Validation of the MAGIC genomic signature on RNAseq in soft-tissue sarcomas

(1) BackgroundGenomic Instability (GIN) plays a critical role in cancer progression and treatment response. Soft tissue sarcomas (STS), are characterized by high levels of chromosomal rearrangements and transcription-associated stress, both of which contribute to poor clinical outcomes. Current standard grading systems, such as FNCLCC, are limited in prognostic accuracy for STS, necessitating novel approaches for risk stratification and treatment guidance. To address this gap, we developed a holistic classifier, the MAGIC (Mixed transcription- and replication-associated GIN classifier), combining transcription- and replication-related GIN indices (iTRAC and iRACIN) to predict metastatic risk. (2) Patients and MethodsThis study utilized RNA sequencing (RNAseq) on 226 STS tumor samples to analyze fusions transcripts break points (BP) distribution and assess GIN. We computed MAGIC indices iTRAC and iRACIN, which are based on chromosomal instability linked to transcription and replication processes, respectively. iTRAC biomarker was evaluated against FNCLCC and CINSARC for metastatic risk stratification. Kaplan-Meier and iPART analyses were used to determine the prognostic relevance of iTRAC levels. (3) ResultsiTRAC significantly stratified patients with distinct metastatic outcomes, outperforming FNCLCC and CINSARC grading systems. STS patients with medium level of iTRAC showed the poorest metastasis-free survival. Patients classified as iTRAC high- and low-risk groups, achieved better prognosis. Furthermore, iTRAC stratified patients metastatic risk in treated and not treated patients, indicating poorer prognosis with chemotherapy in patients with low iTRAC and better for those with with medium iTRAC. (4) Conclusion(s)iTRAC demonstrates a superior prognostic utility in STS over current grading systems, effectively stratifying metastatic risk for patients who might benefit from alternative therapeutic strategies. iTRAC holds potential for personalizing chemotherapeutic approaches, paving the way for a new precision oncology approach in STS.