The early evolutionary landscape of osteosarcoma provides clues for targeted treatment strategies.
Michal KovacBaptiste AmelineSebastian RibiMonika KovacovaWilliam CrossMaxim BarenboimOlaf WittStefan BielackAndreas KriegWolfgang HartmannMichaela NathrathDaniel BaumhoerPublished in: The Journal of pathology (2021)
Osteosarcomas are aggressive primary tumors of bone that are typically detected in locally advanced stages; however, which genetic mutations drive the cancer before its clinical detection remain unknown. To identify these events, we performed longitudinal genome-sequencing analysis of 12 patients with metastatic or refractory osteosarcoma. Phylogenetic and molecular clock analyses were carried out next to identify actionable mutations, and these were validated by integrating data from additional 153 osteosarcomas and pre-existing functional evidence from mouse PDX models. We found that the earliest and thus clinically most promising mutations affect the cell cycle G1 transition, which is guarded by cyclins D3, E1, and cyclin-dependent kinases 2, 4, and 6. Cell cycle G1 alterations originate no more than a year before the primary tumor is clinically detected and occur in >90% and 50% of patients of the discovery and validation cohorts, respectively. In comparison, other cancer driver mutations could be acquired at any evolutionary stage and often do not become pervasive. Consequently, our data support that the repertoire of actionable mutations present in every osteosarcoma cell is largely limited to cell cycle G1 mutations. Since they occur in mutually exclusive combinations favoring either CDK2 or CDK4/6 pathway activation, we propose a new genomically-based algorithm to direct patients to correct clinical trial options. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.
Keyphrases
- cell cycle
- cell proliferation
- end stage renal disease
- clinical trial
- genome wide
- ejection fraction
- newly diagnosed
- single cell
- chronic kidney disease
- peritoneal dialysis
- prognostic factors
- papillary thyroid
- squamous cell carcinoma
- machine learning
- stem cells
- gene expression
- dna methylation
- study protocol
- big data
- electronic health record
- randomized controlled trial
- rectal cancer
- systematic review
- cancer therapy
- quantum dots
- patient reported outcomes
- deep learning
- soft tissue
- phase iii
- squamous cell
- cell therapy
- cross sectional
- double blind
- bone loss
- real time pcr