Transcriptomic Subgroups in Soft Tissue Tumors Correlate with Morphologic Subtype, Genomic Features, and Outcome.

Area of Science:

• Oncology
• Genomics
• Molecular Pathology

Background:

• Soft tissue tumors (STT) are a diverse group of over 100 neoplasms.
• Many STT subtypes lack reliable diagnostic or prognostic markers, highlighting a need for advanced classification methods.

Purpose of the Study:

• To assess the clinical utility of transcriptome sequencing (RNA-seq) for classifying STT.
• To identify prognostically relevant subgroups within STT using RNA-seq data.

Main Methods:

• RNA-sequencing (RNA-seq) was performed on 704 tumors across 56 histologic subtypes.
• Integrated analysis of global gene expression (GGE) profiles, fusion transcripts, genomic features, and clinicopathologic data.
• Unsupervised clustering and machine learning were employed to refine classification and discover prognostic signatures.

Main Results:

• Over 200 pathogenic gene fusions were identified, including 40 novel ones.
• Distinct GGE profiles were observed for many subtypes, particularly those with specific gene fusions.
• Diagnostic reclassifications occurred in 5.7% of cases; transcriptomic subclusters correlated with metastasis-free survival in complex sarcomas.

Conclusions:

• Transcriptomic profiling enhances diagnostic accuracy and uncovers novel oncogenic fusions in STT.
• RNA-seq provides valuable prognostic information for soft tissue tumors.
• Multicenter studies are recommended to fully leverage RNA-seq potential due to STT diversity.