Soft tissue sarcomas (STS) histopathological classification system has several conceptual caveats, impacting prognostication and treatment. The clinical and molecular–based tools currently employed to estimate prognosis also have limitations. Clinically driven molecular profiling studies may cover these gaps. We performed DNA sequencing (DNAseq) and RNA sequencing (RNAseq), portraying the molecular profile of 102 samples of 3 of the most common STS subtypes. The RNAseq data was analyzed using unsupervised machine learning models, unravelling previously unknown molecular patterns and identifying 4 well–defined transcriptomic clusters. These transcriptomic clusters have a clear prognostic value, a finding that was externally validated. This transcriptomic cluster–based classification′s prognostic value is superior to the prognostic accuracy of currently used clinical–based (SARCULATOR nomograms) and molecular–based (CINSARC) prognostication tools. The analysis of DNAseq data from the same cohort of samples revealed a plethora of unique and, in some cases, never documented molecular targets for precision treatment across different transcriptomic clusters.

The authors have declared no competing interest.

This study was sponsored by Roche Foundation Medicine under the RNA LDT Research Programme. Roche Foundation Medicine provided all the FoundationOneCDx and FoundationOneRNA assays, and also provided technical support.

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Ethics committee of Centro Academico de Medicina de Lisboa gave ethical approval for this work. Ethics committee of Instituto Portugues de Oncologia de Lisboa Francisco Gentil gave ethical approval for this work.

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