Comprehensive genomic landscape of osteosarcoma: Systematic review and analysis of 1,058 patients across 20 studies with therapeutic implications from the Fight Osteosarcoma Through European Research (FOSTER) Consortium.

10032 Background: Osteosarcoma exhibits profound genomic complexity, complicating biomarker discovery. Although advances in multi-omics profiling have improved biological understanding, genomic data remain fragmented across heterogeneous cohorts, and actionable alterations are rarely reported. Methods: A systematic review identified 20 studies published between 2005 and 2022, spanning heterogeneous sequencing technologies (whole-genome/exome/RNA sequencing, methylome, and targeted gene panels) and reporting genetic drivers and/or potentially actionable mutations. Mutation data were manually extracted from the published results (e.g., tables, figures, oncoplots) and integrated into a binary Mutation Annotation Format - like matrix (1,041 samples, 471 genes) to provide a comprehensive overview of variants reported to date. Results: 1058 patients were included, with tissue derivation (primary, metastatic, recurrent) described in about 10% of the cases. 3816 germlines or somatic SNVs, CNVs or structural alterations were reported in 471 genes. TP53 (40%), RB1 (18%), and CDKN2A/B (17%) were the most frequently altered genes, alongside with recurrent changes in cell cycle regulators ( CCND family) and angiogenesis-related genes ( VEGFA ). Potentially actionable variants were less common: MYC (13%, all amplifications), CDK4 (10%), PDGFRA (8%); single agent activity against such targets is either lacking or not yet available. Potentially actionable epigenetic targets were rarely identified: TET2 (0.2%), IDH2 (0.3%), DNMT3A (0.7%). TCGA-based pathway analysis revealed consistent involvement of TP53, cell cycle, RTK-RAS, PI3K, and MYC signaling. Co-occurrences detected were TP53 with MAP2K4 , RICTOR , PTPRD and RB1 genes, and structural rearrangements affecting RTK-RAS and PI3K pathways. There was relative exclusivity between mutations of RB1 and CDK4 . Conclusions: The analysis identifies a small number of recurrently mutated genes and a large number of rarely affected, though potentially actionable, mutations, encoding proteins involved in cell surface, nuclear and epigenetic interactions. Lack of harmonization in data processing, mutation calling and definition of ‘over’ and ‘under’ expression, together with absent descriptions of tissue derivation hamper effective large scale analysis and phenotypic subgrouping. TP53 mutation co-occurrences such as RICTOR or RB1 were also identified, with potential therapeutic implications including WEE1 and mTOR inhibitors. Tumors with overactive CDK4/6 typically have functional RB1 and might be sensitive to CDK inhibitors. Harmonization of multi-omic methods and collaborative international analyses are essential to advance precision medicine in osteosarcoma and to inform future trials.