MicroRNA-mRNA networks define translatable molecular outcome phenotypes in osteosarcoma
Christopher B. Lietz, Cassandra C. Garbutt, William T. Barry, Vikram Deshpande, Yen‐Lin Chen, Santiago A. Lozano‐Calderón, Yaoyu Wang, Brian Lawney, David H. Ebb, Gregory M. Coté, Zhenfeng Duan, Francis J. Hornicek, Edwin Choy, G. Petur Nielsen, Benjamin Haibe‐Kains, John Quackenbush, Dimitrios Spentzos
Abstract
There is a lack of well validated prognostic biomarkers in osteosarcoma, a rare, recalcitrant disease for which treatment standards have not changed in over 20 years. We performed microRNA sequencing in 74 frozen osteosarcoma biopsy samples, constituting the largest single center translationally analyzed osteosarcoma cohort to date, and we separately analyzed a multi-omic dataset from a large NCI supported national cooperative group cohort. We validated the prognostic value of candidate microRNA signatures and contextualized them in relevant transcriptomic and epigenomic networks. Our results reveal the existence of molecularly defined phenotypes associated with outcome independent of clinicopathologic features. Through machine learning based integrative pharmacogenomic analysis, the microRNA biomarkers identify novel therapeutics for stratified application in osteosarcoma. The previously unrecognized osteosarcoma subtypes with distinct clinical courses and response to therapy could be translatable for discerning patients appropriate for more intensified, less intensified, or alternate therapeutic regimens.