A Contemporary Assessment of Osteosarcoma: Lessons from a Comparative Approach

Abstract

This review will describe more than two decades of comparative research on primary bone cancer (osteosarcoma). Osteosarcoma is a chaotic disease present in a complex and variable microenvironment composed of many different cell types which interact with each other and lead to high transcriptional heterogeneity. Despite this heterogeneity, common transcriptional patterns can be observed in the bulk transcriptomes of these tumors; additionally, these patterns are associated with outcome, indicating their importance to the molecular biology of the disease. Work from our group and others has led to our current understanding of osteosarcoma as a disease where multiple pathological processes appear to converge into a limited array of tissue organizations with distinct biology. Recurrent as well as distinct events can lead to these states of tissue organization, explaining the heterogeneity of osteosarcoma that is observed among and within species. Yet, despite their chaotic genomes, osteosarcomas seem to be (relatively) genetically stable, with persistent maintenance of essentially the same chromothriptic karyotype throughout the developmental lifetime of the tumor. Importantly, the transcriptional variance between tumors can highlight the underlying biology of the malignant cells themselves, as well as the composition of the osteosarcoma microenvironment and the host response, both of which are prognostically significant for this disease. Initial single cell RNA-seq reports provide further evidence of the importance of the osteosarcoma microenvironment for tumor characterization. Our data suggest that improving patient outcomes in immunologically barren or “cold” osteosarcomas, necessitates generating immune permissive or “warmer” microenvironments within the tumor. Furthermore, the aging bone microenvironment may create specific niches that predispose to cancer, and identification of the drivers that lead to these variable transcriptional patterns will be essential to identify personalized, effective genomic therapy for osteosarcoma.