SUMMARY Sarcomas are a broad family of mesenchymal malignancies exhibiting remarkable histologic diversity. We describe the multi-platform molecular landscape of 206 adult soft tissue sarcomas representing 6 major types. Along with novel insights into the biology of individual sarcoma types, we report three overarching findings: 1) unlike most epithelial malignancies, these sarcomas (excepting synovial sarcoma) are characterized predominantly by copy number changes, with low mutational loads and only a few genes (TP53, ATRX, RB1) highly recurrently mutated across sarcoma types, 2) within sarcoma types, genomic and regulomic diversity of driver pathways defines molecular subtypes associated with patient outcome, and 3) the immune microenvironment, inferred from DNA methylation and mRNA profiles, associates with outcome and may inform clinical trials of immune checkpoint inhibitors. Overall, this large-scale analysis reveals previously unappreciated sarcoma type-specific changes in copy number, methylation, RNA, and protein, providing insights into refining sarcoma therapy and relationships to other cancer types.

Sarcomas are heterogeneous and aggressive mesenchymal tumors. Histological grading has so far been the best predictor for metastasis-free survival, but it has several limitations, such as moderate reproducibility and poor prognostic value for some histological types. To improve patient grading, we performed genomic and expression profiling in a training set of 183 sarcomas and established a prognostic gene expression signature, complexity index in sarcomas (CINSARC), composed of 67 genes related to mitosis and chromosome management. In a multivariate analysis, CINSARC predicts metastasis outcome in the training set and in an independent 127 sarcomas validation set. It is superior to the Fédération Francaise des Centres de Lutte Contre le Cancer grading system in determining metastatic outcome for sarcoma patients. Furthermore, it also predicts outcome for gastrointestinal stromal tumors (GISTs), breast carcinomas and lymphomas. Application of the signature will permit more selective use of adjuvant therapies for people with sarcomas, leading to decreased iatrogenic morbidity and improved outcomes for such individuals.

Purpose: The importance of KIT and PDGFRA mutations in the oncogenesis of gastrointestinal stromal tumors (GIST) is well established, but the genetic basis of GIST metastasis is poorly understood. We recently published a 67 gene expression prognostic signature related to genome complexity (CINSARC for Complexity INdex in SARComas) and asked whether it could predict outcome in GISTs.Experimental Design: We carried out genome and expression profiling on 67 primary untreated GISTs.Results: We show and validate here that it can predict metastasis in a new data set of 67 primary untreated GISTs. The gene whose expression was most strongly associated with metastasis was AURKA, but the AURKA locus was not amplified. Instead, we identified deletion of the p16 (CDKN2A) and retinoblastoma (RB1) genes as likely causal events leading to increased AURKA and CINSARC gene expression, to chromosome rearrangement, and ultimately to metastasis. On the basis of these findings, we established a Genomic Index that integrates the number and type of DNA copy number alterations. This index is a strong prognostic factor in GISTs. We show that CINSARC class, AURKA expression, and Genomic Index all outperform the Armed Forces Institute of Pathology (AFIP) grading system in determining the prognosis of patients with GISTs.

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