Immunologic constant of rejection signature is prognostic in soft-tissue sarcoma and refines the CINSARC signature

Archive ouverte

Bertucci, Francois | Niziers, Vincent | de Nonneville, Alexandre | Finetti, Pascal | Mescam, Léna | Mir, Olivier | Italiano, Antoine | Le Cesne, Axel | Blay, Jean-Yves | Ceccarelli, Michele | Bedognetti, Davide | Birnbaum, Daniel | Mamessier, Emilie

Edité par CCSD ; BMJ Publishing Group -

International audience. Background Soft-tissue sarcomas (STSs) are heterogeneous and aggressive tumors, with high metastatic risk. The immunologic constant of rejection (ICR) 20-gene signature is a signature of cytotoxic immune response. We hypothesized that ICR might improve the prognostic assessment of early-stage STS. Methods We retrospectively applied ICR to 1455 non-metastatic STS and searched for correlations between ICR classes and clinicopathological and biological variables, including metastasis-free survival (MFS). Results Thirty-four per cent of tumors were classified as ICR1, 27% ICR2, 24% ICR3, and 15% ICR4. These classes were associated with patients’ age, pathological type, and tumor depth, and an enrichment from ICR1 to ICR4 of quantitative/qualitative scores of immune response. ICR1 class was associated with a 59% increased risk of metastatic relapse when compared with ICR2-4 class. In multivariate analysis, ICR classification remained associated with MFS, as well as pathological type and Complexity Index in Sarcomas (CINSARC) classification, suggesting independent prognostic value. A prognostic clinicogenomic model, including the three variables, was built in a learning set (n=339) and validated in an independent set (n=339), showing greater prognostic precision than each variable alone or in doublet. Finally, connectivity mapping analysis identified drug classes potentially able to reverse the expression profile of poor-prognosis tumors, such as chemotherapy and targeted therapies. Conclusion ICR signature is independently associated with postoperative MFS in early-stage STS, independently from other prognostic features, including CINSARC. We built a robust prognostic clinicogenomic model integrating ICR, CINSARC, and pathological type, and suggested differential vulnerability of each prognostic group to different systemic therapies.

• Description
• Sujet/Public
• Outil
• Outil d'anticipation
• Mémoire et thèse
• Gestion