Characterization of the diverse malignant and stromal cell states that make up soft tissue sarcomas and their correlation with patient outcomes has proven difficult using fixed clinical specimens. Here, we employed EcoTyper, a machine-learning framework, to identify the fundamental cell states and cellular ecosystems that make up sarcomas on a large scale using bulk transcriptomes with clinical annotations. We identified and validated 23 sarcoma-specific, transcriptionally defined cell states, many of which were highly prognostic of patient outcomes across independent datasets.
View Article and Find Full Text PDFPurpose: Models to study metastatic disease in rare cancers are needed to advance preclinical therapeutics and to gain insight into disease biology. Osteosarcoma is a rare cancer with a complex genomic landscape in which outcomes for patients with metastatic disease are poor. As osteosarcoma genomes are highly heterogeneous, multiple models are needed to fully elucidate key aspects of disease biology and to recapitulate clinically relevant phenotypes.
View Article and Find Full Text PDFObjective: To assess the safety and efficacy of magnetic resonance-guided focused ultrasound (MRgFUS) for the treatment extra-abdominal desmoids.
Methods: A total of 105 patients with desmoid fibromatosis (79 females, 26 males; 35 ± 14 years) were treated with MRgFUS between 2011 and 2021 in three centers. Total and viable tumors were evaluated per patient at last follow-up after treatment.