Establishment of multiple novel patient-derived models of desmoplastic small round cell tumor enabling functional characterization of ERBB pathway signaling and pre-clinical evaluation of a novel targeted therapy approach
Smith, R. S.; Odintsov, I.; Liu, Z.; Hayashi, T.; Lui, A. J. W.; Vojnic, M.; Suehara, Y.; Mattar, M. S.; Hmeljak, J.; Ramirez, H. A.; Shaw, M.; Bui, G.; Hartono, A. B.; Gladstone, E.; Magnan, H.; Khodos, I.; de Stanchina, E.; La Quaglia, M. P.; Yao, J.; Lae, M.; Lee, S. B.; Spraggon, L.; Pratilas, C. A.; Ladanyi, M.; Somwar, R.
Show abstract
Desmoplastic small round cell tumor (DSRCT) is characterized by the t(11;22)(p13;q12) chromosomal translocation, which fuses the transcriptional regulatory domain of EWSR1 with the zinc finger DNA-binding domain of WT1, resulting in the oncogenic transcription factor EWS-WT1. DSRCT primarily affects young males and has a 5-year overall survival of about 15%. Typical treatment approaches for patients with DSRCT involve a multi-modal combination of surgery, chemotherapy and radiation. The paucity of DSRCT disease models has hampered functional and pre-clinical therapeutic studies in this aggressive cancer. Here, we developed robust preclinical disease models and mined DSRCT expression profiling data to identify genetic vulnerabilities that could be leveraged for the identification of rational therapies. Specifically, we developed four new DSRCT cell lines and one patient-derived xenograft (PDX) model. Transcriptomic and proteomic profiling showed evidence of activation of the ERBB pathway. Ectopic expression of EWSR1-WT1 resulted in upregulation of ERRB family ligands and downstream signaling. Treatment of DSRCT cell lines with ERBB ligands resulted in activation of EGFR, ERBB2, ERK1/2 and AKT, and stimulation of cell growth. Conversely, targeting of EGFR using shRNA, small molecule inhibitors (afatinib, neratinib) or an anti-EGFR antibody (cetuximab) inhibited growth and induced apoptosis in DSRCT cells. Finally, treatment of mice bearing DSRCT xenografts with a combination of cetuximab and afatinib significantly reduced tumor growth. These data provide a rationale for the clinical evaluation of EGFR antagonists in patients with DSRCT.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- AXL is a key factor for cell plasticity and promotes metastasis in pancreatic cancer 95%
- KSR1 mediates small-cell lung carcinoma tumor initiation and cisplatin resistance 95%
- ETS1, a target gene of the EWSR1::FLI1 fusion oncoprotein, regulates the expression of the focal adhesion protein TENSIN3 94%
Similar papers in this journal
- A Genomically and Clinically Annotated Patient Derived Xenograft (PDX) Resource for Preclinical Research in Non-Small Cell Lung Cancer 95%
- FGFR1 is critical for Rbl2 loss-driven tumor development and requires PLCG1 activation for continued growth of small cell lung cancer 94%
- Natural killer cell regulation of breast cancer stem cells mediates metastatic dormancy. 94%
Similar papers in this journal
- Tumor suppressor PLK2 may serve as a biomarker in triple-negative breast cancer for improved response to PLK1 therapeutics 95%
- Aberrant Hippo-YAP/TEAD signaling drives malignant transcriptional reprogramming in external auditory canal squamous cell carcinoma 93%
- SREBP-dependent regulation of lipid homeostasis is required for progression and growth of pancreatic ductal adenocarcinoma 93%
Similar papers in this journal
- PRMT1 regulates EGFR and Wnt signaling pathways and is a promising target for combinatorial treatment of breast cancer 95%
- Transcriptomic analyses of MYCN-regulated genes in anaplastic Wilms' tumour cell lines reveals oncogenic pathways and potential therapeutic vulnerabilities 94%
- An inducible BRCA1 expression system with in vivo applicability uncovers activity of the combination of ATR and PARP inhibitors to overcome therapy resistance 94%
"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.