generated knockdown models of the pathognomonic DSRCT fusion oncoprotein (EWSR1::WT1) and combined them with an original systems-biology-based pipeline including patient data and the largest histology collection of DSRCTs and morphological mimics available to date. These novel tools were enriched with curated public datasets including patient- and cell line-derived ChIP-seq, bulk and single-cell RNA-seq studies resulting in a multi-model and multi-omic toolbox for discovery analyses. As a proof of concept, our approach revealed the alpha-2/delta subunit of the voltage-dependent calcium channel complex, CACNA2D2, as a highly overexpressed, super-enhancer driven, direct target of EWSR1::WT1. Single-cell and bulk-level analyses of patient samples and xenografted cell lines highlighted CACNA2D2 as a critical component of our newly established EWSR1::WT1 oncogenic signature, that can be employed to robustly identify DSRCT in reference sets. Finally, we show that CACNA2D2 is a highly sensitive and specific single biomarker for fast, simple, and cost-efficient diagnosis of DSRCT. Collectively, we establish a large-scale multi-omics dataset for this devastating disease and provide a blueprint of how such toolbox can be used to identify new and clinically relevant diagnostic markers, which may significantly reduce misdiagnoses, and thus improve patient care.
Matching journals
The top 11 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- CNTNAP4 signaling regulates osteosarcoma disease progression 94%
- Tumor break load quantitates structural variant-associated genomic instability with biological and clinical relevance across cancers 94%
- Predicting the Tumor Microenvironment Composition and Immunotherapy Response in Non-Small Cell Lung Cancer from Digital Histopathology Images 94%
Similar papers in this journal
- Propagated circulating tumor cells uncovers the rople of NFκB and COP1 in metastasis 96%
- ATRX alteration contributes to tumor growth and immune escape in pleomorphic sarcomas 94%
- The epithelial-mesenchymal transcription factor SNAI1 represses transcription of the tumor suppressor miRNA let-7 in cancer 94%
Similar papers in this journal
- Long-term patient-derived ovarian cancer organoids closely recapitulate tumor of origin and clinical response 94%
- Essential role of PLD2 in hypoxia-induced stemness and therapy resistance in ovarian tumors 94%
- In vitro models to mimic tumor endothelial cell-mediated immune cell reprogramming in lung adenocarcinoma 94%
Similar papers in this journal
- Unveiling the influence of tumor and immune signatures on immune checkpoint therapy in advanced lung cancer 95%
- NAB2-STAT6 drives an EGR1-dependent neuroendocrine program in Solitary Fibrous Tumors 95%
- Metabolic reprogramming of cancer cells by JMJD6-mediated pre-mRNA splicing is associated with therapeutic response to splicing inhibitor 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.