Biological and therapeutic insights from animal modeling of fusion-driven pediatric soft tissue sarcomas.
Jack P KucinskiDelia CalderonGenevieve C KendallPublished in: Disease models & mechanisms (2024)
Survival for children with cancer has primarily improved over the past decades due to refinements in surgery, radiation and chemotherapy. Although these general therapies are sometimes curative, the cancer often recurs, resulting in poor outcomes for patients. Fusion-driven pediatric soft tissue sarcomas are genetically defined by chromosomal translocations that create a chimeric oncogene. This distinctive, almost 'monogenic', genetic feature supports the generation of animal models to study the respective diseases in vivo. This Review focuses on a subset of fusion-driven pediatric soft tissue sarcomas that have transgenic animal tumor models, which includes fusion-positive and infantile rhabdomyosarcoma, synovial sarcoma, undifferentiated small round cell sarcoma, alveolar soft part sarcoma and clear cell sarcoma. Studies using the animal models of these sarcomas have highlighted that pediatric cancers require a specific cellular state or developmental stage to drive tumorigenesis, as the fusion oncogenes cause different outcomes depending on their lineage and timing of expression. Therefore, understanding these context-specific activities could identify targetable activities and mechanisms critical for tumorigenesis. Broadly, these cancers show dependencies on chromatin regulators to support oncogenic gene expression and co-opting of developmental pathways. Comparative analyses across lineages and tumor models will further provide biological and therapeutic insights to improve outcomes for these children.
Keyphrases
- soft tissue
- gene expression
- childhood cancer
- high grade
- young adults
- papillary thyroid
- end stage renal disease
- transcription factor
- minimally invasive
- cell therapy
- dna methylation
- genome wide
- poor prognosis
- prognostic factors
- single cell
- chronic kidney disease
- ejection fraction
- machine learning
- squamous cell carcinoma
- deep learning
- dna damage
- squamous cell
- newly diagnosed
- stem cells
- clear cell
- locally advanced
- coronary artery bypass
- type diabetes
- copy number
- adipose tissue
- acute coronary syndrome
- binding protein
- patient reported
- long non coding rna
- percutaneous coronary intervention
- neural network