Multi-omics analysis identifies osteosarcoma subtypes with distinct prognosis indicating stratified treatment.
Yafei JiangJinzeng WangMengxiong SunDongqing ZuoHongsheng WangJiakang ShenWenyan JiangHaoran MuXiaojun MaFei YinJun LinChongren WangShuting YuLu JiangGang LvFeng LiuLinghang XueKai TianGangyang WangZifei ZhouYu LvZhuoying WangTao ZhangJing XuLiu YangKewen ZhaoWei SunYujie TangZhengdong CaiSheng-Yue WangYingqi HuaPublished in: Nature communications (2022)
Osteosarcoma (OS) is a primary malignant bone tumor that most commonly affects children, adolescents, and young adults. Here, we comprehensively analyze genomic, epigenomic and transcriptomic data from 121 OS patients. Somatic mutations are diverse within the cohort, and only TP53 is significantly mutated. Through unsupervised integrative clustering of the multi-omics data, we classify OS into four subtypes with distinct molecular features and clinical prognosis: (1) Immune activated (S-IA), (2) Immune suppressed (S-IS), (3) Homologous recombination deficiency dominant (S-HRD), and (4) MYC driven (S-MD). MYC amplification with HR proficiency tumors is identified with a high oxidative phosphorylation signature resulting in resistance to neoadjuvant chemotherapy. Potential therapeutic targets are identified for each subtype, including platinum-based chemotherapy, immune checkpoint inhibitors, anti-VEGFR, anti-MYC and PARPi-based synthetic lethal strategies. Our comprehensive integrated characterization provides a valuable resource that deepens our understanding of the disease, and may guide future clinical strategies for the precision treatment of OS.
Keyphrases
- neoadjuvant chemotherapy
- single cell
- locally advanced
- dna damage
- transcription factor
- end stage renal disease
- electronic health record
- big data
- chronic kidney disease
- lymph node
- dna repair
- newly diagnosed
- machine learning
- copy number
- sentinel lymph node
- squamous cell carcinoma
- early stage
- molecular dynamics
- replacement therapy
- gene expression
- artificial intelligence
- rectal cancer
- deep learning
- endothelial cells
- body composition
- climate change
- human health
- label free
- nucleic acid