Systems Biology Approach Identifies Prognostic Signatures of Poor Overall Survival and Guides the Prioritization of Novel BET-CHK1 Combination Therapy for Osteosarcoma.
Pankita H PandyaLijun ChengM Reza SaadatzadehKhadijeh Bijangi-VishehsaraeiShan TangAnthony L SinnMelissa A TrowbridgeKathryn L CoyBarbara J BaileyCourtney N YoungJixin DingErika A DobrotaSavannah DyerAdily ElmiQuinton ThompsonFarinaz BarghiJeremiah ShultzEric A AlbrightHarlan E ShannonMary E MurrayMark S MarshallMichael J FergusonTodd E BertrandL Daniel WurtzSandeep BatraLang LiJamie L RenbargerKaren E PollokPublished in: Cancers (2020)
Osteosarcoma (OS) patients exhibit poor overall survival, partly due to copy number variations (CNVs) resulting in dysregulated gene expression and therapeutic resistance. To identify actionable prognostic signatures of poor overall survival, we employed a systems biology approach using public databases to integrate CNVs, gene expression, and survival outcomes in pediatric, adolescent, and young adult OS patients. Chromosome 8 was a hotspot for poor prognostic signatures. The MYC-RAD21 copy number gain (8q24) correlated with increased gene expression and poor overall survival in 90% of the patients (n = 85). MYC and RAD21 play a role in replication-stress, which is a therapeutically actionable network. We prioritized replication-stress regulators, bromodomain and extra-terminal proteins (BETs), and CHK1, in order to test the hypothesis that the inhibition of BET + CHK1 in MYC-RAD21+ pediatric OS models would be efficacious and safe. We demonstrate that MYC-RAD21+ pediatric OS cell lines were sensitive to the inhibition of BET (BETi) and CHK1 (CHK1i) at clinically achievable concentrations. While the potentiation of CHK1i-mediated effects by BETi was BET-BRD4-dependent, MYC expression was BET-BRD4-independent. In MYC-RAD21+ pediatric OS xenografts, BETi + CHK1i significantly decreased tumor growth, increased survival, and was well tolerated. Therefore, targeting replication stress is a promising strategy to pursue as a therapeutic option for this devastating disease.
Keyphrases
- gene expression
- copy number
- end stage renal disease
- genome wide
- dna methylation
- dna damage
- dna repair
- transcription factor
- ejection fraction
- chronic kidney disease
- mitochondrial dna
- young adults
- dna damage response
- newly diagnosed
- peritoneal dialysis
- healthcare
- prognostic factors
- emergency department
- free survival
- patient reported outcomes
- deep learning
- machine learning
- drug delivery
- patient reported
- cancer therapy
- heat stress
- artificial intelligence
- childhood cancer