The Genomic Grade Index predicts postoperative clinical outcome in patients with soft-tissue sarcoma. (24th October 2017) - Bertucci, F; De Nonneville, A; Finetti, P; Perrot, D; Nilbert, M; Italiano, A; Le Cesne, A; Skubitz, K M; Blay, J Y; Birnbaum, D
Abstract: Background: Soft-tissue sarcomas (STSs) are a group of rare, heterogeneous, and aggressive tumors, with high metastatic risk and relatively few efficient systemic therapies. We hypothesized that the Genomic Grade Index (GGI), a 108-gene signature previously developed in early-stage breast cancer, might improve the prognostic assessment of patients with early-stage STS. Patients and methods: We collected gene expression and clinicopathological data of 678 operated STS, and searched for correlations between the GGI-based classification and clinicopathological variables, including the metastasis-free survival (MFS). Results: Based on GGI, 275 samples (41%) were classified as 'GGI-low' and 403 (59%) as 'GGI-high'. The 'GGI-high' class was more associated with poor-prognosis features than the 'GGI-low' class: pathological grade 3 ( P = 9.50E–11), undifferentiated sarcomas and leiomyosarcomas ( P < 1.00E–06), location in extremities ( P < 1.00E–06), and complex genetic profile ( P = 2.1E–20). The 5-year MFS was 53% (95%CI 47–59) in the 'GGI-high' class versus 78% (95%CI 72–85) in the 'GGI-low' class ( P = 3.02E–11), with a corresponding hazard ratio for metastatic relapse equal to 2.92 (95%CI 2.10–4.07; P = 2.23E–10). In multivariate analysis, the GGI-based classification remained significant, whereas the pathological grade did not. In fact, the GGI-based classification stratified the patients with pathological grades 1 and 2 and those with pathological grade 3 in two classes with different 5-year MFS. Comparison of the GGI and CINSARC multigene signatures revealed similar correlations with clinicopathological variables, which were, however, stronger with GGI than with CINSARC, a strong concordance (71%) in terms of low-risk or high-risk classifications, and independent prognostic value for MFS in multivariate analysis, suggesting complementary prognostic information. Conclusion: GGI refines the prediction of MFS in operated STS and might improve the tailoring of adjuvant chemotherapy. Further clinical validation is warranted in larger retrospective, then prospective series, as well as the functional validation of relevant genes that could provide new therapeutic targets.