An international framework for clinical translation of molecular classifiers in osteosarcoma

Osteosarcoma has been treated with a standardized, intensive chemotherapy regimen for over four decades, despite the disease's well-documented biological heterogeneity. Current clinical risk stratification relies primarily on metastatic status and histologic response to chemotherapy, which are insufficient to guide personalized therapy. This leaves a significant portion of patients over-treated, suffering from unnecessary toxicity, while others remain at high risk of treatment failure. This paper presents the consensus of an international working group aimed at establishing a framework to integrate molecular classifiers into clinical practice.

The working group categorized candidate classifiers into five domains: genomics, transcriptomics, epigenetics, cell-surface proteins, and circulating analytes. They established a four-level evidence framework to prioritize these candidates for clinical translation. The most promising candidate is pre-treatment circulating tumor DNA (ctDNA) burden, which has shown independent prognostic value across multiple retrospective cohorts and is currently undergoing prospective validation in the LEOPARD study. Other high-priority candidates include MYC amplification and three specific transcriptomic classifiers (G1/G2, a four-subtype model, and a tumor microenvironment signature), all of which have demonstrated reproducible prognostic associations.

The authors emphasize that moving toward biologically informed treatment requires more than just identifying markers; it necessitates a coordinated infrastructure. This includes standardizing biospecimen collection, harmonizing clinical data dictionaries, and ensuring equitable access to molecular profiling. The paper proposes hypothetical frameworks for how these classifiers could be integrated into clinical trials, moving from rule-based algorithms to continuous probabilistic risk prediction. The ultimate goal is to link these molecularly defined risk groups to targeted therapeutic strategies, such as de-intensifying treatment for low-risk patients or testing novel agents for those with high-risk molecular profiles.